Reliable RFID Communication and Positioning System for Industrial Iot

Reliable RFID Communication and Positioning System for Industrial IoT

CHUANYING ZHAI

Doctoral Thesis in Information and Communication Technology School of Information and Communication Technology KTH Royal Institute of Technology Stockholm, Sweden 2016 KTH School of Information and Communication Technology TRITA-ICT 2016:29 SE-164 40 Stockholm ISBN 978-91-7729-165-7 SWEDEN

Akademisk avhandling som med tillstånd av Kungl Tekniska högskolan framlägges till oﬀentlig granskning för avläggande av teknologie doktorsexamen i Informations- och kommunikationsteknik måndagen den 12 december 2016 klockan 14.00 i Sal C, Electrum, Kungl Tekniska högskolan, Kistagången 16, Kista.

Tryck: Universitetsservice US AB iii

Abstract

The Internet of Things (IoT) has the vision to interconnect everything of the physical world and the virtual world. Advanced automated and adaptive connectivity of objects, systems, and services is expected to be achieved under the IoT context, especially in the industrial environment. Industry 4.0 with the goal of intelligent and self-adaptable manufacturing is driven by the IoT. The Object Layer, where real-time and reliable information acquisition from the physical objects carried out, is the basic enabler in the 3-layer industrial IoT system. Such acquisition system features deterministic access, reliable communication with failure resistance mechanism, latency-aware real- time response, deployable structure/protocol, and adaptive performance on various QoS demands. This thesis proposes a reliable RFID communication system for acquisition in the industrial environment. A discrete gateway structure and a contention-free communication protocol are designed to fulfill the unique system requirements. Such gateway structure offers a flexible configuration of readers and RF technologies. It enables a full duplex communication between the objects and the gateway. The designed MF-TDMA protocol can enhance the failure resistance and emergency report mechanism thanks to the separation of control link and data link in the gateway. Specifically, an optional ARQ mechanism, an independent/uniform synchronization and control method, and a slot allocation optimization algorithm are designed besides time-division and frequency-division multiplexing. Protocol implementations for different industrial situations illustrate the system ability for supporting the demands of various QoS. Finally, a 2.4-GHz/UWB hybrid positioning platform is explored based on the introduced RFID system. Taking advantage of the UWB technology, the positioning platform can achieve positioning accuracy from meter level to centimeter level. Hybrid tag prototype and specific communication process based on the MF-TDMA protocol are designed. An SDR UWB reader network, capable of evaluating multiple algorithms, is built to realize accurate positioning with an improved algorithm proposed. Keywords: 2.4-GHz/UWB hybrid positioning, industrial IoT, MF-TDMA, QoS, reliable communication, RFID iv

Sammanfattning

Sakernas Internet (IoT) har som vision att koppla samman allt i den fysiska världen med den virtuella världen. Avancerad automatiserad och adaptiv anslutning av objekt, system och tjänster förväntas att uppnås inom ramen för Sakernas Internet. Särskilt i den industriella miljön. Det är således också att betraktas som en av de viktigaste drivkrafterna för den 4:e industriella revolutionen, Industri 4.0, som har som målsättning att uppnå intelligent och självanpassningsbar tillverkning. Arkitekturen i det industriella IoT-systemet består av tre delar: Objektsla- ger, Nätverkslager och Tjänstelager. Objektslagret, där förvärvande av till- förlitlig realtidsinformation från fysiska objekt utförs, är den grundläggan- de möjliggöraren. För dylik inhämtning från Objektlagret tillämpas lämp- ligast RFID-teknik, smart sensor-chipteknik, och kommunikationsprotokoll. Informationsinhämtningssystem för industriella tillämpningar kräver determi- nistisk access, tillförlitlig kommunikation med felmotståndsmekanism. Samt väntetidsmedveten prestanda för realtidssvar, möjlighet att applicera struktur/protokoll i flera områden, och flexibel möjlighet för QoS-krav beroende på den specifika arbetsmiljön. I denna avhandling har ett RFID-kommunikationssystem med hög tillför- litlighet föreslagits för informations-inhämtning i industriell miljö. En diskret Gateway-struktur, där en samordnande enhet, en uppsättning avläsare, och ett konfliktfritt kommunikationsprotokoll är utformade för att uppfylla de unika systemkraven. En sådan Gateway-struktur möjliggör flexibel konfigu- ration av läsare och RF-tekniker med en koordinator. Det möjliggör också full duplex-kommunikation mellan smarta objekt och gateway. Ett adaptivt protokoll, ett utvecklat MF-TDMA-protokoll, för att förbättra fel motstånd och rapportmekanism tillgodoses tack vare oberoende implementation av styrlänk och datalänk i den diskreta Gateway-strukturen. Närmare bestämt; En valfri ARQ-mekanism, en oberoende och enhetlig synkroniserings- och styrmetod och en allokeringsoptimerande algoritm tillhandahålls förutom även schema- lagd tids- och frekvens-multiplexande kommunikationssätt. Implementationer med olika industriella förhållanden visar förmågan hos systemet för att möta kraven från olika QoS. Slutligen utforskas en 2,4-GHz RF- och UWB- hybridpositioneringsplatt- form baserat på den introducerade RFID-systemet. Genom att dra full nyt- ta av den fina upplösningen i tidsdomän för UWB-tekniken, kan positione- ringsplattformen uppnå positioneringsnoggrannhet från meternivå (med 2,4 GHz RF) till centimeternivå (med UWB). En specifik kommunikationspro- cess byggd på MF-TDMA och en prototyp av en hybrid-tagg utvecklades. Ett SDR-UWB-läsarnätverk som kan använda flera algoritmer, har byggts för plattformen för att uppnå exakt positionering med en förbättrad positio- neringsalgoritm. Keywords: 2,4-GHz RF- och UWB- hybridpositionerings, industriell IoT, MF-TDMA, QoS, tillförlitlig kommunikation, RFID v

Acknowledgments

First, I would like to express my sincere gratitude and respect to my supervisors Prof. Hannu Tenhunen, Dr. Zhuo Zou and Prof. Lirong Zheng for the continuous support of my Ph.D study and research. I am grateful to Hannu for his patient guidance in all the time of research and writing of this thesis. I sincerely appreciate to Zhuo, my co-supervisor, without his help, I could not imagine when I can get my first journal paper published. Each progress I have made during the long PhD study, cannot be separated from his encouragement and assistance. My special thanks to Prof. Zheng for providing me the opportunity to join the iPack group. He can always bring creative ideas each time I have discussed with him. I appreciate having such good supervisors and mentors for my Ph.D life. Besides, I would like to thank Prof. Zhonghai Lu. He helped me a lot at the beginning of my PhD study. And many thanks to Dr. Qiang Chen. Dr. Chen has shared a lot of useful and interesting things in both the daily life and research experience in Sweden. Without his help, I cannot get me Swedish summary of this thesis ready. I am very grateful to the former and current members of my group for their continuous and selfless supports. Jia Mao, for his unique insights of UWB transmitter and receiver. The discussions with him have brought me some advice for the implementation of UWB based positioning platform from the circuit aspect. Dr. Majid Baghaei, for his prototype design UWB transmitter. Wei Ouyang, for his advice in embedded system programming. And thanks to Dr. Jue Shen, Qin Zhou, Dr. Ning Ma, Dr. Geng Yang, Dr. Zhi Zhang, Dr. Li Xie, Jie Gao, Qiansu Wan, Dr. Yi Feng, Dr. Liang Rong for accompanying me and supporting me along the research journey. Special thanks to Pei Liu and Qin, for their help of accommoda- tion. Thanks to the new PhD colleagues Kunlong Yang, and Yuxiang Huan. And I really appreciate all my friends, especially to those not mentioned, both in Sweden and in China, thank you indeed for helping me. I sincerely appreciate my colleague Amleset Kelati and Mohammad Badawi. They gave me some advice and helped me a lot during the application process of my thesis defense. Then I would like to present my gratitude to the advanced reviewer, the committee members, my opponent and the chairman for coming to attend and assist my disputation. I would also express my appreciation to all the colleagues from the PhD of- fice and Alina Munteanu for assisting the administrative work and the application process. At the end, I would like to thank my family, without your support and un- derstanding, I cannot have this valuable and memorable time in Sweden. Special thanks to my husband, for his accompany.

Chuanying Zhai, Autumn 2016, Stockholm vi Contents

Contents vii

List of Figures ix

List of Tables xii

List of Acronyms xiii

List of Publications xv

1 Introduction 1 1.1 Background ...... 1 1.1.1 The Internet of Things ...... 1 1.1.2 Enabling Technologies ...... 2 1.2 Motivation ...... 3 1.2.1 The Fourth Generation of Industry ...... 3 1.2.2 Challenges of System Implementation for the Industrial IoT . 4 1.2.3 Imperious Demand on Ubiquitous Positioning ...... 6 1.3 Contributions and Thesis Organization ...... 7

2 Industrial IoT and RFID System 11 2.1 Architecture of the Industrial IoT ...... 11 2.1.1 IoT System ...... 11 2.1.2 Characteristics of the Acquisition System ...... 13 2.2 Industrial IoT System ...... 14 2.2.1 Acquisition System Topology ...... 14 2.2.2 System Topology for Industrial IoT ...... 16 2.3 RFID System for Industrial IoT ...... 18 2.3.1 RFID Components ...... 18 2.3.2 Active RFID Technologies ...... 19 2.4 Protocols and Standards ...... 22 2.5 Summary ...... 24

vii viii CONTENTS

3 Reliable RFID Communication System with QoS Capability 25 3.1 Background ...... 25 3.2 Proposed System ...... 25 3.2.1 System Architecture ...... 25 3.2.2 MAC Protocol ...... 27 3.2.3 Synchronization ...... 28 3.2.4 Optional ARQ ...... 28 3.2.5 Packet ...... 29 3.2.6 Slot Allocation ...... 31 3.2.7 Communication process ...... 31 3.2.8 Protocol Implementation ...... 34 3.3 QoS Protocol Implementation ...... 35 3.3.1 MF-TDMA for Energy-constraint Monitoring ...... 35 3.3.2 MF-TDMA for Latency-constraint Tracking ...... 36 3.3.3 MF-TDMA for Reliability-aware Control ...... 41 3.3.4 Throughput and Packet Delivery Ratio ...... 44 3.4 Summary ...... 46

4 2.4-GHz/UWB Hybrid Positioning Platform 49 4.1 Background ...... 49 4.1.1 Context Awareness of the Industrial IoT ...... 49 4.1.2 Indoor Positioning Techniques ...... 49 4.1.3 Accurate Positioning using the UWB Technology ...... 51 4.2 2.4-GHz/UWB Positioning Platform ...... 52 4.2.1 System Architecture ...... 54 4.2.2 Hybrid tag ...... 55 4.2.3 Communication Process ...... 55 4.2.4 State-of-the-art UWB Receiver ...... 58 4.2.5 The Proposed SDR UWB Receiver ...... 60 4.2.6 Implementation and Experiment ...... 62 4.3 Summary ...... 66

5 Conclusions and Future Work 67 5.1 Thesis Summary ...... 67 5.2 Future Work ...... 68

Bibliography 71 List of Figures

1.1 A overlook of the IoT in industries [26]...... 2 1.2 Development of industry: from the ﬁrst generation to the fourth generation [48]...... 4 1.3 Development roadmap of the IoT [59]...... 6

2.1 The basic IoT system...... 11 2.2 The Industrial IoT structure...... 12 2.3 Basic system topologies...... 15 2.4 System topology for the industrial IoT...... 17 2.5 The operating frequency of the IEEE 802.15.4 compliant technologies [78]. 19 2.6 Pulses and spectrum of the UWB signal compared with the conventional sinusoidal narrow-band signal...... 20 2.7 The coexistence of the UWB and other technologies...... 21 2.8 Development of the RFID towards the industrial IoT and Industry 4.0. 21

3.1 The proposed system architecture. Adapted from Paper I ...... 26 3.2 The topology of the proposed system...... 27 3.3 An overview of the proposed MF-TDMA protocol. Adapted from Paper II ...... 28 3.4 The ARQ method: ARQ for the tags allocated to the same transmission channel. Adapted from Paper I ...... 29 3.5 The packet format.Adapted from Paper I ...... 29 3.6 The ﬂowchart of a tag in the communication process. Adapted from Paper I ...... 32 3.7 The ﬂowchart of the coordinator in the communication process. Adapted from Paper I ...... 33 3.8 Four-phase communication process of the MF-TDMA protocol.Adapted from Paper III ...... 34 3.9 Protocol description for the energy-constraint monitoring application. Adapted from Paper III ...... 35 3.10 Optimization of the guard time and transmission time in each slot for reducing the synchronization rate...... 36

ix x List of Figures

3.11 Protocol description for the latency-constraint tracking application. Adapted from Paper III ...... 37 3.12 Estimation of energy consumption of both energy-constraint and latency- constraint scenarios. Packet length: 60 bytes, reduced packet: 40 bytes, update cycle: 10 minutes, current for transmission, reception and sleep: 30 mA, 15 mA, and 1 µA, synchronization interval: 37.5 minutes and 28 seconds...... 37 3.13 Optimization of the slot allocation for reducing queuing time for transmission. Adapted from Paper I ...... 39 3.14 Queuing time for 50 ms (λ = 1/(50ms)) expectation of generation interval of 1000 tags using np-CSMA protocol, the initial MF-TDMA protocol, and the slot optimized MF-TDMA protocol. Adapted from Paper I ...... 40 3.15 Queuing time for 500 ms (λ = 1/(500ms)) expectation of generation interval of 1000 tags using np-CSMA protocol, the initial MF-TDMA protocol, and the slot optimized MF-TDMA protocol. Adapted from Paper I ...... 41 3.16 Queuing time for 500 ms (λ = 1/(500ms)) expectation of generation interval of 1000 tags using 5 channels. Adapted from Paper I ...... 42 3.17 Queuing time for 500 ms (λ = 1/(500ms)) expectation of generation interval of 1000 tags using 6 channels. Adapted from Paper I ...... 42 3.18 Protocol description for the reliability-aware control application. Adapted from Paper III ...... 43 3.19 The PDR and the throughput curves for diﬀerent tag allocations. . . . . 45

4.1 Comparison of power, coverage, and data rate of tags implemented by different short range wireless technologies...... 52 4.2 Hardware and software structure of the proposed positioning platform. . 53 4.3 The hybrid technology based positioning system...... 54 4.4 The UWB/2.4-GHz hybrid tag...... 55 4.5 The communication process used in each cluster...... 56 4.6 The state transition of the RF module of each hybrid tag...... 56 4.7 The operation flowchart of the 2.4-GHz/UWB hybrid positioning. Adapted from Paper IV ...... 57 4.8 Architecture of the correlator-based (Matched filter) UWB receiver. . . 59 4.9 Architecture of the energy detection (ED) based UWB receiver...... 59 4.10 Architecture of the self-delay based (AcR) UWB receiver...... 59 4.11 The block diagram of the proposed SDR-based ToA estimator...... 60 4.12 The ToA estimation process using the proposed ToA estimator...... 61 4.13 The structure of the SDR UWB reader network...... 62 4.14 The overview of the platform implementation...... 63 4.15 The absolute ToA estimation based on the received UWB signal. . . . . 63 4.16 A positioning example using TDoA estimation...... 64 List of Figures xi

4.17 The RMSE distribution of the estimated locations of one test point over 1600 times estimations ...... 65 4.18 Estimated average power consumption of Tx and Rx of each tag (2.4- GHz: 32 mA for Tx and 27 mA for Rx, the Tx period for position update is 5 ms, the Rx period for command is 5 ms, and Rx period for synchronization is 1 ms; UWB: 16 mA for Tx, the Tx period for position update is 1 ms)...... 65 List of Tables

2.1 The standards proposed for supporting the IoT...... 22

3.1 The MAC control word. Adapted from Paper I ...... 30

4.1 RTLS solutions. Adapted from Paper IV ...... 50

xii List of Acronyms

xiii xiv LIST OF ACRONYMS

AcR AutoCorRelation ADC Analog-to-Digital Converter AoA Angle of Arrival ARQ Automatic Repeat Query ASIC Application-Specific Integrated Circuit CoO Cell of Origin CSMA Carrier Sense Multiple Access DR Dead Reckoning ED Energy Detector ETSI European Telecommunications Standards Institute FDMA Frequency Division Multiple Access FP FingerPrinting GPS Global Positioning System H2C Human-to-Computer HF High Frequency IC Integrated Circuit IETF Internet Engineering Task Force IoT Internet of Tings IR-UWB Impulse Radio UWB ISM Industrial, Scientific and Medical KF Kalman Filter LF Low Frequency LLS Linear Least Square M2M Machine-to-Machine MAC Media Access Control MEMS Micro-ElectroMechanical Systems ms milliseconds NFC Near Field Communication ns nanoseconds PDR Packet Delivery Ratio PLR Packet Loss Rate ps picoseconds PSD Power Spectral Density QoS Quality of Service RFID Radio Frequency IDentification RMSE Root Mean Square Error RSS Received Signal Strength RTLS Real-Time Locating Systems RToF Round-Trip of Flight SDR Software Defined Radio SFD Start Frame Delimiter TDMA Time Division Multiple Access TDoA Time Difference of Arrival ToA Time of Arrival UHF Ultra-High Frequency UWB Ultra-Wide Bandwidth WSN Wireless Sensor Network List of Publications

Papers included in the thesis:

1. Chuanying Zhai, Zhuo Zou, Qiang Chen, Lida Xu, Li-Rong Zheng, and Hannu Tenhunen. "Delay-Aware and Reliability-Aware Contention-Free MF-TDMA Protocol for Automated RFID Monitoring in Industrial IoT," in Journal of Industrial Information Integration, vol.3, 2016, pp. 8-19.

2. Chuanying Zhai, Zhuo Zou, Qiang Chen, Lirong Zheng, and Hannu Ten- hunen. "High-Throughput and High-Eﬃciency Multiple Access Scheme for IEEE802.15.4 based RFID Sensing," 2015 IEEE International Conference on Ubiquitous Wireless Broadband (ICUWB), Montreal, QC, 2015, pp. 1-5.

3. Chuanying Zhai, Zhuo Zou, Yifan Qin, Ning Ma, Yuxiang Huan, Qiang Chen, Lirong Zheng, and Hannu Tenhunen. "QoS based RFID System for Smart Assembly Workshop," RFID Technology and Applications (RFID-TA), 2016 IEEE International Conference on, Shunde, China, 2016.

4. Chuanying Zhai, Zhuo Zou, Qin Zhou, Jia Mao, Qiang Chen, Hannu Ten- hunen, Lirong Zheng, and Lida Xu. "A 2.4-GHz ISM RF and UWB Hy- brid RFID Real-Time Locating System for Industrial Enterprise Internet of Things," in Enterprise Information Systems (Taylor & Francis Group), Avail- able online March 2016, pp. 1-18.

5. Chuanying Zhai, Zhuo Zou, and Lirong Zheng. "Software Deﬁned Radio IR- UWB Positioning Platform for RFID and WSN Application," 2012 IEEE International Conference on Ultra-Wideband (ICUWB), Syracuse, NY, 2012, pp. 501-505. Papers not included in the thesis:

6. Chuanying Zhai, Zhuo Zou, Qiang Chen, Lirong Zheng, and Hannu Tenhunen. "Optimization on Guard Time and Synchronization Cycle for TDMA-based Deterministic RFID System," RFID Technology and Applications (RFID-TA), 2015 IEEE International Conference on, Tokyo, 2015, pp. 71-75.

xv xvi LIST OF PUBLICATIONS

7. Chuanying Zhai, Zhuo Zou, and Lirong Zheng. "A Software Deﬁned Ra- dio Platform for Passive UWB-RFID Localization," Wireless Information Technology and Systems (ICWITS), 2012 IEEE International Conference on, Maui, HI, 2012, pp. 1-4. 8. Zhuo Zou, Botao Shao, Qin Zhou, Chuanying Zhai, Jia Mao, Majid Baghaei- Nejad, Qiang Chen, and Lirong Zheng. "Design and Demonstration of Passive UWB RFIDs: Chipless versus Chip Solutions," RFID-Technologies and Ap- plications (RFID-TA), 2012 IEEE International Conference on, Nice, 2012, pp. 6-11. 9. Dongxuan Bao, Zhuo Zou, Yuxiang Huan, Chuanying Zhai, Hannu Tenhunen, and Lirong Zheng. "A Smart Catheter System for Minimally Invasive Brain Monitoring." in Proceedings of the International Conference on Biomedical Electronics and Devices, SciTePress, 2015, pp. 198-203. Patent: 10. Chuanying Zhai, Zhuo Zou, Qiang Chen, Lirong Zheng, and Hannu Tenhunen. "A Multi-Antenna Ultra-wide Bandwidth (UWB) Receiver," US Provisional Patent, 2015. Chapter 1

Introduction

1.1 Background

1.1.1 The Internet of Things

The concept of the Internet of Things(IoT) was proposed by MIT Auto-ID Lab in 1999 [1] and formally introduced by the ITU Internet Report in 2005 [2]. From 2010, the IoT which has the vision to interconnect everything of both the physical world and the virtual world has attracted large amount of attention [3]. Beyond the conventional human-to-computer (H2C)/machine-to-machine (M2M) communications, advanced connectivity of services, systems, and objects while covering a variety of protocols, domains, and applications [4, 5] is expected to be presented by the introduction of the IoT. It is predicted that by 2025 more than 50 billion objects can be reached through the connection of the IoT [6, 7].

In the IoT, the "thing" can be a person with wearable sensor for remote heart monitoring, a herd attached to a bio-chip identifier for identification and position tracking, a machine cooperated with other tools and raw materials with micro- controller for manufacturing, or a computer with authorization for power management of city electricity. Evolved from wireless technologies, network technologies, micro-electromechanical systems (MEMS), and service, the IoT thus provides an immediate access to information of all the things, both natural and man-made, and motivates high efficiency and productivity in both business and daily life [8]. Especially, under the context of the industrial environment, the IoT technology has introduced a wide range of business opportunities and economic applications in various practical applications including utility grid [9–11], transportations [12, 13], healthcare [14–16], industrial automation [17, 18], manufacturing and assembly [17, 19–21], livestock and agriculture [22–25]. Figure 1.1 is application instances of the IoT from the enterprise aspect of view [26].

1 2 CHAPTER 1. INTRODUCTION

Figure 1.1: A overlook of the IoT in industries [26].

1.1.2 Enabling Technologies From the technology aspect, the IoT is enabled by the improvements of the under- lying technologies in terms of sensor and microprocessor designs, Radio Frequency Identiﬁcation (RFID) and communication technologies, and advanced connectivi- ties and networks [27, 28].

Smart Sensors As the development of the technology of chip electronics, the size and cost of the chip decline and the performance improves. It is feasible to produce wearable and injectable small high-speed sensors even on the ﬂexible substrates. Additionally, the rises of microprocessors and multi-core processors [29, 30] capacitate huge increasing on performance and eﬃciency of the sensors. The sensors that can condition the sensed data, program custom-built functions for collecting valuable information, and communicate with others over wire or wireless medias become intelligent, that is, the smart sensors [31]. The smart sensors make their attached objects closely linked to the IoT via the capabilities of sensing, digital processing and radio communicating [32].

Communication and Network Protocols To connect the tremendous physical objects to the Internet, advanced protocol, on one hand, such as IPv6 [33, 34] was introduced to accommodate the requirements of addressing all the objects. On the other hand, the eﬃcient and reliable networks are needed to promise the communications of the objects with others and computer systems. Besides, various communication technologies and the corresponding protocols such as Wi-Fi, Bluetooth, Bluetooth Low Energy, ZigBee, IEEE 802.15.4, Z-Wave, 1.2. MOTIVATION 3

LTE, Near Field Communication (NFC), RFID, Ultra-Wide Bandwidth (UWB), and IrDA, can be used in the IoT [35–39].

RFID Technology

In addition to sensing, identification, which is crucial to name and match services in the IoT, is another key enabler. Since there are billions of objects connected in the IoT, apart from their addresses for network, unique identification is demanded for correctly collecting information from the object. The RFID technique, which can automatically identify via radio waves the tags attached to the objects, is considered as the prerequisite of the IoT. Compared with conventional barcode, the RFID enables faster identification rate, longer operation range, larger data capability, reuse and read-write feasibility, and higher level of security by storing the identification information of the objects in the RFID tags.

1.2 Motivation

1.2.1 The Fourth Generation of Industry

Increasing competitions and global challenges of customer requirements and volatile market developments force more industrial companies to turn to high-tech method- ologies. The industries are therefore transforming to a new era of evolution which is known as Industry 4.0, as shown in Figure 1.2 [40]. This development is driven by several factors. First is the M2M technology which enables large-scale automated production by introducing self-learn and self-organize machines to the workshops [41, 42]. The second driver is the development of high-speed broadband technology [43, 44]. It makes the real-time data exchanging achievable anytime anywhere. The third driver is the cloud computing and big data. Thanks to the rapid advance of such cloud computing and big data technologies, massive data storage, analysis, and processing become feasible in the industry. The last driver is the IoT, which can connect everything into a huge network. Through the industrial IoT, together with the help of sensors, data acquisition solutions, and network technologies, the industries can possess not only intelligent, and self-adaptable machines/products but also self-organized system and manufacturing [45]. In summary, the fourth generation of industry expects worry-free system with the abilities in terms of self-conﬁguration, self-diagnostics, self-maintenance, and self-optimization [46, 47]. Such system also enables a better visibility into the manufacturing. The resource consumption, equipment performance, and the safety state in the industry can be explicitly observed. This can thus enhance the industry eﬃciency. It is notable that the industry is gradually experiencing the IoT phase, that is, the industrial IoT. 4 CHAPTER 1. INTRODUCTION

First (Industry 1.0) Second (Industry 2.0) Third (Industry 3.0) Fourth (Industry 4.0) Industrial revolution Industrial revolution Industrial revolution Industrial revolution

Introduction of Introduction of a division of Automate Cyber-physical mechanical production labor and mass production production systems facilities

water and steam power electrical energy electric and IT systems First programmable First mechanical First assembly line logic control loom,1784. Cincinnati slaughterhouses, 1870. (PLC), Modicon 084, 1969.

Figure 1.2: Development of industry: from the ﬁrst generation to the fourth generation [48].

1.2.2 Challenges of System Implementation for the Industrial IoT • Scalability The ability of scalable and deployable of an IoT system is highly desired for supporting the industrial environment [49] because of the huge number of objects connected and distributed at indoor, outdoor, or multiple ﬂoors. For example, in a manufacturing workshop, from raw materials, tools, assembly line, products, machines and facilities, to other monitoring equipment are all connected. Along with the production process, some of the components may be moved from one place to another, and some of them may be assembled and packaged. The system should be able keep tracking of the components to promise continuous control. In addition, the system should have the ability to support the change of the states of each object. That is, when an object is used up, or it is assembled together to others, the system has to adjust the control of the object or release the space for the newly involved object.

• Flexibility The ﬂexibility of the industrial IoT system represents its adaptive ability for supporting multiple hardware/protocol infrastructures, the compatibility of providing seamless service for the objects from other systems, and the uniformity for multiple functions in one system. First, there have been a lot of communication systems proposed for collecting information from the objects and interacting for the IoT [50–53]. However, most of the systems and protocols are designed based on a single microprocessor and wireless interface to support only one communication technology and one compliant protocol. As the universal development of the IoT, the smart tag which can comprise multiple processors and support multiple wireless in- 1.2. MOTIVATION 5

terfaces [49, 54] is expected for handling complex computing and communication requirements. Consequently, it requests the system and it corresponding protocol to be feasible to sustain such multi-core multi-interface even multi- protocol hardware infrastructure.

When shifting the existing network infrastructures to the IoT, the compatibility is another challenge for the system implementation. Because the lack of universal protocols/standards, the designed systems and some of the machines are using various technologies and protocols for communication. An- other challenge is that in some of the industrial environments, only specific wireless technologies are applicable. For instance, in a metal fabrication workshop, most of the wireless techniques such UWB, Bluetooth, and ZigBee, can hardly be used. Additionally, in the same industrial environment, there exist various contexts of usage. For example, in a fruit warehouse, there are sensors for monitoring to indicate the state change, and smart tags attached to the packages of fruit for identifying the time and transportation information. The two types of tag feature different information format and various communication characteristic. Consequently, it is desired that the system designed for the industrial IoT should be able to provide equivalent performance when different technologies or multiple communication protocols are employed.

• Time/Energy Eﬃciency

From the market aspect, one of the significant trends of the industrial IoT is to provide greater customer focus and more customer-specific service [55]. The main goal of introducing the IoT to the industry is to achieve shorter lead times by optimizing the process time and reducing the response time of faults to boost the efficiency of industries. From the industrial aspect, it is expected to minimize the energy consumption while maximizing the system throughput and quality by applying the smart machines and the smart networked system [56]. Therefore, as the cornerstone of the implementation of industrial IoT, the data collection system which takes care of the data sensing and interacting based on the physical objects is liable for providing more latency-efficient and energy-efficient service.

• Cost

Cost is what the industries concern most when turning into the industrial IoT. Although most of the technologies employed such as robotics, biotech- nology, sensor technology, and microelectronics technology, are introduced decades ago. It is the reduction in the cost of computing power and microelectronics process enables the technologies to be used in the industrial environment [57, 58]. Consequently, realizing the system in a non-complex and easy-maintenance manner is the goal of the industrial IoT application. 6 CHAPTER 1. INTRODUCTION

1.2.3 Imperious Demand on Ubiquitous Positioning Figure 1.3 illustrates a roadmap of the development of the IoT [59]. It can be seen that accurate positioning capability is an indispensable factor as the growth of the IoT. Under the context of the industrial IoT, the objective of ubiquitous positioning is to locate people, products, machines, and other objects anytime anywhere both in the indoor and the outdoor condition [60].

Advance sensor fusions, services oriented software agents Miniaturization, power- efficient electronics, multi- core, broadband, cloud Teleoperation and computing telepresence. Ability to monitor and control Cyber-physical world Indoor and outdoor distant objects location ability of devices and persons to receive geolocation Locating people and signals the entire objects both in business and daily life Ubiquitous Positioning Cost reduction, universal application Surveillance, security,

Technology Research Technology healthcare, transport, Demand for expedited food safety, document Vertical-Market Applications logistics management

RFID tags for facilitating routing inventorying and loss prevention Supply- Chain Helpers

2000 2010 2020 Time

Figure 1.3: Development roadmap of the IoT [59].

Especially, for the context-aware industrial IoT, the capability of accurate positioning is essential. For example, with the help of the smart sensors and wireless communication, the production process can be automated controlled. However, when mechanical fault or run-time error occurs, it demands the system/network be capable of accurately locating the position of fault/error both in the physical space and in the cyber world [61, 62]. Another practical need appears in the safeguard of valuable objects including people in special working environments. It is demanded in the fields like fresh fruits/assets transportation [63, 64], tracking of worker in mining, fire fighting and other dangerous conditions, and so on [65–67]. Moreover, the ubiquitous positioning systems applied in factories, farms, and utilities enables improvement of productivity and value by providing real-time location information of products, animals, and facilities. The stored location data also offers valuable information for optimizing progress management, resource allocation, and breeding method [68]. From another perspective, to realize the ubiquitous positioning in the industrial IoT, it requires the system to execute both the communicating and the locating. 1.3. CONTRIBUTIONS AND THESIS ORGANIZATION 7

Up to now, there is no international approach for developing such ubiquitous positioning system and its associated infrastructure. That is, the existing positioning systems can hardly offer a satisfied performance in the industrial environment. For instance, the Global Positioning System (GPS) can not provide accurate location information for indoors. The systems designed for using in the indoor environment, such as the radio based WiFi positioning [69] and the non-radio based magnetic positioning [70], are limited either by the expense of the equipment/installation or the accuracy. Moreover, as the working scenarios of the industries contain both indoor and outdoor condition, it requires the positioning system to be capable of supporting seamless positioning and precision. Meanwhile, the various accuracy demands in different applied conditions rely on a number of numerical computing or a group of fitting comparison. This requires the system to have not only a strong computing capability for processing the location data but also an adaptive algorithm for supporting flexible accuracy in complex environments [71].

1.3 Contributions and Thesis Organization

The thesis is organized as follows:

Chapter 2 In Chapter 2, the architecture of the IoT system is ﬁrst introduced. Based on the architecture, an acquisition system which takes care of the communication among the physical objects and the network server is pointed for the industrial IoT architecture. Then the appreciated architecture, the communication techniques and the corresponding protocols design of the acquisition system are discussed.

Chapter 3 Based on the characteristics of the acquisition system, an RFID communication system with the discrete structure gateway and contention-free protocol is designed in this chapter. The detailed implementations of the RFID system and the protocols for resolving the requirements, that is deterministic access, deployable capability, ﬂexible latency, reliable communication, and QoS performance, of the industrial applications are illustrated. Additionally, under the consideration of various QoS demands, instance implementations of the designed RFID communication system are demonstrated to explain the ﬂexibility of the designed system.

Contributions:

• First, a discrete-structure gateway based RFID system is designed. The designed system architecture enables multi-channel multi-technique communications and ﬂexible system capacity by adjusting the occupied number of channels or the number/type of readers. As it separates the conventional 8 CHAPTER 1. INTRODUCTION

function of a gateway device to a coordinator and a set of readers, a dedicated frequency channel can be used in the coordinator to perform all the control work, meanwhile, the readers can focus on reception. Second, a delay-aware and reliability-aware communication protocol is designed. To guarantee the performance in various industrial applications, the designed communication protocol, which is called MF-TDMA protocol, uses an optional ARQ mechanism, the independent/uniform synchronization and control manner, and the slot allocation optimization method to lower the delay, improve the reliability, and support the capability of adaptive quality. Third, QoS based protocol implementations are explores. To illustrate how the system and its protocol can be used for diﬀerent qualities of performance, the communication process is expressed as a 4-stage process. Based on this, instance implementations for diﬀerent performance requests are designed as reference for the industrial applications.

The included papers:

• Paper I: Chuanying Zhai, Zhuo Zou, Qiang Chen, Lida Xu, Li-Rong Zheng, and Hannu Tenhunen. "Delay-Aware and Reliability-Aware Contention-Free MF-TDMA Protocol for Automated RFID Monitoring in Industrial IoT," in Journal of Industrial Information Integration, vol.3, 2016, pp. 8-19.

• Paper II: Chuanying Zhai, Zhuo Zou, Qiang Chen, Lirong Zheng, and Hannu Tenhunen. "High-Throughput and High-Eﬃciency Multiple Access Scheme for IEEE802.15.4 based RFID Sensing," 2015 IEEE International Conference on Ubiquitous Wireless Broadband (ICUWB), Montreal, QC, 2015, pp. 1-5.

• Paper III: Chuanying Zhai, Zhuo Zou, Yifan Qin, Ning Ma, Yuxiang Huan, Qiang Chen, Lirong Zheng, and Hannu Tenhunen. "QoS based RFID System for Smart Assembly Workshop," RFID Technology and Applications (RFID- TA), 2016 IEEE International Conference on, Shunde, China, 2016.

Chapter 4 A positioning platform which hybrids the 2.4-GHz RF and the UWB techniques for providing accuracy from meter level to centimeter level is explored in this chapter. The RFID tag for positioning application is designed to have both the 2.4-GHz RF and the UWB interface for updating the object’s location on demands. To take advantage of the UWB technique in the time domain, time of arrival (ToA) and time diﬀerence of arrival (TDoA) based positioning algorithms are proposed. And a software deﬁned radio (SDR) UWB reader as part of the platform is introduced for demonstrating the positioning.

Contributions: 1.3. CONTRIBUTIONS AND THESIS ORGANIZATION 9

• First, a positioning platform based on hybrid communication technologies is designed. It uses a 2.4-GHz transceiver with 8051 core as the coordinator to send controls to the tags. And both the 2.4-GHz RF and the UWB readers are used as the readers to receive the tags’ information. Second, a hybrid tag which contains the same 2.4-GHz transceiver and a UWB transmitter is designed. It enables the tag to receive the command from the coordinator through the 2.4-GHz receiver, and to transmit data to the readers through either the 2.4-GHz transmitter or the UWB transmitter. Without the UWB receiver, the hybrid tag can work in a power-eﬀective manner while supporting the capability of centimeter level accuracy thanks to the pulse-based UWB signals. Third, an improved positioning algorithm is proposed to provide high conﬁdence of accurate positioning performance. Fourth, an SDR UWB reader network is built based on an oscilloscope. Since it supports the third party program, multiple ToA/TDoA based positioning algorithms are achievable. The implementation of this hybrid positioning platform is considered as an instance demonstration of the designed RFID system.

The included papers:

• Paper IV: Chuanying Zhai, Zhuo Zou, Qin Zhou, Jia Mao, Qiang Chen, Hannu Tenhunen, Lirong Zheng, and Lida Xu. "A 2.4-GHz ISM RF and UWB Hybrid RFID Real-Time Locating System for Industrial Enterprise Internet of Things," in Enterprise Information Systems (Taylor & Francis Group), Available online March 2016, pp. 1-18. • Paper V: Chuanying Zhai, Zhuo Zou, and Lirong Zheng. "Software Deﬁned Radio IR-UWB Positioning Platform for RFID and WSN Application," 2012 IEEE International Conference on Ultra-Wideband (ICUWB), Syracuse, NY, 2012, pp. 501-505.

Chapter 5 This chapter concludes the thesis and discusses the future work under the considerations of the ubiquitous industrial applications towards the IoT.

Chapter 2

Industrial IoT and RFID System

2.1 Architecture of the Industrial IoT

2.1.1 IoT System From the view of application types, the IoT system can be basically modeled by a three-layer architecture [72] consisting of the Object Layer, the Network Layer, and the Application Layer as shown in Figure 2.1. The Objects Layer includes the ’things’ which are connected to the Internet. In

Application Layer

Network Layer

Object Layer

Figure 2.1: The basic IoT system.

11 12 CHAPTER 2. INDUSTRIAL IOT AND RFID SYSTEM

Service Application

Network Layer

Network (WiFi, Fiber, Mobile, etc.)

Gateway Object Layer

RFID Techniques (ZigBee, Bluetooth, UWB, 6LowPAN, etc.)

Objects

Sensors (Temp., Accel., Power, Light, etc.)/Embedded chips

Figure 2.2: The Industrial IoT structure.

this layer, information of the objects is acquired, digitized and then transferred to the Network Layer. It relies on various sensors and embedded chips to achieve state information such as temperature, speed, pressure, etc., and inherent information such as identification, ingredient, usage, etc., respectively. The function of the Network Layer can be divided into three parts: the abstraction, the service management, and the middleware. The abstraction refers to the wireless interface which is used to transport the data from the Object Layer to the service management. Then the data is processed and digested by the server management according to different application requirements. However, there may exist different types of service over the IoT implementations, the objects can only connect and communicate to others using the same service. Thus, before the information is delivered to the Application Layer, a middleware which is employed to perform ubiquitous computations and decisions based on different application requests. As shown in the basic IoT system structure, sensors or embedded chips are supposed to communication directly with the central server. This structure may not fit most of the IoT application well especially in the industrial environment because of the huge amount of objects and the various types of information. Therefore, as illustrated in Figure 2.2, the gateway device is used at the Object Layer for the industrial IoT system [73]. Instead of a direct connection between the objects and the server/Internet, the objects communicate via the gateway. In the practical implementations, there are multiple gateway devices and each device can handle the work of data collection, data exchange, processing, and communication for multiple objects. This guarantees the efficient and accurate management of the 2.1. ARCHITECTURE OF THE INDUSTRIAL IOT 13 huge volume of data in the industrial IoT system. The gateway has both the interface for communicating with the objects and the interface for connecting to the upper network. Hence, one gateway should be able to take care of multiple technologies/standards and objects. The system applied in the Object Layer, that is an acquisition system, is then comprised of the gateway devices and the objects (attached to sensors and embedded chips) as the basis of the industrial IoT system.

2.1.2 Characteristics of the Acquisition System Considered the industrial environments and its requirements, the acquisition system should feature a number of unique characteristics as illustrated in the following parts.

• Determinacy Unlike the IoT system for consumer applications which need to handle a large number of random scenarios, the industrial applications are normally deterministic. That is, first the objects connected to the network such as the workers, machines, products, etc., are relatively fixed. Second, the working processes in the industries are already established. For example, in a manufacturing workshop, the operating steps, sequence, and the involved tools, machines are pre-specified. In other words, the whole process is known. At last, the purpose of each operation, the usage of each component, and the request information of each procedure are all determined. Overall, it can be seen that there is few unpredictable event in most of the industrial applications except errors. Therefore, the acquisition system can use the schedule-based deterministic manner to communicate with the objects. In this case, it also lowers the complexity of implementation and avoids missing objects during the operations.

• Reliability Another property of the industrial IoT system is that it demands high reliability. When it shifts to rely on the information exchange among the objects without manpower, reliable data becomes fatal to promise the normal and eﬃcient operations in the industries. Consequently, failure resistance mechanism is the requisite part in the acquisition system. The mechanism should be able to consist of three aspects: data re-transmit, data overload, and object recovery. The ability of data re-transmit is used to guarantee reliable message delivery during routine operations by enabling re-transmission of the failure message. And the data overload is an ability to recognize the emergency or crucial message and pass it to its destination even the system is operating at a full-load state. It requires the used protocol of the acquisition system to specify such messages in an expedited manner. The object recovery is used to deal with system death (object lost). Sometimes, if one node in the system is down, the following operation may halt unless the node recovered. 14 CHAPTER 2. INDUSTRIAL IOT AND RFID SYSTEM

Otherwise the system may lose control of that node, and the following work may be aﬀected. To resolve this problem, a protocol that supports optimized communication, pre-deﬁned timeout period, and rollback methods is needed. It means that, the communication can skip to the next node without waiting for the failed node when its response period times up. Meanwhile, the system can accept the data from the failed node in a new scheduled communication turn when the node falls back to a previous state for recovery.

• Latency Sensitivity In the industrial IoT system, real-time response is required in most of the applications. Although compared with some mission-critical applications which desire millisecond level, that is 1 ms response, the response delay of normal industrial applications is up to the hundred-millisecond level [74, 75]. The great challenge of latency performance in the acquisition system refers to the multiple objects in one operation. That is, the protocol has to promise the average latency of all the involved objects to obtain a short-delay level. A long delay of one object in the operation may result in a long system latency.

• Deployment and Development As there are multiple gateway devices to handle the objects in the acquisition system. The diversity and variability of industrial applications request the acquisition system to be deployable and expandable for the quantity and information type change of the objects. For example, the industrial environment usually has multiple floors and workshops. And one object may move among different places and change its role in different areas. If all the areas are managed by one gateway device, it should be able to switch to accept different data types of the object in different areas. In addition, the protocol for the system is required to be capable of adapting the varying system capacity.

• Quality of Service The quality of service in the industrial IoT system can be considered as the containment of supporting ﬂexible qualities according to diﬀerent application requirements. Additionally, response delay, energy consumption, and communication reliability are the major concerns of the service quality, the acquisition system needs to be able to boost the corresponding performance on demand of the requirements.

2.2 Industrial IoT System

2.2.1 Acquisition System Topology Various enabling system topologies used in the acquisition system can be categorized into four basic types, point-to-point topology, star topology, bus topology, and mesh topology. 2.2. INDUSTRIAL IOT SYSTEM 15

(a) point-to-point (b) bus (c) star

(d) extended star (e) distributed star (f) mesh

Figure 2.3: Basic system topologies.

Point-to-Point Topology A point-to-point network [76] shown in Figure 2.3(a) is the simplest structure which has a direct connection between two nodes, such as an object and a gateway. Be- cause communication is limited between the two connected nodes, this kind of system can hardly be scaled for a large amount of objects and long operation distance.

Bus Topology The bus topology [76] as shown in Figure 2.3(b) relies on a single cable, named bus, to connect a server (a terminator) and multiple nodes. Data travels in bidirectional along the bus until it ﬁnds the recipient. Because the bus topology only communication through a single bus, the whole network crashes when it fails, and the network scale is limited by the bus.

Star Topology The star topology [76] consists of a master node (server or gateway), and a set of slave nodes that are connected to the master node as shown in Figure 2.3(c). The master node acts as a common connection point for the connected slave nodes. All the slave nodes can interact with the master node by sending data to it and receiving data from it. In this topology, the slave node can communicate with others through the master node by considering it as a hub. 16 CHAPTER 2. INDUSTRIAL IOT AND RFID SYSTEM

Similar to the point-to-point network, data packet travels directly between the master and slave or maximum two hops to reach its destination. A high-throughput low-latency performance can be achieved using this network. Additionally, each slave node is independent and isolated to the other slave nodes. This makes the system more reliable by simply ignoring the error nodes from the network. However, because the whole connection is master dependent, the system fails once the master node meets failure or interruption. Based on the star topology, as can be seen from Figure 2.3(d) and 2.3(e), an extended star topology and a distributed star topology are available [76]. The extended star topology has the ability to cover a larger distance by adding repeaters to build a star link between the master nodes and a higher level node. The extended topology is also referred as a hierarchical star topology. The distributed star topology connects multiple independent star networks. One of the master nodes of these networks is considered as the central node. It can also extend the system’s coverage.

Mesh Topology Figure 2.3(f) is a basic mesh topology network [76]. In the mesh topology, three types of nodes exist: a gateway node performs like a master node in the star topology, router nodes that can both capture/disseminate their own data and act as the routers for other nodes connected to them, and simple nodes which only have data capture/dissemination capabilities. Thus, each node of the mesh system is connected to more than one nodes. A data packet generated by one node may pass through multiple nodes to reach its destination. It is obviously that by using the multi-hopping mechanism, the system based on the mesh topology can work in a long range and large area compared with other topologies. As the path of data transmission is not fixed, the mesh topology features self-healing to re-route the data path once a failure occurs in its original route. This flexible configuration also enables free quantity change of nodes in the network. However, the multiple hopping transmission of data undoubtedly results in high network latency and low communication reliability. And collisions and cor- ruptions are unavoidable under the condition of high latency. The system/protocol complexity is thus extremely increased when there is a large number of nodes which all demand a reliable access.

2.2.2 System Topology for Industrial IoT Among the topologies, the extended star based architecture as shown in Figure 2.4 is considered as one of the most practical topologies for the industrial environment. A point-to-point link is built between the objects and the gateway device to ensure the direct communication. Although it faces some disadvantages compared with the mesh architecture used in the wireless sensor network (WSN). That is, its coverage range is limited, there is no direct communication between two objects, and the 2.2. INDUSTRIAL IOT SYSTEM 17 end-to-end delay may be large. The advantages of the extended star structure outperform others in the industrial applications.

Objects (sensors&chips)

Application/ Gateway service

Application/ Gateway Network/Server service

Application/ Gateway service

Figure 2.4: System topology for the industrial IoT.

1. The direct connection between each object and the gateway device avoids failures caused by another object in the multi-hop case. It also saves the eﬀort spent on the computation of optional paths for fault tolerant in the multi-hop scenarios. For failure or error of one object, the gateway can fast locate and handle it in the extended star system.

2. In this structure, the schedule based protocol is easier to implement than the mesh topology. Because in the multi-hop scenarios, if an optional path is selected, the scheduled communication may not be the optimized one. In other words, the latency may increase, and extra computation is required.

3. The information from one object to another is delivered via the gateway device. For two objects that have large physical distance, this delivery method oﬀers shorter end-to-end delay then. Additionally, it lowers the complex computing and extra energy consumption of the chip attached to each object by avoiding the judgment of each message it received. The power is saved by setting the chip to the low-power mode when idle rather than receiving and transmitting data for other objects. 18 CHAPTER 2. INDUSTRIAL IOT AND RFID SYSTEM

4. The signiﬁcant merit of the extended star structure is that it is uncomplicated to implement and simple to extend. The maintenance cost is low for the industries. And it gives its protocol more space to focus on the improvement of communication reliability and failure resistance. Although the failure of the gateway device is fatal for the system, it can be resolved by employing redundant gateway or other methods. The additional cost is low compared with increasing the complexity of the whole system.

2.3 RFID System for Industrial IoT

An industrial IoT system is implemented on the basis of a reliable data communication between the physical objects and the gateway device in the acquisition system. Consequently, accurate and reliable technologies used for delivering the information for such communication become the key enabler of supporting the operation of the acquisition system. Among others, active RFID technologies such as IEEE 802.15.4 compliant/ZigBee, UWB, and UHF RFID are more appropriate because they are capable of realizing low-power low-cost implementation while promising the communication reliability.

2.3.1 RFID Components RFID is formed as a wireless communication technology which uses radio waves to realize data delivery. A basic RFID system consists of RFID tags and readers. The tag, also known as a transponder, is attached to target item for collecting data (via sensors) and then transmitting it to the RFID reader. The reader is a used to send commands for interrogating the tags, and receive the data delivered from the tags for processing. It works like an access point for the RFID tags so that the information of the target items can be available for further applications and services. An RFID tag is normally an integrated circuit (IC) chip which is basically composed of an RF transceiver and an antenna. Depending on the power solution, there are three types: passive tags, semi-active tags, and active tags. The passive tag has no power supply on the chip. It relies on the received radio waves from the reader. Therefore, an RFID system based on the passive tags can perform limited tasks and can hardly support any extended functions or long-distance communication. Such RFID tag is widely used in retail business to replace the conventional barcode thanks to its extremely low-power low-cost solution [77]. The semi-active tag has a battery for routine work of the RFID chip, but it still needs the reader’s power for broadcasting. It can communicate in a larger area compared to the passive tags, but suﬀers from limited applications too. The active tag usually uses an external battery to fully provide power for the chip. It is thus capable of supporting various peripheral circuits for extra functions. For instance, it can have storage module to save more information than the passive tags, or it can support a microprocessor on 2.3. RFID SYSTEM FOR INDUSTRIAL IOT 19 the chip for multiple sensors. The RFID tag equipped with such microprocessor and sensors can then be considered as a smart tag which is able to not only detect, col- lect data from its attached item, but also pre-process the data or perform self-check before and after communication. Consequently, an RFID system implemented by such active tag becomes a great candidate for the IoT application. An RFID reader, similarly, is comprised of an RFID transceiver, an antenna, the Internet or wireless technology interface, and an optional storage, microprocessor, and so on. The RFID reader can communicate with tags within its operation area. It is able to read and write the tags. Depending on the complexity of the reader, it can perform simple or complicated processing of the data for the connected computer/server. Overall, the whole RFID system can be recognized as a bridge between the target items and the service system which needs the information of the targets. It can deliver information for them. So with the help of a network of the computers/servers which have readers connected, the IoT can be achieved.

2.3.2 Active RFID Technologies

Figure 2.5: The operating frequency of the IEEE 802.15.4 compliant technologies [78].

RFID can be classified into low frequency (LF), that is 120 KHz to 150 KHz, high frequency (HF), that is 13.56 MHz, ultra-high frequency (UHF), that is 433 MHz, 860 MHz to 960 MHz, and microwave that is, 2.45 GHz, 3.1 GHz to 10.6 GHz, according to the operation frequency band [77]. The UHF (433 MHz) and microwave bands such as the IEEE 802.15.4 compliant industrial, scientific and medical (ISM) radio bands, ZigBee, UHF RFID and UWB are appreciatable in the active RFID system. • IEEE 802.15.4 compliant The IEEE 802.15.4 is a standard that specifies a low-power low-cost physical layer and the media access control (MAC) for the wireless link in the 868 20 CHAPTER 2. INDUSTRIAL IOT AND RFID SYSTEM

MHz/915 MHz and 2.45 GHz bands for industrial applications [79] as shown in Figure 2.5. It can be further used with the 6LoWPAN as a network layer to realize the wireless embedded Internet. It works as a basic protocol standard and permits extension for speciﬁcations.

• UWB

The impulse radio UWB (IR-UWB) technology (3.1 GHz to 10.6 GHz) uses ultra-short pulses over a large radio spectrum for data transmission. Unlike the conventional narrow-band communication such as Wi-Fi and UHF-RFID which employs continuous carrier wave for data transmission, the IR-UWB transmits repetition of pulses in pico-second (ps) to nano-second (ns) level in the time domain to represent data information. Compared with duty cycle, the pulse duration is quite short. Thus the average power computation for the same data is reduced. The energy of IR-UWB signal is distributed over an ultra wide bandwidth (> 500 MHz), so it can transmit without interfering with other narrow-band technologies. That is, it is able to coexist with others by sharing the same spectrum. As shown in Figure 2.6 and Figure 2.7, the UWB can share the same advantage of the expanded bandwidth as the spread-spectrum technology. But since the large bandwidth of the UWB signal is generated by the short duration of the pulse, it also enables precise time-domain resolution. This makes the UWB a good candidate for accurate positioning based on time-based solutions [80].

Figure 2.6: Pulses and spectrum of the UWB signal compared with the conventional sinusoidal narrow-band signal. 2.3. RFID SYSTEM FOR INDUSTRIAL IOT 21

Figure 2.7: The coexistence of the UWB and other technologies.

Figure 2.8 exhibits a development map of the RFID technology [81, 82] from the first generation which can only perform identification to the fourth generation and fifth generation which enable active and powerful RFID devices for extra computing and networking operation for acting as smart devices.

Complexity

• Smart ubiquitous device • Monitor/track/control/identify/sense • Fault tolerant • Network support • Remote monitor • Accurate positioning capability • Multi‐ sensing • Multi‐core multi‐antenna multi‐chip • Extra‐computing • Fine power management • Localization th • Sensing 5 Gen • Simple data processing th • Power management 4 Gen Active/self‐adaption (power compensation) Active rd • Anti‐collision 3 Gen • Re‐writable Passive Semi‐passive 2nd Gen Antenna • Identification 1st Gen Passive/backscatter

Time

Figure 2.8: Development of the RFID towards the industrial IoT and Industry 4.0. 22 CHAPTER 2. INDUSTRIAL IOT AND RFID SYSTEM

2.4 Protocols and Standards

Numerous standards are proposed to facilitate the implementations of the IoT system. For example, Internet Engineering Task Force (IETF), Institute of Elec- trical and Electronics Engineers (IEEE), EPCglobal, 6LoWPAN, and European Standards Organizations like European Telecommunications Standards Institute (ETSI), European Committee for Standards and so on. They provide protocols focusing on the whole IoT applications from the physical (sensor device) aspect to the application aspect. Table 2.1 illustrates some of the common standards in support of the IoT [83–86].

Table 2.1: The standards proposed for supporting the IoT.

Service Layer CoAP, MQTT, XMPP, AMQP, TLS/SSL, HTTP, DNS, MODBUS, etc. Network Layer 6LoWPAN, IPv6, IPv4, TCP, UDP, DTLS, etc. Object Layer IEEE 802.15.4 MAC/PHY, IEEE 802.11, Ethernet (IEEE 802.3), Wireless LAN, PLC, UWB, Z-WAVE, etc.

The protocol which is used for establishing the object communications, that is, the link layer (MAC) protocol for the RFID system, dominates the physical-based industrial IoT system. The link layer protocols are generally categorized three groups, contention-based, contention-free and hybrid.

Contention-based The contention-based protocols are implemented based on either synchronous or asynchronous transmission schedules. The nodes, that is the objects, contend for the channel access in various ways to acquire and transmit data depending on the protocols. All contention-based protocols are intending to improve the delay performance. For example, the protocols based on ALOHA and slotted ALOHA [87] aiming to promise a channel access of each node as fast as possible. The drawback of this kind of protocols is the low throughput which is limited by the high collision rate since the nodes using these protocols apply a random mechanism to obtain the channel access. Another cluster of contention-based protocols is developed based on the S-MAC protocol, such as S-MAC-AL [88], DSMAC [89], energy-eﬃcient MAC [90], and carrier sense multiple access (CSMA) [91, 92] protocols. In these 2.4. PROTOCOLS AND STANDARDS 23 protocols, either adaptive active period is adopted to reduce the forwarding delay or path-aware transmission schedules are employed to achieve an optimal delay.

Contention-free In contrast to the contention-based protocols, the contention-free protocols rely on the strictly scheduled coordination of all nodes to eliminate the collision issues in the network. The contention-free protocols can hardly achieve the minimum delay but they can potentially provide a bound of transmission delay for all nodes, thus enabling a guaranteed overall delay performance. Time division multiple access (TDMA) and frequency division multiple access (FDMA) are two common techniques for transmission schedule-based protocols. In addition, relying on the basic TDMA and FDMA techniques, dedicated hardware and multiple channels are employed in some protocols to achieve network-wide synchronization, high throughput and guaranteed delay, such as RT-Link [93] and HyMAC [94].

Hybrid The hybrid protocol combines two or more protocols to take advantage of them aiming to improve both the performance of delay and reliability. For instance, the CSMA-TDMA hybrid protocols [95, 96] employs contention mechanism at the node access phase, and then uses scheduled communication of the accessed nodes. Although the protocol reduces the contention probability for higher throughput, it incurs extra delay and energy consumption compared with the pure TDMA protocol. A contention-FDMA protocol [97] further introduces frequency diversity to enlarge its scalability. It employs one of the available channels as a control channel, and similar contention-to-schedule mechanism in each time intervals as the CSMA-TDMA protocol for communication. Although the contention-FDMA addresses better bandwidth utilization, the overhead and uncertainty of access still exist. Additionally, many protocols employ mechanisms such as ARQ, power control, and path control to support QoS performance in terms of transmission delay, reliability and energy consumption [98, 99].

Protocol for RFID System in Industrial IoT Overall, considered the property of determinacy of the industrial IoT and its high- reliability requirement, the contention-free protocol is a proper candidate. Al- though, the schedule-based suﬀers limited latency sensitivity, it can provide a promised delay bound for all the connected objects. This reliability-aware merit outperforms others in the industrial situations. Besides, certain improvement technique or algorithm can be introduced in the contention-free protocol to minimize the latency in the applications with the high real-time request. 24 CHAPTER 2. INDUSTRIAL IOT AND RFID SYSTEM

2.5 Summary

In the chapter, the basic architecture of the industrial IoT system is introduced. The acquisition system, that is the RFID system, which consists of the objects and the gateway devices, takes care of the information collection, exchange, and control of the Object Layer, is the basis for the implementation of the industrial IoT system. Based on the characteristics of the acquisition system, the appreciated system topology, communication technologies, and protocols are discussed in this chapter. As a result, the extended star topology, the RFID technology with active RFID tags, and a contention-free based protocol are recommended for realizing the acquisition system in the industrial environment to guarantee reliable, deterministic, real-time, deployable, and QoS performance. Chapter 3

Reliable RFID Communication System with QoS Capability

3.1 Background

The RFID system in the industrial IoT application is expected to execute functions of tracking, monitoring, and control. In the tracking application, the system masters the objects’ position information and moving trace via the data sent from the attached RFID tags. This function is widely expected in tools/components tracking in production plant [100, 101] and people/assets positioning in dangerous working environment [65, 102]. The monitoring function is focusing on the sensing information of the objects’ working environment and individual status. For example, air quality monitoring can help in the protection of food, animals, plants and people [103–105]. Automated remote monitoring of body condition is highly demanded in future healthcare industry [106]. And in the control oriented application, the RFID technology is expected as an important pattern in the mass customization manufacturing industries [107, 108] and the non-mission critical interactive-based operations where communication delay is around hundreds of milliseconds [109, 110].

3.2 Proposed System

In view of the characteristics of both the IoT and the industrial environment, we propose an RFID system aiming to support both the hardware ﬂexibility and the communication reliability while minimizing the energy and time consumption.

3.2.1 System Architecture An RFID system architecture proposed for the industrial IoT application is shown in Figure 3.1. In the local area, a server is used to advanced process and store the data in consideration of the security and conﬁdential requirements in the industrial

25 CHAPTER 3. RELIABLE RFID COMMUNICATION SYSTEM WITH QOS 26 CAPABILITY environment. The RFID readers and the local server are then connected through the Ethernet for data interaction within an Intranet [111]. The system can be divided into several sub-systems for large coverage. In each sub-system, a transmitter working as a coordinator, and a cluster of receivers working as the reference readers are employed instead of a conventional gateway.

Internet

Local operators Local Server Computers/users Ethernet

Sub-system Sub-systems Reader2 Reader1

Coordinator . . . ReaderL-1

tag_12 tag_21 tag_22 tag_L-1,1 . . . tag_L-1,2 tag_11 . . . Distributed RFID tags

Figure 3.1: The proposed system architecture. Adapted from Paper I

The system topology as shown in Figure 3.2 is similar to an extended star topology. Each central coordinator together with its corresponding reference readers is considered as a sub-network inside the local server covered area. And the multiple reference readers in each sub-network enable ﬂexible hardware implementation at the sensor/tag side. That is, the system can support multi-channel transceivers and multiple technologies simultaneously. The system scalability is physically enlarged by this architecture. Compared with the number of reference readers, there is a single coordinator in each sub-system. This structure provides a centralized management of the sensors/tags in each sub-system at the cost of limited capacity at the control link under the scenario of single coordinator and multiple reference readers. It is feasible to most of the monitor-based industrial IoT applications such as identifying people, tracking assets, and sensing environment status. To support both the RFID and the Ethernet communication, the coordinator and the reference readers all consist of both an RFID interface and an Ethernet interface. For sensors/tags, each of them is composed of a microprocessor, one or multiple RFID transceivers, and optional sensors depending on the operation 3.2. PROPOSED SYSTEM 27

Central Reference Target reader reader tag

Figure 3.2: The topology of the proposed system.

environment.

3.2.2 MAC Protocol

Based on the system architecture and the characteristics of industrial IoT application, a contention-free MAC protocol using the multi-frequency TDMA (MF- TDMA) scheme is designed as shown in Figure 3.3. The protocol relies on the time schedule of the TDMA and employs multiple frequency channels for communication between the tags and the coordinator/readers. In the protocol, a frame cycle is defined as the period that all the tags have finished their scheduled transmission. And each tag is working in a duty-cycle manner for both transmission and reception to reduce power consumption [111, 112]. Specifically, it can be considered that the protocol provides a multi-channel full- duplex communication capability. That is, an independent channel is allocated to each reference reader and the coordinator. The whole sub-system uses one dedicated channel controlled by the coordinator to pass data from the coordinator to the tags. And multiple channels pre-allocated to the readers are used to transfer data packets from the tags to the reference readers. Each tag can work in at least two channels, one is the dedicated control channel which is fixed for the coordinator, and the other is the transmission channel. One tag can occupy one or more channels to transmit depending on the number of transmitters and the initial channel allocations [111, 112]. CHAPTER 3. RELIABLE RFID COMMUNICATION SYSTEM WITH QOS 28 CAPABILITY

frame cycle frame cycle Slot1 Slot2Slot3 Slot4 SlotM time

masters Listen(CH1~CHL-1) . . . Listen(CH1~CHL-1) . . . tag11 tag12 tag13 tag14 tag12 CH1 ...... tag21 node22 tag23 tag22 tag23 CH2 ...... tag31tag32 tag33 tag3m tag31 tag32 tag35 tag33 tag3m CH3 ...... send

tag(L-1,1) tag(L-1,3) tag(L-1,4) tag(L-1,m) tag(L-1,2) tag(L-1,4) CHL-1 ...... tag(L-1,2) tag11 tag23 tag(L-1,1) tag(L-1,3) tag(L-1,m) tag12 tag33 CHL(syn) ...... tag31 tag21 tag(L-1,4) tag22 frequency

Figure 3.3: An overview of the proposed MF-TDMA protocol. Adapted from Paper II

3.2.3 Synchronization Since the time schedule based protocol is sensitive to clock alignment. A synchronization mechanism is designed to reduce the probability of collision caused by clock shifts of the distributed tags. In each scheduled time slot, a guard time is reserved to tolerate the clock shifts of the tags transmitted at the adjacent slots. As shown in Figure 3.3, although the guard time is used, synchronization of the clocks is required since when the accumulated clock drifts exceed the bound it can tolerate [112]. A synchronization cycle tsyn is assumed to constraint by ϕ m · (α + 2ϕ) ≤ tsyn ≤ (3.1) 2f,max where f,max is the maximum average drift rate under frequency f, ϕ is half of the reserved guard time between two neighboring time slots, α is the eﬀective transmission time of one tag, α + 2ϕ equals to the length of time slot, and m ≥ 1 is a factor that indicates the minimum synchronization interval according to diﬀerent application requirements [113]. Based on the system structure and the characteristics of the MF-TDMA protocol, each tag can enjoy an independent synchronization interval besides a universal synchronization manner. This enables an adaptive synchronization mechanism of the tags according to their unique behaviors. Therefore, both uniform synchronization and independent synchronization are available for the tags.

3.2.4 Optional ARQ To improve the reliability of each transmission, an optional ARQ is used in the protocol. That is, the tags turn to reception mode for receiving the ARQ packet right after each transmission period as can be seen in Figure 3.4. An ARQ packet 3.2. PROPOSED SYSTEM 29 is sent to the tag if its data is not successfully delivered. The ARQ packet indicates the time of re-transmission and other relevant information for the tag. On contrast, if the transmitted data of one tag is correctly received by the reader, there is no ARQ generated for the tag. The tag turns to sleep when the timeout limit for the ARQ packet reaches. Since there may exist multiple transmission channels, multiple tags are waiting for the ARQ packet at the same time. The information for these tags can be combined in one ARQ packet to promise every failed tag can receive it before the time runs out.

tagi1 guard Trans Time out Rec no ARQ guard Trans tagi2 Rec ARQ

CH i . . . guard Trans tagi,m ARQ Rec frame

Figure 3.4: The ARQ method: ARQ for the tags allocated to the same transmission channel. Adapted from Paper I

3.2.5 Packet The packet format of the protocol as shown in Figure 3.5 is similar to the format used in the IEEE 802.15.4 protocol. It contains a 4-byte preamble, 1-byte Start Frame Delimiter (SFD), and 1-byte MAC length indicator for the physical header. And the MAC packet consists of MAC control word, source/destination address, payload data, and the CRC [111]. The MAC control word (1 byte) is used to indicate the packet type as illustrated in Table 3.1.

MAC 4 11 1 0~8 0~80~108 2 preamble SFD Len Con Src Dst Payload CRC

PHY

Figure 3.5: The packet format.Adapted from Paper I

2.5

1.5

1 Average drift (us/s) Average

0.5

0 20 40 60 80 100 120 140 Drift rate (ppm) ∆ Fig. 1. Average drift rate under different maximum drift rate We first plot the average drift in different drift rate in Fig. 4. Based on our simulation, the average drift ∆ is not impacted by slot length, guard time and frame cycle. It increases almost in linear as the maximum drift rate , it is 0.41 us/s at 20 ppm and 2.88 us/s at 140 ppm as shown in the figure. However, since the clock drift of one node can be either positive or negative, the accumulated drifts of the node in one frame cycle do not apply to the linear law. In the worst case, if the clock drift in the same direction during the whole frame cycle, a guard time of 0.41 us is expected to tolerate the node whose clock has a maximum drift rate of 20 ppm under 1 s frame cycle.

Length of data packet 16 bytes/128 bytes Update (duty) cycle 1 s/10 minutes Symbol rate 64 kbps/1.6 kbps Number of devices 100 ~ 10000 CHAPTER 3.Number RELIABLE of transmission RFID COMMUNICATION channels 1SYSTEM ~ 100 WITH QOS 30 Guard time 0.2 ms/120 msCAPABILITY Table 3.1: The MAC control word. Adapted from Paper I

bit Control word Description 7 default: 1 1: data pkt 0: non-data pkt 6~5 default: 00 11: broadcast sync pkt 10: sync pkt 0x: non-sync pkt 4 default: 0 1: control pkt 0: non-control pkt 3~2 default: 00 11: single ARQ pkt 10: multiple ARQ pkt 0x: non-ARQ pkt 1~0 default: 00 reserved

Four types of packet is employed in the protocol:

1. Data packet. It is the packet sent from the tags to the readers. It should at least contains its own address for the readers to match with the schedule map. If the received data is transmitted from a tag not scheduled, the data packet would be dropped and then a synchronization message including schedule correction may be sent to the tag.

2. Synchronization packet. There are two synchronization methods: the uniform and the independent. A broadcast packet is employed in the uniform synchronization. All the tags can receive this packet if they are in the reception mode. In this case, no destination address is required in this synchronization packet. The source address is optional since the synchronization time is appointed.

For the independent synchronization, its packet is sent together with the ARQ packet or control packet. No dedicated independent synchronization packet is recommended in the communication.

3. ARQ packet. If the ARQ packet has only one destination, the corresponding address is explicit shown. Otherwise there would be no data in the address ﬁeld. Both the destination and re-transmission information are written in the payload in serial format. The tags need to recognize their own information according to the addresses. And the tags can tell which type of ARQ packet it is according to the MAC control word.

4. Control packet. This packet is sent from the coordinator to the tags. 3.2. PROPOSED SYSTEM 31

3.2.6 Slot Allocation

The slot allocation in each transmission channel is independent, that is, there can be a diverse number of the slot with diﬀerent period length in each channel. A ﬂexible slot allocation is then available for the tags in each coordinator controlled sub- system. The assigned slot for one tag can be moved from one transmission channel to another or from one sub-system to another on demand of the requirement when idle slot exists.

3.2.7 Communication process

There are 5 timers working in each tag, a transmission timer, a synchronization timer, an ARQ timeout timer, and a re-transmission timeout timer [111]. The transmission timer stores the time point of transmission of one tag in each update cycle. It is scheduled according to the transmission map generated by the server and assigned to the tag at the initial stage of the whole communication (including each initialization of the system refresh). The synchronization timer is used for listening to the broadcast synchronization message. If a synchronization packet is received individually (from control/ARQ packet) before this timer reaches, a new timer is set according to the information of the received synchronization packet. Otherwise, the synchronization timer resets automatically after the synchronization. The ARQ timeout timer is used to record the maximum ARQ waiting period after the tag’s transmission. If one tag does not receive ARQ packet within this period, it turns to another state such as sleep. The re-transmission timeout timer indicates the maximum reserved time length for re-transmission in each transmission channel. It relies on the idle time between the end time of the frame length and the update cycle.

Flow of Tag

As illustrated in Figure 3.6, for each tag, the transmission timer equals to its individual update cycle (it may not be the same as the others). And the ARQ timeout timer is set every time when the transmission is complete. The control timer and the synchronization timer may be much larger than the transmission timer. The synchronization timer can be modiﬁed if an independent synchronization message is sent together with an ARQ packet. CHAPTER 3. RELIABLE RFID COMMUNICATION SYSTEM WITH QOS 32 CAPABILITY

start

Tx timer n reaches? y

Pkt in Tx n buffer?

y Add src&dst addr Then, Tx

n Tx finishes? y Turn to Rx

n ARQ time out? y

Turn to sleep

n Pkt in Rx buffer?

y Delete Tx pkt, reset Tx timer y Re-transmit and ARQ time out? timer, sync clock (if sync n data Rx) Refresh ARQ timer, Tx timer

y Control timer Turn on Rx reaches?

n y n Rx Sync timer finishes? reaches? y

n Set e.g. clock, task, timer Delete Rx pkt Turn to sleep

Figure 3.6: The ﬂowchart of a tag in the communication process. Adapted from Paper I 3.2. PROPOSED SYSTEM 33

start

n Pkt from server?

y control pkt?

y ARQ pkt? Multi-ARQ?

y y n

y Broadcast? Re-organize payload

Add dst addr

n Timer reaches?

n Tx finishes?

Delete pkt

Figure 3.7: The ﬂowchart of the coordinator in the communication process. Adapted from Paper I CHAPTER 3. RELIABLE RFID COMMUNICATION SYSTEM WITH QOS 34 CAPABILITY Flow of Coordinator

Figure 3.7 is the ﬂowchart of the coordinator. During the communication process, the coordinator only in charge of sending control packet, ARQ packet and synchronization packet to the tags. All these packets are generated or triggered by the server according to the received results from the reference readers. For example, if one reader reports the failure of reception of one tag’ data, the server then generates a re-transmission schedule and sends it to the coordinator. To guarantee the synchronization of all the sub-systems and enable ﬂexible moving from one sub-system to another, the system selects the server’s clock as the reference clock.

3.2.8 Protocol Implementation

The implementation of the designed MF-TDMA protocol can be divided into four phases as described in Figure 3.8. Phase 1 is a downlink, that is from the coordinator to the tags, at which the coordinator sends initial information or control commands to the tags. And Phase 2 is an uplink, that is from the tags to the readers. In this phase, the tags transmit sensing/identifying data or response report to the readers based on the received orders of Phase 1. Phase 3 is also a downlink. The tags may receive feedback packets containing error control message such as the ARQ and commands regarding the state of the delivered data from Phase 2. And Phase 4 is employed for those tags which have received a message at Phase 3 to response according to the error control.

Phase 3 Downlink Phase 1 Downlink Phase 2 Uplink Phase 4 Uplink Send ARQ/Sync/ Send command Listen Listen Error control listen

...... send Track idle

Send ......

. success Burst

Monitor error 1234567 4 567 Error

ARQ ...

...... Sync ... 8 910 11 12 13 14 8 12

Control Error 4 control

Figure 3.8: Four-phase communication process of the MF-TDMA protocol.Adapted from Paper III 3.3. QOS PROTOCOL IMPLEMENTATION 35

3.3 QoS Protocol Implementation

3.3.1 MF-TDMA for Energy-constraint Monitoring

The monitoring focused industrial application normally features long data update cycle and diverse update packets depending on diﬀerent types of sensing information and objects. Energy consumption which determines the RFID tags’ lifetime is then the key issue in such RFID system.

Initialization command ARQ update cycle

Cnd(dl) ARQ received re-transmit slot for tagi(ul) slot for tagj(ul) sync ch1(ul) 1 2 1 2 (dl) guard slot for tagi(dl) slot for tagj(dl) chL(ul) 3 3 (dl)

ARQ timeout sensing pkt idle period

Figure 3.9: Protocol description for the energy-constraint monitoring application. Adapted from Paper III

Figure 3.9 illustrates how the proposed MF-TDMA protocol implements in such monitoring applications. It shows a multi-channel implementation, that is, the tags are allocated to transmission channels from ch1 to chL, and the Cnd is the dedicated command channel. Phase 1 as shown in Figure 3.8 is the initial stage for the coordinator to confirm the schedule arrangement with the tags. And this phase is not repeated during the monitoring operation until an appointed system refresh time. The time slot and transmission allocation are pre-defined according to the update cycle of each type of tag to promise all the transmission channels sharing the same frame length. This frame length can be considered as the system update cycle (time period that tags in each channel finish their transmission without considering re-transmission). The frame length is set smaller than the tag’s update cycle. An idle period is reserved between the end of transmission of the last tag of the previous frame, and the start of the following frame. During this idle period, the tags which received an ARQ packet re-transmit their sensing data accordingly (Phase 3). If one tag still fails to deliver its sensing packet and the re-transmission timer is available, another re-transmission time can be sent via a new ARQ packet. It can re-transmit the packet again. The length of this idle period is the maximum length bound of the re-transmission timer. Considered the characteristic of the monitoring application, both the broadcast synchronization and the independent synchronization can be used. The broadcast synchronization packet is recommended for reducing the implementation complexity and energy consumption. CHAPTER 3. RELIABLE RFID COMMUNICATION SYSTEM WITH QOS 36 CAPABILITY

Update cycle Update cycle

tguard ttrans

original CHi

m cycles n>m sync n cycles

optimized CHi

t trans

Update cycle Update cycle

Figure 3.10: Optimization of the guard time and transmission time in each slot for reducing the synchronization rate.

By minimizing the average transmission and reception period, it can further reduce the energy consumption of each tag. Figure 3.10 describes an example of a transmission period and synchronization rate reducing method for the tags with temperature sensors for fruit status monitoring in a warehouse. Assuming that the sensing payload is 12-bit length (−55◦ ∼ +125◦ with 0.1◦accurancy) and the temperature update interval is 10 minutes. Then based on the application environment, it is feasible to reduce the payload to 8 bits for indicating ±10◦ variations compared with the previous update. Therefore, as shown in Figure 3.10, instead of transmitting a full sensing packet (ttrans), the tags can transmit a reduced 0 packet within ttrans. Under the same time slot allocation and required system 0 update rate, larger guard time tguard is achieved between two slots. The broadcast synchronization interval can be then enlarged from m cycles to n cycles.

3.3.2 MF-TDMA for Latency-constraint Tracking On the other hand, the tracking focused application is sensitive to delay rather than energy when a continuous tracking is desired for moving objects. Figure 3.11 is the proposed protocol implementation for latency-constraint tracking applications. In this implementation, payload length of position information is assumed to be the same for diﬀerent tags. To achieve the minimum system latency for frequent position update of each tag, the system update cycle is set to be equal to the frame length (the maximum frame length of all the used transmission channels). Hence, the re-transmission mechanism and the broadcast synchronization are not employed due to the limited idle period between two update intervals. In other words, the tags do not need to turn to reception mode after each transmission for the ARQ packet. And independent synchronization message is sent for each tag with possible control information. Moreover, as can be found from the ﬁgure, various guard length is used for the tags, that is, there exists diverse slot length 3.3. QOS PROTOCOL IMPLEMENTATION 37

Initialization command sync update cycle

Cnd(dl) position pkt

ch1(ul) (dl) slot for tagi(ul) guard chL(ul) (dl) slot for tagi(dl)

Figure 3.11: Protocol description for the latency-constraint tracking application. Adapted from Paper III

0 10 200 mAh battery with 5% leakage per year

-1 10

100 mAh battery with 5% 4*10-2 leakage per year Energy consumption (Ah) consumption Energy full pkt (energy) full pkt (latency) reduced pkt (energy) -2 reduced pkt (latency) 10 0 2 4 6 8 10 Time (year)

Figure 3.12: Estimation of energy consumption of both energy-constraint and latency-constraint scenarios. Packet length: 60 bytes, reduced packet: 40 bytes, update cycle: 10 minutes, current for transmission, reception and sleep: 30 mA, 15 mA, and 1 µA, synchronization interval: 37.5 minutes and 28 seconds. in each frame. This various guard length enables different synchronization interval among the tags. Similar to the energy-constraint monitoring, reduced length of position packet can be used to further decrease the transmission delay and power consumption. Figure 3.12 shows a comparison of energy consumption of the tag’s RF module working in the energy-constraint scenario and the latency-constraint scenario, respectively. In this figure, the following assumptions are made: the packet length CHAPTER 3. RELIABLE RFID COMMUNICATION SYSTEM WITH QOS 38 CAPABILITY for both scenarios is equal, that is 60 bytes, and the transmission period is around 50 ms at 10 Kbps rate. The reduced packet is 40-byte length, and the tag’s update rate is every 10 minutes. No ARQ or re-transmission is used. The average current consumption of transmission, reception, and sleep is considered as 30 mA, 15 mA, and 1 µA, respectively [114, 115]. Different guard length is used for the tag working in the energy-constraint scenario and the latency-constraint scenario to support 37.5 minutes and 28 seconds synchronization interval, respectively. It can be seen, under the same condition, when enlarging the guard length to 9 times of latency-constraint scenario for the energy-constraint application, it consumes about 60% less power. And the lifetime of the tag extends to 5 years from about 2.2 years. When a reduced packet is used, it can further reduce 20% power consumption for the energy-constraint application in this case (from 60 bytes to 40 bytes). For latency-constraint applications, the queuing time of each tag which is the delay from packet generation to packet transmission also affects the performance especially in the time schedule based protocol scenario. In the proposed MF-TDMA protocol, each tag can only transmit at the slot assigned to it at each update cycle. Hence, if the assigned slot is earlier than its packet generation time, the transmission may fail and a re-transmission requires. Alternatively, it may transmit the packet generated at the previous update cycle at this time without re-transmission organized. On the other hand, if the assigned slot is quite late compared to the packet generation time, a long queuing time is also introduced. Therefore, a slot allocation optimization as shown in Figure 3.13 is designed to help to improve the slot allocation according to previous performance of each tag to minimize the queuing delay. It can be described as

• The packet of tag p generates before its assigned slot: Assume that slot tT S,p is assigned and tg,p is its packet generation time. The optimization process is to exchange tT S,p with (or move to a slot if it is idle) an available closer time slot tT S,p0 ∈ (tg,p, tT S,p]. • The packet of tag p generates after its assigned slot: Similarly, the optimization for this case, new time slot tT S,p0 ∈ [tg,p, tframe) is selected from the end of its packet generation time to the end of the frame. This optimization is executed based on an assumption that the packet generation time of each time is a random event at the ﬁrst time. And then the packets generation follows the duty-cycle principle. We predict the packet generation time by a Poisson Distribution, e−λt(λ)n p (t) = (3.2) n n! where n is the number of packet generated at time point t, and the interval of packet generation can be expressed as −λt P0(t) = e (3.3) 3.3. QOS PROTOCOL IMPLEMENTATION 39

delay guard pkt gen

initial allocation

frame re-trans

duty cycle

optimized allocation

Figure 3.13: Optimization of the slot allocation for reducing queuing time for transmission. Adapted from Paper I

It indicates that from time 0 to t, no packet is generated. 1/λ is then the expectation of packet generation interval. Here we deﬁne the density of packet in one transmission channel as λ. If λ is large, that is, the expectation of packet generation interval is small, which indicates that most of the tags in the same channel generate packets at a contiguous time. In this case, a long queuing delay is expected in the MF-TDMA protocol due to the scheduled principle. Otherwise, when λ is small, the packet generation time of the tags is distributed over a long period, there may exist more space for the slot optimization method to decrease the queuing delay. Then the packet generation time can be assumed as 1 1 t = − log(1 − rand(N)) (3.4) g λ λ where N is the quantity of tags in each transmission channel. As a comparison, Figure 3.14 and Figure 3.15 draw the statistical results of the queuing time (waiting time) for the diﬀerent expectation of density of packet generation of 1000 tags allocated in one transmission channel, respectively. To better illustrate the performance of the proposed MF-TDMA protocol and the improvement after slot optimization, the contention-based np-CSMA [116] protocol is shown as a reference. In the simulation, a 16-byte data packet is assumed for each CHAPTER 3. RELIABLE RFID COMMUNICATION SYSTEM WITH QOS 40 CAPABILITY

180 np-CSMA 160 initial MFTDMA optimized MFTDMA 140

120

100

Number of tag (single CH) 40

0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Waiting time (s)

Figure 3.14: Queuing time for 50 ms (λ = 1/(50ms)) expectation of generation interval of 1000 tags using np-CSMA protocol, the initial MF-TDMA protocol, and the slot optimized MF-TDMA protocol. Adapted from Paper I tag, the data rate is 256 Kbps, the guard length is 0.2 ms, and the data update cycle is every 1 second (the packet generation cycle is assumed to equal to the update cycle). It can be seen that, in Figure 3.14 the packet density is large in a short period. The contention-based np-CSMA method can provide a better performance in queuing delay since the transmission period of each packet is quite short. It manages to provide 17% of the tags to transmit within 50 ms queuing time, which is twice of the number of the proposed MF-TDMA method. For MF-TDMA protocol, it provides a bound of the queuing time at 550 ms and 700 ms depending on if the slot allocation is optimized or not. This is at least 100 ms shorter compared with the np-CSMA protocol in this case. And as shown in Figure 3.15, when the packet density drops to 1/(500 ms), the MF-TDMA protocol with slot optimization achieves a great progress. It can provide 50 ms queuing time for 60% of the tags, while promising all the tags queuing time no more than 400 ms [111]. When multiple transmission channels are employed, as shown in Figure 3.16 and Figure 3.17, the MF-TDMA with slot optimization guarantees less than 100 ms queuing time. In the simulation for the two ﬁgures, 5 and 6 channels are used for the 1000 tags, respectively. For the proposed MF-TDMA protocol, since one channel is occupied dedicated for control, it employs 1 channel less for transmission compared with the np-CSMA protocol. It is shown that although the MF-TDMA 3.3. QOS PROTOCOL IMPLEMENTATION 41

900 np-CSMA 800 initial MFTDMA optimized MFTDMA 700

600

500

400

300

Number of tag (single CH) 200

100

0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Waiting time (s)

Figure 3.15: Queuing time for 500 ms (λ = 1/(500ms)) expectation of generation interval of 1000 tags using np-CSMA protocol, the initial MF-TDMA protocol, and the slot optimized MF-TDMA protocol. Adapted from Paper I

protocols use 1 channel less, it can promise almost 100% of the tags to achieve a minimum of 50 ms queuing delay after slot optimization when 6 channels are used. It can be concluded that with the slot optimization, the proposed MF-TDMA protocol can provide guaranteed short queuing delay of all the tags for latency- constraint applications. The scheduled transmission also prevent unnecessary collisions occurred when using contention-based protocols.

3.3.3 MF-TDMA for Reliability-aware Control When turning the RFID technology to the control application in the industrial ﬁeld, cost and reliability are the primary concerns. Under the IoT context, the RFID tags attached to ubiquitous objects such as raw materials, components, tools, and machines should be able to communicate with the process controller for corresponding executions. Hence, in the control oriented applications, the coordinator is mainly used to send control commands to the tags. Depending on the objects each tag attached, they either complete the command by showing the identiﬁcation or forward the order to its microprocessor to direct other executions of the object. To enable reliable control, execution report is required from the RFID tags which may notice CHAPTER 3. RELIABLE RFID COMMUNICATION SYSTEM WITH QOS 42 CAPABILITY

700 np-CSMA(5CH) optimized MFTDMA(4CH) 600

500

400

300 Number of tag 200

100

0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Waiting time (s)

Figure 3.16: Queuing time for 500 ms (λ = 1/(500ms)) expectation of generation interval of 1000 tags using 5 channels. Adapted from Paper I

1000 np-CSMA(6CH) 900 optimized MFTDMA(5CH)

800

700

600

500

400 Number of tag 300

200

100

0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Waiting time(s)

Figure 3.17: Queuing time for 500 ms (λ = 1/(500ms)) expectation of generation interval of 1000 tags using 6 channels. Adapted from Paper I 3.3. QOS PROTOCOL IMPLEMENTATION 43 the controller (server) whether the order is carried out successfully or not. If errors are reported, the server needs to turn to error control to ensure the process. It is notable that all the actions of the RFID tags rely on the control orders. Normally, one working process can be divided into several independent stages. And each stage also includes several actions. Each action may require multiple objects to perform diﬀerent operations. The whole process is pre-divided into multiple stages. The time period used by executing each stage is deﬁned as a control cycle. The length of each control cycle thus may vary. The control cycle is composed of several commands. One or multiple objects may collaborate for completing one command. Therefore, in one control cycle, the tags need to know when to start to listen to their corresponding orders. And then they have to report the operation results to the readers to indicate if the coordinator can continue to send the following command. Figure 3.18 shows how the protocol implemented for control based on the above principle. The communication process of the protocol can be described as follows:

error control control cycle error control for normal error schedule command command continue Cnd(dl)

burst error wait for next cycle ch1(ul) (dl) error resolved slot for tagi-k(ul) chL(ul) (dl)

success slot for tagi-k(dl) normal error interrupted by error control response

Figure 3.18: Protocol description for the reliability-aware control application. Adapted from Paper III

• An initial control packet is sent at the start of the whole control process. It is used to synchronize the tags and assign the schedule map. The tags obtain the start time of each control cycle from this schedule map and turn on their reception module at the corresponding time.

• After initialization, the process starts. At the beginning of the control cycle, all the tags receive a command packet which indicates the listening time of each tag within this control cycle.

• Then the tags start to listen for commands in turn. When the corresponding task of one object is accomplished, the tag sends a report to indicate its completion state.

• If errors are reported, the server needs to turn to different error handlers according to the types of error. In brief, there two types of error. They are defined as normal error and burst error. The normal error can be considered as an independent error. In detail, a normal error only affects the object who CHAPTER 3. RELIABLE RFID COMMUNICATION SYSTEM WITH QOS 44 CAPABILITY meets the problem, the other objects can finish their tasks in the control cycle without the success of this object. In this case, the server doesn’t handle this problem immediately. Instead, it sends a packet including information for error control, that is an ARQ with the appointed error control time. The error handler would be carried out at the end of the whole control cycle when all the other commands are completed. Since extra time is demanded for this error control and its period is unpredictable. To avoid missing the scheduling of the next control cycle, all the tag needs to turn on the reception mode when the success report of its last command is sent. For the burst error, the other objects or the following commands cannot be continued when it occurs. An immediate error handler or manpower is thus demanded. Once a report of burst error is received, the server lets the coordinator interrupt the current command process to handle it. On the other hand, the objects involved in this error are waiting for the error handler after the burst error report. Meanwhile, the timers which are used for record the listening schedule within the control cycle are paused for all the involved tags. For the other objects which are not involved, they have to keep listening until the end of the error handler. Additionally, their timers for schedule can be updated by the following commands accordingly to ensure time alignment within this control cycle.

3.3.4 Throughput and Packet Delivery Ratio The packet loss rate (PLR) is an important factor to measure the communication reliability. Excluding collisions due to access contention, channel interference is another element that may generate the packet loss. However, it is inevitable to meet such interference which is caused by occlusions, metallic items, and other technologies compared with the conventional wired methods when using wireless communication technologies in the industrial environment. Since multiple channels are employed in the proposed MF-TDMA protocol, the tags may suffer from high packet loss if their transmission channel has got stronger interference than others. Thanks to the flexible and independent tag allocation mechanism among different transmission channels, we are able to assign the tags to the channel with better performance. However, higher latency is then introduced in this case when the number of tags is fixed. We use the packet delivery ratio (PDR) and the system throughput to evaluate the performance of the protocol. The PDR is defined as the ratio of the successfully delivered packet to the total packets sent.

Pi=L−1 q · P PDR = i=1 i i (3.5) Pi=L−1 i=1 Pi where qi is the factor of the quality of channel i, it represents the degree of interfer- Pi=L−1 ence of one transmission channel. i=1 Pi is the number of the whole transmitted 3.3. QOS PROTOCOL IMPLEMENTATION 45

1 8 PDR 0.95 Throughput

0.9 6

0.85

0.8 4 Packet delivery ratio

0.75 Throughput (packets/second)

2 500 1000 1500 2000 2500 3000 3500 4000 4500 Number of tags at the best−performed transmission channel Figure 3.19: The PDR and the throughput curves for diﬀerent tag allocations.

Pi=L−1 packets of the tags, and i=1 qi · Pi is the number of packets received by the readers through the whole L − 1 transmission channels [112]. The system throughput is expressed as the number of packets received within each update cycle. It depends on both the successful delivery packets and the transmission delay, Pi=L−1 q · P throughput = i=1 i i (3.6) tupdate To observe how the channel performance affects the packet delivery ratio and the system throughput, an instance under the following assumptions is shown. There are 10 available transmission channels and 5000 tags working in an energy- constraint manner. Each tag requires 50 ms to transmit its data packet, and 450 ms guard time is reserved for the adjacent transmissions. For simplicity, the 10 transmission channels are supposed to encounter the average PLR of 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, and 50%, respectively. The default update cycle is 10 minutes, and it can be enlarged up to every 37.5 minutes. Within each update cycle, at least 10 seconds are reserved for re-transmission and optional synchronization. Thus up to 1180 tags can be allocated to the same transmission channel when the update cycle is 10 minutes. And this number grows to 4480 for a 37.5-minute update cycle. The curves are drawn in Figure 3.19, where the greedy algorithm is employed to re-allocate the tags to better transmission channels. When the 5000 tags are equally allocated to the 10 channels, due to the assumed PLR, it obtains the lowest PDR, and the PDR increases as more tags are re-allocated to the channels with better quality. For the throughput, when the best channel has less than 1180 tags, CHAPTER 3. RELIABLE RFID COMMUNICATION SYSTEM WITH QOS 46 CAPABILITY the throughput grows as the increase of the PDR. Since more than 1180 tags are assigned to one channel, the increase of update cycle resulting in a decrease of the throughput correspondingly. Therefore, on demand the QoS requirements of the PDR and the throughput performance, it can always achieve an optimal allocation method of different application environments depending on the update cycle, the number of tags, and the number of available transmission channels thanks to the flexible allocation mechanism.

3.4 Summary

In this chapter, first we describe an RFID system designed based on a discrete gateway architecture and the corresponding MF-TDMA protocol for the industrial IoT. Second, for various QoS requirements, instance designs for energy-constraint monitoring, latency-constraint tracking, and reliability-aware control scenarios are introduced based on a four-phase communication process. The discrete gateway architecture is composed of a coordinator and multiple RFID readers. This structure explicitly separates the tag control and data collection by employing the coordinator working in a dedicated frequency band. The multiple RFID readers can be distributed to a large coverage to receive data while the central coordinator can work in a comparably high power manner to control the tags. As mentioned in this chapter, this structure also enables multiple RFID techniques to be used when the tags are equipped with a uniform receiver for listening to the coordinator’s control. Based on the system architecture, a contention-free MF-TDMA protocol is designed to offer schedule-based communication for applying in the industrial environment. It can take care of multi-channel communication simultaneously while supporting an independent control channel for all the tags within one gateway’s control area. To handle the latency and reliability demands, an optional ARQ, independent/uniform synchronization and control mechanism, as well as a slot allocation optimization algorithm are used. With the help of these methods, the protocol can provide high communication reliability and low transmission delay with guaranteed delay bound as illustrated in the instance simulations in this chapter. To explain how to support the QoS performance, the communication process of the system is summarized in four phases. Examples for different performance requests are shown independently according to the four-phase communication stage: For application with the energy-constraint requirement, a various length of transmission packet enables flexible slot allocation for sensing tags with different functions. With the help of the re-arranged slot length for the reduced packet, it can be optimized to enlarge the guard length for longer synchronization cycle and decrease the turn-on time of the tag’s RF module. The power consumption is thus further reduced. Additionally, in the energy-constraint applications, there may be a long idle period before a new update turn, synchronization at the broadcast manner can lower the implementation complexity compared with the independent 3.4. SUMMARY 47 synchronization manner. It is notable that the update interval may be large in such application, the ARQ mechanism, on the other hand, provides high transmission reliability. For the aspect of the latency-constraint tracking applications, the long queuing time is a fatal defect of the schedule-based protocols which restricts fast access of the packets. In our design, the server presumes the packet generation time based on the previous transmission delay of each tag, and re-allocated the tags to new transmission slots to decrease the queuing time. With the help of the optimization, the smaller average transmission delay of each tag and smaller access latency of each transmission channel are achieved. Furthermore, the employment of parallel transmission of multiple channels can improve the optimization by re-allocating the tags to the new transmission channels for reducing queuing time. On the other hand, in each update cycle, various guard length is used to compensate the interval between the transmission of two slots resulting in independent synchronization capability. By carrying out such independent synchronization, the server can customize the synchronization cycle for each tag. To improve the communication reliability is seriously desired in the control- orient industrial applications. The MF-TDMA supports strict error control mechanism. Two types of error handler are provided to resolve the error in a real-time manner or a time-lapse manner depending on its level of effect. Another method introduced to improve the reliability is implemented on demand of requirement of the packet delivery ratio and system throughput under the consideration of the channel quality. Different allocation methods can be used to obtain higher packet delivery ratio or throughput thanks to the flexible slot allocation mechanism.

Chapter 4

2.4-GHz/UWB Hybrid Positioning Platform

4.1 Background

4.1.1 Context Awareness of the Industrial IoT The value of location information in the industrial environment lies in the ability not only to identify and locate any products, machines, and workers, but also to deliver their information to users or applications for real-time context-aware industrial intelligence. Therefore, the location information for the industrial application is both accuracy and time dependent. As the emergence of sensor-based networks and wireless communication solutions, real-time location information for indoor environments attracts more atten- tions especially in the industrial applications. The Real-time Locating Systems (RTLS) based on diﬀerent wireless technologies such as RFID, UWB, ZigBee, Blue- tooth and WiFi, as shown in Table 4.1, towards industrial applications [117–125] can continuously monitor targets to reduce search time and improve operational eﬃciency.

4.1.2 Indoor Positioning Techniques Proximity Detection

The proximity detection provides symbolic location information by cell of origin (CoO) method with known position of antennas. That is, when a target is detected by one or more antennas, its location is estimated by the one that receives the strongest signal [126].

Xˆ ∈ kX − aik < r, where maxi∈Q{RSS (ai)} (4.1)

49 50 CHAPTER 4. 2.4-GHZ/UWB HYBRID POSITIONING PLATFORM

Table 4.1: RTLS solutions. Adapted from Paper IV

Solution/System Technology Accuracy Scalability Maintenance System Cost & Cycle capacity Reliability AeroScout Wi-Fi 1 - 3 m < 200 m < 4 years @ >300 per Medium (RSS, TDoA) @ outdoor reduced rate second (From < 60 m @ & turned off per cell $10 per indoor tag ) Ekahau Wi-Fi 1 - 3 m Wi-Fi < 5 years @ >3000 High (RSS) @ RSS>- covered reduced rate each (From 75dB distance & turned off time $50 per tag) Awarepoint ZigBee/IEEE802.15.4 1-3 m in-room < 3 years N/A Low (bed based on proximity) movement detection Nebusens ZigBee/IEEE802.15.4 > 1m < 500 m Several N/A Low (RSS) @ >- months 100dBm Zebra Active RFID + IEEE 0.9 m @ 120 m @ < 7 years @ 300 per High 802.11b 50 m indoor 5 days second (System (ToA, TDoA) radius 1000 m @ update rate begin 1.6 m @ outdoor from 95 radius $50,000) Mojix Passive RFID 1 m by < 10000 Without 1200 Low adding m2 battery reads (1/100 eNodes @ 1 per cost of receiver & second active 512 RFID tag) eNodes RF Controls Passive UHF RFID ± 1’ @ 3- < 100 m Without Depends Low (based on patent- D battery on the pending BESPA & 30 cm sweep antenna) speed Ubisense UWB 30 cm 100-1000 4-6 years Depends High (TDoA, AoA) & 15 cm m depends on on cell (Research @ 40m×40m tag type and and package controlled with location rate update from area standard 4 rate €17000) sensors Time Domain UWB 2 cm @ 40-80 m < 4.2 W N/A High (two-way ToA) LoS power < 50 cm consumption @ NLoS

where ai is the position of antenna i which receives the strongest signal from the target, and r is the circle of its cell. The location of the target is determined inside r.

Triangulation The triangulation is the basic measuring principle for positioning. It relies on the geometric properties of triangles to determine the target’s position. Techniques based on measurements of distances or angles between the target and known points like ToA, TDoA, round-trip time of ﬂight (RToF), received signal strength (RSS) measures the distance either by calculating the propagation time of signal or com- paring the received signal strength with a certain propagation model to locates the 4.1. BACKGROUND 51 target. The angle of arrival (AoA) technique can further estimate the target location by computing the angle of received waveform relative to the reference points [127, 128]. In the distance measurement based triangulation principle, the location Xˆ of the target can be estimated using the linear least square (LLS) algorithm by minimizing the diﬀerence between the real distance d and the estimated distance dˆ based on the position of a known reference,

ˆ ˆ X = argminX∈R d − d (4.2)

Fingerprinting The ﬁngerprinting (FP) method is basically a pattern matching based location estimation. It includes a calibration phase to build a measurement module/map, either empirically or computed analytically, and an estimation phase to compare the received signal with the model to determine the target’s position [129].

Dead Reckoning and Kalman Filter The dead reckoning (DR) method estimates the target’s position by its previous location and estimated moving speed. A speed sensor may be needed in the operating process, and the positioning error grows as it is accumulated by the former position estimates [130]. The Kalman Filter (KF) algorithm is widely used for DR-based location estimation by seeking the maximum conditional probability of the target’s state to predict its position [131].

Xˆ(k + 1) = Φ[Xˆ(k), u(k)] (4.3) where X(k) = [x, y, z, θ] is the target’s position at step k, and u(k) = [d, α] is the estimated input, that is, the target ﬁrst moves distance d, and then turns an angle α at step k. Its position at step k + 1 can then be estimated by Xˆ(k + 1).

4.1.3 Accurate Positioning using the UWB Technology In contrast to the conventional narrow-band technologies, the UWB transmits the pulses with length from pico-seconds to nano-seconds in a duty cycle manner. The ultra-short pulse duration of the IR-UWB signal enables accurate ranging/positioning potential of centimeter-level using the ToA-based methods in an indoor environment. The pulses can better resist to multi-path components in the time domain compared with the carrier-based narrow-band waveforms. In addition, the structure of the UWB transmitter can be realized in a simple and low-power method. In Figure 4.1, a comparison of the performance of tag transmits using the UWB technology and other technologies in terms of coverage, data rate, positioning accuracy and power consumption is summarized [132–134]. It can be concluded that 52 CHAPTER 4. 2.4-GHZ/UWB HYBRID POSITIONING PLATFORM

Satellite Positioning accuracy:6m~10m

Cellular (GSM/UMTS/LTE) Positioning accuracy:3m~10m

Active RFID (IEEE WiFi (IEEE 802.11) 100m 1km 802.15.4/ZigBee) Positioning accuracy:1m~5m Positioning accuracy:1m~3m

10m UWB Bluetooth Positioning Power consumption Positioning accuracy:0.01m Passive RFID accuracy:1m~3m ~1m (UHF/HF) Coverage Positioning accuracy:1m~2m

Passive NFC RFID

1cm 10cm Positioning Positioning accuracy: accuracy: 0.1m~1m 0.1m~1m 1mm 1m

1kbps 10kbps 100kbps 1Mbps 10Mbps 100Mbps 1Gbps 10Gbps Data rate

Figure 4.1: Comparison of power, coverage, and data rate of tags implemented by diﬀerent short range wireless technologies.

considering the coverage range, accuracy and power consumption, the UWB technology is the most power-efficient candidate for short-range (<10m) accurate positioning. And the IEEE 802.15.4 compliant 2.4-GHz active RF is one of the most cost-efficient technologies for long-range positioning implementations in the industrial environment. However, as a trade-off, at the receiver side the ultra-short pulses and large bandwidth of the IR-UWB signal increase the complexity and power consumption for signal recovery. The high cost of the UWB receiver thus limits the massive application of the UWB-based positioning system, especially in the industry which is sensitive to cost and power consumption. Although the large bandwidth is a burden in the receiver design, the low-power emission spectrum also enables coexistence of the UWB signal with the other narrow-band technologies such as the 2.4-GHz RF, without interfering each other [135].

4.2 2.4-GHz/UWB Positioning Platform

The software and hardware architecture of the positioning platform is shown in Figure 4.2. The main contributions of the platform include ﬁve aspects: the design of an asymmetric system structure, hybrid tag, communication protocol, ranging/positioning algorithm, and SDR UWB reader network. 4.2. 2.4-GHZ/UWB POSITIONING PLATFORM 53

User Graphic GUI Access engine Operating system

Drivers Control Positioning Optimization ... /protocol algorithm Software Other Security control drivers core

Other Database Sensor Internet Internet wireless storage repository

Server

Other Internet Power USB I/O interface interface control

UWB Database interface Schedule/ Ranging storage ... control /positioning Reader

2.4-GHz interface MCU Flash Hardware Hardware UWB interface Tag Schedule ... MCU

2.4-GHz Power Flash Sensors Other I/O interface control

Figure 4.2: Hardware and software structure of the proposed positioning platform. 54 CHAPTER 4. 2.4-GHZ/UWB HYBRID POSITIONING PLATFORM

server

clusterM clusterN A coordinator & A coordinator & ... multiple 2.4-GHz, UWB multiple 2.4-GHz, UWB readers readers

control position 2.4-GHz downlink uplink 2.4-GHz RF RF/UWB Tags with 2.4-GHz transceiver and UWB transmitter

objects

Figure 4.3: The hybrid technology based positioning system.

4.2.1 System Architecture

Based on the characteristics of the UWB technology, and the requirements of the positioning in the industrial IoT, a 2.4-GHz RF and UWB hybrid locating system which features asymmetric communication links, that is, optional 2.4-GHz RF or UWB uplink (from tag to reader), and 2.4-GHz RF downlink (from reader to tag), is proposed. In the system, a designed hybrid tag which has both the 2.4-GHz transceiver and the UWB transmitter is used. The 2.4-GHz RF technology is used by the readers to transmit commands to control and organize the tags. And UWB technology is not used in the downlink, not only relaxes the tags design and the power consumption, but also retains long control/operation coverage of the system. Figure 4.3 illustrates the system. Considered the complex background and coverage problem in large-area in the industrial environment, the system is composed of multiple clusters. Each cluster is arranged to cover a specific area according to divisions, such as different floors and various functional workshop, and controlled by one central-controlled coordinator (CCR) and a group of reference readers (both UWB readers and 2.4-GHz RF readers). According to positioning accuracy demands, there exists critical site and non-critical site in each cluster. The UWB based positioning is employed at the critical site. The reference reader listens to the data sent by the tags while the CCR sends commands and synchronization information to the tags [136]. 4.2. 2.4-GHZ/UWB POSITIONING PLATFORM 55

Power management

2.4-GHz 2.4-GHz transceiver Interface MCU UWB UWB tranmitter Interface

Memory

Debug ports 6cm Battery ASIC UWB TX

4cm

TopCC2530 2.4-GHz antenna Bottom UWB antenna

Figure 4.4: The UWB/2.4-GHz hybrid tag.

4.2.2 Hybrid tag Figure 4.4 illustrates a prototype of the designed hybrid tag which contains both a 2.4-GHz transceiver and an ASIC UWB transmitter [115, 137]. In the tag’s structure, a microprocessor is used to control both the 2.4-GHz RF/UWB based transmission, and the 2.4-GHz RF based reception. A UWB antenna and a 2.4-GHz RF antenna are connected to the 2.4-GHz transceiver and the UWB transmitter, respectively.

4.2.3 Communication Process A communication process of the positioning in one cluster of the system is described in Figure 4.5. Both frequency and time multiplexing are employed as shown. Specif- ically, each tag is assigned to a unique time slot and frequency channel to send either through a 2.4-GHz RF channel or through a UWB channel depending on the de- tecting area. To maximum improve energy eﬃciency, both the transmission and the reception of the tags are working in a duty cycle manner, that is, not only the data transmissions but also the data receptions including order/commands and 56 CHAPTER 4. 2.4-GHZ/UWB HYBRID POSITIONING PLATFORM synchronization information must obey to the schedules. The RF module of each tag will not turn on until a pre-deﬁned transmission or reception time is reached.

Frame n Frame n+1 Frame n+2

Ref. Listen Listen Listen reader1 ...

Ref. Listen Listen Listen readerL

Server Pos&F Pos&F Pos. . . . Pos&F Pos Pos&F Pos Pos Pos&F Pos

CCR Command Command Command Command Command . . . Sync. New Info. assigned Sync. Ch 1 S(Tag1) Listen S(Tag1) Listen S(Tag1) New Info assigned

S(Tag2) Listen S(Tag2) S(Tag2) Listen

S(Tag3) S(Tag3) S(Tag3)

... S(Tag4) S(Tag4) S(Tag4)

Sync. Sync. . . .

Ch L S(Tag1) Listen S(Tag1) Listen Sync. S(Tag1) Sync. S(Tag2) S(Tag2) S(Tag2) Listen

S(Tag3) S(Tag3) Listen S(Tag3)

S(Tag4) S(Tag4) S(Tag4)

Figure 4.5: The communication process used in each cluster.

TS or Tx not complete

2.4- TS end & Tx GHz complete Tx

TS end & Tx Tx in 2.4-GHz Rx time reached complete Rx time not mode reached Tx timer not reached

TS or Rx not complete Tx timer reached Rx Sleep TS end & Rx Active (2.4-GHz) complete Tx setup Initialization Start TS end & Tx complete TS end & Tx complete Rx timer Rx timer not reached reached Tx in UWB mode

UWB Tx

TS or Tx not complete

Figure 4.6: The state transition of the RF module of each hybrid tag. 4.2. 2.4-GHZ/UWB POSITIONING PLATFORM 57

Start

System Initialization: (CCR, 2.4-GHz & UWB readers, tags)

Yes No In critical site?

Data transmission Data transmission by Tag (UWB/2.4- by Tag (2.4-GHz, GHz, Channel_n) Channel_m)

Data reception Data reception (2.4-GHz & UWB (2.4-GHz Reader) Reader)

ToA/RSS detection by reader, RSS detection by reader, Position calculation by server Position calculation by server

Yes Moving to Moving to Yes normal site ? critical site? No No Time to No No Time to Synchronize? Synchronize? Yes Yes Tag Initialization/ Tag Initialization/ update(controlled by update (controlled by CCR): (TH, channel, CCR): (TH, channel, update rate, etc.) update rate, etc.)

Tag Tag “Sleep” “Sleep”

Yes Yes Time to transmit? Time to transmit?

No No

Figure 4.7: The operation ﬂowchart of the 2.4-GHz/UWB hybrid positioning. Adapted from Paper IV 58 CHAPTER 4. 2.4-GHZ/UWB HYBRID POSITIONING PLATFORM

The state transition of one hybrid tag is shown in Figure 4.6. And the operation flow of the positioning system including a switch between the 2.4-GHz RF based transmission manner and the UWB based transmission manner is described in Figure 4.7. As shown in Figure 4.6 and Figure 4.7, each tag keeps its RF module turned-off until its scheduled position update, that is Tx ,or commands/synchronization reception, that is Rx, time reached. Once its RF module is turned on and it is responsible for positioning update, it first detects if it is working in a critical site where the UWB transmission mode should be selected. Then the position update is sent by the corresponding UWB or 2.4-GHz transmitter. The transmitter would be turned off when the transmission is completed. Otherwise, if the RF module is activated for the reception, information of transmission modes or frequency channels switch, new transmission time slot, and new reception schedule for synchronization or command may probably be received. In this case, the tag then re-configure these parameters and employ the new setups in the following operations.

4.2.4 State-of-the-art UWB Receiver From the reader aspect, two kinds of receiver, that is, the 2.4-GHz RF receiver and the UWB receiver, are needed. Compared with the 2.4-GHz narrow-band receiver, the UWB receiver design is more challenging due to its large bandwidth. Classiﬁed by whether full sampling is required, there are coherent and non-coherent UWB receivers.

Coherent UWB Receiver Figure 4.8 shows the classic correlator-based coherent UWB receiver architecture. The received signal is correlated with a local template signal by a mixer after filtered and amplified by an RF front-end. And the output of the mixer is directly sampled by an analog-to-digital converter (ADC) for baseband processing. It is not feasible to realize such coherent receiver for the UWB signals because of the challenging of not only the high sampling rate but also the difficult selection of the local template, which is used as a matched filter to distinguish the UWB pulses, . This kind of receiver is also called as a Matched Filter receiver [138].

Non-coherent UWB Receiver The non-coherent receiver used a simpler structure is an optimum selection compared with the conventional coherent structure. It is capable of processing the UWB signal at an aﬀordable expense of power and cost based on an energy detector (ED) or an autocorrelation (AcR) receiver scheme. Figure 4.9 and Figure 4.10 show the architectures of the ED receiver and the AcR receiver, respectively. In the non-coherent UWB receiver structure, an integration window is chosen to 4.2. 2.4-GHZ/UWB POSITIONING PLATFORM 59

Filter Amplifier Mixer r(t) UWB ADC Back- end

s(t-τ ) RF front-end Template

Figure 4.8: Architecture of the correlator-based (Matched ﬁlter) UWB receiver.

Filter Amplifier Mixer Integrator r(t) UWB ʃ ADC Back- r(t) end

Square-law RF front-end device Figure 4.9: Architecture of the energy detection (ED) based UWB receiver.

Filter Amplifier Integrator r(t) UWB ʃ ADC Back- end

r(t-τ ) RF front-end Delay

Figure 4.10: Architecture of the self-delay based (AcR) UWB receiver. accumulate the energy of the received signal including all multi-path components within the window period. Unlike the coherent receiver, the ED receiver only exploits the envelope, that is, instantaneous power, of the received signal by using the square-law device and its following energy integrator [139, 140]. In this case, no phase comparison is performed, so that the phase-based modulation is useless. To maintain the phase comparison and beneﬁt from the phase information, the AcR receiver is preferred. The AcR receiver uses an analog delay line and a mixer to compare the received signal. This processing is equivalent to an autocorrelation device which has a ﬁxed delay [141, 142]. 60 CHAPTER 4. 2.4-GHZ/UWB HYBRID POSITIONING PLATFORM

4.2.5 The Proposed SDR UWB Receiver To achieve accurate positioning estimate, a ToA-based distance measurement is required at the UWB receiver for obtaining a fine time domain resolution of the received UWB signals. This makes the SDR UWB receiver a more feasible solution, because 1) it is power demanding and expensive to make the ADC work at least at the Nyquist rate, that is, twice the signal bandwidth. And it is challenging to implement a conventional Rake receiver structure to capture enough amount of energy as the coherent receiver demands; 2) it is crucial to synchronize the UWB pulses at the scale of nanosecond duration without fine complex processing algorithms or low clock jitter; 3) it is difficult to realize a nanosecond-level integrator using the analog circuit to achieve centimeter-level distance estimation by the non- coherent UWB receiver.

ToA Estimator and Algorithm By using the SDR receiver, it replaces the analog circuits by digital as much as possible and realizes complex processing of the received UWB signal. The proposed digital implementation of the UWB receiver for ranging/positioning, that is, a ToA estimator, is illustrated in Figure 4.11.

(c)ToA Estimator (e) square UWB integration (d) integration threshold signal ADC (.)2 ∑ max compare ∑ (a) (b) Coarse search Fine search

Figure 4.11: The block diagram of the proposed SDR-based ToA estimator.

The ToA estimator exploits the ED receiver structure. An ADC is used ﬁrst to convert the received signal r(t) to digital format r[n]. Then it collects the energy by squaring and accumulating it over a given time and frequency window Wi

i+Tint,c X 2 zj[k] = tnr [n] (4.4) n=i where i denotes index of the sample of the kth integration window, Tint,c is the length of the integration window used in the coarse search, t is the start time of j k i the integration window. n = 1, ..., N, N = Tf is the index of samples of signal in tsp

j Tf k one pulse repetition cycle Tf at sampling time tsp, and k = 1, ..., K, K = Tint,c represents the index of integration output. zj[k] is considered as the energy block of the kth integration window of the jth pulse repetition interval. 4.2. 2.4-GHZ/UWB POSITIONING PLATFORM 61

The coarse search in the ToA estimator is aiming to rapid distinguish the pulse position in each repetition interval out of the received signals. Thus, considered the multi-path components and the impact of the RF front-end, the integration window length can be selected from several nanoseconds to milliseconds depending on the data rate and the demanded searching speed. A maximum search is used after the integrator to find out the maximum energy block zj[k]max. An index comparison between the maximum energy block zj[k]max and the squared signal r2[n] is carried out to indicate the start point of of the location of 2 the jth pulse, kmax,j, inside r [n]. Then a fine search for ToA is employed for r2[n] in the distinguished area of pulse location. Similarly, a smaller integration 0 window Tint,f is used, and we can get a group of integration blocks zj[k]. Unlike the maximum search used in the coarse pulse location search, a threshold-based leading edge [143, 144] search is used to estimate the ToA of the received signal as the first energy block that exceeds the threshold η,

0 ktoa,j = first{k|zj[k] > η} (4.5) And the absolute ToA of the jth pulse of the received signal is estimated as (T − 1)k tˆ = (T − 1)k + int,f toa,j (4.6) toa,j int,c max,j 2 An instance illustration of the ToA estimator process corresponding to the proposed blocks shown in Figure 4.11 can be found in Figure 4.12. Power Voltage

0 500 1000 1500 2000 2500 3000 3500 4000 0 500 1000 1500 2000 2500 3000 3500 4000 Samples Samples (a) UWB signal (b) Signel after square-law

j j+1 max block Energy

Energy k k max max

0 500 1000 1500 2000 2500 3000 3500 4000 0 500 1000 1500 2000 2500 3000 3500 4000 4500 Samples Samples (c) Output of coarse search (d) Located pulses

j j+1

k toa, j+1 k toa, j Energy Energy

0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400 Samples Samples (e) Fine search of j (f) Fine search of j + 1

Figure 4.12: The ToA estimation process using the proposed ToA estimator. 62 CHAPTER 4. 2.4-GHZ/UWB HYBRID POSITIONING PLATFORM

SDR UWB Reader Network An SDR UWB receiver for TDoA location estimation is built based on a Lecroy oscilloscope. The oscilloscope can perform up to 40-GHz sampling rate and support real-time built-in program for the ToA and TDoA calculation [145]. The block diagram of the SDR TDoA estimation based receiver network is shown in Figure 4.13. Four of the input channels of the oscilloscope is considered as four independent UWB receiver front-ends, and a four-channel ToA estimator based on the structure shown in Figure 4.11 is implemented by the built-in programmable software. Ab- solute ToA estimations achieved by the ToA estimator are exploited in the TDoA positioning program for location estimation after a calibration unit.

CH1 Filter LNA

CH2 Filter LNA t Calibration t’ToA1 ToA1 TDoA ToA t unit t’ToA2 ToA2 (Sychronization) Estimator t (Signal t’ToA3 ToA3 & Positioning CH3 t alignment) t’ToA4 Filter LNA ToA4

CH4 Filter LNA

Oscilloscope

Figure 4.13: The structure of the SDR UWB reader network.

4.2.6 Implementation and Experiment As shown in Figure 4.14, implementation of the positioning platform for experimental setup consists of a computer (local server), a CCR (2.4-GHz RF transceiver with 8051 core), a SDR UWB reader network (4 independent ToA estimator and a uniform TDoA positioning and processing back-end), two 2.4-GHz readers (work in diﬀerent frequency channels), and three hybrid tags (one in critical site, and two in non-critical site).

TDoA Positioning In the experiment, only basic RSS distance estimation is used for the 2.4-GHz RF based positioning. Hence, only the positioning results using the TDoA method under the communication context of the UWB signal are shown. In Figure 4.15, a 3000-times statistical result of absolute ToA estimation of one tag at a ﬁxed location is drawn. This result can be described by a Gaussian distribution with mean µ and variance σ2. The ﬁnal ToA is computed according 4.2. 2.4-GHZ/UWB POSITIONING PLATFORM 63 32

Server Cable/Ethernet 2.4-GHz readers

UWB reader Ethernet network Cable data 2.4-GHz coordinator 2.4-GHz control 2.4-GHz data UWB

Critical site Non-critical site

Figure 4.14: The overview of the platform implementation. to weighted algorithm after ﬁltering out estimations with large errors, P ˆ i∈[µ−ε,µ+ε] nittoa,i ttoa = P (4.7) i∈[µ−ε,µ+ε] ni where |ε| ∈ [σ, 2σ] is the bound of ﬁlter.

400

300

200 Number

100

0 4.95 5 5.05 5.1 5.15 5.2 Time (s) x 10−8 Figure 4.15: The absolute ToA estimation based on the received UWB signal.

Then by employing the TDoA method, the relative ToA measurement results using one of the input channels as the reference channel can be obtained for location 64 CHAPTER 4. 2.4-GHZ/UWB HYBRID POSITIONING PLATFORM estimations. The Newton estimation algorithm is used to compute the locations according to Equation 4.2. An instance of the positioning results of 28 test locations is shown in Figure 4.16.

3 estimate reference

distance (m) 1

0 1 2 3 distance (m)

Figure 4.16: A positioning example using TDoA estimation.

To analyze the positioning accuracy, the root-mean-square error (RMSE) is introduced. A statistic of 1600 times position estimations of one test point is shown in Figure 4.17. It can be seen that over 75% of the location estimations can achieve less than 10 cm positioning accuracy, that is, 1220 out of 1600.

Energy Budget

Figure 4.18 shows a comparison of the estimated average power requested for both data transmission and commands/synchronization information reception of one tag by using a single 2.4-GHz RF technology and the 2.4-GHz RF and UWB [137] hybrid technologies. It draws the average power consumption as a function of the position update rate where the rate of receiving commands sent by the CCR is ﬁxed assumed as every hour, and the synchronization rate of the tags is 10 times of their position update rate in average. It can be seen by using the UWB to transmit data, the hybrid tag can consume about 37.5% less energy compared with the one using the 2.4-GHz RF to transmit. 4.2. 2.4-GHZ/UWB POSITIONING PLATFORM 65

700

600

500

400

300 Number of tests

200

100

0 0 20 40 60 80 100 RMSE (cm)

Figure 4.17: The RMSE distribution of the estimated locations of one test point over 1600 times estimations

100 Normal Tag (2.4−GHz for Tx and Rx) Hybrid Tag 10−1 (UWB for Tx and 2.4−GHz for Rx)

10−2

10−3

−4 Estimated average power of Tx and Rx (Ah) 10 10−3 10−2 10−1 100 Location upade rate (times/s)

Figure 4.18: Estimated average power consumption of Tx and Rx of each tag (2.4- GHz: 32 mA for Tx and 27 mA for Rx, the Tx period for position update is 5 ms, the Rx period for command is 5 ms, and Rx period for synchronization is 1 ms; UWB: 16 mA for Tx, the Tx period for position update is 1 ms). 66 CHAPTER 4. 2.4-GHZ/UWB HYBRID POSITIONING PLATFORM

4.3 Summary

In this chapter, a positioning platform based on the 2.4-GHz RF and UWB hybrid techniques is illustrated. This platform is implemented based on the extension of the architecture described in Chapter 3. That is, multiple RFID techniques, the UWB and the 2.4-GHz RF, are used in the communication to provide different positioning accuracy. The designed RFID tag prototype which consists of both the UWB interface and the 2.4-GHz RF interface demonstrates the feasibility of handling multiple RFID communication techniques in one RFID tag. And the corresponding communication process and system architecture enable such communications. An SDR UWB reader network is built based on an oscilloscope. With the help of the designed improved ToA/TDoA-based positioning algorithms, the positioning platform can provide ﬂexible positioning accuracy up to less than 10 cm error. Furthermore, this platform structure also capacitates other techniques and algorithms to be implemented depending on other application requirements. Chapter 5

Conclusions and Future Work

5.1 Thesis Summary

This thesis describes an RFID communication and positioning system with high- reliability, low-latency, and accurate-positioning capabilities for the industrial IoT system. As the development of the automation and Internet technologies, the 4th generation of the industrial revolution which relies on the basis of the IoT is introduced. Among the three-layer architecture of the industrial IoT system, the performance of data acquisition in the Object Layer dominates the whole network and service. Consequently, the appreciated system architecture, communication technology, and the corresponding implementation protocols in the acquisition system of the Object Layer are discussed in Chapter 2. According to the specific working environment, the requirements of the acquisition system for the industrial applications are: deterministic number of objects/functions under observation/operation process/task purpose, reliable communications among the objects and the network server with failure resistance, data overload and recovery capabilities, latency-aware performance for real-time response, deployable structure/protocol to handle the diversity tasks in multiple industrial environments, and flexible adaptive ability to fulfill QoS demands in one or multiple working scenarios. Under the considerations of the requests and the features illustrated in Chapter 2, a designed discrete-structure gateway based RFID system and a contention-free communication protocol are explored in Chapter 3. One discrete gateway is composed of a coordinator and a set of readers. On the basis of an extended star topology, multiple gateways are connected to a local server, and the RFID tags attached to the objects are connected to the coordinator and the readers of each gateway accordingly. This system structure can benefit deployment for larger capacities and coverage. It also supports flexible configuration of the number of readers inside one coordinator’s coverage in various industrial environments. Additionally, an independent control link and data link is achieved by the separation of the coordinator

67 68 CHAPTER 5. CONCLUSIONS AND FUTURE WORK and the readers in each gateway. It enables extra failure resistance and emergency report mechanism in the protocol as mentioned in the instance design for control application in Chapter 3. The contention-free protocol, MF-TDMA, which uses both time and frequency multiplexing method is specified for the discrete system architecture. It employs scheduled communication for both data link and control link to provide deterministic access. The capability of multiple communication channels/techniques offers larger system capacity then. And the dedicated control channel enables the system to work in a full duplex manner. In the protocol, a basic optional ARQ mechanism is used to improved the communication reliability, an independent/uniform synchronization and control method and the slot allocation optimization algorithm are employed to reduce the transmission delay. From the instance implementations of the designed protocol for energy-constraint, latency- constraint, and control-oriented industrial applications, the usages of the designed methods shown above are introduced in the concrete ways. A positioning platform design based on the designed RFID system architecture is introduced in Chapter 4. The 2.4-GHz RF and the UWB hybrid technologies are used in the platform to support flexible positioning accuracy from meter level to centimeter level on demand of QoS. Besides the proposed asymmetric communication structure, a prototype of hybrid RFID tag with both the 2.4-GHz RF interface and the UWB interface is designed. The RFID tag is able to send information via both the 2.4-GHz radio and the UWB radio. And it only receives data via the 2.4-GHz radio, that is no UWB receiver supported on the tag, which relaxes the pressure of power consumption at the tag side. From the reader side, to take advantage of the fine positioning resolution in the time domain of the UWB signal while providing a flexible testbed for multiple positioning algorithms, an SDR UWB reader network is implemented based on an oscilloscope. It can perform both ToA test and TDoA positioning in real time with the help of the execution of the embedded third-party program. An improved ranging and positioning algorithm is realized for the received UWB signals, and it is implemented in the SDR UWB reader network. From the demonstrated experimental implementation, an average of 10 cm positioning accuracy is achievable when using the UWB signal. The positioning platform is a practical implementation of the designed RFID system which employs two techniques within one coordinator’s coverage. On this basis, more flexible system implementations for different industrial applications can be expected under the designed system architecture and protocol.

5.2 Future Work

As the growing demands of automation, networking, and informatization, the sug- gested future works of the industrial IoT system are distributed mainly over the following aspects:

• Hardware improvement 5.2. FUTURE WORK 69

From the tag side, to connect "everything" to the network requires the RFID tag to be able to perfectly attached to the objects without affecting their physical property while keeping a reliable connection to them. Therefore, the hardware improvement of the tag includes: 1) an efficient power management module which can support continuous long-time power for the RFID tag; 2) be able to implement the RFID tag using printing electronics technology on flexible types of substrate, such as paper and plastic, for special industries; 3) reasonable tag design of RF antenna with omni-directional radiation capability for different substrates. • Standards and compatibility improvement The diversity of industrial applications increases the difficulty of applying one protocol in all systems. The protocol should be extremely flexible and adaptive for not only the RF communication but also the function employed each specific application. Therefore, based on the proposed system structure and communication protocol, further simplification of the protocol, reduction of the implementation complexity, and improvement of the compatibility are the main future effort. • Security improvement Both the system software and the network security are the indispensable issue research under the context of the industrial IoT. In the Object Layer, data generated from some special machines, tool, or operation process should not be revealed to the whole network. Additionally, a local backup of some of the operation-related data is needed for errors or statistic analysis. In future work, the security modules with specific algorithms are requested to be implemented for the industrial IoT system.

Bibliography

[1] Kevin Ashton. That ’internet of things’ thing, in the real world things matter more than ideas. RFID journal.

[2] Ismael Peña-López et al. Itu internet report 2005: the internet of things. 2005.

[3] Michael Chui, Markus Löﬄer, and Roger Roberts. The internet of things. McKinsey Quarterly, 2(2010):1–9, 2010.

[4] Ovidiu Vermesan and Peter Friess. Internet of Things-Global Technological and Societal Trends From Smart Environments and Spaces to Green ICT. River Publishers, 2011.

[5] Lu Tan and Neng Wang. Future internet: The internet of things. In Advanced Computer Theory and Engineering (ICACTE), 2010 3rd International Con- ference on, volume 5, pages V5–376–V5–380, Aug 2010.

[6] Margaret Ranken. M2M Global Forecast & Analysis 2014. Machina Research, 2015.

[7] Karen Tillman. How Many Internet Connections are in the World? Right. Now. Cisco, 2013.

[8] Jayavardhana Gubbi, Rajkumar Buyya, Slaven Marusic, and Marimuthu Palaniswami. Internet of things (iot): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7):1645–1660, 2013.

[9] Yan Zhang, Rong Yu, Maziar Nekovee, Yi Liu, Shengli Xie, and Stein Gjess- ing. Cognitive machine-to-machine communications: visions and potentials for the smart grid. IEEE network, 26(3):6–13, 2012.

[10] Dragan S Markovic, Dejan Zivkovic, Irina Branovic, Ranko Popovic, and Dragan Cvetkovic. Smart power grid and cloud computing. Renewable and Sustainable Energy Reviews, 24:566–577, 2013.

71 72 BIBLIOGRAPHY

[11] Pierluigi Siano. Demand response and smart grids: A survey. Renewable and Sustainable Energy Reviews, 30:461–478, 2014.

[12] TM Anand, K Banupriya, M Deebika, and A Anusiya. Intelligent transportation systems using iot service for vehicular data cloud. International Journal for Innovative Research in Science and Technology, 2(2):80–86, 2015.

[13] Yongheng Wang and Kening Cao. A proactive complex event processing method for large-scale transportation internet of things. International Journal of Distributed Sensor Networks, 2014, 2014.

[14] RSH Istepanian, Sijung Hu, NY Philip, and Ala Sungoor. The potential of internet of m-health things ’m-iot’ for non-invasive glucose level sensing. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 5264–5266. IEEE, 2011.

[15] Charalampos Doukas and Ilias Maglogiannis. Bringing iot and cloud computing towards pervasive healthcare. In Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), 2012 Sixth International Conference on, pages 922–926. IEEE, 2012.

[16] Prosanta Gope and Tzonelih Hwang. Bsn-care: A secure iot-based modern healthcare system using body sensor network. IEEE Sensors Journal, 16(5):1368–1376, 2016.

[17] Chengen Wang, Zhuming Bi, and Li Da Xu. Iot and cloud computing in automation of assembly modeling systems. IEEE Transactions on Industrial Informatics, 10(2):1426–1434, 2014.

[18] Kleanthis Thramboulidis. A cyber–physical system-based approach for industrial automation systems. Computers in Industry, 72:92–102, 2015.

[19] Zhang Duzhong, Liu Quan, and Xu Wenjun. Access convergence of mechanical equipments to heterogeneous networking environment in internet of things for manufacturing. Journal of Convergence Information Technology, 8(4), 2013.

[20] Zhuming Bi, Li Da Xu, and Chengen Wang. Internet of things for enterprise systems of modern manufacturing. IEEE Transactions on Industrial Informatics, 10(2):1537–1546, 2014.

[21] Yun-long Wan, Hai-ping Zhu, Yan-ping Mu, and Hong-chen Yu. Research on iot-based material delivery system of the mixed-model assembly workshop. In Proceedings of 2013 4th International Asia Conference on Industrial Engi- neering and Management Innovation (IEMI2013), pages 581–593. Springer, 2014. BIBLIOGRAPHY 73

[22] Ying Shengbin, Lei Bicheng, Zhou Kun, and Wang Jiangwei. The design of intelligent breeding livestock monitoring system based on the internet of things (iot). Electronic Measurement Technology, 11:021, 2014.

[23] Yu Zhang, Qiyu Chen, Guanting Liu, Weizheng Shen, and Guanlin Wang. Environment parameters control based on wireless sensor network in livestock buildings. International Journal of Distributed Sensor Networks, 2016, 2016.

[24] Alexandros Kaloxylos, Robert Eigenmann, Frederick Teye, Zoi Politopoulou, Sjaak Wolfert, Claudia Shrank, Markus Dillinger, Ioanna Lampropoulou, Eleni Antoniou, Liisa Pesonen, et al. Farm management systems and the future internet era. Computers and electronics in agriculture, 89:130–144, 2012.

[25] Fan TongKe. Smart agriculture based on cloud computing and iot. Journal of Convergence Information Technology, 8(2), 2013.

[26] Shri Kaushal Jani. Internet of things in automobile industry, January 2016.

[27] A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash. Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys Tutorials, 17(4):2347–2376, Fourthquarter 2015.

[28] R. Want, B. N. Schilit, and S. Jenson. Enabling the internet of things. Com- puter, 48(1):28–35, Jan 2015.

[29] D. F. Wendel, R. Kalla, J. Warnock, R. Cargnoni, S. G. Chu, J. G. Clabes, D. Dreps, D. Hrusecky, J. Friedrich, S. Islam, J. Kahle, J. Leenstra, G. Mit- tal, J. Paredes, J. Pille, P. J. Restle, B. Sinharoy, G. Smith, W. J. Starke, S. Taylor, A. J. Van Norstrand, S. Weitzel, P. G. Williams, and V. Zyuban. a highly parallel, scalable multi-core high end server processor. IEEE Journal of Solid-State Circuits, 46(1):145–161, Jan 2011.

[30] Ning Ma, Zhuo Zou, Zhonghai Lu, Lirong Zheng, and S. Blixt. A hierarchical reconﬁgurable micro-coded multi-core processor for iot applications. In Re- conﬁgurable and Communication-Centric Systems-on-Chip (ReCoSoC), 2014 9th International Symposium on, pages 1–4, May 2014.

[31] C. Perera, C. H. Liu, and S. Jayawardena. The emerging internet of things marketplace from an industrial perspective: A survey. IEEE Transactions on Emerging Topics in Computing, 3(4):585–598, Dec 2015.

[32] C. Zhu, V. C. M. Leung, L. Shu, and E. C. H. Ngai. Green internet of things for smart world. IEEE Access, 3:2151–2162, 2015. 74 BIBLIOGRAPHY

[33] A. Sehgal, V. Perelman, S. Kuryla, and J. Schonwalder. Management of resource constrained devices in the internet of things. IEEE Communications Magazine, 50(12):144–149, December 2012. [34] T. Savolainen, J. Soininen, and B. Silverajan. Ipv6 addressing strategies for iot. IEEE Sensors Journal, 13(10):3511–3519, Oct 2013. [35] M. R. Palattella, M. Dohler, A. Grieco, G. Rizzo, J. Torsner, T. Engel, and L. Ladid. Internet of things in the 5g era: Enablers, architecture, and business models. IEEE Journal on Selected Areas in Communications, 34(3):510–527, March 2016. [36] J. Nieminen, C. Gomez, M. Isomaki, T. Savolainen, B. Patil, Z. Shelby, M. Xi, and J. Oller. Networking solutions for connecting bluetooth low energy enabled machines to the internet of things. IEEE Network, 28(6):83–90, Nov 2014. [37] M. R. Palattella, N. Accettura, X. Vilajosana, T. Watteyne, L. A. Grieco, G. Boggia, and M. Dohler. Standardized protocol stack for the internet of (important) things. IEEE Communications Surveys Tutorials, 15(3):1389– 1406, Third 2013. [38] V. Maximov, O. Tabarovsky, and D. Filgus. Distributed localisation algorithm for iot network. In Indoor Positioning and Indoor Navigation (IPIN), 2015 International Conference on, pages 1–7, Oct 2015. [39] Z. Zou, B. Shao, Q. Zhou, C. Zhai, J. Mao, M. Baghaei-Nejad, Q. Chen, and L. Zheng. Design and demonstration of passive uwb rﬁds: Chipless versus chip solutions. In RFID-Technologies and Applications (RFID-TA), 2012 IEEE International Conference on, pages 6–11, Nov 2012. [40] Da Zhang, Feng Xia, Zhuo Yang, Lin Yao, and Wenhong Zhao. Localization technologies for indoor human tracking. In Future Information Technology (FutureTech), 2010 5th International Conference on, pages 1–6. IEEE, 2010. [41] Amr El Mougy, Aymen Kamoun, Mohamed Ibnkahla, Saïd Tazi, and Khalil Drira. A context and application-aware framework for resource management in dynamic collaborative wireless m2m networks. Journal of Network and Computer Applications, 44:30–45, 2014. [42] Zhongshan Zhang, Keping Long, Jianping Wang, and Falko Dressler. On swarm intelligence inspired self-organized networking: its bionic mechanisms, designing principles and optimization approaches. IEEE Communications Surveys & Tutorials, 16(1):513–537, 2014. [43] Pablo Punal Pereira, Jens Eliasson, Rumen Kyusakov, Jerker Delsing, Asma Raayatinezhad, and Mia Johansson. Enabling cloud connectivity for mobile internet of things applications. In Service Oriented System Engineering BIBLIOGRAPHY 75

(SOSE), 2013 IEEE 7th International Symposium on, pages 518–526. IEEE, 2013.

[44] Alessio Botta, Walter De Donato, Valerio Persico, and Antonio Pescapé. On the integration of cloud computing and internet of things. In Future Internet of Things and Cloud (FiCloud), 2014 International Conference on, pages 23– 30. IEEE, 2014.

[45] Malte Brettel, Niklas Friederichsen, Michael Keller, and Marius Rosenberg. How virtualization, decentralization and network building change the manufacturing landscape: An industry 4.0 perspective. International Journal of Mechanical, Industrial Science and Engineering, 8(1):37–44, 2014.

[46] Jay Lee, Behrad Bagheri, and Hung-An Kao. A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 3:18–23, 2015.

[47] Stephan Weyer, Mathias Schmitt, Moritz Ohmer, and Dominic Gorecky. To- wards industry 4.0-standardization as the crucial challenge for highly mod- ular, multi-vendor production systems. IFAC-PapersOnLine, 48(3):579–584, 2015.

[48] C Alessi and C Gummer. Germany bets on ’smart factories’ to keep its manufacturing edge. The Wall Street Journal, 2014.

[49] Anitha Varghese and Deepaknath Tandur. Wireless requirements and challenges in industry 4.0. In Contemporary Computing and Informatics (IC3I), 2014 International Conference on, pages 634–638. IEEE, 2014.

[50] Boyi Xu, Li Da Xu, Hongming Cai, Cheng Xie, Jingyuan Hu, and Fenglin Bu. Ubiquitous data accessing method in iot-based information system for emergency medical services. IEEE Transactions on Industrial Informatics, 10(2):1578–1586, 2014.

[51] Muhammad S Khan, Mohammad S Islam, and Hai Deng. Design of a re- conﬁgurable rﬁd sensing tag as a generic sensing platform toward the future internet of things. IEEE Internet of things journal, 1(4):300–310, 2014.

[52] Jiong Jin, Jayavardhana Gubbi, Slaven Marusic, and Marimuthu Palaniswami. An information framework for creating a smart city through internet of things. IEEE Internet of Things Journal, 1(2):112–121, 2014.

[53] Ju-Su Kim, Hak-Jun Lee, and Ryum-Duck Oh. Smart integrated multiple tracking system development for iot based target-oriented logistics location and resources services. International Journal of Smart Home, 9(5):195–204, 2015. 76 BIBLIOGRAPHY

[54] Roman Obermaisser and Donatus Weber. Architectures for mixed-criticality systems based on networked multi-core chips. In Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA), pages 1–10. IEEE, 2014.

[55] Marcos A Pisching, Fabrício Junqueira, Diolino J Santos Filho, and Paulo E Miyagi. Service composition in the cloud-based manufacturing focused on the industry 4.0. In Doctoral Conference on Computing, Electrical and Industrial Systems, pages 65–72. Springer, 2015.

[56] Florian Ernst. Industry 4.0/industrial internet of things-related technologies and requirements for a successful digital transformation: An investigation of manufacturing businesses worldwide. Available at SSRN 2698137, 2015.

[57] Erik Jan Marinissen, Yervant Zorian, Mario Konijnenburg, Chih-Tsun Huang, Ping-Hsuan Hsieh, Peter Cockburn, Jeroen Delvaux, Vladimir Rožić, Bohan Yang, Dave Singelée, et al. Iot: Source of test challenges. In 2016 21th IEEE European Test Symposium (ETS), pages 1–10. IEEE, 2016.

[58] Rajeev Alur, Emery Berger, Ann W Drobnis, Limor Fix, Kevin Fu, Gregory D Hager, Daniel Lopresti, Klara Nahrstedt, Elizabeth Mynatt, Shwetak Patel, et al. Systems computing challenges in the internet of things. arXiv preprint arXiv:1604.02980, 2016.

[59] SRI Consulting Business Intelligence. Disruptive tech-nologies: Global trends2025, appendix f: The inter-net of things (background).

[60] Tiia Muhonen. Standardization of industrial internet and iot (iot–internet of things)–perspective on condition-based maintenance. University of Oulu, Finland, 2015.

[61] Albert Nubiola, Mohamed Slamani, Ahmed Joubair, and Ilian A Bonev. Com- parison of two calibration methods for a small industrial robot based on an optical cmm and a laser tracker. Robotica, 32(3):447–466, 2014.

[62] Rahul Ravi and Li-Chung Milton Wu. DEMYSTIFYING INDUSTRY 4.0: IMPLICATIONS OF INTERNET OF THINGS AND SERVICES FOR THE CHEMICAL INDUSTRY. PhD thesis, 2016.

[63] Zhuo Zou, Qing Chen, Ismail Uysal, and Lirong Zheng. Radio frequency iden- tiﬁcation enabled wireless sensing for intelligent food logistics. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 372(2017):20130313, 2014.

[64] Rui-Yang Chen. An intelligent value stream-based approach to collaboration of food traceability cyber physical system by fog computing. Food Control, 71:124–136, 2017. BIBLIOGRAPHY 77

[65] Mohammad Ali Moridi, Youhei Kawamura, Mostafa Sharifzadeh, Em- manuel Knox Chanda, Markus Wagner, Hyongdoo Jang, and Hirokazu Okawa. Development of underground mine monitoring and communication system integrated zigbee and gis. International Journal of Mining Science and Technology, 25(5):811–818, 2015. [66] AG Kostianoy, EV Bulycheva, AV Semenov, and A Krainyukov. Satellite monitoring systems for shipping and offshore oil and gas industry in the baltic sea. Transport and Telecommunication Journal, 16(2):117–126, 2015. [67] M Zuba, T Lojka, and I Zolotov. Iot gateway and industrial safety with computer vision. In 2016 IEEE 14th International Symposium on Applied Machine Intelligence and Informatics (SAMI), pages 183–186. IEEE, 2016. [68] Kaivan Karimi and Gary Atkinson. What the internet of things (iot) needs to become a reality. White Paper, FreeScale and ARM, pages 1–16, 2013. [69] Chouchang Yang and Huai-Rong Shao. Wifi-based indoor positioning. IEEE Communications Magazine, 53(3):150–157, 2015. [70] Greg Sterling. Magnetic positioning? the arrival of indoor gps. Technical report, Opus Research Report, 2014. [71] Qingsong Hu, Dian Zhang, and Wei Liu. Precise positioning of moving objects in coal face: Challenges and solutions. International Journal of Digital Content Technology and its Applications, 7(1):213, 2013. [72] Pablo López, David Fernández, Antonio J Jara, and Antonio F Skarmeta. Survey of internet of things technologies for clinical environments. In Ad- vanced Information Networking and Applications Workshops (WAINA), 2013 27th International Conference on, pages 1349–1354. IEEE, 2013. [73] RF Wireless World. Iot architecture: Iot software and hardware architecture. [74] Yalcin Sadi and Sinem Coleri Ergen. Energy and delay constrained maximum adaptive schedule for wireless networked control systems. IEEE Transactions on Wireless Communications, 14(7):3738–3751, 2015. [75] Brendan Galloway and Gerhard P Hancke. Introduction to industrial control networks. IEEE Communications surveys & tutorials, 15(2):860–880, 2013. [76] Javier Espina, Thomas Falck, Athanasia Panousopoulou, Lars Schmitt, Oliver Mülhens, and Guang-Zhong Yang. Network topologies, communication protocols, and standards. In Body sensor networks, pages 189–236. Springer, 2014. [77] Xiaowei Zhu, Samar K Mukhopadhyay, and Hisashi Kurata. A review of rfid technology and its managerial applications in different industries. Journal of Engineering and Technology Management, 29(1):152–167, 2012. 78 BIBLIOGRAPHY

[78] Drew Gislason. Zigbee applications - part 3: Zigbee pans, July 2010.

[79] IEEE 802.15 WPAN Task Group 4. Ieee standard for local and metropoli- tan area networks - part 15.4: Low-rate wireless personal area networks (lr- wpans), 2011.

[80] Sinan Gezici, Zhi Tian, Georgios B Giannakis, Hisashi Kobayashi, Andreas F Molisch, H Vincent Poor, and Zafer Sahinoglu. Localization via ultra- wideband radios: a look at positioning aspects for future sensor networks. IEEE signal processing magazine, 22(4):70–84, 2005.

[81] Chris M Roberts. Radio frequency identiﬁcation (rﬁd). computers & security, 25(1):18–26, 2006.

[82] Simon Holloway. Rﬁd: An introduction.

[83] Rodrigo Roman, Pablo Najera, and Javier Lopez. Securing the internet of things. Computer, 44(9):51–58, 2011.

[84] Tiia Muhonen, Heikki Ailisto, and Pekka Kess. Standards in iot, industrial internet and condition-based maintenance. OF THINGS/////Finland, 21:56, 2015.

[85] Aref Meddeb. Internet of things standards: who stands out from the crowd? IEEE Communications Magazine, 54(7):40–47, 2016.

[86] Andrea Zanella, Nicola Bui, Angelo Castellani, Lorenzo Vangelista, and Michele Zorzi. Internet of things for smart cities. IEEE Internet of Things Journal, 1(1):22–32, 2014.

[87] Haifeng Wu, Yu Zeng, Jihua Feng, and Yu Gu. Binary tree slotted aloha for passive rﬁd tag anticollision. Parallel and Distributed Systems, IEEE Transactions on, 24(1):19–31, 2013.

[88] YZ Zhao, CY Miao, and M Ma. An energy-eﬃcient self-adaptive duty cycle mac protocol for traﬃc-dynamic wireless sensor networks. Wireless personal communications, 68(4):1287–1315, 2013.

[89] Gianpaolo Conte and Patrick Doherty. Vision-based unmanned aerial vehicle navigation using geo-referenced information. EURASIP Journal on Advances in Signal Processing, 2009:10, 2009.

[90] Angelos Antonopoulos, Christos Verikoukis, Charalabos Skianis, and Ozgur B Akan. Energy eﬃcient network coding-based mac for cooperative arq wireless networks. Ad Hoc Networks, 11(1):190–200, 2013. BIBLIOGRAPHY 79

[91] Jian Ni, Bo Tan, and Rayadurgam Srikant. Q-csma: queue-length-based csma/ca algorithms for achieving maximum throughput and low delay in wireless networks. Networking, IEEE/ACM Transactions on, 20(3):825–836, 2012. [92] Sana Ullah, Min Chen, and Kyung Sup Kwak. Throughput and delay analysis of ieee 802.15. 6-based csma/ca protocol. Journal of medical systems, 36(6):3875–3891, 2012. [93] Anthony Rowe, Rahul Mangharam, and Raj Rajkumar. Rt-link: A time- synchronized link protocol for energy-constrained multi-hop wireless networks. In Sensor and Ad Hoc Communications and Networks, 2006. SECON’06. 2006 3rd Annual IEEE Communications Society on, volume 2, pages 402–411. IEEE, 2006. [94] Mastooreh Salajegheh, Hamed Soroush, and Antonis Kalis. Hymac: Hybrid tdma/fdma medium access control protocol for wireless sensor networks. In Personal, Indoor and Mobile Radio Communications, 2007. PIMRC 2007. IEEE 18th International Symposium on, pages 1–5. IEEE, 2007. [95] Mohammad Hossein Sedighi Gilani, Iman Sarraﬁ, and Maghsoud Abbaspour. An adaptive csma/tdma hybrid mac for energy and throughput improvement of wireless sensor networks. Ad Hoc Networks, 11(4):1297–1304, 2013. [96] Yi Liu, Chau Yuen, Xianghui Cao, Naveed Ul Hassan, and Jiming Chen. Design of a scalable hybrid mac protocol for heterogeneous m2m networks. Internet of Things Journal, IEEE, 1(1):99–111, 2014. [97] Chen-Yu Hsu, Chi-Hsien Yen, and Chun-Ting Chou. An adaptive multichan- nel protocol for large-scale machine-to-machine (m2m) networks. In Wireless Communications and Mobile Computing Conference (IWCMC), 2013 9th In- ternational, pages 1223–1228. IEEE, 2013. [98] Mario Strasser, Andreas Meier, Koen Langendoen, and Philipp Blum. Dwarf: Delay-aware robust forwarding for energy-constrained wireless sensor networks. In Distributed Computing in Sensor Systems, pages 64–81. Springer, 2007.

[99] Petcharat Suriyachai, Utz Roedig, and Andrew Scott. Implementation of a mac protocol for qos support in wireless sensor networks. In Pervasive Computing and Communications, 2009. PerCom 2009. IEEE International Conference on, pages 1–6. IEEE, 2009. [100] Danilo De Donno, Fabio Ricciato, and Luciano Tarricone. Listening to tags: Uplink rﬁd measurements with an open-source software-deﬁned radio tool. Instrumentation and Measurement, IEEE Transactions on, 62(1):109–118, 2013. 80 BIBLIOGRAPHY

[101] Anneleen Van Geystelen, Kristof Benaets, Dirk C de Graaf, MHD Larmuseau, and Tom Wenseleers. Track-a-forager: a program for the automated analysis of rﬁd tracking data to reconstruct foraging behaviour. Insectes Sociaux, 63(1):175–183, 2016.

[102] Robert R Friedlander and James R Kraemer. Monitoring container conditions of intermodal shipping containers on a cargo ship through use of a sensor grid, 2015. US Patent App. 14/623,345.

[103] Susanne Steinle, Stefan Reis, and Clive Eric Sabel. Quantifying human exposure to air pollution moving from static monitoring to spatio-temporally resolved personal exposure assessment. Science of the Total Environment, 443:184–193, 2013.

[104] Samah Mansour, Nidal Nasser, Lutful Karim, and Ahmad Ali. Wireless sensor network-based air quality monitoring system. In Computing, Networking and Communications (ICNC), 2014 International Conference on, pages 545–550. IEEE, 2014.

[105] Ricardo Badia-Melis, Luis Ruiz-Garcia, Javier Garcia-Hierro, and Jose I Robla Villalba. Refrigerated fruit storage monitoring combining two different wireless sensing technologies: Rﬁd and wsn. Sensors, 15(3):4781–4795, 2015.

[106] Peter B Southard, Charu Chandra, and Sameer Kumar. Rﬁd in healthcare: a six sigma dmaic and simulation case study. International journal of health care quality assurance, 25(4):291–321, 2012.

[107] Peter Marsh. The new industrial revolution: consumers, globalization and the end of mass production. Yale University Press, 2012.

[108] Ray Y Zhong, QY Dai, T Qu, GJ Hu, and George Q Huang. Rﬁd-enabled real-time manufacturing execution system for mass-customization production. Robotics and Computer-Integrated Manufacturing, 29(2):283–292, 2013.

[109] Sotiris Makris, George Michalos, and George Chryssolouris. Rﬁd driven robotic assembly for random mix manufacturing. Robotics and Computer- Integrated Manufacturing, 28(3):359–365, 2012.

[110] Zhixin Yang, Wei Xu, Pak-Kin Wong, and Xianbo Wang. Modeling of rﬁd- enabled real-time manufacturing execution system in mixed-model assembly lines. Mathematical Problems in Engineering, 2015, 2015.

[111] Chuanying Zhai, Zhuo Zou, Qiang Chen, Lida Xu, Li-Rong Zheng, and Hannu Tenhunen. Delay-aware and reliability-aware contention-free mf–tdma protocol for automated rﬁd monitoring in industrial iot. Journal of Industrial Information Integration, 3:8–19, 2016. BIBLIOGRAPHY 81

[112] Chuanying Zhai, Zhuo Zou, Qiang Chen, Lirong Zheng, and Hannu Tenhunen. High-throughput and high-eﬃciency multiple access scheme for ieee802. 15.4 based rﬁd sensing. In 2015 IEEE International Conference on Ubiquitous Wireless Broadband (ICUWB), pages 1–5. IEEE, 2015.

[113] Chuanying Zhai, Zhuo Zou, Qiang Chen, Lirong Zheng, and Hannu Tenhunen. Optimization on guard time and synchronization cycle for tdma-based deterministic rﬁd system. In RFID Technology and Applications (RFID-TA), 2015 IEEE International Conference on, pages 71–75. IEEE, 2015.

[114] Measuring Power Consumption of CC2530 With Z-Stack.

[115] A True System-on-Chip Solution for 2.4-GHz IEEE 802.15.4 and ZigBee Ap- plications.

[116] Mario E Rivero-Ángeles, Domingo Lara-Rodríguez, and Felipe A Cruz- Pérez. Random-access control mechanisms using adaptive traﬃc load in aloha and csma strategies for edge. IEEE transactions on vehicular technology, 54(3):1160–1186, 2005.

[117] Pelvin Ye Xiang Lim, Yan Ting Tan, and Justin Win Shern Lim. Patient tracking technology with active rﬁd technology. 2013.

[118] Caner Kilinc, Seraj Al Mahmud Mostafa, Raihan Ul Islam, Kashif Shahzad, and Karl Andersson. Indoor taxi-cab: Real-time indoor positioning and location-based services with ekahau and android os. In IMIS, pages 223– 228, 2014.

[119] Ygal Bendavid. Rﬁd-enabled real-time location system (rtls) to improve hospi- tal’s operations management: An up-to-date typology. International Journal of RF Technologies, 5(3-4):137–158, 2013.

[120] Óscar García, Ricardo S Alonso, Dante I Tapia, and Fabio Guevara. Wire- less sensor networks and real-time locating systems to ﬁght against maritime piracy. International Journal of Interactive Multimedia and Artiﬁcial Intelli- gence, 1(5), 2012.

[121] Gideon Kaplan. Wide-area dynamic rﬁd system using uwb, October 31 2014. US Patent App. 14/529,991.

[122] Ramin Sadr. Rﬁd systems with low complexity implementation and pallet coding error correction, March 17 2015. US Patent 8,981,908.

[123] Jianqi Liu, Qinruo Wang, Jiafu Wan, and Jianbin Xiong. Towards real-time indoor localization in wireless sensor networks. In Computer and Information Technology (CIT), 2012 IEEE 12th international conference on, pages 877– 884. IEEE, 2012. 82 BIBLIOGRAPHY

[124] Time Domain. Precise location ultra wideband system. [125] Chris Hook. Applications for bidirectional electronically steerable phased array (bespa) antenna systems with passive uhf rfid tags, 2010. [126] Eric Gross, Anthony J Guarino, Scott Easterling, Ernest Peek, Charles L Zahm, Leonard R Reinhart, and Michael S Gottfried. Method and system for proximity detection and location determination, April 17 2001. US Patent 6,218,961. [127] Zahid Farid, Rosdiadee Nordin, and Mahamod Ismail. Recent advances in wireless indoor localization techniques and system. Journal of Computer Networks and Communications, 2013, 2013. [128] Hui Liu, Houshang Darabi, Pat Banerjee, and Jing Liu. Survey of wireless indoor positioning techniques and systems. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 37(6):1067–1080, 2007. [129] Kamol Kaemarungsi and Prashant Krishnamurthy. Modeling of indoor positioning systems based on location fingerprinting. In INFOCOM 2004. Twenty-third AnnualJoint Conference of the IEEE Computer and Communi- cations Societies, volume 2, pages 1012–1022. IEEE, 2004. [130] E. J. Krakiwsky, C. B. Harris, and R. V. C. Wong. A kalman filter for integrating dead reckoning, map matching and gps positioning. In Position Location and Navigation Symposium, 1988. Record. Navigation into the 21st Century. IEEE PLANS ’88., IEEE, pages 39–46, Nov 1988. [131] Ruizhi Chen. Ubiquitous positioning and mobile location-based services in smart phones. IGI Global, 2012. [132] S. Dhawan. Analogy of promising wireless technologies on different frequen- cies: Bluetooth, wifi, and wimax. In Wireless Broadband and Ultra Wideband Communications, 2007. AusWireless 2007. The 2nd International Conference on, pages 14–14, Aug 2007. [133] Z. Zhu, S. Lambotharan, W. H. Chin, and Z. Fan. Overview of demand management in smart grid and enabling wireless communication technologies. IEEE Wireless Communications, 19(3):48–56, June 2012. [134] Tifenn Rault, Abdelmadjid Bouabdallah, and Yacine Challal. Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks, 67:104–122, 2014. [135] Thaier Hayajneh, Ghada Almashaqbeh, Sana Ullah, and Athanasios V Vasi- lakos. A survey of wireless technologies coexistence in wban: analysis and open research issues. Wireless Networks, 20(8):2165–2199, 2014. BIBLIOGRAPHY 83

[136] Chuanying Zhai, Zhuo Zou, Qin Zhou, Jia Mao, Qiang Chen, Hannu Ten- hunen, Lirong Zheng, and Lida Xu. A 2.4-ghz ism rf and uwb hybrid rfid real-time locating system for industrial enterprise internet of things. Enter- prise Information Systems, pages 1–18, 2016. [137] Jia Mao, Zhuo Zou, and Li-Rong Zheng. A 35 pj/pulse injection-locking based uwb transmitter for wirelessly-powered rfid tags. In ESSCIRC (ESSCIRC), 2013 Proceedings of the, pages 379–382. IEEE, 2013. [138] John WM Rogers, Calvin Plett, and Ian Marsland. Radio Frequency System Architecture and Design. Artech House, 2013. [139] H. Ding, W. Liu, X. Huang, and L. Zheng. First path detection using rank test in ir uwb ranging with energy detection receiver under harsh environments. IEEE Communications Letters, 17(4):761–764, April 2013. [140] Z. Zou, D. S. Mendoza, P. Wang, Q. Zhou, J. Mao, F. Jonsson, H. Tenhunen, and L. R. Zheng. A low-power and flexible energy detection ir-uwb receiver for rfid and wireless sensor networks. IEEE Transactions on Circuits and Systems I: Regular Papers, 58(7):1470–1482, July 2011. [141] Mario R Casu and Giuseppe Durisi. Implementation aspects of a transmitted- reference uwb receiver. Wireless Communications and Mobile Computing, 5(5):537–549, 2005. [142] Q. H. Dang, A. Trindade, A. J. van der Veen, and G. Leus. Signal model and receiver algorithms for a transmit-reference ultra-wideband communication system. IEEE Journal on Selected Areas in Communications, 24(4):773–779, April 2006. [143] Ismail Guvenc and Zafer Sahinoglu. Threshold selection for uwb toa estimation based on kurtosis analysis. Communications Letters, IEEE, 9(12):1025– 1027, 2005. [144] Andrea Giorgetti and Marco Chiani. Time-of-arrival estimation based on information theoretic criteria. Signal Processing, IEEE Transactions on, 61(8):1869–1879, 2013. [145] Shingo Mandai, Matthew W Fishburn, Yuki Maruyama, and Edoardo Char- bon. A wide spectral range single-photon avalanche diode fabricated in an advanced 180 nm cmos technology. Optics express, 20(6):5849–5857, 2012.