DOCSLIB.ORG

A Study on Smart Dust Networks

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Study on Smart Dust Networks A Study on Smart Dust Networks Maryam Abrishami LITH-EX-11/0101 2011-05-31 Abstract This thesis work is done for the department of Electronic System at The Institute of Technology at Linköping University (Linköpings Tekniska Högskolan). Study's focus is to design and implement a protocol for smart dust networks to improve the energy consumption algorithm for this kind of network. Smart dust networks are in category of distributed sensor networks and power consumption is one of the key concerns for this type of network. This work shows that by focusing on improving the algorithmic behavior of power consumption in every network element (so called as mote), we can save a considerable amount of power for the whole network. Suggested algorithm is examined using Erlang for one mote object and the whole idea has put into test for a small network using SystemC. Acknowledgment I would like to express my gratitude to my supervisor Dr. J. Jacob Wikner for introducing me to the topic as well for the support on the way, useful comments, remarks and engagement through the learning process of this master thesis. Furthermore I would like to thank Specifically Mr. Joe Armstrong and Mr. Jan Hederen for their patient and kind support through this thesis work. Also, I like to thank Mr. Fred Hebert, Mr. Parthaserathi Murali, Mr. Vahid Keshmiri and Mr. Joakim Alvbrant for their kind supports on the way. List of abbreviations Abbreviation A network of modules that are interconnected through WSN Wireless Sensor Networks a wireless communication interface A kind of modulation that different version of IEEE FHSS Frequency Hopping Spread Spectrum 802.11 covers different frequency bandwidths for this modulation in indoor and outdoor applications A kind of modulation that different version of IEEE DSSS Direct Sequence Spread Spectrum 802.11 covers different frequency bandwidths for this modulation in indoor and outdoor applications SOM self-organizing Map MEMS Microelectromechanical Sensors MANET Mobile Ad-hoc Network WMN Wireless Mesh Network Defense Advanced Research Projects DARPA Agency PRNET Packet Radio Networks Areal Locations of Hazardous ALOHA Atmospheres CSMA Carrier Sense Medium Access SURAN Survivable Adaptive Radio Networks GloMo Global Mobile Information Systems NTDR Near-term Digital Radio NTDRS Near Term Digital Radio System IETF Internet Engineering Task Force eNB E-UTRAN (or Evolved) Node B SON Self-Organizing Network D-SON Distributed Self-Organizing Network C-SON Centralized Self-Organizing Network Table of Contents A Study on Smart Dust Networks ........................................................................................................1 Maryam Abrishami ..................................................................................................................................1 LITH-EX-11/0101 ......................................................................................................................................1 2011-05-31 ................................................................................................................................................1 1 Introduction .........................................................................................................................10 1.1 Motivation .........................................................................................................................................10 1.2 Working environment ......................................................................................................................10 1.3 Work specification ...........................................................................................................................11 1.4 Outline of thesis ...............................................................................................................................11 2 Distributed networks ............................................................................................................13 2.1 Introduction ......................................................................................................................................13 2.2 Wireless Sensor Networks (WSNs) ................................................................................................13 2.3 Smart Dust ........................................................................................................................................14 2.3.1 What is a smart dust network? ...............................................................................................................................15 2.3.2 Examples of existing applications smart dust ........................................................................................................15 2.3.3 Future applications for smart dust .........................................................................................................................15 2.4 Other network types? ......................................................................................................................16 2.4.1 Ad-hoc Network ......................................................................................................................................................16 2.4.1.1 Ad-hoc Sensor Networks.................................................................................................................................17 2.4.1.2 Ad-hoc Mobile Networks..................................................................................................................................18 2.4.2 Ad-hoc Standards ..................................................................................................................................................18 2.4.3 Autonomous Sensor Networks ...............................................................................................................................18 3 Artificial Intelligence for Smart Dust Networks ....................................................................20 3.1 Introduction ......................................................................................................................................20 3.2 Artificial Intelligence ........................................................................................................................20 3.2.1 Fuzzy logic ............................................................................................................................................................20 3.3 self-organizing Networks (SON) .....................................................................................................21 3.3.1 Introduction ............................................................................................................................................................ 21 3.3.2 SON Architectural types .........................................................................................................................................21 3.3.2.1 Distributed SON................................................................................................................................................22 3.3.2.2 Centralized SON...............................................................................................................................................22 3.3.2.3 Hybrid SON ......................................................................................................................................................22 3.3.3 SON Functions .......................................................................................................................................................22 3.3.3.1 Self-configuration..............................................................................................................................................23 3.3.3.2 Self-optimization...............................................................................................................................................23 3.3.3.3 Self-healing.......................................................................................................................................................23 3.3.3.4 SON Self-Configuration....................................................................................................................................23 3.3.3.5 SON Self-Optimization......................................................................................................................................24 3.3.3.5.1 Self-optimizing motivation...........................................................................................................................................24 3.3.3.5.2 Self-optimizing network functionality..........................................................................................................................25 3.3.3.6 SON Self-Healing..............................................................................................................................................27 3.3.3.6.1 Self-healing network basics........................................................................................................................................27 3.3.3.6.2 Cell Degradation ........................................................................................................................................................27 3.3.3.6.3 Cell outage compensation..........................................................................................................................................28
Load more

Recommended publications
  • A Comparative Study on Operating System for Wireless Sensor Networks
    ICACSIS 2011 ISBN: 978-979-1421-11-9 A Comparative Study on Operating System for Wireless Sensor Networks Thang Vu Chien, Hung Nguyen Chan, and Thanh Nguyen Huu Thai Nguyen University of Information and Communication Technology Quyet Thang Commune, Thai Nguyen City, Vietnam E-mail: [email protected], [email protected], [email protected] Abstract—Wireless Sensor Networks (WSNs) them work correctly and effectively without a conflict have been the subject of intensive research over the in support of the specific applications for which they last years. WSNs consist of a large number of are designed. Each sensor node needs an OS that can sensor nodes, and are used for various applications control the hardware, provide hardware abstraction to such as building monitoring, environment control, application software, and fill in the gap between wild-life habitat monitoring, forest fire detection, applications and the underlying hardware. industry automation, military, security, and health- The basic functionalities of an OS include resource care. Operating system (OS) support for WSNs abstractions for various hardware devices, interrupt plays a central role in building scalable distributed management and task scheduling, concurrency control, applications that are efficient and reliable. Over and networking support. Based on the services the years, we have seen a variety of operating provided by the OS, application programmers can systems (OSes) emerging in the sensor network conveniently use high-level application programming community to facilitate developing WSN interfaces (APIs) independent of the underlying applications. In this paper, we present OS for WSNs. We begin by presenting the major issues for hardware.
    [Show full text]
  • Smart Dust: Communicating with a Cubic- Millimeter Computer
    COVER FEATURE Smart Dust: Communicating with a Cubic- Millimeter Computer The Smart Dust project is probing microfabrication technology’s limitations to determine whether an autonomous sensing, computing, and communication system can be packed into a cubic-millimeter mote to form the basis of integrated, massively distributed sensor networks. Brett ecreasing computing device size, increased • monitoring environmental conditions that affect Warneke connectivity, and enhanced interaction with crops and livestock; the physical world have characterized com- • building virtual keyboards; Matt Last puting’s history. Recently, the popularity of • managing inventory control; Brian Dsmall computing devices, such as handheld • monitoring product quality; Liebowitz computers and cell phones, burgeoning Internet • constructing smart-office spaces; and growth, and the diminishing size and cost of sensors— • providing interfaces for the disabled. Kristofer S.J. especially transistors—have accelerated these trends. Pister The emergence of small computing elements, with SMART DUST REQUIREMENTS University of sporadic connectivity and increased interaction with Smart Dust requires both evolutionary and revolu- California, the environment, provides enriched opportunities to tionary advances in miniaturization, integration, and Berkeley reshape interactions between people and computers energy management. Designers can use microelectro- and spur ubiquitous computing research.1 mechanical systems (MEMS) to build small sensors, The Smart Dust project2 is exploring whether an optical communication components, and power sup- autonomous sensing, computing, and communication plies, whereas microelectronics provides increasing system can be packed into a cubic-millimeter mote (a functionality in smaller areas, with lower energy con- small particle or speck) to form the basis of integrated, sumption. Figure 1 shows the conceptual diagram of massively distributed sensor networks.
    [Show full text]
  • Kent Academic Repository Full Text Document (Pdf)
    Kent Academic Repository Full text document (pdf) Citation for published version Arief, Budi and Blythe, Phil and Fairchild, Richard and Selvarajah, Kirusnapillai and Tully, Alan (2008) Integrating Smartdust into Intelligent Transportation System. In: 10th International Conference on Application of Advanced Technologies in Transportation (AATT 2008), 27-31 May 2008, Athens, Greece. DOI Link to record in KAR https://kar.kent.ac.uk/58716/ Document Version UNSPECIFIED Copyright & reuse Content in the Kent Academic Repository is made available for research purposes. Unless otherwise stated all content is protected by copyright and in the absence of an open licence (eg Creative Commons), permissions for further reuse of content should be sought from the publisher, author or other copyright holder. Versions of research The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record. Enquiries For any further enquiries regarding the licence status of this document, please contact: [email protected] If you believe this document infringes copyright then please contact the KAR admin team with the take-down information provided at http://kar.kent.ac.uk/contact.html INTEGRATING SMARTDUST INTO INTELLIGENT TRANSPORTATION SYSTEMS Budi Arief1, Phil Blythe2, Richard Fairchild3, Kirusnapillai Selvarajah4, Alan Tully5 ABSTRACT. The last few years have seen the emergence of many new technologies that can potentially have major impacts on Intelligent Transportation Systems (ITS). One of these technologies is a micro-electromechanical device called smartdust. A smartdust device (or a mote) is typically composed of a processing unit, some memory, and a radio chip, which allows it to communicate wirelessly with other motes within range.
    [Show full text]
  • Key Infection: Smart Trust for Smart Dust
    Key Infection: Smart Trust for Smart Dust Ross Anderson Haowen Chan Adrian Perrig University of Cambridge Carnegie Mellon University Carnegie Mellon University [email protected] [email protected] [email protected] Abstract and building safety. As sensor networks become cheaper and more commoditised, they will become attractive to Future distributed systems may include large self- home users and small businesses, and for other new appli- organizing networks of locally communicating sen- cations. sor nodes, any small number of which may be sub- A typical sensor network consists of a large number of verted by an adversary. Providing security for these small, low-cost nodes that use wireless peer-to-peer com- sensor networks is important, but the problem is compli- munication to form a self-organized network. They use cated by the fact that managing cryptographic key ma- multi-hop routing algorithms based on dynamic network terial is hard: low-cost nodes are neither tamper-proof and resource discovery protocols. To keep costs down and nor capable of performing public key cryptography effi- to deal with limited battery energy, nodes have fairly min- ciently. imal computation, communication, and storage resources. In this paper, we show how the key distribution problem They do not have tamper-proof hardware. We can thus ex- can be dealt with in environments with a partially present, pect that some small fraction of nodes in a network may be passive adversary: a node wishing to communicate securely compromised by an adversary over time. with other nodes simply generates a symmetric key and An interesting example of a sensor network technology sends it in the clear to its neighbours.
    [Show full text]
  • Shared Sensor Networks Fundamentals, Challenges, Opportunities, Virtualization Techniques, Comparative Analysis, Novel Architecture and Taxonomy
    Journal of Sensor and Actuator Networks Review Shared Sensor Networks Fundamentals, Challenges, Opportunities, Virtualization Techniques, Comparative Analysis, Novel Architecture and Taxonomy Nahla S. Abdel Azeem 1, Ibrahim Tarrad 2, Anar Abdel Hady 3,4, M. I. Youssef 2 and Sherine M. Abd El-kader 3,* 1 Information Technology Center, Electronics Research Institute (ERI), El Tahrir st, El Dokki, Giza 12622, Egypt; [email protected] 2 Electrical Engineering Department, Al-Azhar University, Naser City, Cairo 11651, Egypt; [email protected] (I.T.); [email protected] (M.I.Y.) 3 Computers & Systems Department, Electronics Research Institute (ERI), El Tahrir st, El Dokki, Giza 12622, Egypt; [email protected] 4 Department of Computer Science & Engineering, School of Engineering and Applied Science, Washington University in St. Louis, St. Louis, MO 63130, 1045, USA; [email protected] * Correspondence: [email protected] Received: 19 March 2019; Accepted: 7 May 2019; Published: 15 May 2019 Abstract: The rabid growth of today’s technological world has led us to connecting every electronic device worldwide together, which guides us towards the Internet of Things (IoT). Gathering the produced information based on a very tiny sensing devices under the umbrella of Wireless Sensor Networks (WSNs). The nature of these networks suffers from missing sharing among them in both hardware and software, which causes redundancy and more budget to be used. Thus, the appearance of Shared Sensor Networks (SSNs) provides a real modern revolution in it. Where it targets making a real change in its nature from domain specific networks to concurrent running domain networks. That happens by merging it with the technology of virtualization that enables the sharing feature over different levels of its hardware and software to provide the optimal utilization of the deployed infrastructure with a reduced cost.
    [Show full text]
  • Overview of Wireless Sensor Network (WSN) Security
    Overview of Wireless Sensor Network (WSN) Security Aparicio Carranza Heesang Kim Xiao Lin Chen NYC College of Technology NYC College of Technology NYC College of Technology 186 Jay Street – V626 186 Jay Street 186 Jay Street Brooklyn, NY, USA Brooklyn, NY, USA Brooklyn NY, USA 718 – 260 – 5897 703 – 232 – 2001 917 – 285 - 5155 [email protected] [email protected] xiaolin,[email protected] ABSTRACT of different transmission schemes, allowing the connection Our civilization has entered an era of big data networking. to be made between different models. A router is a good Massive amounts of data is being converted into bits for example of a gateway. There are two common Wireless transmitting over the Internet and shared all over the world network topologies commonly used in WSN systems, they every second. The concept of “smart” devices has are “relay nodes” and “leaf nodes”. The relay network is a conquered preconceived notions of daily routines. broad network topology where the source and destination Smartphones and wearable device technologies have made are interconnected through some node. In these networks, humans to be continuously interconnected, making distance the source and destination cannot communicate directly a factor of no limitation. “Smart” systems are based on a with each other because the distance between the source single concept called the Internet of Things (IoT). IoT is a and destination is greater than the transmission range, due web of wirelessly networked devices embedded with to this an intermediate node needs to relay. The advantage electronic components and sensors that monitors physical of using the relay nodes is that the information can travel and environmental conditions and acquires data to be long distances even when the caller and recipient are far shared with other systems.
    [Show full text]
  • Smart Dust Technology
    250, Mini-Project Report Amy Haas Smart Dust Technology Smart dust is an emerging technology with a huge potential for becoming an internationally successful commercial venture. Now is the best time to invest in smart dust – right at the point at which the scientific community is close to perfecting the technology and its applications but its potential has not yet been realized by the capitalist market. Fundamental Concept Smart dust is a theoretical concept of a tiny wireless sensor network, made up of microelectromechanical sensors (called MEMS), robots, or devices, usually referred to as motes, that have self-contained sensing, computation, communication and power1. According to the smart dust project design created by a team of UC Berkeley researchers2, within each of these motes is an integrated package of “MEMS sensors, a semiconductor laser diode and MEMS beam-steering mirror for active optical transmission, a MEMS corner-cube retroreflector for passive optical transmission, an optical receiver, signalprocessing and control circuitry, and a power source based on thick-film batteries and solar cells.” The intent is that these motes will eventually become the size of a grain of sand or a dust particle, hence the name “smart dust”, but the technology still has a long way to go to achieve its target size. In current sensor technology, the sensors are about the size of a bottle cap or small coin. As with most electronic devices or technologies, the biggest challenges in achieving actual smart dust are the power consumption issues and making the batteries small enough. Although there is a lot of work being done on solar cells to keep the motes self-sustaining, the components are just not small enough yet to actually be considered smart dust.
    [Show full text]
  • Jul 2 7 2000
    THE SNAP! TOOLKIT FOR DEVELOPING SENSOR NETWORKS AND APPLICATION TO XC SKIING BY MATTHEW B. DEBSKI Submitted to the Department of Electrical Engineering and Computer Science in Partial Fulfillment of the Requirements for the Degrees of Bachelor of Science in Electrical Engineering and Computer Science and Master of Engineering in Electrical Engineering and Computer Science at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY May 2000 2 20C: MASSACHUSETTS INSTITUTE © Massachusetts Institute of Technology 2000. All rights reserved. OF TECHNOLOGY JUL 2 7 2000 LIBRARIES Author Department of Electrical Engineering and Computer Science May 18, 2000 Certified by MichaelJ. Hawley Assistant Prolssor of Media Arts and Sciences Alex Dreyfoos Jr. (1954) Career Development Professor of Media Arts and Sciences Thesis Supervisor Accepted by Arthur C. Smith Chairman, Department Committee on Graduate Theses ' pT THE SNAP! TOOLKIT FOR DEVELOPING SENSOR NETWORKS AND APPLICATION TO XC SKIING BY MATTHEW B. DEBSKI Submitted to the Department of Electrical Engineering and Computer Science in Partial Fulfillment of the Requirements for the Degrees of Bachelor of Science in Electrical Engineering and Computer Science and Master of Engineering in Electrical Engineering and Computer Science May 18, 2000 ABSTRACT Athletes, health professionals, animal specialists, meteorologists, and other investigators increasingly employ small, human-scale sensor networks in their disciplines. The sensor networks used by each field and study have unique requirements. Regardless, these networks all collect and store data, and often display or transmit them. The Snap! toolkit takes advantage of the similarities among sensor networks to make prototyping them fast and easy. Simultaneously, it acknowledges their inherent differences, remaining flexible in order to accommodate the needs of different systems.
    [Show full text]
  • Monitoring High Voltage Power Lines Using Efficient WSN
    Advances in Energy and Power 7(1): 10-19, 2020 http://www.hrpub.org DOI: 10.13189/aep.2020.070102 Monitoring High Voltage Power Lines Using Efficient WSN Khaled Al-Maitah, Batool Al-Khriesat, Abdullah Al-Odienat* Department of Electrical Engineering, Faculty of Engineering, Mutah University, Jordan Received March 13, 2020; Revised April 22, 2020; Accepted April 27, 2020 Copyright©2020 by authors, all rights reserved. Authors agree that this article remains permanently open access under the terms of the Creative Commons Attribution License 4.0 International License Abstract The power transmission line is one of most [1]. Moreover, power systems are now highly critical components in electrical power system. In order to interconnected which allows the transfer of a large enhance the reliability of the large power systems, an electrical energy over long distances far from the source of advanced and efficient Wireless Sensor Network (WSN) generation to locations suffer of lack in electrical energy [2] must be employed along power transmission line. However, [3]. time delay has emerged as one of the most critical issues in In power systems, transmission lines are critical the design of WSN-based monitoring network of components from the reliability and stability view point. transmission lines. This paper proposes a new model for There is a real need for an efficient monitoring system of WSN for monitoring High Voltage Transmission Line the transmission lines to enhance the normal operation of (HVTL) which improves the needed time delay to transmit the power system and avoid the major disconnections and the measured quantity from transmission line towers to the blackouts [4].
    [Show full text]
  • System Architecture Directions for Post-Soc/32-Bit Networked Sensors Hyung-Sin Kim, Michael P Andersen, Kaifei Chen, Sam Kumar, William J
    System Architecture Directions for Post-SoC/32-bit Networked Sensors Hyung-Sin Kim, Michael P Andersen, Kaifei Chen, Sam Kumar, William J. Zhao, Kevin Ma, and David E. Culler Department of Electrical Engineering and Computer Sciences, University of California, Berkeley (hs.kim,m.andersen,kaifei,samkumar,william_zhao,kevinma.sd,culler)@berkeley.edu ABSTRACT those that have guided design over the past 20 years, since the The emergence of low-power 32-bit Systems-on-Chip (SoCs), which emergence of the field [31, 37]. While it is easy to observe high- integrate a 32-bit MCU, radio, and flash, presents an opportunity level trends and infer that at some point conventional concurrency to re-examine design points and trade-offs at all levels of the sys- models and networking stacks will fit on this class of computing tem architecture of networked sensors. To this end, we develop a platform, when dealing with low power embedded operation, the post-SoC/32-bit design point called Hamilton, showing that using devil is always in the details. integrated components enables a ∼$7 core and shifts hardware mod- The datasheets for modern 32-bit System-on-Chip units (SoCs) ularity to design time. We study the interaction between hardware with an integrated microcontroller (MCU) and radio are truly im- and embedded operating systems, identifying that (1) post-SoC pressive [10, 43]. Thread-based OSes have reemerged (e.g., RIOT motes provide lower idle current (5.9 µA) than traditional 16-bit [14]), and commercially-driven IPv6 network protocols have arrived motes, (2) 32-bit MCUs are a major energy consumer (e.g., tick (e.g., OpenThread [71]).
    [Show full text]
  • Concepts General Concepts Wireless Sensor Networks (WSN)
    Wireless Sensor Networks – Concepts General Concepts Wireless Sensor Networks (WSN) are built based on a combination of multiple sensors placed in diverse locations, wireless communication network infrastructure and software data processing to monitor and record multiple parameters. Commonly monitored parameters are temperature, atmospheric pressure, humidity, vibration, illuminance, sound level, power consumption, chemical concentration, body health signals and many others, dependant on the selected available sensors. The WSN are used in multiple fields, ranging from remote environment monitoring, medical health, to home surveillance and industrial machines monitoring. In some cases, WSN can also be additionally used for control functions, apart from monitoring functions. Typically a WSN is made of sensor nodes that are wirelessly connected to a gateway that is then connected to a main computer (Fig. 1). In some WSN the sensor nodes can also be connected to each other, so that is possible to implement multi-hop wireless mesh networks. The gateway connects to the main computer through a cabled or wireless connection. Figure 1 – Wireless sensor network The wireless communications used in WSN depend on the application requirements, taking into consideration the needs in terms of transmission distance, sensor data bandwidth, energy source and power consumption. Common communications include standard protocols such as 2.4 GHz radio based on either IEEE802.15.4 (ZigBee, ISA 100, WirelessHart, MiWi) or IEEE802.11 (WiFi) standards. Each sensor node typically includes an embedded microcontroller system with adequate electronic interface with a sensor (or set of sensors), a radio transceiver with antenna (internal or external) and an energy source, usually a battery, or in some cases an energy harvesting circuit.
    [Show full text]
  • Flexis—A Flexible Sensor Node Platform for the Internet of Things
    sensors Article FlexiS—A Flexible Sensor Node Platform for the Internet of Things Duc Minh Pham and Syed Mahfuzul Aziz * UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia; [email protected] * Correspondence: [email protected]; Tel.: +61-08-8302-3643 Abstract: In recent years, significant research and development efforts have been made to transform the Internet of Things (IoT) from a futuristic vision to reality. The IoT is expected to deliver huge economic benefits through improved infrastructure and productivity in almost all sectors. At the core of the IoT are the distributed sensing devices or sensor nodes that collect and communicate information about physical entities in the environment. These sensing platforms have traditionally been developed around off-the-shelf microcontrollers. Field-Programmable Gate Arrays (FPGA) have been used in some of the recent sensor nodes due to their inherent flexibility and high processing capability. FPGAs can be exploited to huge advantage because the sensor nodes can be configured to adapt their functionality and performance to changing requirements. In this paper, FlexiS, a high performance and flexible sensor node platform based on FPGA, is presented. Test results show that FlexiS is suitable for data and computation intensive applications in wireless sensor networks because it offers high performance with low energy profile, easy integration of multiple types of sensors, and flexibility. This type of sensing platforms will therefore be suitable for the distributed data analysis and decision-making capabilities the emerging IoT applications require. Keywords: Internet of Things (IoT); wireless sensor networks (WSN); sensor node; field-programmable Citation: Pham, D.M.; Aziz, S.M.
    [Show full text]