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Power Analysis and Optimization of Wireless Sensor Nodes

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Power Analysis and Optimization of Wireless Sensor Nodes IT 19 049 Examensarbete 30 hp Augusti 2019 Power analysis and optimization of wireless sensor nodes Tobias Mages Institutionen för informationsteknologi Department of Information Technology Abstract Power analysis and optimization of wireless sensor nodes Tobias Mages Teknisk- naturvetenskaplig fakultet UTH-enheten Wireless sensors offer the possibility to monitor critical parameters in our environment, which enables applications to optimize processes or anticipate and Besöksadress: detect critical events. Environmental monitoring and predictive maintenance of Ångströmlaboratoriet Lägerhyddsvägen 1 non-electric systems are two important application domains that have different Hus 4, Plan 0 computational requirements but similar power constraints. In this thesis is presented an iterative wireless sensor node design that supports both Postadress: applications equally well. In particular, the platform is useful for building Box 536 751 21 Uppsala demonstrators and evaluating proof of concept designs because the system can be used for the rapid prototyping of models out of standard machine learning Telefon: frameworks with reasonable performance. At the same time, the platform can run for 018 – 471 30 03 several years during the environmental monitoring with a battery. Additionally can the Telefax: system be powered by solar harvesting to enable its use in a "deploy and forget" 018 – 471 30 00 manner. For this purpose, the system hardware has been optimized and a radio module was Hemsida: selected which enables the transmission of measurements over several kilometers. To http://www.teknat.uu.se/student recommend the radio configuration for a minimal energy consumption, different settings have been compared in terms of their required energy and transmission range. The power budget of the platform has been generated and optimized, to increase the system run-time and enable the maximal amount of measurements within its energy constraints. Finally, the illumination in greenhouses has been analyzed which showed to provide enough energy to power the platform with a 45x15mm photovoltaic module. In combination with a single coin cell battery could be achieved a continuous system run-time of more than ten years for environmental monitoring applications with this platform. Handledare: Nils Weber Ämnesgranskare: Christian Rohner Examinator: Phillip Rummer IT 19 049 Tryckt av: Reprocentralen ITC Acknowledgement This project would not have been possible without the help of several people, to which I would like to express my gratitude here. I would like to thank Christian Rohner for reviewing this project and giving invaluable feedback and guidance during the entire period. I would like to thank Nils Weber for supervising the project and the welcoming working environment at Bitroot. I would also like to thank Biroot AB for enabling this project by financing the required equipment and prototypes. Finally, I would like to thank Michael Forschlé for his helpful feedback on the written work. Contents Glossary .................................................. I 1 Introduction ............................................. 1 1.1 Motivation and project environment . 1 1.2 Goals . 1 1.3 Structure of the approach . 2 2 Background .............................................. 4 2.1 Power management . 4 2.2 Power source . 7 2.3 Previous work . 12 2.4 Measurement methodology . 15 3 Hardware design ........................................... 17 3.1 Analysis and optimization . 17 3.2 Edge AI modification . 26 3.3 Radio comparison . 31 4 Solar harvesting analysis ...................................... 39 4.1 System power budget . 39 4.2 Power source design . 44 4.3 Available energy analysis . 47 4.3.1 Single diode model analysis . 49 4.3.2 Reference measurement analysis . 53 5 Discussion ............................................... 58 6 Conclusion and future work .................................... 63 List of Figures .............................................. III List of Tables ............................................... V Bibliography ............................................... VI Appendices ................................................XIII Glossary 4-FSK 4-Frequency-Shift Keying AC Alternating Current AHB Advanced High-performance Bus AI Artificial Intelligence ANN Artificial Neural Network aSi Amorphous Silicon BSS Base Station Subsystem CIGS Copper Indium Gallium Selenide CRC Cyclic Redundancy Check CS Control Signal cSi Crystalline Silicon DC Direct Current DSSC Dye-Sensitized Solar Cell EEPROM Electrically Erasable Programmable ROM EMF Electromagnetic Field ESD Electrostatic Discharge FET Field-Effect Transistor FOCV Fractional Open-Circuit Voltage FPU Floating-Point Unit GFSK Gaussian Frequency-Shift Keying GSM Global System for Mobile communications I2C Inter-Integrated Circuit IC Integrated Circuit IMU Inertial Measurement Unit IoT Internet of Things ITM Irregular Terrain Model LDO Low Dropout Regulator LED Light-Emitting Diode Li-ion Lithium-ion LoRa Long Range LR Linear Regulator LSD Low Self-Discharge LSTM Long Short-Term Memory MAC Multiply-Accumulate I MOS Metal-Oxide-Semiconductor MOSFET Metal-Oxide-Semiconductor Field-Effect Transistor MPP Maximum Power Point MPPT Maximum Power Point Tracker MSI Multi Speed Internal MSK Minimum-Shift Keying NCV Nominal Cell Voltage NiMH Nickel–Metal Hydride opamp Operational Amplifier OSI Open Systems Interconnection PCB Printed Circuit Board PLL Phase-Locked Loop PV Photovoltaic PWM Pulse-Width Modulation ROM Read-Only Memory RSSI Received Signal Strength Indicator RTC Real-Time Clock SEK Swedish Krona SMPS Switching Mode Power Supply SPI Serial Peripheral Interface SR Sampling Rate USB Universal Serial Bus WSN Wireless Sensor Network II 1 Introduction 1.1 Motivation and project environment The number of wireless sensors in our environment can be expected to increase during the next years. This offers the possibility to monitor critical parameters for optimizing processes or to detect issues before they arise. One of the start-ups that focuses on different Internet of Things (IoT) solutions in Uppsala is Bitroot, which was founded at the end of 2017. Bitroot’s main focus is on sustainable greenhouse farming, where they monitor the growing conditions of plants. Their first prototypes could measure the soil moisture, air humidity, temperature and ambient illumination. By monitoring these parameters together with the yields of the crops, they search for correlations to find the parameters with most impact on the growing conditions of different plant species. At the same time, the farmer can use this information to monitor his own systems and be alarmed in special cases. This requires the wireless sensor nodes to be designed as energy efficient as possible, to reduce the required maintenance for recharging batteries. Since the collection of data for this project requires several years, Bitroot started working on industrial applications in parallel. Here, they are trying to monitor non-electric systems to reduce their down-time with predictive maintenance. In this field edge Artificial Intelligence (AI) is gaining popularity, where the different algorithms are executed on the sensor nodes directly, instead of being transmitted to a server. This has the main advantages of enabling real-time operations and that a data connection is not required at each time anymore. These two applications have very different computational requirements, but share the issue of an energy constrained operation. For evaluating different projects, it is a key aspect of quickly being able to prepare specific demonstrations for customers or prototyping setups for a case study. For this, it would be beneficial to have a single low-power platform which is as suitable for both applications. It should be able to operate over several years by monitoring environmental conditions, but at the same time be able to efficiently compute models for the predictive maintenance of machines. Ideally, Bitroot would be looking for a system that could even be powered by solar harvesting, to target use cases with a “deploy and forget” manner. 1.2 Goals The goal of this project is to develop an edge-ready low-power wireless sensor platform, that could be used for the rapid prototyping of both, low-power environmental monitoring and industrial predictive maintenance applications. This could increase the reusability of Bitroot’s projects and enable the quick setup of different case studies or demonstrations. The power consumption of Bitroot’s platform shall be analyzed in detail. Based on these results should the hardware be optimized and it should be identified which aspects of the system have the biggest impact on its possible run-time. One part of this should also include the comparison of different voltage converters, to select which setup would be most suitable for Bitroot’s applications. 1 1.3. Structure of the approach It shall be analyzed how the platform needs to be modified for running models out of different machine learning frameworks. Based on this, the required energy for the local computation of a model should be compared with the transmission of equivalent data, to see if this opens new possibilities in reducing the energy consumption. Additionally, the system performance for computing a specified predictive maintenance network should be analyzed and shown how the required hardware modifications would impact the system run-time in environmental monitoring applications. The current transmission range of Bitroot’s wireless sensor nodes is insufficient
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