Energy Efficiency in Wireless Sensor Networks, Through Data Compression Srod Karim Master’s Thesis, Autumn 2017 Abstract Wireless sensor networks are an ever-growing market in today’s society; Smart homes with wirelessly connected and controllable devices, continuous monitoring in environmental, health care and industrial settings, and automated, self-adjusting systems in homes and factories are all growing technologies that are built using wireless sensor networks. In many of these systems, energy consumption can be a major concern; Most devices in the network rely on small batteries as their energy source. Because of this, any energy usage optimization can lead to greatly increased lifetime. Often, radio communication between microcontrollers and other devices is the biggest energy drain in the system. Compression of sensor data before transfer between nodes can be used to reduce the energy consumed by the radio transmissions. By first compressing the data, it is possible to reduce the size of the message transmitted over radio, which in turn will lead to less energy consumed. However, energy is required to perform the compression and decompression. It can be important to have a good idea of when compression may be beneficial for the overall energy efficiency of a wireless sensor network, and when it might be better to send the data without compression. In this thesis we will look at typical properties of wireless sensor networks, wireless transfer protocols for sensor networks, and sensor data. We will analyze the effects of compressing sensor data before sending it over the network, and perform experiments and measurements in a simple wireless sensor network, to assess the effects data compression can have on the energy consumption in wireless sensor networks. i Acknowledgments My thanks go to my advisor Kjetil Østerås, who provided guidance, advice, proofreading, suggestions and general feedback, and for helping me all around throughout my thesis. I also want to thank Silicon Labs, for giving me the opportunity, and for providing me with the microcontrollers and tools required to perform the experiments. I also owe thanks to my advisor at the University of Oslo, Associate Professor Arne Maus, for providing guidance, and for giving feedback on the structure and content of this thesis. I would also like to thank Jonathan Ringstad for providing in depth proofreading and valuable feedback on the structure and wording of the text. ii Preface Overview In this thesis, we perform experiments and measurements to asses the effects of compression in wireless sensor networks. For this purpose, we have divided our thesis into 6 distinct chapters that each cover a aspect of our thesis. In chapter 1, we provide motivation for our thesis, and look at previous work in the field of energy efficiency in wireless sensor networks. In chapter 2, we look at Wireless sensor networks and wireless data transfer, including many factors that contribute to the overall energy usage of wireless transmissions. In chapter 3, we outline various encoding and compression algorithms that can be useful for wireless sensor networks, and create a basis for the algorithms we use for our experiments. In chapter 4, we explain the experiments we perform, and examine the algorithms and datasets we use for our experiments. In chapter 5, we look at the results from our experiments, and asses the effects compression has on energy usage in wireless sensor networks. Finally, in chapter 6 we discuss potential future work in the fields of energy efficient wireless data transfer. iii Contents 1 Introduction 1 1.1 Motivation . .1 1.2 Energy efficient hardware . .2 1.2.1 Energy efficient processors . .2 1.2.2 Energy efficient microcontrollers . .3 1.3 Energy efficient software . .4 1.3.1 Clockspeeds and dynamic speed scaling . .4 1.3.2 Parallelization and vectorization . .5 1.3.3 Wireless sensor networks and compression . .5 2 Wireless data transfer 7 2.1 Wireless sensor networks . .7 2.2 Energy usage in radio transmissions . .9 2.2.1 Energy calculations . .9 2.2.2 Transmission energy usage . 10 2.2.3 Network packets . 11 2.2.4 Receiver timing . 12 2.3 Wireless transfer protocols . 14 2.3.1 Wi-Fi . 16 2.3.2 Bluetooth . 16 2.3.3 ZigBee . 17 2.3.4 Proprietary Protocols . 17 3 Encoding and compression 18 3.1 Sensor Data . 19 3.2 Reducing range or precision . 20 3.3 Run-length encoding . 20 3.4 Delta encoding . 21 3.5 Huffman Encoding . 23 3.6 Arithmetic coding . 25 3.6.1 Prediction by partial matching . 27 3.7 Lempel–Ziv 77 . 28 iv Contents Energy efficiency in wireless sensor Section Contents networks, through data compression 3.8 Lempel–Ziv–Welch . 31 3.8.1 Sensor Lempel–Ziv–Welch . 34 3.8.2 Lempel–Ziv–Welch with minicache . 34 4 Experiments 36 4.1 Compression algorithms . 36 4.1.1 Delta encoding . 36 4.1.2 Variable-width delta encoding . 37 4.1.3 Snappy . 40 4.1.4 LZ4 . 41 4.1.5 LZW . 41 4.1.6 PPMs . 42 4.1.7 fpaq0 . 42 4.2 Datasets . 43 4.2.1 Relative humidity . 43 4.2.2 Sea surface temperature . 45 4.2.3 Mixed data . 46 4.3 Experiments . 47 4.3.1 Compression efficiency . 47 4.3.2 Energy cost of compression . 48 5 Results and Conclusion 51 5.1 Compression efficiency . 51 5.1.1 Results . 51 5.1.2 Conclusion . 55 5.2 Energy cost of compression . 57 5.2.1 Results . 57 5.2.2 Conclusion . 76 5.3 Summary . 78 6 Future work 79 6.1 Compression algorithms . 79 6.2 Retained compression information . 80 6.3 Struct delta encoding . 80 7 Appendix 84 7.1 Results . 84 7.1.1 Experiment 1 . 84 7.1.2 Experiment 2 . 88 v Introduction 1.1 Motivation Energy consumption is an emerging topic in the field of computer science. Excessive production and consumption of electricity can have hazardous effects on the environment, due to the use of non renewable energy sources. Energy consumption is also a big concern for many companies with focus on computation and IT. Power consumption has been a design constraint for many businesses, most importantly due to the high monetary costs of supplying power. The electricity costs of modern computers have become one of the biggest strains on the budget of many firms and data centers. In 2005, Google engineer Luiz André Barosso published an article in which he analyzed the performance costs of their computer severs. In this article he stated that electricity costs made up for roughly one third of the total cost of running the servers. He additionally theorized that energy costs could end up becoming the most expensive part of maintaining a computer server by the end of the decade, even more expensive than the cost of the computer hardware[1]. Energy consumption is also a major concern in mobile, battery-driven devices. For laptops and phones, long battery times are an important feature. Yet, it can be hard to maintain long battery times, given the power consumption of the various components of modern laptops and phones[2][3]. Phones are not the only mobile devices that depend on getting energy through battery usage, as more and more devices using microcontrollers are being produced. Microcontrollers are small processors used in a variety of fields. These include remote controls, power tools, toys, office machines and automatically controlled products and devices such as sensor networks, control systems and implantable medical devices, as well as other embedded systems. Given their small structure and autonomous nature, they tend to be heavily power constrained. Many microcontrollors reside in locations that are hard to access, such as 1 Chapter 1. Introduction Energy efficiency in wireless sensor Section 1.2. Energy efficient hardware networks, through data compression medicinal devices implanted in the body, or distributed sensor networks operating in forests, seas, and other locations far from civilization. In some cases, the changing of batteries for these products can be a difficult or maybe even impossible task, further underlining the importance of energy conservation. Additionally, microcontrollers in IoT devices and wireless sensor networks in homes aim to be easily installable, without requiring rewiring by electricians or drawing long extension cords, and therefore have to rely on batteries. Finally, a lot of devices operating on microcontrollers are intended to reduce energy consumption in larger systems, such as smart light bulbs that automatically turn on and off when people enter and leave the room, and that adjusts brightness depending on the time of day. It is of course important that the microcontrollers are are energy efficient as well, to reduce the overall energy consumption in the system. In this thesis, we want to add to the scientific knowledge of energy consumption in mobile devices, with focus on wireless sensor networks. We want to measure the energy consumption of data transfer in wireless sensor networks, and asses how this property is affected by using compression algorithms to compress data before transfer. 1.2 Energy efficient hardware Due to the importance of power consumption, and the benefits energy efficiency can have, there has emerged a market for the design and production of energy friendly solutions in large portions of the computer science landscape. There is a constant flow of newer and more effective hardware and software designed with energy consumption in mind; tools, peripherals, code and even specifications are being made to be more energy-aware. Perhaps most important is the development of energy efficient Central Processing Units (CPUs), as these can be the most energy hungry part of any microcontroller. With the rise of the Internet Of Things (IoT) and smart devices, there has been a rise to the energy friendly processors market.