Lecture notes on Wireless sensor networks

how to design wireless sensor networks and how wireless sensor network works. And wireless sensor networks current status and future trends | pdf free download
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An Introduction to Wireless Sensor Networks Draft, version 1.8 Carlo Fischione September 2014Contents List of Acronyms 9 Preface 11 1 Introduction to WSNs 15 1.1 WSN Architecture and Protocol Stack . . . . . . . . . . . . . 15 1.2 Challenges and Constraints . . . . . . . . . . . . . . . . . . . 19 1.3 WSN Applications . . . . . . . . . . . . . . . . . . . . . . . . 22 1.4 WSN Integration with the Internet . . . . . . . . . . . . . . . 24 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2 Wireless Channel 31 2.1 Physical Sources of Distortion . . . . . . . . . . . . . . . . . . 32 2.1.1 Attenuation (Path Loss) . . . . . . . . . . . . . . . . . 32 2.1.2 Reflection and refraction . . . . . . . . . . . . . . . . . 32 2.1.3 Diffraction . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.1.4 Scattering . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.2 Statistical fading models . . . . . . . . . . . . . . . . . . . . . 33 2.3 Large Scale Fading . . . . . . . . . . . . . . . . . . . . . . . . 34 2.3.1 Path Loss . . . . . . . . . . . . . . . . . . . . . . . . . 34 2.3.2 Shadowing . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.4 Small Scale Fading . . . . . . . . . . . . . . . . . . . . . . . . 37 2.4.1 Multipath Fading . . . . . . . . . . . . . . . . . . . . . 37 2.4.2 Doppler Spread . . . . . . . . . . . . . . . . . . . . . . 40 2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3 Physical Layer 45 3.1 Basic Components . . . . . . . . . . . . . . . . . . . . . . . . 46 3.2 Modulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.2.1 Binary Phase Shift Keying (BPSK) . . . . . . . . . . . 48 3.2.2 Quadrature Phase Shift Keying (QPSK) . . . . . . . . 49 3.2.3 Amplitude Shift Keying . . . . . . . . . . . . . . . . . 50 3.3 Communication over Gaussian Channel . . . . . . . . . . . . 51 3Contents 4 3.3.1 Error Probability for BPSK . . . . . . . . . . . . . . . 52 3.3.2 Error Probability for 4-PAM . . . . . . . . . . . . . . 53 3.3.3 Error Probability for QAM . . . . . . . . . . . . . . . 54 3.4 Communication over Fading Channel . . . . . . . . . . . . . . 56 3.5 Channel Coding (Error Control Coding) . . . . . . . . . . . . 58 3.5.1 Block Codes . . . . . . . . . . . . . . . . . . . . . . . . 59 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 4 Medium Access Control 65 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4.2 Problems and Performance Requirements for MAC Protocols 66 4.2.1 Energy Efficiency . . . . . . . . . . . . . . . . . . . . . 66 4.2.2 The Hidden Terminal Problem . . . . . . . . . . . . . 68 4.2.3 The Exposed Terminal Problem . . . . . . . . . . . . . 68 4.2.4 Characteristics of MAC Protocols . . . . . . . . . . . . 69 4.3 Definition and Classification of MAC Protocols . . . . . . . . 70 4.3.1 Schedule-based MAC Protocols . . . . . . . . . . . . . 70 4.3.2 Contention-based MAC Protocols . . . . . . . . . . . . 72 4.4 The IEEE 802.15.4 Standard for WSNs . . . . . . . . . . . . . 76 4.4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . 76 4.4.2 An IEEE 802.15.4 Network . . . . . . . . . . . . . . . 77 4.4.3 Physical Layer . . . . . . . . . . . . . . . . . . . . . . 79 4.4.4 MAC Layer . . . . . . . . . . . . . . . . . . . . . . . . 81 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 5 Routing 99 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.2 Routing Challenges . . . . . . . . . . . . . . . . . . . . . . . . 100 5.3 Routing Protocols Classification . . . . . . . . . . . . . . . . . 102 5.3.1 Network Structure . . . . . . . . . . . . . . . . . . . . 102 5.3.2 Route Discovery . . . . . . . . . . . . . . . . . . . . . 107 5.3.3 Protocol Operation . . . . . . . . . . . . . . . . . . . . 109 5.3.4 In-network Data Processing . . . . . . . . . . . . . . . 110 5.4 The Shortest Path Routing . . . . . . . . . . . . . . . . . . . 110 5.4.1 The Shortest Path Optimization Problem . . . . . . . 111 5.4.2 The Generic Shortest Path Algorithm . . . . . . . . . 112 5.4.3 Routing Metrics . . . . . . . . . . . . . . . . . . . . . 115 5.5 RPL Routing Protocol . . . . . . . . . . . . . . . . . . . . . . 117 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 6 Topology Control 125 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 6.2 Connectivity Problems . . . . . . . . . . . . . . . . . . . . . . 130 6.2.1 Range Assignment Problems . . . . . . . . . . . . . . 130Contents 5 6.2.2 Unicast and Broadcast Topologies . . . . . . . . . . . 140 6.3 Coverage Problems . . . . . . . . . . . . . . . . . . . . . . . . 144 6.3.1 Full coverage . . . . . . . . . . . . . . . . . . . . . . . 146 6.3.2 Barrier coverage . . . . . . . . . . . . . . . . . . . . . 148 6.3.3 Sweep covarage . . . . . . . . . . . . . . . . . . . . . . 151 6.4 Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 6.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 7 Distributed Detection 155 7.1 Basic Theory of Detection . . . . . . . . . . . . . . . . . . . . 155 7.2 Detection from Single Sensor in Additive Noise . . . . . . . . 156 7.3 Detection from Multiple Sensors . . . . . . . . . . . . . . . . 159 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 8 Distributed Estimation 167 8.1 Optimal Mean Square Estimate of a Random Variable . . . . 167 8.2 Network with a Star Topology . . . . . . . . . . . . . . . . . . 169 8.2.1 Static Sensor Fusion . . . . . . . . . . . . . . . . . . . 169 8.2.2 Dynamic Sensor Fusion . . . . . . . . . . . . . . . . . 176 8.3 Non-ideal Networks with Star Topology . . . . . . . . . . . . 182 8.3.1 Sensor Fusion in Presence of Message Loss . . . . . . . 183 8.3.2 Sensor Fusion with Limited Bandwidth . . . . . . . . . 187 8.4 Network with Arbitrary Topology . . . . . . . . . . . . . . . . 197 8.4.1 Static Sensor Fusion with Limited Communication Range . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 8.5 Computational Complexity and Communication Cost . . . . . 199 8.5.1 On Computational Complexity . . . . . . . . . . . . . 200 8.5.2 On Communication Cost . . . . . . . . . . . . . . . . . 200 8.5.3 Summary of the computational complexity and com- munication cost . . . . . . . . . . . . . . . . . . . . . . 201 8.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202 9 Distributed Learning 207 9.1 Learning in General . . . . . . . . . . . . . . . . . . . . . . . 207 9.1.1 Supervised Learning . . . . . . . . . . . . . . . . . . . 208 9.1.2 ARMA-time Series . . . . . . . . . . . . . . . . . . . . 217 9.1.3 Optimization in Learning Algorithms . . . . . . . . . . 222 9.2 Learning in WSNs . . . . . . . . . . . . . . . . . . . . . . . . 224 9.2.1 Star Topology . . . . . . . . . . . . . . . . . . . . . . . 225 9.2.2 General Topology . . . . . . . . . . . . . . . . . . . . . 228 9.2.3 Distributed Learning Using Kernel Methods . . . . . . 239 9.2.4 Distributed Learning Using ARMA-time Series . . . . 243 9.2.5 Convergence Speed and Precision . . . . . . . . . . . . 247Contents 6 9.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 9.4 Consulted Material . . . . . . . . . . . . . . . . . . . . . . . . 253 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254 10 Positioning and Localization 257 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 10.2 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258 10.2.1 Physical Layer Measurements . . . . . . . . . . . . . . 258 10.2.2 Computational Constraints . . . . . . . . . . . . . . . 259 10.2.3 Lack of GPS . . . . . . . . . . . . . . . . . . . . . . . 259 10.2.4 Low-End Sensor Node . . . . . . . . . . . . . . . . . . 259 10.3 Ranging Techniques . . . . . . . . . . . . . . . . . . . . . . . 259 10.3.1 Time of Arrival . . . . . . . . . . . . . . . . . . . . . . 259 10.3.2 Time Difference of Arrival . . . . . . . . . . . . . . . . 260 10.3.3 Angle of Arrival . . . . . . . . . . . . . . . . . . . . . 261 10.3.4 Received Signal Strength . . . . . . . . . . . . . . . . 262 10.4 Range-Based Localization . . . . . . . . . . . . . . . . . . . . 262 10.4.1 Triangulation . . . . . . . . . . . . . . . . . . . . . . . 262 10.4.2 Trilateration . . . . . . . . . . . . . . . . . . . . . . . 263 10.4.3 Iterative and Collaborative Multilateration . . . . . . 265 10.5 Range-Free Localization . . . . . . . . . . . . . . . . . . . . . 265 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268 11 Time Synchronization 273 11.1 Node Clocks and Synchronization Problem . . . . . . . . . . . 274 11.1.1 Challenges for Time Synchronization . . . . . . . . . . 277 11.2 Basics of Time Synchronization . . . . . . . . . . . . . . . . . 278 11.2.1 One-Way Message Exchange . . . . . . . . . . . . . . . 280 11.2.2 Two-Way Message Exchange . . . . . . . . . . . . . . 280 11.2.3 Receiver-Receiver Synchronization . . . . . . . . . . . 281 11.3 Time Synchronization Protocols . . . . . . . . . . . . . . . . . 282 11.3.1 MMSE Technique in Time Synchronization Protocols . 283 11.3.2 The Network Time Protocol . . . . . . . . . . . . . . . 284 11.3.3 Timing-Sync Protocol for Sensor Networks . . . . . . . 285 11.3.4 Lightweight Tree-Based Synchronization . . . . . . . . 286 11.3.5 Flooding Time Synchronization Protocol . . . . . . . . 287 11.3.6 Reference Broadcast Synchronization protocol . . . . . 287 11.3.7 Time-Diffusion Synchronization Protocol . . . . . . . . 288 11.3.8 Mini-Sync and Tiny-Sync . . . . . . . . . . . . . . . . 289 11.4 The Gradient Time Synchronization Protocol . . . . . . . . . 290 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293Contents 7 12 Wireless Sensor Network Control Systems 297 12.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . 298 12.1.1 State space representation . . . . . . . . . . . . . . . . 298 12.1.2 Stability of difference equations . . . . . . . . . . . . . 299 12.2 The Wireless Sensor Network Control System . . . . . . . . . 302 12.2.1 Definition . . . . . . . . . . . . . . . . . . . . . . . . . 302 12.2.2 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 12.3 Challanges for system stability . . . . . . . . . . . . . . . . . 305 12.3.1 Network delay . . . . . . . . . . . . . . . . . . . . . . 305 12.3.2 Packet losses . . . . . . . . . . . . . . . . . . . . . . . 310 12.3.3 Multiple-packet transmission . . . . . . . . . . . . . . 311 12.4 Sampling methods . . . . . . . . . . . . . . . . . . . . . . . . 313 12.4.1 Event-triggered sampling . . . . . . . . . . . . . . . . 313 12.4.2 Self-triggered sampling . . . . . . . . . . . . . . . . . . 313 12.4.3 Adaptive self-triggered sampling . . . . . . . . . . . . 314 12.5 System design . . . . . . . . . . . . . . . . . . . . . . . . . . . 314 12.5.1 The Top-down approach . . . . . . . . . . . . . . . . . 315 12.5.2 The Bottom-up approach . . . . . . . . . . . . . . . . 315 12.5.3 The System-level approach . . . . . . . . . . . . . . . 315 12.6 Model based network control system . . . . . . . . . . . . . . 317 12.6.1 A model of the MB-NCS . . . . . . . . . . . . . . . . . 318 12.6.2 MB-NCS stability . . . . . . . . . . . . . . . . . . . . 320 12.7 WSN-CS with Multiple Sensors . . . . . . . . . . . . . . . . . 322 12.7.1 WCN Model . . . . . . . . . . . . . . . . . . . . . . . 323 12.7.2 WSN-CS stability . . . . . . . . . . . . . . . . . . . . 324 12.7.3 Advantages of the WCN . . . . . . . . . . . . . . . . . 326 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 Appendix A Random Variables 335 A.1 Basic Definitions . . . . . . . . . . . . . . . . . . . . . . . . . 335 A.2 Random Variables . . . . . . . . . . . . . . . . . . . . . . . . 337 A.3 Probability Distribution . . . . . . . . . . . . . . . . . . . . . 337 Appendix B Sampling Theory 343 B.1 Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 B.2 Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . 344 B.3 Z-Transform . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 Appendix C Optimization Theory 347 C.1 Optimization Theory . . . . . . . . . . . . . . . . . . . . . . . 347 C.2 Basic Tools of Numerical Analysis . . . . . . . . . . . . . . . 347 C.3 Convex Optimizations . . . . . . . . . . . . . . . . . . . . . . 349 C.4 Non-convex Optimizations . . . . . . . . . . . . . . . . . . . . 350Contents 8 Appendix D Matrix Algebra 353 D.1 Matrix Inversion Formula . . . . . . . . . . . . . . . . . . . . 353 Appendix E Graph Theory 355 E.1 Basic definitions . . . . . . . . . . . . . . . . . . . . . . . . . 355 E.2 Proximity Graphs . . . . . . . . . . . . . . . . . . . . . . . . . 357 Appendix F WSNs Programming 361 F.1 TinyOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366 Bibliography 374List of Acronyms ACK Acknowledgement AODV Ad Hoc On-Demand Distance Vector ASK Amplitude Shift Keying ARQ Automatic Request Retransmission AWGN Additive White Gaussian Noise BER Bit Error Rate BPSK Bit Phase Shift Keying CAP Contention Access Period CCA Clear Channel Assessment CDF Cumulative Distribution Function CFP Contention Free Period CSMA Carrier Sense Multiple Access CSMA/CA Carrier Sense Multiple Access/Collision Avoidance CTS Clear-to-Send FDMA Frequency Division Multiple Access FFD Full Functionality Device FSK Frequency Shift Keying LEACH Low Energy Adaptive Clustering Hierarchy MAC Medium Access Control MMSE Minimum Mean Square Error NLOS Non Line of Sight 9Contents 10 PAM Pulse Amplitude Modulation PER Packet Error Rate PDF Probability Distribution Function QPSK Quadrature Phase Shift Keying RFD Reduced Functionality Device RPL Routing over Low Power Lossy Networks RTS Request-to-Send SNR Signal to Noise Ratio TDMA Time Division Multiple Access TDOA Time Difference of Arrival TOA Time of Arrival WLAN Wireless Local Area Network WPAN Wireless Personal Area Network WSN Wireless Sensor Network WSN-CS Wireless Sensor Network-Control SystemsPreface Recent technological advances led to the development of very small and low-cost sensor devices with computational, processing, data storage and communicational capabilities. These devices, called wireless sensor nodes, when deployed in an area (indoors or outdoors) form a Wireless Sensor Net- work (WSN). The initial development of WSN was motivated by military applications such as enemy detection, battlefield surveillance, etc. As years went by, considerable amounts of research efforts have enabled the actual implementation and deployment of sensor networks tailored to the unique re- quirements of certain sensing and monitoring applications. Nowadays WSNs are a very promising tool of monitoring events and are used in many other fields, such as agriculture, environmental monitoring of air-water pollution, greenhouse, health monitoring, structural monitoring and more. Given the benefits offered by WSNs compared to wired networks, such as, simple de- ployment, low installation cost, lack of cabling, and high mobility, WSNs present an appealing technology as a smart infrastructure for building and factory automation, and process control applications. The book is intended as a textbook for senior undergraduate or graduate- level students with the goal of helping them to gain an understanding of the challenges and promises of this exciting field. It is also targeted at academicandindustrialresearchersworkinginthefield, andalsoatengineers developing actual solutions for WSNs. Each chapter ends with a number of exercises that will allow students to practice the described concepts and techniques. Thisbookcoversfundamentaltopicstodesign, understand, andperforma a performance analysis of WSNs. The structure is organized as follows: • Chapter 1 provides an introduction to the basic characteristics and the architecture of a WSN. Moreover, a brief description of the main WSN applications is given; • Chapter 2 deals with the wireless channel in WSNs. Emphasis is given on the fading models and their effects on the signals that carry the communication messages; • Chapter 3 presents the physical layer in WSNs. In particular, basic 11Contents 12 elements of modulation theory are provided while the probability of error in various channels is studied; • Chapter 4 covers the medium access control mechanisms in WSNs and the way nodes access the channel is examined. The chapter focuses also on the IEEE 802.15.4 standard; • Chapter 5 is dedicated to routing in WSNs. Routing protocols are classified, the basic optimization theory for routing is introduced, and an iterative solution for the shortest path optimization problem is pre- sented; • Chapter 6 presents the fundamental theoretical results for the topology control of WSNs. Emphasis is put on the NP hardness of connectivity and coverage control problems; This is a fairly advanced theoretical chapter. • Chapter 7 provides an introduction to the basics of detection theory. How events are detected out of uncertain (noisy) measurements from one/multiple sensors is studied; • Chapter 8 presents the fundamental aspects of distributed estimation over WNSs. Star and ad-hoc networks are studied. Estimation in the presence of limited communication resources is also mentioned. This is a fairly advanced theoretical chapter. • Chapter 9 introduces the fundamentals of distributed learning over WNSs. After a review of the basics of learning theory, the specific application to WSNs is presented. This is a fairly advanced theoretical chapter. • Chapter 10 presents the basic of positioning and localization in WSNs. Node positioning methods require the combination of common mea- surements (e.g. time, range, and angle) together with estimation tech- niques in order to locate the nodes; This chapter is an application of the results of Chapter 6; • Chapter 11 introduces the concept of time synchronization and pro- vides an overview of several synchronization strategies; This chapter is an application of the results of Chapter 6; • Chapter 12 provides an overview of control over WSNs. The basics of automaticcontroltheoryarereviewed. Conditionensuringthestability of closed loop control over WSNs are studied, both in the presence of delays and message losses. The effects of WSNs networking protocol is characterized;Contents 13 • Appendix 1 provides a basic mathematical background for random variables and probability distribution functions; • Appendix 2 provides a basic mathematical background for sampling theory; • Appendix 3 gives some basic useful concepts regarding optimization theory; • Appendix 4 gives one useful result of Matrix Algebra; • Appendix 5 gives fundamental definitions of Graph Theory; • Appendix 6 contains an introduction to sensor network programming accompaniedwithexplanatoryexampleswritteninNesC,theprogram- ming language for WSNs. This draft book results from the material that has been taught at the 2012 and 2013 editions of the course "Principles of Wireless Sensor Networks" at KTH Royal Institute of Technology, Stockholm, Sweden. The work so far employed to put together this book corresponds to 1.5 years of full time work of one researcher. I acknowledge the work Eric Ahlqvist (Topology Control), Piergiuseppe Di Marco (IEEE 802.15.4 MAC),CharalamposKalalas(allthechapters,excludedTopologyCon- trol and Distributed Learning), Fredrik Isaksson (Distributed Estima- tion), Gustav Zickert (WSN-Control Systems), Ahsan Mahmood (all the chapters, excluded Topology Control and Distributed Learning), Rasmus Nilsson (Distributed Learning), Yuzhe Xu (Distributed Esti- mation).Chapter 1 Introduction to WSNs Sensor nodes offer a powerful combination of distributed sensing, com- puting and communication. The ever-increasing capabilities of these tiny sensor nodes, which include sensing, data processing, and communicating, enable the realization of WSNs based on the collaborative effort of a number of other sensor nodes. They enable a wide range of applications and, at the same time, offer numerous challenges due to their peculiarities, primarily the stringent energy constraints to which sensing nodes are typically subjected. As illustrated in Figure 1.1, WSNs incorporate knowledge and technolo- gies from three different fields; Wireless communications, Networking and Systems and Control theory. In order to realize the existing and potential applications for WSNs, sophisticated and extremely efficient communication protocols are required. This chapter provides a first introduction to the WSNs, including architecture, specific characteristics and applications. 1.1 WSN Architecture and Protocol Stack WSNs, asshowninFigure1.2, arecomposedofanumberofsensornodes, which are densely deployed either inside a physical phenomenon or very close to it. Thesensornodesaretransceiversusuallyscatteredinasensorfieldwhere each of them has the capability to collect data and route data back to the sink/gateway and the end-users by a multi-hop infrastructureless architec- ture through the sink. They use their processing capabilities to locally carry out simple computations and transmit only the required and partially pro- cessed data. The sink may communicate with the task manager/end-user via the Internet or satellite or any type of wireless network (like WiFi, mesh net- works, cellular systems, WiMAX, etc.), making Internet of Things possible. However, in many cases the sink can be directly connected to the end-users. Note that there may be multiple sinks/gateways and multiple end-users in the architecture. 15Chapter 1. Introduction to WSNs 16 Figure 1.1 Areas of study that concur to the definition of WSNs Figure 1.2 A WSN connected to the Internet via a sink node. As illustrated in Figure 1.3, each sensor node is consisting of five main components; a microcontroller unit, a transceiver unit, a memory unit, a power unit and a sensor unit. Each one of these components is determinant in designing a WSN for deployment. The microcontroller unit is in charge of the different tasks, data process- ing and the control of the other components in the node. It is the main controller of the wireless sensor node, through which every other component is managed. The controller unit may consist of an on-board memory or may be associated with a small storage unit integrated into the embedded board. It manages the procedures that enable the sensor node to perform sensing operations, run associated algorithms, and collaborate with the other nodes through wireless communication. Through the transceiver unit a sensor node performs its communication with other nodes and other parts of the WSN. It is the most power consump- tion unit. The memory unit is for temporal storage of the sensed data and can be RAM, ROM and their other memory types (SDRAM, SRAM, EPROM,Chapter 1. Introduction to WSNs 17 Figure 1.3 Components of a node of a WSN. etc.), flash or even external storage devices such as USB. Thepowerunit,whichisoneofthecriticalcomponents,isfornodeenergy supply. Power can be stored in batteries (most common) rechargeable or not or in capacitors. For extra power supply and recharge, there can be used natural sources such as solar power in forms of photovoltaic panels and cells, wind power with turbines, kinetic energy from water, etc. Last but not least is the sensor unit, which is the main component of a wireless sensor node that distinguishes it from any other embedded system with communication capabilities. It may generally include several sensor units, which provide information gathering capabilities from the physical world. Each sensor unit is responsible for gathering information of a certain type, such as temperature, humidity, or light, and is usually composed of two subunits: a sensor and an analog-to-digital converter (ADC). The ana- log signals produced by the sensor based on the observed phenomenon are converted to digital signals by the ADC, and then fed into the processing unit. In WSNs, the sensor nodes have the dual functionality of being both data originators and data routers. Hence, communication is performed for two reasons: • Source function: Each sensor node’s primary role is to gather data from the environment through the various sensors. The data generated from sensing the environment need to be processed and transmitted to nearby sensor nodes for multi-hop delivery to the sink. • Router function: In addition to originating data, each sensor node is responsible for relaying the information transmitted by its neighbors. The low-power communication techniques in WSNs limit the commu- nication range of a node. In a large network, multi-hop communication is required so that nodes relay the information sent by their neighbors to the data collector, i.e., the sink. Accordingly, the sensor node is responsible for receiving the data sent by its neighbors and forwarding these data to one of its neighbors according to the routing decisions. Except for their transmit/receive operation state, transceivers can be putChapter 1. Introduction to WSNs 18 Figure 1.4 Power consumption of a node to receive or transmit messages. Figure 1.5 WSN having a star topology. into an idle state (ready to receive, but not doing so) where some functions in hardware can be switched off, reducing energy consumption. The breakdown of the transceiver power consumption in Figure 1.4 shows that a transceiver expends a similar amount of energy for transmitting and receiving, as well as when it is idle. Moreover, a significant amount of energy can be saved by turning off the transceiver to a sleep state whenever the sensor node does not need to transmit or receive any data. In this state, significant parts of the transceiver are switched off and the nodes are not able to immediately receive something. Thus, recovery time and startup energy to leave sleep state can be significant design parameters. When the transmission ranges of the radios of all sensor nodes are large enough and the sensors can transmit their data directly to the centralized base station, they can form a star topology as shown in Figure 1.5. In this topology, eachsensornodecommunicatesdirectlywiththebasestationusing a single hop. However, sensor networks often cover large geographic areas and radio transmissionpowershouldbekeptataminimuminordertoconserveenergy; consequently, multi-hop communication is the more common case for sensor networks (shown in Figure 1.6). In this mesh topology, sensor nodes must not only capture and disseminate their own data, but also serve as relays for other sensor nodes, that is, they must collaborate to propagate sensor data towards the base station. This routing problem, that is, the task ofChapter 1. Introduction to WSNs 19 Figure 1.6 WSN having with multihop communication. finding a multi-hop path from a sensor node to the base station, is one of the most important challenges and has received large attention from the research community. When a node serves as a relay for multiple routes, it often has theopportunitytoanalyzeandpre-processsensordatainthenetwork, which can lead to the elimination of redundant information or aggregation of data that may be smaller than the original data. Routing is examined in detail in chapter 5. The reduced ISO-OSI protocol stack used by the sink and all sensor nodes is given in Figure 1.7. This protocol stack combines power and routing awareness, integrates data with networking protocols, communicates power efficiently through the wireless medium, and promotes cooperative efforts of sensor nodes. The protocol stack consists of the physical layer, medium access control layer, routing layer and application layer. The physical layer addresses the needs of simple but robust modulation, transmission, and re- ceiving techniques. Since the environment is noisy and sensor nodes can be mobile, the medium access control layer is responsible for ensuring reliable communication through error control techniques and manage channel access to minimize collision with neighbors’ broadcasts. The routing layer takes care of routing the data and depending on the sensing tasks, different types of application software can be built and used on the application layer. The above mentioned layers are thoroughly examined in the following chapters. 1.2 Challenges and Constraints WhileWSNssharemanysimilaritieswithotherdistributedsystems, they are subject to a variety of unique challenges and constraints. These con- straints impact the design of a WSN, leading to protocols and algorithms that differ from their counterparts in other distributed systems.Chapter 1. Introduction to WSNs 20 Figure 1.7 ISO-OSI protocol stack for WSNs. Energy The intrinsic properties of individual sensor nodes pose additional chal- lenges to the communication protocols primarily in terms of energy con- sumption. As will be explained in the following chapters, WSN applications and communication protocols are mainly tailored to provide high energy efficiency. Sensor nodes carry limited power sources. Typically, they are powered through batteries, which must be either replaced or recharged (e.g., using solar power) when depleted. For some nodes, neither option is ap- propriate, that is, they will simply be discarded once their energy source is depleted. Whether the battery can be recharged or not significantly affects the strategy applied to energy consumption. Therefore, while traditional networks are designed to improve performance metrics such as throughput and delay, WSN protocols focus primarily on power conservation. Node Deployment The deployment of WSNs is another factor that is considered in de- veloping WSN protocols. The position of the sensor nodes need not be engineered or predetermined. This allows random deployment in inacces- sible terrains or disaster relief operations. On the other hand, this ran- dom deployment requires the development of self-organizing protocols for the communication protocol stack. In particular, sensor nodes must be self- managing in that they configure themselves, operate and collaborate with other nodes, and adapt to failures, changes in the environment, and changes in the environmental stimuli without human intervention. Moreover, many sensor networks, once deployed, must operate unattended, that is, adapta- tion, maintenance, and repair must be performed in an autonomous fashion. In energy-constrained sensor networks, all these self-management features must be designed and implemented such that they do not incur excessive energy overheads.

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