ENERGY-EFFICIENT PROTOCOLS AND SYSTEMS FOR WIRELESS SENSOR NETWORKS AND SMART ENVIRONMENTS by GIACOMO GHIDINI Presented to the Faculty of the Graduate School of The University of Texas at Arlington in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY THE UNIVERSITY OF TEXAS AT ARLINGTON December 2012 Copyright c by GIACOMO GHIDINI 2012 All Rights Reserved To my family. ACKNOWLEDGMENTS During my graduate studies, I have been very fortunate to receive the constant support of many great family, friends, and colleagues. Prof. Das has been a great supervisor, and I especially appreciate how he has always known when to motivate and push me, as well as when to let me search for the right path on my own. My Ph.D. committee members are experts in their research areas, and I am very proud to have discussed my research work with them. The Center for Research in Wireless Mobility and Networking is a great community of researchers, and I am thankful to its current and past members, as well as all the visitors who have shared their knowledge with us during seminars and meetings. The Department of Computer Science and Engineering at UT Arlington is a great place to work at, and its faculty and staff have always helped me sort out any issue related to my studies. Many other people have played important roles in my graduate studies, and I would like to especially thank Dr. Vipul Gupta of Oracle Labs. My family and friends deserve a lot of credit for always being there for me: cheering me up when I was going through hard times, as well as rejoicing at my achievements. It is thank to all these great people that I was able to achieve my research goals and graduate from the Ph.D. program. November 12, 2012 iv ABSTRACT ENERGY-EFFICIENT PROTOCOLS AND SYSTEMS FOR WIRELESS SENSOR NETWORKS AND SMART ENVIRONMENTS GIACOMO GHIDINI The University of Texas at Arlington, 2012 Supervising Professor: Sajal K. Das In a wireless sensor network, small computing devices, called sensors, sense the surrounding environment and relay the sensed data to a base station over a multi-hop wireless network, eventually processing them en-route. Wireless sensor networks and other devices, such as smartphones, smart meters, and smart appliances, cooperate in smart environments to obtain information about the environment, and then use this information to improve the experience of the users. Since most of these systems rely on battery power, there is a need for energy-efficient solutions for their operation. The objective of this dissertation is to design algorithms and protocols to improve the energy efficiency of such systems, and validate them using mathematical analysis, software simulations, and testbed experiments. In the first part of the dissertation, we look at two fundamental problems in wireless sensor networks: localization and duty cycling. In the area of localization, we describe a novel protocol for duty cycling wireless actor and sensor networks, and present a mathematical analysis based on the coupon collector's problem and the the- v ory of coverage processes, as well as simulation results. Our analysis and results show that the proposed protocol achieves the user-requested localization accuracy while maximizing the sleep time of sensor nodes. As far as duty cycling is concerned, we present novel Markov chain-based randomized schemes, and discuss the probabilis- tic analysis, as well as the experiments we conducted on Sun SPOT sensors. These results show that our proposed schemes reduce the sleep latency, while not affecting other performance metrics such as the energy efficiency, or vice versa. In the second part of the dissertation, we shift our focus to smart environments, and present our research work on data fusion and visualization aimed to provide lay users with actionable information. We introduce a framework, called FuseViz, to leverage already existing data sources such as smartphones, online databases and ser- vices, and wireless sensor networks, while addressing the challenges posed by large, live, heterogeneous, and autonomous data streams. We demonstrate the concepts behind our framework with a case study in building energy efficiency, and introduce E2Home, a Web-based application for this problem developed on top of the frame- work. Preliminary experiment results for the proposed E2Home system not only show that the actionable information can be easily computed, but also demonstrate energy savings of about 10%. Finally, we conclude our dissertation with an overview of a system-level energy model, built using data from the above-mentioned sources, that can be tailored for each home, its location, and residents, and can help further minimize energy consumption. vi TABLE OF CONTENTS ACKNOWLEDGMENTS . iv ABSTRACT . v LIST OF ILLUSTRATIONS . xi LIST OF TABLES . xiv Chapter Page 1. INTRODUCTION . 1 2. AN INTRODUCTION TO WIRELESS SENSOR NETWORKS AND THE COMMUNICATION STACK . 4 2.1 MAC Layer . 6 2.1.1 MAC Protocol Classes . 7 2.2 Network Layer . 12 2.2.1 IPv6 in Low-Power Wireless Personal Area Networks . 13 2.2.2 The Routing Protocol for Low Power and Lossy Networks (RPL) . 14 2.2.3 RPL Implementations . 16 2.2.4 RPL Analyzes . 18 2.3 Application Layer . 21 2.3.1 The Constrained Application Protocol (CoAP) . 22 2.3.2 CoAP Implementations . 25 2.3.3 Internetworking Between HTTP and CoAP . 28 2.4 Discussion . 28 2.4.1 MAC Layer . 29 vii 2.4.2 Network Layer . 31 2.4.3 Application Layer . 32 2.5 Summary . 34 3. A SEMI-DISTRIBUTED LOCALIZATION PROTOCOL FOR WIRELESS SENSOR AND ACTOR NETWORKS . 36 3.1 Related Work . 39 3.1.1 Our Contributions . 41 3.2 Models . 43 3.2.1 Virtual Infrastructure . 43 3.2.2 Actor Communications . 44 3.2.3 Sensor Communications . 45 3.2.4 Working (or Sleep-Awake) Schedules . 47 3.2.5 Deployment Model . 48 3.3 Localization Protocol . 48 3.3.1 Sensor and Actor Algorithms . 49 3.3.2 Analysis of Localization Protocol . 54 3.3.3 Simulation Results . 67 3.4 Summary . 71 4. ENERGY-EFFICIENT MARKOV CHAIN-BASED DUTY CYCLING SCHEMES . 73 4.1 Motivation and Preliminary Experiments . 76 4.1.1 Randomized Scheme Model . 77 4.1.2 Aggregate Duty Cycle . 78 4.1.3 Connection Delay . 78 4.1.4 Connection Duration . 81 4.1.5 Time and Energy Efficiency . 81 viii 4.1.6 Rationale Behind Markov Chain-based Scheme . 84 4.2 Markov Chain-based Duty Cycling Scheme . 86 4.3 Analysis of Markov Chain-based Randomized Scheme . 89 4.3.1 Assumptions and Pairwise Markov Chain Model . 90 4.3.2 Aggregate Duty Cycle . 91 4.3.3 Connection Delay . 92 4.3.4 Connection Duration . 95 4.3.5 Time Efficiency . 96 4.4 Experimental Results for Randomized Scheme . 99 4.5 Related Work . 103 4.6 Summary . 105 5. MARKOV CHAIN-BASED PARTIALLY RANDOMIZED DUTY CYCLING SCHEMES . 107 5.1 Partially Randomized Markov Chain-based Duty Cycling Schemes . 108 5.2 Analysis of Partially Randomized Duty Cycling Scheme . 109 5.2.1 Assumptions and Pairwise Markov Chain Model . 109 5.2.2 Aggregate Duty Cycle . 111 5.2.3 Connection Delay . 112 5.2.4 Connection Duration . 114 5.2.5 Time Efficiency . 115 5.3 Experimental Results for Partially Randomized Scheme . 118 5.4 Comparison of Randomized Schemes . 122 5.5 Optimal Duty Cycling for Wireless Sensor Networks . 124 5.6 Summary . 126 6. FuseViz: A FRAMEWORK FOR WEB-BASED DATA FUSION AND VISUALIZATION IN SMART ENVIRONMENTS . 127 ix 6.1 Related Work . 132 6.2 Requirements and Challenges . 134 6.3 Proposed FuseViz Framework . 136 6.3.1 Architecture . 137 6.3.2 Application . 144 6.3.3 Implementation . 144 6.4 Case Study: E2Home . 145 6.5 Discussion . 150 6.6 Summary . 153 7. CONCLUSIONS . 155 REFERENCES . 157 BIOGRAPHICAL STATEMENT . 170 x LIST OF ILLUSTRATIONS Figure Page 1.1 Sample wireless sensor network . 2 1.2 Scenario and research work . 3 2.1 Internet and WSN communication stacks . 6 2.2 Sample scheduled MAC protocol . 9 2.3 Example of schedule distribution in SMAC . 10 2.4 Example of preamble sampling-based MAC protocol . 11 2.5 Directed acyclic graph constructed by RPL . 15 2.6 Unicast routing with RPL . 17 2.7 Internetworking between the Internet and WSN . 22 2.8 ReSTful networking between Internet device and sensor node . 23 2.9 Examples of exchanges between CoAP client and server . 24 2.10 Dynamic selection of MAC protocol . 30 3.1 WSAN scenario . 37 3.2 Duty cycling WSAN . 39 3.3 Example of coordinate polar system on top of WSAN . 40 3.4 Virtual infrastructure for WSAN . 43 3.5 Analytical results for varying number of coronas . 64 3.6 Analytical results for varying duty cycle . 65 3.7 Analytical and experimental results for varying number of coronas . 68 3.8 Analytical and experimental results for varying duty cycle . 69 3.9 Experimental results for seeds, trained, and mistrained sensors . 70 xi 3.10 Analytical and experimental results for varying number of coronas with suboptimal density . 70 3.11 Experimental results for varying number of coronas and density . 71 4.1 Block diagram for randomized scheme operation . 77 4.2 Sample random working schedules . 79 4.3 Experimental results for randomized scheme with i.i.d. r. v.'s . 80 4.4 Ideal and real duty cycling transitions . ..