Leveraging Heterogeneity for Maximizing Network Lifetime in Wireless Sensor Networks

Technological advances in integrated circuitry, micro-electromechanical systems, wireless communication, and storage battery in combination have driven the development of low-cost, low-power sensor nodes. Sensors can be scattered all over the region to build a wireless sensor network (WSN) for many applications such as environmental observations, habitat monitoring, intruder detection, military surveillance, and so on. Since the major role of these applications is to gather information over a long period of time, the network lifetime of WSNs has become a major concern for network designers. The increasing heterogeneity of sensor nodes, namely nodes with enhanced computation capacity, energy budget, or communication capability, is one direction along which network designers can leverage to increase the network lifetime. In this work, we explore different types of heterogeneity in terms of link heterogeneity, energy heterogeneity, and computation heterogeneity in WSNs and then consider the communication strategy to leverage those resources for maximizing the network lifetime. We formulate our problem as a linear programming (LP) problem for finding the communication strategy and investigate the performance benefits of link heterogeneity, energy heterogeneity, and computation heterogeneity. Based on the insights obtained from the theoretical formulation, we then propose a distributed algorithm to achieve the maximum network lifetime by leveraging heterogeneous nodes in a distributed manner. Simulation results shows that leveraging heterogeneity can improve the network lifetime in the wireless sensor networks.ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii CHAPTER 1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . 1 CHAPTER 2 BACKGROUND . . . . . . . . . . . . . . . . . . . . . . 5 2.1 Wireless Sensor Devices . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2.1 Characteristics and Challenges . . . . . . . . . . . . . . . . . 8 2.2.2 System Evaluation Metrics . . . . . . . . . . . . . . . . . . . 10 2.2.3 Research Overview . . . . . . . . . . . . . . . . . . . . . . . 12 2.3 Heterogeneous Wireless Sensor Networks . . . . . . . . . . . . . . . 17 2.3.1 Source of Heterogeneity . . . . . . . . . . . . . . . . . . . . . 17 2.3.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.3.3 Leveraging Heterogeneity for Maximizing Network Lifetime . 20 CHAPTER 3 PROBLEM FORMULATION . . . . . . . . . . . . . . 21 3.1 System Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.1.1 Network Model . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.1.2 Energy Model . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.1.3 Lifetime Model . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.1.4 Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.2 LP Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 CHAPTER 4 SIMULATION RESULTS AND OBSERVATIONS . 36 4.1 Evaluation Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 4.1.1 Solving LPs – lp solve . . . . . . . . . . . . . . . . . . . . . . 36 4.1.2 Network Topology . . . . . . . . . . . . . . . . . . . . . . . . 36 4.1.3 Simulation Parameters . . . . . . . . . . . . . . . . . . . . . 37 4.1.4 Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.2 Link Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.2.1 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . 41 4.2.2 Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.3 Energy Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.3.1 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . 47 4.3.2 Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.4 Computation Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . 55 4.4.1 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . 57 4.4.2 Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 4.5 Practical Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.5.1 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . 59 CHAPTER 5 DISTRIBUTED ALGORITHM . . . . . . . . . . . . . 61 5.1 Leveraging Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . 61 5.1.1 Distributed Algorithm . . . . . . . . . . . . . . . . . . . . . . 62 5.2 Simulation Environment . . . . . . . . . . . . . . . . . . . . . . . . 64 5.2.1 The IEEE 802.15.4 Standard . . . . . . . . . . . . . . . . . . 64 5.2.2 The ZigBee Routing Protocol . . . . . . . . . . . . . . . . . . 66 5.2.3 Power Consumption on Mica Mote . . . . . . . . . . . . . . . 67 5.2.4 Network Simulator – ns-2 . . . . . . . . . . . . . . . . . . . . 69 5.3 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 69 5.3.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 69 5.3.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . 70 CHAPTER 6 CONCLUSION AND FUTURE WORK . . . . . . . 72 6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7