Energy-Aware Topology Control and Data Delivery in Wireless Sensor Networks
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The objective of this thesis is to address the problem of energy conservation in wireless sensor networks by tackling two fundamental problems: topology control and data delivery. We first address energy-aware topology control taking into account throughput per unit energy as the primary metric of interest. Through both experimental observations and analysis, we show that the optimal topology is a function of traffic load in the network. We then propose a new topology control scheme, Adaptive Topology Control (ATC), which increases throughput per unit energy. Based on different coordinations among nodes, we proposed three ATC schemes: ATC-CP, ATC-IP, and ATC-MS. Through simulations, we show that three ATC schemes outperform static topology control schemes, and particularly the ATC-MS has the best performance under all environments. Secondly, we explore an energy-aware data delivery problem consisting of two sub-problems: downstream (from a sink to sensors) and upstream (from sensors to a sink) data delivery. Although we address the problems as two independent ones, we eventually solve those problems with two approaches: GARUDA-DN and GARUDA-UP which share a common structure, the minimum dominating set. For the downstream data delivery, we consider reliability as well as energy conservation since unreliable data delivery can increase energy consumption under high data loss rates. To reduce energy consumption and achieve robustness, we propose GARUDA-DN which is scalable to the network size, message characteristics, loss rate and the reliable delivery semantics. From ns2-based simulations, we show that GARUDA-DN performs significantly better than the basic schemes proposed thus far in terms of latency and energy consumption. For the upstream data delivery, we address an energy efficient aggregation scheme to gather correlated data with theoretical solutions: the shortest path tree (SPT), the minimum spanning tree (MST) and the Steiner minimum tree (SMT). To approximate the optimal solution in case of perfect correlation among data, we propose GARUDA-UP which combines the minimum dominating set (MDS) with SPT in order to aggregate correlated data. From discrete event simulations, we show that GARUDA-UP outperforms the SPT and closely approximates the centralized optimal solution, SMT, with less amount of overhead and in a decentralized fashion.