Energy Consumption Optimization in Wireless Sensor Networks (bibtex)
by Georgios Tsoumanis
Abstract:
Prolonging a wireless sensor network's lifetime is closely related to energy consumption and particularly to the energy hole problem, where sensor nodes close to the sink node consume a considerable amount of their energy for relaying purposes. In order to tackle the energy hole problem's effects, this thesis proposes two approaches that counter the problem from two perspectives: (i) the minimization of the energy consumption by approaching the sink placement problem as a k-median problem and (ii) the prolongation of the network's lifetime by recharging its sensor nodes. In the first approach, an analytical model for analyzing the available energy in the network is proposed. The next step is to analytically model the overall energy consumption as a k-median facility location problem, its solution corresponding to the location of k sinks in the network. As analytically shown, when k sinks are placed according to the solution of the previous facility location problem, then the overall energy consumption is minimized, resulting in a higher energy-saving system. Thus, the saved energy can be further utilized, e.g., to extend the network's lifetime and support modern replenishing techniques such as energy harvesting and battery recharging. Simulation results validate the analytical model that is the basis of the analysis and confirm the results with respect to the available energy in the network. In particular, significant energy savings are observed when the analytical results are applied, thus resulting in better energy utilization and subsequent network lifetime increment. The second approach is focused on two proposed recharging policies. The first one is a simple recharging policy that permits a mobile recharger, initially stationed at the sink node, to move around and replenish any node's exhausted battery when a certain recharging threshold is violated. This policy, as well as the second pro- posed recharging policy (i.e., the enhanced recharging policy), refer to on-demand recharging policies which base their operation on local information, allowing the mobile recharger to move -- upon request -- to a node of reduced energy level and replenish its battery. When under the enhanced recharging policy and after completing the latter replenishment, the mobile recharger continues operating in a hop-by-hop manner to the neighbor nodes of the lowest energy level, thus replenishing their batteries too. It is shown that the minimization of the recharging distance covered by the mobile recharger is a facility location problem, and particularly an 1-median one. Simulation results, regarding the simple recharging policy, investigate various aspects of it related to the recharging threshold and the level of the energy left in the network nodes' batteries. In addition, it is shown that when the sink's location is set to the solution of the particular facility location problem, then the recharging distance is minimized irrespectively of the recharging threshold. As for the enhanced recharging policy's simulation results, its effectiveness is investigated using simulation results and compared against an existing well-known on-demand recharging policy that exploits global knowledge (i.e., knowledge of both the energy level of all nodes and the network topology). It is shown that the enhanced recharging policy, even though based on local information, maintain the average energy level and termination time higher than that under the existing one that exploits global knowledge. Furthermore, it is observed that the network's lifetime is maximized when the basis of the mobile recharger is located at the solution of the mentioned median problem for all studied policies. The approaches studied in this thesis establish a relation between facility location problems (particularly the k-median problem) and energy consumption and battery replenishment. This is a significant contribution that is expected to trigger future work in the area and reveal further aspects of the energy consumption issues and how lifetime may be prolonged in wireless sensor networks.
Reference:
Georgios Tsoumanis, "Energy Consumption Optimization in Wireless Sensor Networks", Ph.D. Thesis, Ionian University, 2018.
Bibtex Entry:
@phdthesis{tsoumanis2018thesis,
	Abstract = {Prolonging a wireless sensor network's lifetime is closely related to energy consumption and particularly to the energy hole problem, where sensor nodes close to the sink node consume a considerable amount of their energy for relaying purposes. In order to tackle the energy hole problem's effects, this thesis proposes two approaches that counter the problem from two perspectives: (i) the minimization of the energy consumption by approaching the sink placement problem as a k-median problem and (ii) the prolongation of the network's lifetime by recharging its sensor nodes.
	In the first approach, an analytical model for analyzing the available energy in the network is proposed. The next step is to analytically model the overall energy consumption as a k-median facility location problem, its solution corresponding to the location of k sinks in the network. As analytically shown, when k sinks are placed according to the solution of the previous facility location problem, then the overall energy consumption is minimized, resulting in a higher energy-saving system. Thus, the saved energy can be further utilized, e.g., to extend the network's lifetime and support modern replenishing techniques such as energy harvesting and battery recharging. Simulation results validate the analytical model that is the basis of the analysis and confirm the results with respect to the available energy in the network. In particular, significant energy savings are observed when the analytical results are applied, thus resulting in better energy utilization and subsequent network lifetime increment.
	The second approach is focused on two proposed recharging policies. The first one is a simple recharging policy that permits a mobile recharger, initially stationed at the sink node, to move around and replenish any node's exhausted battery when a certain recharging threshold is violated. This policy, as well as the second pro- posed recharging policy (i.e., the enhanced recharging policy), refer to on-demand recharging policies which base their operation on local information, allowing the mobile recharger to move -- upon request -- to a node of reduced energy level and replenish its battery. When under the enhanced recharging policy and after completing the latter replenishment, the mobile recharger continues operating in a hop-by-hop manner to the neighbor nodes of the lowest energy level, thus replenishing their
	batteries too. It is shown that the minimization of the recharging distance covered by the mobile recharger is a facility location problem, and particularly an 1-median one. Simulation results, regarding the simple recharging policy, investigate various aspects of it related to the recharging threshold and the level of the energy left in the network nodes' batteries. In addition, it is shown that when the sink's location is set to the solution of the particular facility location problem, then the recharging distance is minimized irrespectively of the recharging threshold. As for the enhanced recharging policy's simulation results, its effectiveness is investigated using simulation results and compared against an existing well-known on-demand recharging policy that exploits global knowledge (i.e., knowledge of both the energy level of all nodes and the network topology). It is shown that the enhanced recharging policy, even though based on local information, maintain the average energy level and termination time higher than that under the existing one that exploits global knowledge. Furthermore, it is observed that the network's lifetime is maximized when the basis of the mobile recharger is located at the solution of the mentioned median problem for all studied policies.
	The approaches studied in this thesis establish a relation between facility location problems (particularly the k-median problem) and energy consumption and battery replenishment. This is a significant contribution that is expected to trigger future work in the area and reveal further aspects of the energy consumption issues and how lifetime may be prolonged in wireless sensor networks.},
	Author = {Tsoumanis, Georgios},
	Date-Modified = {2020-01-27 23:16:32 +0200},
	Keywords = {stphdthesis,olinet},
	Month = {September},
	School = {Ionian University},
	Title = {{{Energy Consumption Optimization in Wireless Sensor Networks}}},
	Type = {{Ph.D. Thesis}},
	Year = {2018}}
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