Multi-objective evolutionary optimization of 3D differentiated sensor network deployment
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
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In dense wireless sensor networks, density control is an important technique for prolonging network驴s lifetime while providing sufficient sensing coverage. In this paper, we develop three new density control protocols by considering the tradeoff between energy usage and coverage. The first one, Non-Overlapping Density Control, aims at maximizing coverage while avoiding the overlap of sensing areas of active sensors. Under the ideal case, a set of optimality conditions are derived to select sensors such that the sensing space is covered systematically to maximize the usage of each sensors and the coverage gap is minimized. Based on the optimality conditions, we develop a distributed protocol that can be efficiently implemented in large sensor networks. Next, we present a protocol called Non- Overlapping Density Control Based on Distances that does not require location information of the nodes. This protocol is more flexible and easier to implement than existing location-based methods. Finally, we present a new rangeadjustable protocol called Non-Overlapping Density Control for Adjustable Sensing Ranges. It allows heterogenous sensing ranges for different sensors to save energy consumption. Extensive simulation shows promising results of the new protocols.