Minimization of the Redundant Sensor Nodes in Dense Wireless Sensor Networks
Most sensor networks are deployed with high density and then node duty cycle is dynamically managed in order to prolong the network lifetime. In this paper, we address the issue of maintaining sensing coverage of surveillance target in large density wireless sensor networks and present an efficient technique for the selection of active sensor nodes. First, the At Most k-Coverage Problem (AM k-Coverage) is modeled as a nonlinear integer programming. Then Genetic Algorithm is designed to solve the multi-objective nonlinear integer programming, which is a quasi-parallel method. And later by using Genetic Algorithm, a central algorithm is designed to organize a sensor network into coverage sets. Considering that the central base station consumes a great deal of energy when it collects the coverage information from every node, we also propose a localized manner on the basis of the proposed central algorithm. Finally, Experimental results show that the proposed algorithm can construct the coverage sets reliably and reduce the number of active sensor nodes which is helpful to reduce system energy consumption and prolong the network lifespan.
Keywordscoverage sets multi-objective optimization genetic algorithm Pareto-optimal local central node maximal remaining energy
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