Dynamic Sensor Selection Optimization Strategy for Wireless Sensor Networks

Energy consumption and effective coverage rate are both significant problem in wireless sensor networks (WSNs). The dynamic sensor selection optimization strategy refers to the optimization of the tradeoff between energy consumption and effective coverage rate, which is adopted to enhance energy efficiency, enlarge the effective coverage rate and prolong the lifetime of WSN. A strategy for dynamic sensor selection optimization, called HN-GA, is proposed, which uses the genetic algorithm (GA) to implement global searching and adopts the Hopfield network (HN) to reduce the search space of genetic algorithm and ensure the validity of each gene. In terms of evaluating the optimized sensor selection results, a combined metric is introduced, which is based on several practically feasible measures of the energy consumption and the effective coverage rate. The simulation results verify that the proposed HN-GA algorithm performs well in dynamic sensor selection optimization strategy. Under the guidance of HN-GA based dynamic sensor selection optimization strategy, the lifetime and the effective coverage performance of WSN can be significantly improved. Compared with GA algorithm and HN, HN-GA has better performance on regional convergence and global searching. It can achieve dynamic sensor selection optimization more efficiently and rapidly.