无线传感器网络动态节点选择优化策略

Dynamic Sensor Selection Optimization Strategy for Wireless Sensor Networks

无线传感器网络的能耗和有效覆盖率是衡量其性能的两个重要指标.无线传感器网络动态节点选择优化策略通过合理配置各无线传感器节点状态,平衡网络能耗和有效覆盖率,提高网络能效性,延长网络寿命.提出一种结合了Hopfield网络与遗传算法的动态节点选择优化策略,简称为HN-GA.该策略通过遗传算法实现全局搜索,采用Hopfield网络缩小遗传算法的搜索范围,保证遗传算法中每个基因对应待选解的有效性,并针对动态节点选择优化提出一种基于无线传感器网络能耗、寿命和有效覆盖率的综合指标.仿真实验表明,HN-GA算法能有效完成无线传感器网络动态节点选择优化,并在确保网络有效覆盖率的前提下,通过动态配置各无线传感器节点状态,降低网络能耗,延长网络寿命.与遗传算法和Hopfield网络相比,HN-GA算法不仅全局搜索能力强,且收敛速度快、耗时少.

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.