无线传感器网络中的最大生命期基因路由算法

无线传感器网络(wireless sensor networks,简称WSNs)由一组低功率且能量受限的传感器节点构成,设计此类网络的一个基本挑战便是最大化网络生命期的问题.在WSNs中,由于邻近传感器节点所收集的数据之间往往具有时空相关性,多采用数据聚合技术作为去除数据冗余、压缩数据大小的有效手段.合理地应用数据聚合技术,可以有效地减少数据传递量,降低网络能耗,从而延长网络生命期.研究了WSNs中结合数据聚合与节点功率控制的优化数据传递技术,提出了一种新的最大化网络生命期的路由算法.该算法采用遗传算法(genetic algorithm,简称GA)最优化数据聚合点的选择,并采用梯度算法进一步优化结果.该算法均衡节点能耗,并最大化网络生命期.仿真结果表明,该算法极大地提高了网络的生命期.

Maximum Lifetime Genetic Routing Algorithm in Wireless Sensor Networks

Wireless sensor networks (WSNs) consist of low-power and energy-constrained sensor nodes, and a fundamental challenge in the design of such networks is to maximize the network lifetime. In WSNs, data collected by adjacent sensor nodes usually have spatial-temporal correlations, and data aggregation technique is often used as an effective approach to remove data redundancy. Efficient usage of data aggregation technique can significantly reduce the amount of data delivery, lower the cost of overall power consumption of the network, hence increase the network lifetime. This paper studies the optimal data delivery in WSNs that takes advantage of data aggregation and nodal power control, and presents a novel routing algorithm that maximizes the network lifetime. The algorithm uses genetic algorithm (GA) to achieve an optimal selection of aggregation points, and gradient algorithm is also used to further optimize the result. The algorithm balances the power consumption of sensor nodes, and maximizes the network lifetime. Numerical results show that the proposed approach has substantially improved the network lifetime.