一种模拟退火遗传算法的传感器网络数据融合技术研究

研究无线传感器网络(WSN)数据融合技术。传感器节点计算能力、通信能力有限,WSN采用交叉重叠方式部署,导致冗余数据量大,需采用数据融合技术消除冗余和无效数据,节约网络通信能耗。结合遗传算法全局搜索和模拟退火算法局部搜索的优点,提出一种模拟退火遗传算法的WSN数据融合方法(SA-GA)。采用模拟退火遗传算法快速找到移动代理路由最优传感器节点序列,并实现数据融合。仿真实验结果表明,与遗传算法、模拟退火算法相比,SA-GA更能快速找到全局最优数据融合节点序列,并对数据进行有效融合,具有更小的网络能耗和网络延时。

Research on wireless sensor networks data aggregation on simulated annealing genetic algorithm

This paper researched the wireless sensor network (WSN) data fusion. Sensor node computing ability and communication ability were limited. WSN used overlapping deployment, leading to large redundant data quantity, so as to use the data fusion technology to eliminate redundancy and invalid data, save network communication energy. Combination of genetic algorithm and simulated annealing algorithm for global search and local search advantages, this paper proposed a simulated annealing genetic algorithm ( SA-GA ) WSN data fusion method. By using simulated annealing genetic algorithm, it could quickly find the mobile agent routing optimal sensor node sequence and fuse the data. The simulation results show that, comparing with the genetic algorithm and simulated annealing algorithm, SA-GA can quickly find optimal data fusion node sequence, integrate the data effectively, and it has smaller energy consumption of the network and network delay.