基于PSO的信息熵数据融合非均匀分簇路由算法

针对无线传感器网络分簇算法中能量分布不均衡导致的"热区"和簇头负载过重问题,提出了一种基于PSO算法优化簇头选举的非均匀分簇算法。在候选簇头选举和竞争半径计算过程中综合考虑节点动态能量、节点密度和节点距基站距离,将网络进行非均匀分簇,并引入PSO算法进行最终簇头选举。根据节点能量、节点密度和距基站距离确定簇间单跳多跳结合的路由规则,选取代价函数小的节点作为下一跳节点。基于节点信息熵确定融合阈值,进行簇内数据融合剔除冗余数据。仿真结果表明,改进算法的数据传输量比EEUC算法和UCRA算法分别提高了20%和10%,提升了数据的融合效率,有效延长了网络生命周期,簇头能量消耗得到均衡,减少了网络能量消耗,网络的整体性能显著优于其他对比算法。

Uneven clustering routing algorithm based on particle swarm optimization and entropy theory

Aiming at solving the ''hot spots'' and cluster head energy consumption problem in wireless sensor networks(WSNs) caused by unbalanced energy consumption, an uneven clustering routing algorithm based on adaptive particle swarm optimization (PSO) is proposed. The node energy, node density and the distance to the BS are considered during the cluster head election and competition radius calculation. Network is divided into clusters of unequal size. The PSO is used to select the final cluster heads according to the cluster size. Meanwhile, hybrid routing rules of hop routing and multi-hop routing are adopted based on node energy, node destiny and the distance to BS, and the cluster heads of minimum cost function are chosen as the next hop. Data fusion in the cluster is conducted to eliminate redundant data based on entropy theory. Simulation results show that the data transmission amount of the improved algorithm is 20% and 10% higher than those of EEUC and UCRA algorithms. The lifetime of the improved algorithm is extended, the energy consumption of cluster head is balanced, the network energy consumption is effectively reduced, and the performance of the network is significantly better than the other comparison algorithms.