基于萤火虫算法优化FCM的WSN路由算法

针对无线传感器网络节点能源有限,容易出现能量负载不均衡的问题,提出了一种基于萤火虫算法优化模糊C均值(FCM)的无线传感器网络(WSN)路由算法(FFACM),优化了分簇路由算法中的分簇阶段和簇间路由建立阶段。在分簇阶段,使用萤火虫算法计算初始聚类中心,避免模糊C均值算法因初始聚类中心而陷入局部最优的问题。在选择簇首节点上,建立关于剩余能量和距离的适应度函数,选取适应度值最大的节点作为簇首节点并动态更新。通过计算节点间的链路代价并根据剩余能量和到sink节点的距离建立代价函数,选择代价函数值最小的节点建立簇间多跳路由,使得簇首节点的负载降到最低。从仿真实验结果可知,相比于其他无线传感器网络的路由算法,FFACM算法能有效均衡网络负载,降低节点能耗,从而延长网络的使用周期。

WSN Routing Algorithm Based on Firefly Algorithm to Optimize FCM

The limited energy of nodes in wireless sensor networks is prone to unbalanced energy load. To solve this problem, a routing algorithm for wireless sensor networks (WSN) is proposed based on the firefly algorithm to optimize fuzzy C-means (FCM). Both the clustering stage and the inter-cluster routing establishment stage in clustering routing algorithm are optimized in firefly algorithm to optimize fuzzy C-means (FFACM) algorithm. In the clustering stage, the firefly algorithm is used to calculate the initial clustering center, so as to avoid the problem that fuzzy C-means algorithm falls into local optimum due to the initial clustering center. To select the cluster head node, the fitness function about residual energy and distance is established, and the cluster head node with the largest fitness value is selected and dynamically updated. We calculate the link cost between nodes and establish the cost function according to the residual energy and the distance to the sink node, and then select the node with the smallest cost function value to establish the multi-hop routing between clusters, which minimizes the load of the cluster head node. The simulation results show that compared with other routing algorithms in wireless sensor networks, FFACM algorithm can effectively balance the network load, reduce the energy consumption of nodes, and thus it can prolong the network life cycle.