基于隶属云蚁群算法的长链型无线传感器网络路由优化

针对用于监测系统的长链树状无线传感器网络数据传输的实时性和高可靠性等要求,并考虑到近汇聚节点处易形成"漏斗"等问题,本文提出用基于云模型的多蚁群算法对无线传感器网络路由进行跨层优化的设计思想。算法通过种群间信息素的相互作用并行地完成路径的搜索并及时对信息素进行更新,在搜索的过程中把节点的时延、跳数、负载及分组成功率作为路径的启发值,利用多规则云发生器对更新策略中的信息素残留系数和信息素强度进行自适应调整。仿真结果表明,该路由算法能够保证无线传感器网络具有很强的实时性、可靠性及鲁棒性,实现了网络的负载平衡及拥塞控制机制。

A Cloud Model Based Multiple Ant Colony Algorithm for the Routing Optimization of WSN with a Long-Chain Structure

The requirement of real timeness and high reliability of data transmission in the WSN based monitoring systems with a long chain tree like topology , the network hotspots problem which is likely to occur around the sink node must be taken into account. This paper proposes a new cross layer routing algorithm for wireless sensor networks, which is named Cloud model based Multiple Ant Colony routing Optimization (CMACO). Through the interaction of pheromones between populations, the routing algorithm searches for the feasible paths in parallel and updates the pheromones in time. In the path finding process, the routing metrics of single hop delay, hop count, queue length and packet reception rate are regarded as heuristics correction factors. A multiple rules generator is used to optimize the residual factor ρ and the total pheromone information Q so as to update the pheromones adaptively. The simulation results demonstrate that the routing algorithm can guarantee the real timeness, reliability and robustness of wireless sensor networks. It can also achieve the network load balancing and the congestion control mechanism.