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Cloud Model and Ant Colony Optimization Based QoS Routing Algorithm for Wireless Sensor Networks

机译:基于云模型和蚁群优化的无线传感器网络的QoS路由算法

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This paper presents CMACRO (Cloud Model and Multiple Ant Colonies Optimization based Routing), a new cross-layer QoS routing algorithm for wireless sensor networks. Basing on the principle of cross-layer design, the algorithm adapts delay, nodes' load and link quality as QoS metrics, and provides differentiated services for real time event-driven data streams and delay-tolerant periodic sampling data. The QoS routing metrics are regarded as heuristics correction factors in ant colony algorithm (ACA). The ants are divided into a number of different populations. Through the interaction of pheromone between multi populations, the routing algorithm searches for the feasible paths in parallel and updates the pheromone in time. To overcome the slow convergence of ant colony algorithm, improvements to control the randomness of the ants via cloud model are proposed. The simulation results demonstrate that the routing algorithm can guarantee the real time, reliability and robustness of wireless sensor networks. It can also achieve the network load balancing and congestion control mechanism.
机译:本文介绍了CMACRO(云模型和基于蚁群优化的路由),一种新的无线传感器网络的跨层QoS路由算法。基于跨层设计的原理,该算法适应延迟,节点的负载和链路质量作为QoS度量,并为实时事件驱动数据流和延迟定期采样数据提供差异化​​服务。 QoS路由指标被视为蚁群算法(ACA)中的启发式校正因子。蚂蚁分为许多不同的人群。通过多人物之间信息素的相互作用,路由算法并行地搜索可行的路径并及时更新信息素。为了克服蚁群算法的缓慢收敛,提出了通过云模型控制蚂蚁随机性的改进。仿真结果表明,路由算法可以保证无线传感器网络的实时,可靠性和鲁棒性。它还可以实现网络负载平衡和拥塞控制机制。

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