多源多汇WSNs中基于概率路由的数据融合方法

Probabilistic routing-based data fusion method in multi-source and multi-sink WSNs

针对传统多对一数据融合方法无法有效支持多源多汇无线传感器网络(WSNs)应用场景,难以适应网络动态性和可扩展性等问题,提出了一种基于概率路由的分布式多对多数据融合方法。在无需全局知识的前提下,多个汇聚节点能同时高效地收集来自多个源节点的感知数据。新方法使用混沌蚁群优化指导数据传输方向,结合数据融合和多播技术降低通信负载。当网络环境保持稳定时,路由结构逐渐收敛为近似最优数据融合结构,如果网络环境发生变化,路由结构也会迅速调整。通过与现有方法的比较,实验仿真验证了新方法的可行性和高效性。

In view of the problems that the traditional many-to-one data fusion methods cannot effectively support the application scenarios with multi-source and multi-sink wireless sensor networks(WSNs),and these methods are difficult to adapt to the dynamics and scalability of network,a distributed many-to-many data fusion method based on probabilistic routing is proposed.Without the requirement of global knowledge,multiple sink nodes can efficiently collect the sensing data from multiple source nodes at the same time.The new method exploits the chaotic ant colony optimization to guide the direction of data transmission,also combines data fusion and multicast technology to reduce the communication load.When the network environment remains stable,the routing structure gradually converges to an approximate optimal data fusion structure,but if any change happens on network environment,the routing structure can quickly adjust.Experimental simulation results show that the new method is feasible and efficient when comparing with the existing methods.