无线传感器网络的任意覆盖率节点配置

Node Deployment with Arbitrary Coverage Percentage in Wireless Sensor Networks

研究了任意覆盖率下的无线传感器网络分布式节点自动配置问题.首先,针对正六边形拓扑架构下的网络覆盖,给出了节点密集分布条件下的覆盖率与相邻工作节点间距的解析关系,从而得到了理想条件下部分覆盖的最优节点配置.考虑到实际系统中有限的节点密度和节点的随机分布,进一步提出了一种可以在此条件下实现任意覆盖率的部分覆盖协同优化算法(Optimized collaborative partial coverage,OCPC).OCPC通过节点间的动态协同唤醒最接近于理想配置的工作节点并使其他节点睡眠以节省能量.以尽可能少的工作节点达到网络的覆盖和连通需求并降低网络的能耗,进而达到网络的感知任务和能量消耗的有效折衷.仿真表明,OCPC可以有效地实现任意期望覆盖率下的网络配置并保持网络连通,同时,与经典覆盖算法PEAS(Probing environment and adaptive sleeping)和OGDC(Optimal geographic density control)相比,在网络的节能方面也具有明显的优越性.

This paper addresses the automatic sensor node deployment problem of partial coverage with arbitrary percentage in wireless sensor networks. It presents the analytical relationship between sensor node deployment and coverage percentage based on the optimal network coverage topology of equilateral-hexagon structure with dense distribution of sensor nodes, through which the optimal sensor node deployment of partial coverage is obtained in the ideal case. Furthermore, due to the limited density and random distribution of sensor nodes in real systems, an optimized collaborative partial coverage （OCPC） algorithm is proposed for practical application of obtaining partial coverage with arbitrary percentage. In OCPC, the working nodes which most approximate to the optimal deployment are woken up to work and the other nodes are turned off for energy conservation via the dynamic collaboration among sensor nodes, while the requirement of both coverage and connectivity are satisfied with the fewest possible sensor nodes so that the network energy consumption can be reduced. Simulation results show that the OCPC can effectively achieve coverage of arbitrary percentage and maintain the network connectivity, and its superiority in respect of energy conservation is demonstrated by comparison with PEAS （Probing environment and adaptive sleeping） and OGDC （Optimal geographic density control） algorithms.