基于区域分簇的大规模无线传感器网络生命周期优化策略

针对环境监测、电网冰灾监测等大规模监测系统中监测区域覆盖广、传感器数量大等特性,为节约网络能耗以延长生命周期,提出了一种基于区域分簇的大规模无线传感器网络生命周期优化策略(RCS).该策略首先利用传感器节点的位置信息进行凝聚的层次聚类(AGNES)算法将大规模网络分区以优化簇首的分布;其次,候选簇首节点竞选簇首成功后进行不均匀分簇,同时加入时间阈值来均衡簇首节点的能耗;最后,采用簇间多跳路由,根据节点剩余能量、与汇聚点距离计算网络能耗代价来构建最小生成树进行路由选择.在仿真实验中,该策略与经典的低功耗自适应分簇(LEACH)协议和能量高效的非均匀分簇(EEUC)算法比较,簇首能耗平均分别减少了45.1%和2.4%,网络生命周期分别延长了38%和3.7%.实验结果表明,RCS在大规模网络中能有效均衡整体网络能耗,显著延长了网络的生命周期.

Regional cluster-based lifetime optimization strategy for large-scale wireless sensor networks

In view of the characteristics of wide monitoring area and large number of sensors in large-scale monitoring systems like environment monitoring and power grid ice-disaster monitoring system, a Regional Cluster-based lifetime optimization Strategy for large-scale wireless sensor network (RCS) was proposed to save the network energy consumption and prolong the lifetime of the network. The strategy firstly used AGNES (Agglomerative Nesting) algorithm to divide the network into several subareas based on node location for optimizing the distribution of cluster heads. Secondly, uneven clusters would be conducted after cluster heads were generated, and a time threshold value was set to balance node energy consumption. Finally, for inter-cluster communication, a multi-hop routing was adopted by constructing minimum spanning tree on the basis of calculating network energy cost to balance the energy consumption of the cluster heads. In the simulation, compared with LEACH (Low Energy Adaptive Clustering Hierarchy) and EEUC (Energy-Efficient Uneven Clustering) algorithm, RCS respectively reduced the cluster head nodes' energy consumption by 45.1% and 2.4% on average; and respectively extend the network lifetime by 38% and 3.7%.The simulation results show that RCS can be more efficient to balance the overall network energy consumption, and significantly prolong the network lifetime.