一种无线传感器网络健壮性可调的能量均衡拓扑控制算法

无线传感器网络中, 应用环境的干扰导致节点间距不能被准确度量. 所以利用以节点间距作为权重的闭包图(EG)模型构建的拓扑没有考虑环境的干扰, 忽略了这部分干扰带来的能耗, 缩短了网络生存时间. 针对无线传感器网络拓扑能量不均的特点和EG模型的缺陷, 首先引入节点度调节因子, 建立通信度量模型和节点实际生存时间模型; 其次量化网络节点度, 从而获取满足能量均衡和网络生命期最大化需求的节点度的取值规律; 然后利用该取值规律和函数极值充分条件解析推导出网络最大能量消耗值和最长生存时间, 并获得最优节点度; 最后基于以上模型提出一种健壮性可调的能量均衡拓扑控制算法. 理论证明该拓扑连通且为双向连通. 仿真结果说明网络能利用最优节点度达到较高的健壮性, 保证信息可靠传输, 且算法能有效平衡节点能耗, 提高网络健壮性, 延长网络生命周期.

Energy balance and robustness adjustable topology control algorithm for wireless sensor networks

In wireless sensor networks, the interference around the application environment may cause the actual distance between any pair of nodes to fail to be measured accurately. Enclosure graph (EG) model uses this distance between nodes as its weight to construct the topology, which does not fully consider the interference. Consequently it will lead to a large amount of energy consumption induced by the application environment. Even it shortens the survival time. According to the feature of network energy inequality in a wireless sensor network and the defect of EG, we first introduce the adjustable factor of node degree, establish a model of communication metric and a model for the node actual survival time. Then according to the demand of network energy equalization and maximum network lifetime, we quantitatively analyze the network node degree, and achieve its regular pattern. In accordance with this regular pattern and sufficient conditions of function extremum, the maximum node energy consumption and the maximum node actual survival time are deduced. And the corresponding optimal node degree is achieved. Finally, according to the above model, in this paper we propose an energy balance and robustness adjustable topology control algorithm for wireless sensor networks. Theoretical analyses show that this algorithm can guarantee that the network is connected and the link of the network is bi-directionally connected. Experiments show that the network takes advantage of this optimal node degree to obtain the high robustness, thus guaranteeing that the information can be transferred unfailingly. This algorithm can effectively balance the node energy, improve the node survival time, enhance the network robustness, and prolong the network's lifetime.