高密度无线传感器网络分簇定位算法

Cluster localization scheme for high-density wireless sensor networks

节点自身定位是无线传感器网络应用的支撑技术之一。提出了一种适用于大规模高密度无线传感器网络的分簇定位算法。首先定义了节点的势作为簇首选举依据,网络中节点间的距离由接收信号强度和通信半径的关系间接计算得到,各簇内的拓扑信息由簇首保存,簇首利用线性规划法实现簇内相对定位;随后从sink节点开始逐步进行簇间位置融合,最终实现全网的绝对定位。相比集中式的凸规划定位算法,所提算法计算复杂度低、通信量小、定位精度高,且不需要预先知道环境中的信号衰减因子,有一定的抗噪声干扰能力。仿真结果显示,在节点按均匀网格分布和均匀随机分布两种情况下,所提算法能取得较好的定位效果。

The node self-localization is one of the supporting technologies in wireless sensor networks. A distributed cluster localization scheme （linear programming-cluster localization scheme, LP-CLS） is introduced, which is based on linear programming. First, the authority of node is defined as the criterion for voting cluster and the distance between neighbor nodes is estimated by the relation between received signal strength indicator and communication range. Then, according to linear programming, the relative coordinates of nodes in a cluster are calculated out by the relevant cluster node. Finally, starting from sink node, coordinates between neighbor clusters are combined and the whole relative coordinates are converted to absolute coordinate. The simulation results indicate that LP-CLS outperforms Convex significantly in the aspect of localization accuracy and algo- rithm complication whether nodes are placed regularly or randomly.