层次分簇的无线传感网络多级优化测量

Multi-step Optimized Measurement in Hierarchically Clustered Wireless Sensor Networks

层次化的无线传感网络由骨干传感节点和普通传感节点组成。由于节点能量受限,无线传感网络跟踪测量目标时需同时考虑目标跟踪精度和跟踪方法的能效性。提出一种层次分簇的多级优化无线测量方法。将骨干节点作为簇首,采用粒子滤波算法预测运动目标位置,运用DELAUNAY三角剖分优化选择目标附近的节点作为测量节点,并根据测量节点地理位置判断是否转移簇首。在测量节点同步的感知目标后,簇首利用熵来逐次选择能效性最高的测量节点数据进行融合,实现目标定位测量。试验表明,该方法能满足目标跟踪精度,并可有效的减少网络能耗,提高无线传感网络测量使用寿命。

Hierarchical wireless sensor networks （WSNs） consist of backbone nodes and normal sensor nodes. Because of limited energy resource, tracking targets in WSN should consider both the tracking accuracy and energy consumption. A multi-step optimized measurement approach based on dynamic clustering is proposed. The backbone node implements particle filter to predict target locations, and selects normal sensor nodes around the target to be measurement sensors based on DELAUNAY triangulation. It then judges whether the cluster head role needs to be transferred. When the measurement is done, the backbone node selects the sensor with the most energy-efficiency, which is computed by using entropy, to localize the target step by step. The experiments verify that the proposed approach can well satisfy the target tracking accuracy, and reduce energy consumption efficiently.