大规模WSN协同检测的节点临界密度

以移动目标的协同检测概率为网络覆盖的评价指标，提出了一种面向移动目标协同检测的大规模无线传感器网络（WSN）在二维平面上随机撒播节点归一化密度和检测概率三角形分析模型（TAM），采用归一化和二项式随机分布分析方法,得到了协同节点数为3、目标归一化路径长度小于1时最坏情况的节点归一化临界密度（NNCDT）上限；并分析了节点归一化密度对网络协同检测性能的影响。根据仿真结果分析了目标移动速度和传感器的检测时间和判决门限对NNCDT影响规律，结果表明TAM的NNCDT可以较为准确地被协同检测概率和归一化路径长度所确定，可为节点实际部署提供参考。

Critical Node Density Thresholds for Collaborative Detection in Large Scale WSN

Taking collaborative detection probability of mobile targets as coverage evaluating index in large-scale wireless sensor networks（WSN）, a triangular analytical model（TAM）is proposed to analyze the normalization density of randomly deployed nodes in a 2-dimensional region. Binomial distribution and normalization methods are used to calculate the worst-case upper bounds on the nodes normalized critical density threshold (NNCDT) with 3 collaborative sensors and normalized path length (NPL) less than 1. The effects of sensor nodes normalized density on the collaborative detection performance are discussed. The influences of mobile targets speed, sensor’s measurement time and decision threshold on NNCDT are analyzed. Simulations show that NNCDT can be exactly determined by the collaborative detection probability and NPL, and can provide a reference for determination of sensors density needed for certain performance in random deployment.