无线传感器网络精度优选RSSI协作定位算法

针对目前无线传感器网络（WSN）定位算法中未知节点间接收信号强度指示（RSSI）冗余信息利用不足以及信息无筛选利用问题，提出一种新的精度优选RSSI协作定位算法。首先，利用RSSI阈值，从大量粗定位的未知节点中筛选出定位精度相对较高的节点；接着，利用subset子集判断方法从经过RSSI阈值筛选的节点中提取出受环境影响较小的节点，作为次选协作骨干节点；然后，使用锚节点置换准则，根据置换锚节点的定位误差，从次选协作节点中进一步提取出高精度的节点作为优选协作骨干节点；最后，以协作骨干节点为协作对象，根据精度优先级参与协作求精，对未知节点进行未知修正。仿真实验表明，该算法在100 m×100 m网格区域内的平均定位精度小于1.127 m。在定位精度方面，相同条件下，相较于改进的采用RSSI模型的无线传感器网络定位算法，该算法平均定位精度提高了15%；在时间效率方面，相同条件下，对比传统RSSI协作定位算法，该算法在时间效率上提高了20%。可见，所提算法可以有效提高节点定位精度，减小计算复杂度，提高时间效率。

RSSI collaborative location algorithm of selecting preference accuracy for wireless sensor network

Concerning insufficient and blind use of the Received Signal Strength Indicator (RSSI) information among unknown nodes, a new RSSI collaborative location algorithm of selecting preference accuracy for Wireless Sensor Network (WSN) was proposed. Firstly, the nodes with high locating accuracy were selected from coarsely located unknown nodes based on the RSSI thresholds. Secondly, subset judgment method was used to seek out the unknown nodes which were less affected by the environment as the second collaboration backbone nodes. Then, based on the positioning errors of the anchor nodes, anchor node replacement criterion was used to further extract the high-precision node from the secondary selected cooperative nodes as the optimal cooperative backbone nodes. Finally, the collaborative backbone nodes were used as the cooperative objects, and the unknown nodes were modified according to the precision priorities. In the simulation experiments, the average localization accuracy of the proposed algorithm was within 1.127 m in 100 m*100 m grids. In terms of locating accuracy, the average locating accuracy of the proposed algorithm is improved by 15% compared with the improved WSN locating algorithm using RSSI model. In terms of time efficiency, compared with the traditional RSSI collaborative location algorithm, the proposed algorithm improves the time efficiency by 20% under the same condition. It can be seen that the proposed algorithm can effectively enhance the locating accuracy, reduce computational complexity and improve time efficiency.