RSSI weighted centroid location algorithm optimized by self-adaptive grey wolf optimizer algorithm

In the localization process of wireless sensor network nodes, the number of neighboring anchor nodes can affect the localization accuracy of unknown nodes. Therefore, a self-adaptive grey wolf algorithm optimized RSSI weighted centroid location (AD-GWO-RSSI) algorithm was proposed. Firstly, the algorithm calculated the average number of anchor nodes for all unknown nodes and the number of neighboring anchor nodes for each unknown node based on the connectivity relationship of the sensor network. Secondly the average value of the two was used as the threshold of the GWO algorithm to optimize the RSSI weighted centroid positioning process. Finally, the optimized high-precision localization node was used as a pseudo-anchor node to locate other unknown nodes. The experimental results show that the GWO Algorithm-based RSSI weighted centroid localization algorithm, which obtains an adaptive threshold through the number of neighboring anchor nodes, achieves high localization accuracy, and has strong generalization and stability.