Wireless Sensor Node Location based on IGWO-LSSVM

摘要

In view of the low node accuracy in wireless sensor node positioning, this paper proposes a node positioning algorithm based on the Improved Grey Wolf Optimization and Least Squares Support Vector Machine (IGWO-LSSVM). First, a wireless sensor positioning model in two-dimensional space is established. Then, the least squares support vector machine is used to model and locate unknown nodes. Finally, the least square vector machine parameters are optimized based on chaos mapping, adaptive factors, and the golden sine gray wolf algorithm to obtain node positioning. Simulation experiments show that compared with other algorithms, this algorithm has a better effect on the accuracy of node positioning.