Location Optimization Enhancement of Wireless Sensor Networks in Intelligent Transportation System Based on Reverse Elite Large Model

In Wireless Sensor Networks (WSNs), location technology is crucial for information sensing and processing. However, due to cost constraints, most sensor nodes are unable to obtain their own location information, making determining the location of unknown sensor nodes a critical issue. This paper aims to address three key issues in WSNs: accurate positioning accuracy, high positioning coverage, and minimizing energy consumption. Therefore, we model the coverage control problem of WSNs as a multi-objective programming problem and propose a Multi-Objective Whale Optimization Algorithm (MOWOA) based on reverse elite retention and Levy mutation. Our proposed algorithm combines reverse learning strategies and improves congestion calculation, adds learning rates and external solution set strategies, and combines a non-dominated sorting method based on reference points. Experimental results show that MOWOA can provide higher-quality non-dominated solutions, select suitable node locations, and optimize their performance metrics