A Study of Wireless Sensing Multi-Mobile Sink Path Planning Based on Improved AOA

Abstract

Wireless Sensor Networks are essential for real-time data collection in disaster response, military surveillance, and environmental monitoring. Each sensor detects environmental parameters and sends data to a base station (BS) or sink. Using multiple mobile sinks (MSs) based on clustering algorithms is more efficient than static sink-based methods, especially in scenarios where the number of sensor nodes (SNs) is large. This scheme can improve path planning. This paper presents a Multi-mobile Sink Path planning algorithm with Dual Clustering (MSPDC) to improve rescue efficiency in earthquake scenarios. The algorithm combines the K-means algorithm, improved Density Peak Clustering (DPC), and an improved Arithmetic Optimization Algorithm (AOA). First, we use the K-means algorithm to pinpoint the cluster centers, which serve as rendezvous points (RPs), and adjust them for better coverage. Secondly, we apply an improved DPC algorithm to cluster these RPs, further increasing coverage. Finally, we use an improved AOA for 3D path planning. By calculating the average Euclidean distance between each cluster center and its RPs, we pick the center with the smallest distance as the best starting spot for our robots. As the algorithm searches for paths, it cleverly adapts its step size based on the density of obstacles in the environment, dynamically adjusting arithmetic operators and setting a safe distance to avoid obstacles. Algorithm simulation analysis shows that the improved AOA, compared with the traditional AOA, the Bat Algorithm, the Improved Grey Wolf Optimization Algorithm, the Genetic Algorithm, and the Whale Optimization Algorithm, can search for the shortest path more quickly. It significantly shortens the total path traversal time, reduces the number of turns, and improves the success rate of obstacle avoidance in complex environments. The MSPDC proposed in this paper demonstrates stronger applicability and reliability in complex scenarios such as earthquake rescue.