An Age-Based Data Collection and Path Planning Algorithm in UAV-Assisted Wireless Sensor Networks

Abstract

In view of the importance of Age of Information (AoI) in delay sensitive applications of Wireless Sensor Networks (WSNs), an improved gray wolf algorithm (POPAGA) based on the combination of particle swarm optimization possibility fuzzy C-mean clustering is proposed. POPAGA is optimized from the clustering stage and the path planning stage. In the clustering stage, the particle swarm optimization algorithm is first used to optimize the possibility fuzzy hybrid clustering algorithm, which not only overcomes the problem that the fuzzy C-means is sensitive to the initial clustering center, but also avoids the poor initialization effect of the possibility fuzzy c-means clustering, so as to determine the Hovering Collection Data points (HCD) and their associated Sensor Nodes (SNs). In the path planning stage, based on the hover collection data points obtained in the previous stage, the improved gray wolf optimization algorithm (GWO) is used to find the optimal path to minimize the maximum AoI and the average AoI. The simulation results show that POPAGA can obtain the global minimum AoI optimal value, whether compared with the traditional genetic algorithm (GA) and simulated annealing algorithm (SA) for solving TSP problem, or compared with the genetic algorithm (GA) and greedy algorithm based on AoI.