Optimal path planning for UAV using NSGA-II based metaheuristic for sensor data gathering application in Wireless Sensor Networks

摘要

Data Collection from the remote or very callous environments is very challenging issue in wireless sensor network. In a remote or very callous environment, when sensor nodes are randomly deployed, it is observed that all sensor nodes are not fully connected. Therefore, to facilitate an efficient data collection from remote sensing region, application of Unmanned Arial Vehicles (UAVs) has been mulled over with the wireless sensor networks. This paper considers optimal path planning problem for UAV using NSGA-II-based meta-heuristic algorithm. In the proposed scheme, first Depth-First-Search (DFS) algorithm is used for selecting the best sequence of flying cells to be visited by a UAV for data collection. After selection of the best sequence of flying cells, NSGA-II-based meta-heuristic algorithm is used for computing the optimal path of the UAV for covering the all designated flying cells within the network. This paper derives a novel fitness function for judging the optimal path for UAV. Simulation results of the proposed scheme and its comparisons with the existing GA-based scheme are done using a custom simulator in Python 3.