Multiple UAV Path-Planning for Data Collection in Cluster-based Wireless Sensor Network

In a multiple UAV-based data collection from the cluster-based wireless sensor network, selection of an optimal set of load-balanced cluster heads and calculation of optimal path for each Unmanned Aerial Vehicles(UAV) are very challenging research issues. Most of the existing cluster head selection schemes do not consider the uniform distribution of cluster heads and also do not consider delay incurs in data collection. Thus, in this paper, the Salp-Swam optimization method is used for the selection of an optimal set of load-balanced Cluster Heads(CHs) and Differential Evolution(DE) based meta-heuristic is used for estimation of delay efficient optimal path for each UAV. In this paper, multiple-UAV is deployed for data collection task in order to reduce data collection time. All UAVs start its journey from the base station, collect data from its cluster heads and return back to the base station. The performance of the proposed scheme is compared with two well-known existing schemes in a different network scenario in terms of travel distance and travel time. Through simulation results, it is observed that the proposed scheme outperforms the existing schemes with respect to the travel time.