CEDAN: Cost-Effective Data Aggregation for UAV-Enabled IoT Networks

One of the crucial challenges in networked Unmanned Aerial Vehicles (UAVs) is to configure them to serve as aerial base stations (BSs) for collecting data from distributed Internet of Things (IoT) devices in a region devoid of backbone connectivity. To address this challenge, it is required to compute optimized trajectories of UAVs to collect data while considering the different activation patterns of IoT devices. We propose a scheme to optimize the trade-off between the number of covered IoT devices and the travel time of UAVs. The formulated cost minimization problem is the capacitated single depot vehicle routing problem (CSDVRP), which is NP-hard. We propose a solution scheme named Cost-Effective Data Aggregation for UAV-Enabled IoT Networks (CEDAN), which operates in four steps. First, it determines the optimized hovering locations (HLs) for UAVs. Subsequently, CEDAN determines the optimized route adopting Christofides’s approximation algorithm for Travelling Salesman Problem (TSP). Further, a split function produces the optimized trajectories for all UAVs. Finally, a route adjustment algorithm applies the cost function and rearranges the order of visiting each HL. Extensive simulation results depict that the CEDAN outperforms than Clarke-Wright (CW) savings heuristics, CEDAN without route adjustment (CWRA), and Zhan
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