Analysis of Data Harvesting by Unmanned Aerial Vehicles
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This paper explores an emerging wireless architecture based on Unmanned Aerial Vehicles (UAVs), i.e., drones. We consider a network where UAVs at fixed altitude harvest data from Internet-of-Things (IoT) devices on the ground. In such a system, the UAVs' motion activates IoT uplink transmissions and so the motion triggers the interference field and determines the network performance. To analyze the performance, we propose a stochastic geometry model. The coverage area of each UAV, referred to as the activation window, is modeled for simplicity as a rectangle where at most one IoT device is scheduled to transmit at a time. In this setting, we analyze the signal-to-interference and data rate from two typical perspectives, namely from a typical UAV's and from a typical IoT device's points of view. Our stochastic geometry model enables us to explore the size of the activation window which maximizes the UAV networks' harvesting capacity. Finally, we present a network extension of the proposed model and derive the network performance.
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