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DADCA: An Efficient Distributed Algorithm for Aerial DataCollection from Wireless Sensors Networks by UAVs

Unmanned Aerial Vehicles (UAVs) are increasingly used as data collectors for Wireless Sensors Networks (WSN) on the ground. Most current research proposes optimizations for the itinerary creation of a single UAV. Our work, however, proposes a distributed algorithm for collecting WSN data using a dynamic set of UAVs. The algorithm takes into account the fact that UAVs could leave or join the group due to either recharging requirements or malfunctions. In our work we also consider the fact that UAVs only have medium-range communication capabilities (a few meters) in order to deliver collected data. Compared to the computationally costly and non-real-time Traveling Salesman Problem (TSP) approach, our algorithm delivers approximately 13% more efficient sensory visiting at certain scenarios without using the optimized tour.