Unmanned Aerial Vehicles Data Gathering Scheme Based on Sparse Sampling

In wireless sensor network (WSN) deployment scenarios such as natural disasters and accident sites that lack conditions for establishing base stations, successfully collect and transmit regional environmental data such as disasters and accidents is challenging. To realize reliable and energy-saving data collection and transmission, this work proposes a novel unmanned aerial vehicles (UAVs) data gathering scheme based on sparse sampling for efficient WSN communication. To improve the efficiency of UAV data collection, instead of optimizing the hovering position and the flight trajectories of UAV, the proposed scheme achieves a balance between intra-cluster distance and the UAV flight path length by selecting the most reasonable number of clusters. To reduce WSN energy consumption, instead of reducing the transmission distance between nodes and UAV, a sparse sensing method is adopted to reduce the frequency and quantity of data transmission. Simulation experiments demonstrate that the proposed scheme can greatly extends the life of the sensor network while ensuring the successful transmission of sensing data to the ground command center.