Unmanned Aerial Vehicle for Sparse Sampling in Wireless Sensor Networks

Abstract

In Wireless Sensor Networks, sensory data among adjacent sensor nodes have strong spatial correlation. If all sensor nodes collect and transmit data, energy consumption is very large and the network life is shortened. To address this issue, we put forward an Unmanned Aerial Vehicle (UAV)-assisted sparse sampling algorithm. In this algorithm, Compressive Sensing is applied for sparse sampling, and the UAV is utilized as a mobile relay to collect the sensory data of partial nodes. To reduce energy consumption for data transmission, we combine greedy algorithm with the rules of genetic algorithms to plan the route of the UAV. The numerical results given by the simulations indicate that the proposed algorithm has less energy consumption than other baseline algorithms, and can better recover a great number of raw data by the fewer measurements.