Unmanned Aerial Vehicle-Assisted Sparse Sensing in Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) suffer from enormous energy consumption when sensor nodes transmit data over long distances. Hence, this letter develops an Unmanned Aerial Vehicle (UAV)-assisted sparse sensing algorithm to address this issue. Specifically, the data from the sensor nodes are locally stored and are sparsely sampled through Compressive Sensing (CS), while a UAV is utilized as a mobile base station to collect data from the partial sensor nodes. In the proposed UAV and WSN system, the energy consumption minimization problem is formulated as a combinatorial optimization problem, which is solved utilizing a joint sparse sensing and Greedy algorithm based on a Neighborhood Search Strategy (GNSS). Extensive experiments demonstrate that the proposed algorithm achieves a sparse measurement with a smaller error and decreases energy consumption.