Charging UAV deployment for improving charging performance of wireless rechargeable sensor networks via joint optimization approach

Abstract Wireless power transfer based on charging unmanned aerial vehicles (CUAVs) is a promising method for enhancing the lifetime of wireless rechargeable sensor networks (WRSNs). However, how to deploy the CUAVs so that enhancing the charging efficiency is still a challenge. In this work, we formulate a CUAV deployment optimization problem (CUAVDOP) to jointly increase the number of the sensor nodes that within the charging scopes of CUAVs, improve the minimum charging efficiency in the network and reduce the motion energy consumptions of CUAVs. Moreover, the formulated CUAVDOP is analyzed and proven as NP-hard. Then, we propose an improved firefly algorithm (IFA) to solve the formulated CUAVDOP. IFA introduces three improved items that are the opposition-based learning model, attraction model and adaptive step size factor to enhance the performance of conventional firefly algorithm, so that making it more suitable for solving the formulated CUAVDOP. Simulation results demonstrate that the proposed algorithm is effective for dealing with the formulated joint optimization problem. Moreover, the superiority of IFA is verified by tests.