Energy‐efficient optimisation for UAV‐aided wireless sensor networks. Issue 8 (1st May 2019)

• Record Type: Journal Article
• Authors: Hua, Meng; Wang, Yi; Zhang, Zhengming; Li, Chunguo; Huang, Yongming; Yang, Luxi
• Is Part Of: IET communications. Volume 13:Issue 8(2019)
• Page Start: 972
• Page End: 980
• Publication Date: 2019-05-01
• Subjects: remotely operated vehicles -- convex programming -- concave programming -- autonomous aerial vehicles -- wireless sensor networks -- iterative methods -- optimisation; SNs -- formulated problem -- nonconvex fractional optimisation problem -- original problem -- successive convex optimisation technique -- optimal UAV time allocation -- given UAV trajectory constraints -- given UAV time allocation -- energy‐efficient optimisation -- UAV‐aided wireless sensor networks -- novel unmanned aerial vehicle‐based wireless sensor network -- flying base station -- sensor nodes -- system energy efficiency; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2018.5441
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : This study investigates a novel unmanned aerial vehicle (UAV)‐based wireless sensor network, where the UAV acts as a flying base station to serve multiple wireless sensor nodes (SNs). The authors goal is to maximise the system energy efficiency of the UAV while satisfying the fairness among SNs by jointly optimising the UAV trajectory and UAV time allocation. The formulated problem is shown to be a non‐convex fractional optimisation problem, which is hard to tackle. To this end, they decompose the original problem into two sub‐problems, and the block coordinate descent method and successive convex optimisation technique are employed to solve these two sub‐problems iteratively. Specifically, in the first sub‐problem, the optimal UAV time allocation is obtained by maximising the minimum achievable rate of SNs with given UAV trajectory constraints. In the second sub‐problem, the UAV trajectory is achieved by minimising the energy consumption of the UAV with the given UAV time allocation. Subsequently, an iterative algorithm is proposed to optimise the time allocation and UAV trajectory alternately. Furthermore, the convergence and complexity of their proposed algorithm are provided. Numerical results show that the proposed scheme outperforms the existing benchmark strategies in terms of energy efficiency.