Open AccessEnergy‐efficient optimisation for UAV‐aided wireless sensor networks
Author(s) -
Hua Meng,
Wang Yi,
Zhang Zhengming,
Li Chunguo,
Huang Yongming,
Yang Luxi
Publication year - 2019
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2018.5441
Subject(s) - computer science , coordinate descent , benchmark (surveying) , trajectory , wireless sensor network , energy consumption , base station , mathematical optimization , convergence (economics) , trajectory optimization , wireless , energy (signal processing) , efficient energy use , block (permutation group theory) , real time computing , algorithm , mathematics , computer network , optimal control , telecommunications , engineering , statistics , physics , geometry , geodesy , astronomy , economic growth , electrical engineering , economics , geography
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.