Open AccessDistributed resource allocation optimisation algorithm based on particle swarm optimisation in wireless sensor network
Author(s) -
Hao Xiaochen,
Yao Ning,
Wang Jiaojiao,
Wang Liyuan
Publication year - 2020
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.2020.0368
Subject(s) - computer science , particle swarm optimization , wireless sensor network , energy consumption , mathematical optimization , resource allocation , topology control , power control , wireless network , channel (broadcasting) , wireless , distributed computing , computer network , power (physics) , key distribution in wireless sensor networks , algorithm , engineering , telecommunications , mathematics , physics , quantum mechanics , electrical engineering
This study concentrates on the optimal resource allocation problem in a wireless sensor network with rare spectrum resources and improper topology structure. To explore the interdependence of various resources and achieve better anti‐interference property, the authors analyse the problem of joint power control and channel allocation based on bit error rate (BER) model and energy consumption model. Specifically, low‐energy consumption and BER are both crucial design objectives for a number of multi‐hop wireless network applications with constrained network resources and battery‐powered sensors. As these two objectives that influenced by power control and channel allocation are conflicting with each other, it becomes important to achieve the trade‐off between them. Aiming at the aforementioned problem, this study formulates a multi‐objective optimisation model to minimise BER and energy consumption under the constraints of link interference, link capacity, and network connectivity. On the basis of this model, they propose a distributed resource allocation optimisation algorithm based on particle swarm optimisation (DRAPSO) to achieve Pareto optimal solutions. Furthermore, the information complexity and time complexity of DRAPSO are theoretically analysed. The simulation results show that DRAPSO can effectively increase network capacity, decrease energy consumption, and BER. Besides, the trade‐off of multi‐performances can be significantly achieved.