Joint resource allocation algorithm based on multi-objective optimization for wireless sensor networks

被引:22
|
作者
Hao, Xiaochen [1 ]
Yao, Ning [1 ,2 ]
Wang, Liyuan [1 ]
Wang, Jiaojiao [1 ]
机构
[1] Yanshan Univ, Sch Elect Engn, Qinhuangdao 066004, Hebei, Peoples R China
[2] Beijing Jiaotong Univ, Dept HaiBin Coll, Huanghua 061199, Peoples R China
基金
中国国家自然科学基金;
关键词
Multi-radio multi-channel wireless sensor networks; Resource allocation; Graph coloring; Multi-objective optimization; Hybrid particle swarm optimization; VARIABLE NEIGHBORHOOD SEARCH; PARTICLE SWARM OPTIMIZATION; POWER-CONTROL; ENERGY EFFICIENCY; ASSIGNMENT; MULTIHOP;
D O I
10.1016/j.asoc.2020.106470
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the limitations of the network resources and battery energy of wireless sensors, the competition of resources in the process of communication will increase the network energy consumption and reduce the Quality of Service (QoS), resulting in that the application of Multi-Radio Multi-Channel (MRMC) Wireless Sensor Networks (WSNs) face many challenges. In this paper, we concentrate on the resource allocation of joint time slot assignment, channel allocation and power control for MRMC WSNs. Due to the diversity of research objectives and the computational complexity of the non-convex problem, this paper develops a two-stage resource allocation optimization algorithm by analyzing the interdependence of various resources. Specifically, to exchange information with conflict-free transmission among all sensors, a graph coloring algorithm for time slot assignment is designed firstly. Then based on the first stage of this algorithm, the problem of joint power control and channel allocation is studied and formulated as a multi-objective optimization problem to achieve the trade-off between energy efficiency and network capacity maximization under the constraints of link interference and load balance. Multi-objective hybrid particle swarm optimization is introduced to obtain the Pareto optimal solutions. The simulation results show that the proposed algorithm significantly performs better in terms of achieving the trade-off of multi-performance. (C) 2020 Elsevier B.V. All rights reserved.
引用
收藏
页数:15
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