Resource allocation optimization algorithm based on double populations differential evolution in WSN

Xiaochen HAO; Liyuan WANG; Jinshuo LIU; Lixia XIE; Wenhuan ZHANG

doi:10.11959/j.issn.1000-436x.2018061

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Resource allocation optimization algorithm based on double populations differential evolution in WSN

Papers | 更新时间：2024-06-05

- Resource allocation optimization algorithm based on double populations differential evolution in WSN
- Journal on Communications Vol. 39, Issue 4, Pages: 68-75(2018)
- 作者机构：
  
  燕山大学电气工程学院，河北秦皇岛 066004
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61403336);The Natural Science Foundation of Hebei Province(F2015203342);The Independent Research Project for Young Teacher of Yanshan University(15LGB007)
- DOI：10.11959/j.issn.1000-436x.2018061
  CLC： TP393
- Online First：2018-04，
  
  Published：25 April 2018
- 稿件说明：
移动端阅览
Xiaochen HAO, Liyuan WANG, Jinshuo LIU, et al. Resource allocation optimization algorithm based on double populations differential evolution in WSN[J]. Journal on Communications, 2018, 39(4): 68-75.
DOI：

Xiaochen HAO, Liyuan WANG, Jinshuo LIU, et al. Resource allocation optimization algorithm based on double populations differential evolution in WSN[J]. Journal on Communications, 2018, 39(4): 68-75. DOI： 10.11959/j.issn.1000-436x.2018061.

摘要

针对多射频多信道（MRMC

multi-radio multi-channel）无线传感器网络中的链路冲突和链路干扰过大而导致的网络能耗过大、容量受限、资源分配不均衡的问题，提出一种基于双群体差分进化的联合资源分配优化算法（RADEA）。RADEA 综合考虑了信道分配、功率控制和时隙分配之间相互影响的关系，以链路的冲突和干扰为约束条件，以减小网络能耗、最大化网络容量、提高资源分配的均衡性为目标函数，构建了系统的资源分配多目标优化模型。考虑到解决多目标优化问题的复杂性，采用双群体差分进化算法对模型进行迭代求解。仿真实验表明，该算法能够有效地避免链路冲突，同时能有效地降低网络干扰，提高网络容量和资源分配均衡性。

Abstract

A resource allocation algorithm was proposed for improving the network performance through jointing channel allocation

power control and timeslot allocation in multi-radio multi-channel wireless sensor network.More specifically

the network was modeled as a multi-objective optimization problem where the energy efficient

resource allocation balanced

networks capacity maximized were considered under the link interference and link conflict constraints.Due to the problem was NP-Hardness

a simple centralized algorithm——differential evolution based on double populations was used to solve the constrained multi-objective optimization problem.The simulation results show that the proposed algorithm significantly improves the network capacity and energy efficiency and guarantees the resource allocation balancing while reducing link interference and avoiding link conflict.

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Keywords

references

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