吉林大学学报(理学版) ›› 2023, Vol. 61 ›› Issue (4): 922-928.

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基于改进遗传算法的物联网链路负载均衡控制方法

景雯1, 张杰1, 傅文博1, 陈富2   

  1. 1. 山西大同大学 计算机与网络工程学院, 山西 大同 037009; 2. 山西大同大学 数学与统计学院, 山西 大同 037009
  • 收稿日期:2022-10-24 出版日期:2023-07-26 发布日期:2023-07-26
  • 通讯作者: 景雯 E-mail:jingwen@sxdtdx.edu.cn

Load Balancing Control Method of IoT Link Based on Improved Genetic Algorithm

JING Wen1, ZHANG Jie1, FU Wenbo1, CHEN Fu2   

  1. 1. School of Computer and Network Engineering, Shanxi Datong University, Datong 037009, Shanxi Province, China;
    2. School of Mathematics and Statistics, Shanxi Datong University, Datong 037009, Shanxi Province, China
  • Received:2022-10-24 Online:2023-07-26 Published:2023-07-26

摘要: 针对链路负载控制受物联网搜索空间的影响, 搜索空间过小会降低负载均衡度的问题, 提出一种基于改进遗传算法的物联网链路负载均衡控制方法. 首先, 构建物联网链路的频带传输模型, 利用抽头间隔采样控制物联网链路传输, 建立物联网链路频带模型获得均衡调度函数, 整合频带完成负载均衡配置; 其次, 加入分数间隔均衡设计链路, 用频带分配原则得到频带匹配概率, 调节均衡器的抽头数值, 设置链路码间干扰项约束; 再次, 给出遗传算法的参数编码, 把所有请求都按一维顺序排列, 对适应度函数进行线性尺度转换, 完成遗传算法的改进; 最后, 组合基因进化染色体, 扩展物联网搜索空间, 令迭代数量小于最大系数, 实现链路传输负载的均衡控制. 实验结果表明, 该方法能较好控制物联网链路负载均衡, 链路负载均衡度可达92%, 并且能减少能量消耗.

关键词: 改进遗传算法, 物联网链路, 负载均衡控制, 频带分配, 适应度函数

Abstract: Aiming at the problem that the control of link load was affected by the search space of the Internet of Things, a small  search space could reduce the load balancing degree, we proposed a load balancing control method of the Internet of Things  link based on  improved genetic algorithm. Firstly, the frequency band transmission model of the Internet of Things link was constructed, and tap interval sampling was used to control the transmission of the Internet of Things link, the frequency band model of the Internet of Things link was established  to obtain the balanced scheduling function, and the frequency band was integrated to complete the load balancing configuration. Secondly, we added  fractional interval equalization to design the link, used the frequency band allocation principle to obtain the frequency band matching probability, adjusted the tap value of the equalizer, and set the inter symbol interference term constraint of the link. Thirdly, we gave the parameter code of genetic algorithm, arranged all requests  in one-dimensional order, and  transformed  linear scale on the fitness function to complete the improvement of genetic algorithm. Finally, we combined  gene evolution chromosomes to expand the search space of the Internet of Things, made the number of iterations less than the maximum coefficient, and realized the balancing control of link transmission load. The experimental results show that the proposed method can effectively  control the load balancing of the Internet of Things link, the link load balancing degree can reach 92%, and can reduce energy consumption.

Key words: improved genetic algorithm, Internet of Things link, load balancing control, frequency band allocation, fitness function

中图分类号: 

  • TP391