摘要
针对传统能量优化方法没有进行节点分簇处理,导致可配置能量低、能量配置效率低的问题,提出基于粒子群算法的物联网可配置服务能量优化方法。根据物联网一阶无线电模型计算可配置服务所需要消耗的能量,利用惯性权重和学习因子改进传统粒子群算法,解决其容易陷入局部最优的问题。采用改进后的粒子群算法获取合理的适应度函数,通过最小生成树模型获得连通图的权重,得到可配置服务传输数据的最优路径。根据最优路径上节点之间的簇头竞争,完成簇头选择和节点分簇处理,实现物联网可配置服务能量的优化。仿真结果表明:研究方法的可配置能量高、能量配置效率更高,实用性强。
In order to solve the problems of low configurable energy and low energy configuration efficiency caused by the traditional method without node clustering,this article presented a method of configurable service energy optimization in the Internet of things based on particle swarm optimization algorithm.According to the first-order radio model of the Internet of things,the energy consumed by configurable service was calculated.The traditional particle swarm optimization algorithm was improved by inertia weights and learning factors,so that the problem that the algorithm was easy to fall into local optimization can be solved.The improved particle swarm optimization algorithm was adopted to get the reasonable fitness function.The weight of connected graph was obtained by the minimum spanning tree model,and the optimal path of configurable service data transmission was obtained.According to the cluster head competition among nodes on the optimal path,the cluster head selection and the node clustering were completed to optimize the configurable service energy of the Internet of things.Simulation results show that the proposed method has higher configurable energy and allocation efficiency,so its practicability is stronger.
作者
赵瑞玉
席兵
ZHAO Rui-yu;XI Bing(College of Mobile Telecommunications Chongqing University of Posts and Telecom,Chongqing 401520,China)
出处
《计算机仿真》
北大核心
2021年第8期360-363,411,共5页
Computer Simulation
基金
重庆市教育委员会科学技术研究项目(KJQN20190240)。
关键词
粒子群优化
物联网
可配置服务
能量优化
Particle swarm optimization algorithm(PSO)
Internet of things(IoT)
Configurable services
Energy optimization
