基于改进蝙蝠算法的无线传感器网络动态任务调度
Dynamic Task Scheduling for Wireless Sensor Networks Based on an Improved Bat Algorithm
翟羽婷 1王欣 1白蕾2
作者信息
- 1. 张家口学院数学与信息科学学院,河北 张家口 075000
- 2. 北华大学工程训练中心,吉林 吉林 132021
- 折叠
摘要
无线传感器网络是一种处理感知信息的无线网络,在处理过程中因节点分配不均,导致网络资源利用率较低、任务调度中节点能量消耗较高.为此,提出了基于改进蝙蝠算法的无线传感器网络动态任务调度方法.通过增加权值系数改进蝙蝠算法,降低无线传感器网络节点密度,定义二进制变量,确定任务分配节点.采用可分负载理论,计算两种分群结构网络环境下任务运行时间与传输时间,得到群内和群间阶段无线传感器网络节点动态任务调度方案.仿真结果表明:所提算法在迭代次数为 200 次时即可获取目标函数值,当虚拟机数量为 1 200 时,所需无线传感器网络节点数量为 38 个,网络资源利用率始终高于 68%,说明所提方法能够有效降低调度过程中节点的能量消耗,提高网络资源利用率.
Abstract
Wireless sensor network is a kind of wireless network that processes sensing information.Due to uneven node allocation in the process of processing,the network resource utilization rate is low,and the node energy consumption in task scheduling is high.There-fore,a dynamic task scheduling method for wireless sensor networks based on improved bat algorithm is proposed.The bat algorithm is improved by increasing the weight coefficient,reducing the node density of wireless sensor networks,defining binary variables,and de-termining task allocation nodes.According to the separable load theory,the task running time and transmission time under two kinds of clustering structure network environment are calculated,and the dynamic task scheduling scheme of wireless sensor network nodes in the intra group and inter group stages is obtained.The simulation results show that the proposed algorithm can obtain the objective function value when the number of iterations is 200.When the number of virtual machines is 1 200,the required number of wireless sensor net-work nodes is 38,and the network resource utilization rate is always higher than 68%,demonstrating that the proposed method can effec-tively reduce the energy consumption of nodes in the scheduling process and improve the network resource utilization rate.
关键词
无线传感器网络/动态任务调度/改进蝙蝠算法/二进制变量/可分负载理论/权值系数Key words
wireless sensor network/dynamic task scheduling/improved bat algorithm/binary variables/separable load theory/weight coefficient引用本文复制引用
基金项目
吉林省教育厅科学技术研究规划课题(十三五)(JJKH20180339KJ)
出版年
2024
国家科技期刊平台
NSTL
万方数据