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论文:2022,Vol:40,Issue(6):1394-1403 |
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引用本文： |
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朱鹏, 任继军, 任智源. 基于路径计算方法的WSN时延优化研究[J]. 西北工业大学学报 |
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ZHU Peng, REN Jijun, REN Zhiyuan. WSN latency optimization based on path calculation method[J]. Journal of Northwestern Polytechnical University |
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| 基于路径计算方法的WSN时延优化研究 |
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| 朱鹏1, 任继军1, 任智源2 |
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1. 西安邮电大学 通信与信息工程学院, 陕西 西安 710121;
2. 西安电子科技大学 综合业务网理论及关键技术国家重点实验室, 陕西 西安 710071 |
| 摘要: |
| 随着物联网(IoT)行业的快速发展,无线传感器网络(WSN)融合云计算技术面临着任务处理时延高、传感器节点能量有限的挑战。因此,提出了一种基于云雾网络架构的路径计算方法,利用雾计算层的网络边缘设备计算资源,将WSN监测任务合理地部署到指定边缘设备上完成处理,以减少能耗制约下的任务处理时延。为了将任务有效地分配到雾计算层,采用了一种任务映射规则,将有向无环图表示的监测任务映射到无向图表示的雾计算层网络;结合时延和能耗约束建立了一个关于寻求最优映射关系的二值优化问题;采用模拟退火-离散二值粒子群优化(SA-BPSO)算法实现了对该优化问题的求解。仿真结果显示,在数据量为10 Mb时,该方法的时延性能相比较WSN融合云计算技术提高了约40%。 |
| 关键词:
无线传感器网络
路径计算
云雾网络架构
模拟退火-离散二值粒子群优化
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| WSN latency optimization based on path calculation method |
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| ZHU Peng1, REN Jijun1, REN Zhiyuan2 |
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1. School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China;
2. State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China |
| Abstract: |
| With the rapid development of the Internet of Things (IoT) industry, wireless sensor network (WSN) fusion cloud computing technology is encountering the challenges of high task processing latency and limited sensor node energy. Therefore, a path calculation method based on cloud computing network architecture is proposed. WSN monitoring tasks are deployed to specific edge devices reasonably by using the computing resources of network edge devices in the fog computing layer to reduce the task processing latency under the constraints of energy consumption. In order to efficiently assign tasks to the fog computing layer, a task mapping rule is used to map the monitoring tasks represented by the directed acyclic graph to the fog computing layer network represented by the acyclic graph. At the same time, a binary optimization problem for finding the optimal mapping relationship is established with time latency and energy constraints. Finally, the simulated annealing-discrete binary particle swarm optimization (SA-BPSO) algorithm is used to solve the optimization problem. The simulation results show that the latency performance under this method is about 40% higher than that of WSN fusion cloud computing technology when the data volume is 10 Mb. |
| Key words:
wireless sensor network
path calculation
cloud and fog network architecture
SA-BPSO
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| 收稿日期: 2022-03-25
修回日期:
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| DOI: 10.1051/jnwpu/20224061394 |
| 基金项目: 陕西省重点研发计划(2021GY-100)资助 |
| 通讯作者: 任继军(1980—),西安邮电大学高级工程师、硕士生导师,主要从事软件无线电研究。e-mail:renjijun@xupt.edu.cn。
Email：renjijun@xupt.edu.cn |
| 作者简介: 朱鹏(1999—),西安邮电大学硕士研究生,主要从事边缘计算研究
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| 作者相关文章 |
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| 朱鹏 在本刊中的所有文章 |
| 任继军 在本刊中的所有文章 |
| 任智源 在本刊中的所有文章 |
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