计算机工程与应用 ›› 2021, Vol. 57 ›› Issue (9): 109-117.DOI: 10.3778/j.issn.1002-8331.2011-0213

• 网络、通信与安全 • 上一篇    下一篇

雾计算中跨层感知分簇路由协议

孙泽宇,徐琛,苏艳超,李传锋,聂雅琳   

  1. 1.洛阳理工学院 计算机与信息工程学院,河南 洛阳 471023
    2.上海应用技术大学 计算机科学与信息工程学院,上海 201418
    3.河南省高技术创业服务中心 信息技术部,郑州 450008
  • 出版日期:2021-05-01 发布日期:2021-04-29

CCRP:Cross-Layer-Sensing Clustering Routing Protocol in Fog Computing

SUN Zeyu, XU Chen, SU Yanchao, LI Chuanfeng, NIE Yalin   

  1. 1.School of Computer and Information Engineering, Luoyang Institute of Science and Technology, Luoyang, Henan 471023, China
    2.School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China
    3.Department of Information Technology, Henan High Technology Entrepreneurship Center, Zhengzhou 450008, China
  • Online:2021-05-01 Published:2021-04-29

摘要:

传统的以数据为中心的路由协议,往往会导致传感网中出现在大量的“能量空洞”或“热点”现象。为了克服上述现象,借助雾计算理论模型,提出了一种基于雾计算跨层感知分簇路由协议(A Cross-layer-sensing Clustering Routing Protocol Based on Fog Computing,CCRP)。该协议通过跨层映射原理,利用感知事件驱动机制将雾节点映射到传感层,构成功能强大的虚拟控制节点,将传感网分簇路由协议的控制过程上传至雾层,通过雾计算实现事件域节点分布式成簇路由汇聚中心,从而建立以映射雾节点为中心的优化数据聚合路由,取代传感网底层路由中的数据,进一步平衡并减少网络负载。在路由协议优化阶段,利用粒子群优化算法(Particle Swarm Optimizations,PSO)采用无竞争开销方式选举一组最佳节点担任簇首,能有效地均衡全网能量的开销,抑制传感器节点能量的快速消耗,延长了网络生存周期。仿真实验表明,CCRP协议能够有效抑制网络开销的同时还可以高效完成对数据的优化过程。

关键词: 雾计算, 传感网, 路由协议, 粒子群优化算法, 网络生存周期

Abstract:

Traditional data-centric routing protocols often lead to a large number of “energy holes” or “hot spots” in the sensor network. In order to overcome the above phenomenon, this paper proposes a Cross-layer-sensing Clustering Routing Protocol Based on Fog Computing(CCRP) based on the fog computing theoretical model. The protocol uses the cross-layer mapping principle to map the fog node to the sensor layer using the perceptual event-driven mechanism to form a powerful virtual control node. The control process of the sensor network clustering routing protocol is uploaded to the fog layer and realized through fog computing. The event domain nodes are distributed in clusters and the routing convergence center, thereby establishing an optimized data aggregation routing centered on the mapped fog node, replacing the data in the underlying routing of the sensor network, further balancing and reducing network load. In the routing protocol optimization stage, the Particle Swarm Optimization(PSO) algorithm is used to elect a group of best nodes as cluster heads in a non-competitive cost method, which can effectively balance the energy cost of the entire network and suppress the rapid energy consumption of sensor nodes, extend the network life cycle. Finally, the simulation experiment shows that the CCRP protocol in this paper can effectively suppress the network overhead while efficiently completing the data optimization process.

Key words: fog computing, sensor networks, routing protocol, particles swarm optimization, network lifetime