arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1909.07585cs.NI
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物联网中增强的分布式数据聚合方法

Enhanced distributed data aggregation method in the internet of things

Mohammad Hossein Homaei, Parisa Rafiee

摘要:

\emph{"Internet of Things (IoT)"}作为一个新的概念在技术和通信领域已经出现。在这样的现代技术中,为每个生物体(例如,人、动物、物体等)提供了通过数据通信网络(诸如因特网或内联网)发送数据的能力。由于有限的硬件和通信操作能力以及小尺寸,物联网经历了相当多的挑战。这些固有的挑战不仅会导致节点之间的聚合、传输和通信效率的根本限制,而且还会降低路由性能。为了应对可用时间缩短和节点间通信不稳定的问题,网络中的数据聚合和传输方法被设计得更加智能化。本文提出了一种分布式的方法来设置节点间的子平衡。该方法通过限制度来增加网络图的高度,从而减少网络拥塞。此外,一个动态的数据聚合方法-命名为LA-RPL-提出了RPL网络。更具体地,每个节点配备有学习自动机,以便执行数据聚合和传输。仿真结果表明,所提出的方法优于以前建议的基础方法的能量消耗,网络控制开销,和丢包率。

英文摘要:

As a novel concept in technology and communication world, \emph{"Internet of Things (IoT)"} has been emerged. In such a modern technology, the capability to transmit data through data communication networks (such as Internet or Intranet) is provided for each organism (e.g. human being, animals, things, and so forth). Due to the limited hardware and communication operational capability as well as small dimensions, IoT undergoes quite a few challenges. Such inherent challenges not only cause fundamental restrictions in the efficiency of aggregation, transmission, and communication between nodes; but they also degrade routing performance. To cope with the reduced availability time and unstable communications among nodes, data aggregation and transmission approaches in such networks are designed more intelligently. In this paper, a distributed method is proposed so as to set child balance among nodes. In this method, the height of network graph is increased through restricting the degree; and network congestion is reduced as a result. In addition, a dynamic data aggregation approach -named as LA-RPL- is proposed for RPL networks. More specifically, each node is equipped with a learning automata in order to perform data aggregation and transmissions. Simulation results demonstrate that the proposed approach outperforms previously suggested base approaches in terms of energy consumption, network control overhead, and packet loss rate.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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