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Distributed Dynamic Cluster-Head Selection and Clustering for Massive IoT Access in 5G Networks

机译:分布式动态集群 - 头选择和群集用于5G网络中的大规模物联网访问

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摘要

With the rapid growth of Internet-of-things (IoT) devices, IoT communication has become an increasingly crucial part of 5G wireless communication systems. The large-scale IoT devices access results in system overload and low utilization of energy efficiency under the existing network framework. In this paper, the cluster head uses the LTE-M protocol, and the intra-cluster uses the low-power wide-area network (LPWAN) self-networking protocol in the wireless sensor network. By a detailed analysis of the messages exchanged between the device and the base station, we describe the causes of overload and the steps of data aggregate combined with the physical channel. Then, we explore the cluster head quantity and the optimal scale in the intra-cluster under the traditional K-mean algorithm. When K is 30 under specific resources, the simulation results show that the system’s access success probability and resource utilization are optimal. Also, we propose a distributed dynamic cluster-head selection and clustering scheme based on an improved K-means algorithm. Simulation results show that the proposed scheme can reach 88.07% on the access success probability. The throughput and resource utilization are 3.5 times high than that of the optimal K-means.
机译:随着互联网(物联网)设备的快速增长,物联网通信已成为5G无线通信系统的越来越重要的一部分。大规模的IOT设备在现有网络框架下的系统过载和低利用率下降。在本文中,群集头使用LTE-M协议,内部集群在无线传感器网络中使用低功耗广域网(LPWAN)自网协议。通过对设备与基站之间交换的消息进行详细分析,我们描述了过载的原因和数据聚合与物理信道的步骤。然后,我们在传统的K均值算法下探讨群集头数量和帧内集群中的最佳规模。当k在特定资源下30时,仿真结果表明,系统的访问成功概率和资源利用率是最佳的。此外,我们提出了一种基于改进的K均值算法的分布式动态集群头选择和聚类方案。仿真结果表明,拟议方案可达到88.07%的访问成功概率。吞吐量和资源利用率高于最佳k均值的3.5倍。

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