# Distributed Dynamic Cluster-Head Selection and Clustering for Massive IoT Access in 5G Networks
## 1 Introduction  
Massive machine type communication is one of the main scenes of 5G wireless communication. Cisco estimates that 70% of IoT device connections will serve by the 3 rd Generation Partner Project (3 GPP) developed networks [1] because the existing cellular network has broad coverage, a large amount of deployed infrastructure, a complete user service management system, and so on. In order to adapt to the IoT device service well, in the future wireless network, the primary solution is to optimize the existing cellular network for service characteristics. Therefore, the 3 GPP organization has been working on the standardization of IoT networks since Release-8, which is more inclined to choose optimization schemes that have less impact on existing networks to enable cellular networks to support massive IoT device communication [2]. With a large number of IoT devices application service centered on the uplink, the transmission data is small, the transmission is frequent, and the battery is difficult to replace. When large-scale devices request to access the network, the collision caused by random access is the main reason for low resource utilization of small data IoT devices. There are two main problems in the existing network for the future massive IoT access. * Massive IoT devices access will generate a large number of collisions and especially the congestion of random access, which will seriously reduce the access success probability and result in a system overload. * IoT devices transmit small amounts of data requiring more than 20 handshakes to establish a data connection with the base station (BS) in LTE-M [3], which leads to high signaling overhead and results in low Resource Utilization. The solutions are optimized for these problems as follows: on the one hand, the research mainly considers the high-efficiency random access process of the Media Access Control (MAC) layer. It is primarily to improve the random access process to alleviate the problem of high collision probability, thereby reducing waste and achieving energy efficiency optimization. Also, it mainly divides into two research ideas. One is a dynamic allocation of random access process resources including preamble, time, and frequency-domain. The other is access control including dynamic control methods [4; 5] and multiple back-off methods [5; 6]. On the other hand, based on the idea of cooperative relaying, it can be combined with other lower-power technologies [7] for intra-cluster communication. In the relay-based transmission mechanism, device clustering algorithm and the selection of cluster head are the critical factors affecting energy efficiency. Specific rules group all devices, one of which acts as a coordinator. The Conventional clustering method is based on the fixed geographical position of the device before the network starts to operate. The cluster head is stationary regardless of whether the device is in an active state or a dormant state. This approach improves the performance of the system, but it requires the cluster head to have an unlimited energy supply. Literatures [8; 9; 10; 11; 12; 13; 14] propose a low-energy adaptive clustering hierarchy (LEACH) algorithm and related improvements. LEACH is a self-organizing, adaptive clustering protocol that uses randomization to distribute the energy load evenly among the sensors in the network. Sensors elect themselves to be local cluster-heads at any given time with a certain probability. A series of K-means selection algorithms based on grouping and coordinator are introduced in ref. [15]. Researches [16; 17] discuss within their clustering mechanism some criteria, such as the communication interest among the devices. Ref. [18] explains in detail the exploitation of the device-to-device communication scenario; it can support efficient resource management, especially in the considered Orthogonal Frequency-Division Multiple Access (OFDMA) setup.