Intelligent Security Management and Control in the IoT. Mohamed-Aymen Chalouf

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      Using Reinforcement Learning to Manage Massive Access in NB-IoT Networks

       Yassine HADJADJ-AOUL and Soraya AIT-CHELLOUCHE

       Inria, CNRS, IRISA, University of Rennes 1, France

2.1. Introduction

      Communications between objects in the Internet of Things (IoT) and particularly machine-to-machine (M2M) communications are considered as one of the most important evolutions of the Internet. Supporting these devices is, however, one of the most significant challenges that network operators need to overcome (Lin et al. 2016). In fact, the considerable number of these devices that might attempt to access a network at the same time could lead to heavy congestion, or even a total saturation, with all the consequences this might cause. In fact, as can be seen in Figure 2.1, a very limited number of devices trying simultaneously to access the network can reduce the network’s performance to zero, independently of the access opportunities available (Bouzouita et al. 2016). In these circumstances, it seems clear that effective access control mechanisms are needed to maintain a reasonable number of access attempts.

The Third Generation Partnership Project (3GPP) group has identified the overload on the Random Access Network (RAN) as a priority at a very early stage and has suggested several solutions. Among the approaches suggested, the Access Class Barring (ACB), suggested in version 8, and its extension, the Extended Access Barring (EAB), suggested in version 11, are certainly the most effective strategies (3GPP 2011). In fact, these approaches tackle the problem at its roots by preventing any attempts to access the network. However, these approaches merely provide a framework for congestion management, without providing a ready-made solution.

      Figure СКАЧАТЬ