Group Mobility Management for Large-Scale Machine-to-Machine Mobile Networking

Abstract

Machine-to-machine (M2M) communications have emerged as a new communication paradigm to support Internet of Things (IoT) applications. Millions to trillions of machines will connect to mobile communication networks (MCNs) to provide IoT applications. This group-based behavior is considered one of the features of M2M communications. That is, machines are likely with correlated mobility and may perform mobility management at the same time. As a result of this scenario, signaling exchanges for machines are more likely to occur at the same time, and the random access channel (RACH) for the signaling is more likely to be congested. In this paper, we propose a group mobility management (GMM) mechanism where machines are grouped based on the similarity of their mobility patterns at the location database (LDB), and only the leader machine performs mobility management on behalf of the other machines in the same group. The GMM mechanism attempts to mitigate the signaling congestion problem. Through our performance study, we show how the GMM mechanism can reduce registration signaling from machines.