A case study for M2M traffic characterization in a smart city environment

This paper presents a case study to characterize Machine-To-Machine (M2M) traffic in a smart city environment. Real data on the position of machines and three different probability distributions (Poisson, Beta, and deterministic) are used to model the packet generation of some realistic IoT applications. A web application is presented and allows to perform a variety of analyses on M2M traffic characterization. The island of Montreal is used as study case: realistic data on the position of machines and of eNodeB stations in a real LTE network are employed to demonstrate the possibilities of the tool. In the numerical results, the traffic generated by different M2M applications is presented and some differences between M2M and human traffic and their impact on the LTE infrastructure are highlighted.