Macquarie University

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Machine-to-machine (M2M) communication in vehicle management

posted on 2022-03-28, 17:10 authored by Sayidul Morsalin
Vehicle tracking by automated machine-to-machine (M2M) communication over cellular wireless networks will be a key component in future "intelligent transport" systems in which existing transport infrastructure is used more efficiently, resulting in reduced traffic congestion, improved fuel efficiency, etc. Furthermore, as transport becomes electrified, M2M communication will enable the impact of electric vehicles (EVs) on the electricity distribution network to be managed. In this research, we have implemented a Raspberry Pi based data logging system (DLS) operating over a commercial 4G wireless network. The DLS sends trip information, such as state of charge (SoC) and location of EVs to a remote server at regular intervals. We also address the question of scalability of the wireless DLS, and report both numerical simulations and analytical results showing that up to 250 vehicles can be supported per base station before communication delays and blocking disrupt system operation. Lastly, we implement a neural-network-based intelligent decision-making system to utilise the M2M logged data for charge scheduling and load management of EVs in the power grid.


Table of Contents

1. Introduction -- 2. Review of machine-to-machine communication -- 3. Raspberry Pi based data logging system -- 4. Scalability of vehicles -- 5. M2M communication with scheduling -- 6. ANN-controlled charge scheduling of EVs -- 7. Final conclusion and future work.


Bibliography: pages 63-69 Empirical thesis.

Awarding Institution

Macquarie University

Degree Type

Thesis MRes


MRes, Macquarie University, Faculty of Science and Engineering, Department of Engineering

Department, Centre or School

Department of Engineering

Year of Award


Principal Supervisor

Graham Town


Copyright Sayidul Morsalin 2016. Copyright disclaimer:




1 online resource (xviii, 69 pages) diagrams, graphs, tables

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