The University of New Brunswick

Published purpose

Logistics, which is the process of planning, implementing and controlling the flow and storage of goods and services from the point of origin to the point of consumption, is essential for successful and sustainable economic growth in Canada. However, due to pressure from saturated markets, new customer demands and adverse seasonal weather conditions, the logistics systems in Canada face many new complex challenges. Recently, the rapid technological convergence between Internet of Things (IoT), Fog/Cloud Computing, and AI has made Logistics 4.0 possible, which has significant potential to improve the quality of logistics in Canada by providing benefits such as real-time big data analytics for optimized routing and reduced storage requirement without information disruption. Nevertheless, Logistics 4.0 still faces many security and privacy challenges, such as veracity of data, robustness of IoT and Fog devices, and privacy-preserving big data AI processing. The proposed research project aims to address these challenges by contributing to the development of a secure and resilient fog computing framework for Intelligent Transportation Systems (ITS) to address the threats to ITS, including those to field devices such as IoT devices, sensors, traffic signals, roadside equipment, and those to transportation management centers. Specifically, we will secure data in its whole cycle within ITS, i.e., secure data collection, data transmission, data storage, and data processing within ITS. Novel techniques, including privacypreserving AI data processing, lightweight authentication for IoT and Fog devices, and efficient malware detection techniques for IoT and Fog devices will be exploited in the framework for ITS.