Visible to the public Dependable Adaptive Mobility in Vehicular Networks for Resilient Mobile Cyber Physical Systems

TitleDependable Adaptive Mobility in Vehicular Networks for Resilient Mobile Cyber Physical Systems
Publication TypeConference Paper
Year of Publication2020
AuthorsOlowononi, F. O., Rawat, D. B., Liu, C.
Conference Name2020 IEEE International Conference on Communications Workshops (ICC Workshops)
Date PublishedJune 2020
ISBN Number978-1-7281-7440-2
Keywordsadaptive cruise control, adaptive manner, Adaptive Mobility, advanced driver-assistance systems, Autonomous vehicles, collision avoidance, communication technologies, composability, connected moving vehicles, connected vehicles, corresponding developments, dependable adaptive mobility, Dependable Mobility, driver information systems, efficient functionality, environmental concerns, future locations, Global Positioning System, high mobility, improved safety, intelligent transportation systems, Kalman filter, Kalman filters, lane departure warning, legal speed limit, Mathematical model, Metrics, network accountability, optimal information exchange, predicted exchanged information, Prediction algorithms, pubcrawl, resilience, Resiliency, resilient mobile cyber physical systems, Resilient Vehicular Networking, road safety, road traffic, Safety, secure movement-prediction, traffic engineering computing, Vehicles, vehicular ad hoc networks, Vehicular Networks

Improved safety, high mobility and environmental concerns in transportation systems across the world and the corresponding developments in information and communication technologies continue to drive attention towards Intelligent Transportation Systems (ITS). This is evident in advanced driver-assistance systems such as lane departure warning, adaptive cruise control and collision avoidance. However, in connected and autonomous vehicles, the efficient functionality of these applications depends largely on the ability of a vehicle to accurately predict it operating parameters such as location and speed. The ability to predict the immediate future/next location (or speed) of a vehicle or its ability to predict neighbors help in guaranteeing integrity, availability and accountability, thus boosting safety and resiliency of the Vehicular Network for Mobile Cyber Physical Systems (VCPS). In this paper, we proposed a secure movement-prediction for connected vehicles by using Kalman filter. Specifically, Kalman filter predicts the locations and speeds of individual vehicles with reference to already observed and known information such posted legal speed limit, geographic/road location, direction etc. The aim is to achieve resilience through the predicted and exchanged information between connected moving vehicles in an adaptive manner. By being able to predict their future locations, the following vehicle is able to adjust its position more accurately to avoid collision and to ensure optimal information exchange among vehicles.

Citation Keyolowononi_dependable_2020