Dissemination and reachability of alert messages in intelligent transportation systems for improving road safety

World Congress on Industrial Automation

July 20-22, 2015 San Francisco, USA

Hadj Bourdoucen, Mohammed Oul Khaoua, Osama H Rehman, Maryam Al Lawati and Shaima M Ahmed

Posters-Accepted Abstracts: Adv Robot Autom

Abstract :

Over the last decade, numerous research works have been performed on active road safety applications that fall under the theme of intelligent transportation systems (ITS). Among the list of plausible ITS based on-road applications, alert messages dissemination protocols based on vehicular ad hoc networks (VANETs) have received a special attention worldwide and was supported by governments, industries and research institutes. The main goal on the work summarized here is to target reduction of road accidents by providing early event-driven warning messages to endangered vehicles and hence increasing the capability of avoiding accidents or traffic congestion spots. Few ideas pointed out that broadcast based alert messages dissemination services can lead towards 75% of reduction in both urban and rural road traffic accidents. The current work is directed towards design of new relay node selection protocols based on link quality estimation for better message dissemination and reachability. Simulations demonstrated very good results in terms of alert messages reachability and robustness with an acceptable improvement in end-to-end delay. In this regard, several mobility models were investigated including a realistic model that mimics road designs in Oman, namely, car following model with roundabout layout (CFM-RL). The algorithm performance was evaluated over different mobility models and was proved that the models affect the performance of broadcasting beacon messages in VANETs. Additionally, the prototyping platform based on socket programming was developed in multiplatform operating systems to be used for implementing and testing the prototype. These are underway for different road scenarios to study and determine the performance of the developed models.