Efficient Safety Message Dissemination in Vehicular Ad Hoc Networks

Abstract

Over the past few years, the occurrence of enormous human, societal, environmental and economic losses due to traffic accidents has led toward a search for highly innovative and practical solutions to improve safety on the roads. One such initiative is the introduction of Intelligent Transportation Systems (ITS), whereby a vital application is to ensure road safety by fast and reliable dissemination of safety messages. This research develops novel and practical schemes to efficiently and reliably disseminate safety information in Vehicular Ad Hoc Networks (VANETs) using Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication to improve the transportation safety. Firstly, an innovative multi-hop broadcasting protocol is developed, which exploits a smart forwarder selection process, handshake-less broadcasting, ACK Decoupling and efficient collision resolution mechanism. This protocol significantly improves the speed of safety message propagation without compromising on the reliability. Secondly, this research proposes a novel architecture that facilitates the effective sharing of safety information in VANETs by exchanging and storing the data (about potential threats) obtained from the neighboring vehicles as well as from on-board sensor technologies. The architecture leverages entirely on BSMs and improves the visibility and situational awareness of vehicles. The key attraction of this architecture is its novelty, simplicity, practicality, and applicability. Both of the proposed schemes were evaluated under simulation and real-world experimental conditions. The results establish and validate the performance gain of the proposed schemes. The highlight of the above techniques is that the exchange of safety information among vehicles takes place using the existing V2V standards, without requiring any modifications to the standards. Finally, these techniques can be readily deployed to improve safety on the roads, and thus, reduce human causalities as well as lower the social, environmental and economic expenses.