VANET Traffic Congestion Detection and Avoidance

The main objectives behind the development of congestion detection algorithms are to detect areas of high traffic density with low speeds. Each vehicle captures and disseminates information such as location and speed and process the information received from other vehicles in the network,   which can be possible through VANET. Vehicular Ad-hoc   Networks are self-organizing networks established among   vehicles equipped with communication facilities Due to recent advancements in vehicular technologies vehicular communication has emerged. Multiple approaches have been proposed to implement congestion detection in VANET. Traffic congestion is a very serious problem in big cities. With the number of vehicles increasing rapidly, especially in cities whose economy is booming, the situation is getting even worse. In this paper we are presenting Detection of Traffic Congestion using proposed approach and analysis of result

Using Trust and Possibilistic Reasoning to Deal with Untrustworthy Communication in VANETs

International audienceVANETs allow for unprecedented amounts of information to be sent between participants in traffic. Unfortunately, without countermeasures, they also allow selfish agents to take advantage of communication to improve their own utility. In this paper we present a novel framework for dealing with potentially untrustworthy information. The framework consists primarily of two components: a computational trust model for estimating the amount of uncertainty in received information and a possibilistic beliefs-desires-intentions agent system for reasoning about this uncertain information in order to achieve the driver's goals. We demonstrate the framework's effectiveness in an easy to understand but realistic scenario of a freeway system in which we also show that deceit may have a larger impact on traffic flow than previously thought