Abstract
Without pre-defined infrastructures, vehicles connect to each other to form Vehicular Ad-Hoc Networks (VANETs) to deliver data among vehicles. Dissemination of messages, for example accident alert messages or congestion messages is critical in VANETs. People’s lives may be at stake in accidents. Modeling and predicting VANETs’ message dissemination opens an opportunity to adopt appropriate strategies to alert severe accidents, manage traffic, evacuate vehicles from disasters, or recover from accidents. However, modeling data dissemination is challenging due to vehicles’ high mobility, which results in network topologies rapidly changing and data transmission being unstable. This paper presents analytical data dissemination models on VANETs. We model data dissemination as a new production adoption process and as a time-dependent stochastic process. The fact that information value or importance is decreasing with time and distance is also considered. The analytical models allow prediction and evaluation of information diffusion and enable intelligent traffic management.
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