Efficient Vehicular Crowdsourcing Models in VANET for Disaster Management

Route planning in a vehicular network is a well known problem. Static solutions for finding a shortest path have proven their efficiency, however in a dynamic network such as a vehicular network, they are confronted to dynamic costs (travel time, consumption, waiting time,…) and time constraints (traffic peaks, ghost traffic jam, accidents…). This is a practical problem faced by several services providers on traffic information who want to offer a realistic computation of a shortest path. This paper propose a model based on the communication between vehicles (Vehicle to Vehicle: V2V) to reduce the time spend by travels taking into account the travel time registered and exchanged between vehicles in real time. In our model, vehicles act as ants and they choose their itineraries thanks to a pheromone map affected by the phenomenon of evaporation. The presented algorithms are evaluated in real world traffic networks and by modeling and simulating extreme cases such as accidents, act of terrorism and disasters.