Vehicular Ad Hoc Networks (VANETs) are highly dynamic and unstable due to the heterogeneous nature of the communications, intermittent links, high mobility and constant changes in network topology. Currently, some of the most important challenges of VANETs are the scalability problem, congestion, unnecessary duplication of data, low delivery rate, communication delay and temporary fragmentation. Many recent studies have focused on a hybrid mechanism to disseminate information implementing the store and forward technique in sparse vehicular networks, as well as clustering techniques to avoid the scalability problem in dense vehicular networks. However, the selection of intermediate nodes in the store and forward technique, the stability of the clusters and the unnecessary duplication of data remain as central challenges. Therefore, we propose an adaptable destination-based dissemination algorithm (DBDA) using the publish/ subscribe model. DBDA considers the destination of the vehicles as an important parameter to form the clusters and select the intermediate nodes, contrary to other proposed solutions. Additionally, DBDA implements a publish/subscribe model. This model provides a context-aware service to select the intermediate nodes according to the importance of the message, destination, current location and speed of the vehicles; as a result, it avoids delay, congestion, unnecessary duplications and low delivery rate.
The Vehicular Ad hoc Network (VANET) takes the advantage of the relative mobility of the vehicles to opportunistically share dynamic information when they meet, aiming to avoid accidents and traffic jams, get local information of the nearby places, enjoy entertainment applications among others. However, the shared information must be handled properly, saving bandwidth and tackling the unnecessary duplication of data, preventing the scalability problem and congestion. The current solutions of these problems incorporate the concept of Relevance to share the information in a clever way but they do not exploit the advantage of knowing the destination of the vehicles and context-aware services to follow behavior patterns. Therefore, we propose an efficient destination-based data management policy, which follows the vehicular behavior and context to determine the relevance or importance of the information using a publisher/subscriber model and clusterbased dissemination. In order to demonstrate the feasibility of the proposal we implemented sets of simulation in which the results reported an outstanding performance in terms of congestion and delivery ratio.
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