Learning Automata-based Opportunistic Data Aggregation and Forwarding scheme for alert generation in Vehicular Ad Hoc Networks

Due to the highly mobile and continuously changing topology, the major problem in Vehicular Ad Hoc Networks (VANETs) is how and where the collected information is to be transmitted. An intelligent approach can adaptively selects the next hop for data forwarding and aggregation from the other nodes in the networks. But due to high velocity and constant topological changes, it is a challenging task to meet address the above issues. To address these issues, we proposed a Learning Automata-based Opportunistic Data Aggregation and Forwarding (LAODAF) scheme for alert generation in VANETs. Learning automata (LA) operate separately which are deployed to the nearest Road Side Units (RSUs) to collect and forward the data from respective regions along with alert generation. Once data is aggregated, LA adaptively selects the destination for data transfer, based on the newly defined metric known as Opportunistic Aggregation and Forwarding (OAF). LA predicts the mobility of the vehicle and adaptively selects the path for forwarding, based on the value of OAF. Moreover, it updates its action probability vector and learning rate based on the values of OAF. This will reduce network congestion and the load on the network as it is aggregated and forwarded only when required. An algorithm for opportunistic data aggregation and forwarding is also proposed. The proposed strategy is evaluated using various metrics such as a number of successful transmissions, connectivity, link breakage rate, traffic density, packet reception ratio, and delay. The results obtained show that the proposed scheme is more effective for opportunistic Data Aggregation and Forwarding in VANETs.