A Framework For Handling Local Broadcast Storm Using Probabilistic Data Aggregation In VANET
Data aggregation is used to combine correlated data items from different vehicles before redistributing to other vehicles in the vehicular ad hoc networks (VANET). The number of retransmissions and the communication overhead can be reduced considerably by using aggregation. It is a prerequisite for applications that require periodic dissemination of information into a large region so that, drivers can be informed well in advance and can take alternative route in case of traffic congestion. Dissemination of information to vehicles through broadcasting creates a broadcast storm problem in VANET. In this paper a novel framework is proposed for handling the local broadcast storm problem using probabilistic data aggregation which reduces the bandwidth consumption and hence improves the information dissemination. This system exploits the knowledge base and stores the decisions for aggregation and is based on a flexible and extensible set of criteria. These criterias can be application specific and can enable a dynamic fragmentation of the road according to the various application requirements. The framework is evaluated for VANET based traffic information system through simulation for strictly limited bandwidth and local broadcast problem. The results demonstrate that completely structure-free probabilistic data aggregation reduces the bandwidth consumption by eliminating the local broadcast problem.
data aggregation vanet knowledge base kd-tree