Intelligent Multiple-Vehicular-Attributes (iMVA) Broadcast Protocol for VANETs

Abstract

Any vehicular network attributes in VANETs (e.g., sender-receiver distance and number of neighboring vehicles) can be used to determine candidate rebroadcast vehicles. The use of only limited number of attributes, however, may rule out potential broadcast candidates. For example, a counter-based scheme broadcast will never take specific nodes (vehicles) into consideration even though they have great sender-receiver distance. This research employs multiple vehicular attributes to tackle such an issue. The use of naïve-Bayes probability in our protocol combines the individual strengths of the incorporated vehicular attributes to obtain a broadcast decision. The results suggest that the proposed method is intelligent to the variation of node density and its performance outperformed both the distance-based scheme and the Efficient Counter-based Scheme (ECS) broadcast scheme at any network densities experimented. Our broadcast method contributes to the increase of 1.8% average reachability, 30.3% saving on rebroadcast tries, 5% reduction on delivery cost and shortens the total delay time by about 19.6% over the ECS scheme. An analytical model justifies the simulated reachability and saved-rebroadcast results showing similar trends over network density experimented.