Vehicular ad hoc networks (VANETs) enable the timely broadcast dissemination of event-driven messages to interested vehicles. Especially when dealing with broadcast communication, data dissemination protocols must achieve a high degree of scalability due to frequent deviations in the network density. In dense networks, suppression techniques are designed to prevent the so-called broadcast storm problem. In sparse networks, protocols incorporate store-carry-forward mechanisms to take advantage of the mobility of vehicles to store and relay messages until a new opportunity for dissemination emerges. Despite numerous efforts, most related works focus on either highway or urban scenarios, but not both. Highways are mostly addressed with a single directional dissemination. For urban scenarios, protocols mostly concentrate on either using infrastructure or developing methods for selecting vehicles to perform the store-carry-forward task. In both cases, dense networks are dealt with suppression techniques that are not optimal for multi-directional dissemination. To fill this gap, we present an infrastructure-less protocol that combines a generalized time slot scheme based on directional sectors and a store-carry-forward algorithm to support multi-directional data dissemination. By means of simulations, we show that our protocol scales properly in various network densities in both realistic highway and urban scenarios. Most importantly, it outperforms state-of-the-art protocols in terms of delivery ratio, end-to-end delay, and number of transmissions. Compared to these solutions, our protocol presents up to seven times lower number of transmissions in dense highway scenarios.
For highway or urban scenarios, a protocol supports multidirectional data dissemination by combining a generalized time slot scheme based on directional sectors and a store-carry-forward algorithm REF .
A scalable data dissemination protocol for both highway and urban vehicular environments
EURASIP Journal on Wireless Communications and Networking
Computer Science