Adaptive Copy and Spread data dissemination in vehicular ad-hoc networks

Abstract

Vehicular ad-hoc networks (VANETs) are promising for more and more intelligent transportation applications, where information is disseminated over a group of vehicles. VANETs are characterized for its unstable framework, in which the isolated vehicles communicate with others only when the unexpected meetings occur. As a result, some available data dissemination schemes do not work well in VANETs. In this paper, we propose an opportunistic auto-adaptive dissemination scheme - Adaptive Copy and Spread (ACS) Algorithm - for VANETs, which makes use of the moving patterns of vehicles like direction and velocity, and dynamically adjusts the dissemination strategies. This scheme dynamically increases or decreases the number of message copies inside the broadcast area, and vehicles can intelligently decide when to start or stop broadcast a message. ACS is designed to keep the message at the effective area's boundary to inform the entering vehicles as soon as possible. The simulation results show that ACS enhance the data dissemination performance significantly with a smaller network load compared with random choice algorithm and Epidemic dissemination algorithm over a series of scenarios.