Road safety applications envisaged for Vehicular Ad Hoc Networks (VANETs) depend largely on the dissemination of warning messages to deliver information to concerned vehicles. The intended applications, as well as some inherent VANET characteristics, make data dissemination an essential service and a challenging task in this kind of networks. This work lays out a decentralized stochastic solution for the data dissemination problem through two game-theoretical mechanisms. Given the non-stationarity induced by a highly dynamic topology, diverse network densities, and intermittent connectivity, a solution for the formulated game requires an adaptive procedure able to exploit the environment changes. Extensive simulations reveal that our proposal excels in terms of number of transmissions, lower end-to-end delay and reduced overhead while maintaining high delivery ratio, compared to other proposals.
Obstacles in Vehicular ad hoc networks (VANETs) in urban scenarios are an important issue. Normally, in traffic simulators vehicles can send/receive packets between each other if they are in the same transmission range no matter if an obstacle is presented or not between them. For this reason, checking if there is an obstacle between sender and receiver is an important goal. In this paper, we present a program named REVsim1.0 (Realistic Environment for Vanets simulation) [1] capable to detect at each instant of time if between a sender and a receiver a communication can be established or conversely, if an obstacle is found and such a communication is not possible. Parameters such as α, β, road resolution and transmission range have been defined and used in our proposed algorithm. Finally, a validation of our algorithm is shown.Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.
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