Abstract
Due to high velocity of the vehicles, data dissemination and mobile data offloading are most difficult tasks to be performed in vehicular ad hoc networks (VANETs). In recent years, due to an exponential increase in the data generated from various sources such as smart devices, gadgets, and actuators, there arises a need of usage of an efficient communication infrastructure to handle the aforementioned issues. Most of the earlier solutions reported in the literature for data offloading problem have used the cellular communication, which may be congested in handing a large number of requests from community of users. This may result a performance bottleneck in terms of call drops and data dissemination to the other vehicles in the VANET environment. Also, these schemes lack a comprehensive approach of data dissemination to meet the quality of service (QoS) in real time. Hence, to overcome this problem, some of the mobile data can be disseminated using the existing vehicular infrastructure and Wi-Fi access points (APs). In this paper, we propose a new schedule based on game theoretic approach where the APs and vehicles act as players in a game and compete for offloading the cellular data. The proposed scheme is based on the selection of the best vehicle or AP based on the utility of the players (vehicles and APs) in the game. The utility of vehicle and AP is decided based on the parameters such as distance, velocity, connectivity to destination, bandwidth, and area of the network. A novel algorithm has been designed using the proposed game theoretic approach for handling mobile data offloading and data dissemination. The proposed solution not only successfully offloads the data but also maintains QoS with respect to the parameters such as end-to-end delay, message progress, and message dissemination speed. Results obtained confirm the superiority of the proposal in comparison with the other existing schemes. Specifically, the proposed scheme achieves improvement of 4.16 and 20.5 % in message progress, 18.91 and 4.75 % in extra messages generated, 11.26 and 54.94 % in message dissemination speed, and 78.71 and 87.94 % in end-to-end delay in sparse network as compared to GyTAR and GPCR, respectively.