Future Generation Computer Systems 48 (2015) 60–72 Contents lists available at ScienceDirect Future Generation Computer Systems journal homepage: www.elsevier.com/locate/fgcs Bayesian Coalition Game for Contention-Aware Reliable Data Forwarding in Vehicular Mobile Cloud Neeraj Kumar, Rahat Iqbal ∗ , Sudip Misra, Joel J.P.C. Rodrigues Department of Computer Science and Engineering, Thapar University, Patiala, India Department of Computing and Digital Environment, Coventry University, Coventry, UK School of Information Technology, IIT Kharagpur (W.B.), India Instituto de Telecomunicações, University of Beira Interior, Covilhã, Portugal University ITMO, Saint-Petersburg, Russia highlights • A Bayesian Coalition game-based reliable data transmission is proposed for vehicular cloud. • Learning Automata (LA) are assumed to be the players in the game. • For each action taken by the players in the game, they may get a reward or a penalty based upon which all the future actions to be taken are decided. • The results obtained are convincing as compared to other approaches of its category. article info Article history: Received 28 February 2014 Received in revised form 25 September 2014 Accepted 8 October 2014 Available online 18 October 2014 Keywords: Bayesian Coalition Game Data dissemination Learning automata Vehicular cloud abstract The exponential growth in the demands of users to access various resources during mobility has led to the popularity of Vehicular Mobile Cloud. Vehicular users may access various resources on road from the cloud which acts as a service provider for them. Most of the existing proposals on vehicular cloud use unicast sender-based data forwarding, which results in an overall performance degradation with respect to the metrics such as packet delivery ratio, end-to-end delay, and reliable data transmission. Most of the applications for vehicular cloud have tight upper bounds with respect to reliable transmission. In view of the above, in this paper, we formulate the problem of reliable data forwarding as a Bayesian Coalition Game (BCG) using Learning Automata concepts. Learning Automata (LA) are assumed as the players in the game stationed on the vehicles. For taking adaptive decisions about reliable data forwarding, each player observes the moves of the other players in the game. For this purpose, a coalition game is formulated among the players of the game for taking adaptive decisions. For each action taken by a player in the game, it gets a reward or a penalty from the environment, and accordingly, it updates its action probability vector. An adaptive Learning Automata based Contention Aware Data Forwarding (LACADF) is also proposed. The proposed scheme is evaluated in different network scenarios with respect to parameters such as message overhead, throughput, and delay by varying the density and mobility of the vehicles. The results obtained show that the proposed scheme is better than the other conventional schemes with respect to the above metrics. © 2014 Elsevier B.V. All rights reserved. 1. Introduction In the past few decades, there has been growing interests of the research communities in the area of Vehicular Cloud (VCloud) due ∗ Corresponding author at: Department of Computing and Digital Environment, Coventry University, Coventry, UK. E-mail addresses: neeraj.kumar@thapar.edu (N. Kumar), r.iqbal@coventry.ac.uk (R. Iqbal), smisra@sit.iitkgp.ernet.in (S. Misra), joeljr@ieee.org (J.J.P.C. Rodrigues). to their use in various domains such as Intelligent Transport Sys- tems (ITS), Urban Surveillance Systems, safety and security in com- munity networks, and emergency applications. Many researchers across the globe are working to design new solutions to provide facilities to the users on-board their vehicles to make use of them in case of emergency situations such as collision on the road, traf- fic block, safety alarms for fire, and theft. For all of these applica- tions, the broadcasting of message is to be done efficiently with minimum contention of available resources [1,2]. In the current pa- per, we use the concepts of both vehicular networks and cloud for http://dx.doi.org/10.1016/j.future.2014.10.013 0167-739X/© 2014 Elsevier B.V. All rights reserved.