Paper Info
Title
Networks of learning automata for the vehicular environment: a performance analysis study
Abstract
Due to the stringent constraints of constant topological changes and low end-to-end delay, data forwarding in the vehicular enviornment is always a challenging task. In this article, we have analyzed the performance of networks of learning automata (LA) using the concepts of the Bayesian coalition game in the vehicular environment. LA are assumed to be the players in the game, which form a coalition based on some predefined strategy from the strategy space. Each action taken by the players in the game may be rewarded or penalized by the environment in which they operate. The environment provides a feedback for each action taken by the LA. The probability of selection of an action is estimated using the Bayesian conditional probability on the payoff corresponding to each player. After fetching input from the environment, the LA update their action probability vector. The performance of the proposed scheme is evaluated in different network conditions in a real environment by varying the learning rates of the automaton. A 20-30 percent enhancement of the successful packet delivery ratio has been observed using the proposed scheme in the vehicular environment.
Year
DOI
Venue
2014
10.1109/MWC.2014.7000970
Wireless Communications, IEEE  
Keywords
DocType
Volume
learning automata,vehicular ad hoc networks,Bayesian conditional probability,VANET,end-to-end delay,learning automata networks,learning rates,performance analysis,probability vector,vehicular ad hoc networks,vehicular environment
Journal
21
Issue
ISSN
Citations 
6
1536-1284
17
PageRank 
References 
Authors
0.61
10
4
Name
Order
Citations
PageRank
Neeraj Kumar12889236.13
Sudip Misra22471200.95
Obaidat, M.S.329039.97
JOEL J. P. C. RODRIGUES43484341.72