کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
425192 685700 2015 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Bayesian Coalition Game for Contention-Aware Reliable Data Forwarding in Vehicular Mobile Cloud
ترجمه فارسی عنوان
بازی ائتلاف بیزی برای اطمینان اطلاعات اطمینان در حمل و نقل موبایل
کلمات کلیدی
بازی ائتلاف بیزی، انتشار اطلاعات، اتوماتای ​​یادگیری، ابر وسایل نقلیه
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
چکیده انگلیسی


• 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.

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Future Generation Computer Systems - Volume 48, July 2015, Pages 60–72
نویسندگان
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