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Satisfactory content delivery scheme for QoS provisioning in delay tolerant networks

机译:容错网络中用于QoS设置的令人满意的内容交付方案

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We deal in this article with the content forwarding problem in delay tolerant networks (DTNs). We first formulate the content delivery interaction as a noncooperative satisfaction game. On one hand, the source node seeks to ensure a delivery probability above some given threshold. On the other hand, the relay nodes seek to maximize their own payoffs. The source node offers a reward (virtual coins) to the relay, which caches and forwards the file to the final destination. Each relay has to solve the dilemma of accepting/rejecting to cache the source's file. Cooperation incurs energy cost due to caching, carrying, and forwarding the source's file. Yet when a relay accepts to cooperate, it may receive some reward if it succeeds to be the first relay to forward the content to the destination. Otherwise, the relay may receive some penalty in the form of a constant regret; the latter parameter is introduced to make incentive for cooperation. Next, we introduce the concept of satisfaction equilibrium (SE) as a solution concept to the induced game. Now, the source node is solely interested in reaching a file delivery probability greater than some given threshold, while the relays behave rationally to maximize their respective payoffs. Full characterizations of the SEs for both pure and mixed strategies are derived. Furthermore, we propose two learning algorithms allowing the players (source/relays) to reach the SE strategies. Finally, extensive numerical investigations and some learning simulations are carried out to illustrate the behavior of the interacting nodes and to give some insightful thoughts on how to fine-tune the network setting.
机译:我们在本文中讨论了容错网络(DTN)中的内容转发问题。我们首先将内容交付交互公式化为非合作满意度游戏。一方面,源节点试图确保传递概率高于某个给定阈值。另一方面,中继节点试图最大化其自身的收益。源节点向中继提供奖励(虚拟硬币),中继将文件缓存并转发到最终目的地。每个中继都必须解决接受/拒绝以缓存源文件的难题。由于缓存,携带和转发源文件,因此合作会产生能源成本。但是,当中继接受合作时,如果它成功成为将内容转发到目的地的第一个中继,则它可能会收到一些回报。否则,接力者可能会不断感到遗憾;引入后一个参数以激励合作。接下来,我们介绍满意度均衡(SE)的概念作为对诱导博弈的解决方案。现在,源节点只对达到大于某个给定阈值的文件传递概率感兴趣,而中继则合理地行为以最大化其各自的收益。得出了纯策略和混合策略的SE的完整特征。此外,我们提出了两种学习算法,允许玩家(源/中继)达到SE策略。最后,进行了广泛的数值研究和一些学习模拟,以说明相互作用的节点的行为,并就如何微调网络设置给出了一些有见地的想法。

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