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首页> 外文会议>IEEE 10th International Conference on Ubiquitous Intelligence and Computing; IEEE 10th International Conference on Autonomic and Trusted Computing >Using the Model of Markets with Intermediaries as an Incentive Scheme for Opportunistic Social Networks
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Using the Model of Markets with Intermediaries as an Incentive Scheme for Opportunistic Social Networks

机译：使用中介人市场模型作为机会主义社交网络的激励方案

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摘要

The popularity of smart phones and other intelligent devices has made it possible for us to organize social activities by means of opportunistic social networks. However, message dissemination performance in opportunistic social networks is highly dependent on the cooperation between different users. In this paper, we put forward the model of markets with intermediaries as an incentive mechanism. In this model, users can overcome their selfishness in order to get profits, thus cooperation can be strengthened. Based on this incentive mechanism, we propose a broker selection algorithm: the Ranger Algorithm. Rangers are those users who not only have met with users in other communities for multiple times, but also have a high probability of meeting these users. Experiments are implemented using the MIT Reality Mining Dataset. Results show that the model of markets with intermediaries can serve as an incentive mechanism to stimulate collaboration, and Ranger Algorithm outperforms other baseline algorithms in improving message dissemination performance. On the basis of the above work, a prototype system is built to help organize offline social activities.
机译：智能电话和其他智能设备的普及使我们能够通过机会性社交网络组织社交活动。但是，机会社交网络中的消息传播性能高度依赖于不同用户之间的合作。本文提出了以中介人为激励机制的市场模型。在这种模式下，用户可以克服自己的自私以获取利润，从而加强合作。基于这种激励机制，我们提出了经纪人选择算法：Ranger算法。护林员是不仅与其他社区的用户多次会面，而且很可能会见这些用户的那些用户。实验是使用MIT现实采矿数据集实现的。结果表明，具有中间人的市场模型可以用作激励合作的激励机制，并且Ranger算法在改善消息传播性能方面优于其他基准算法。在上述工作的基础上，构建了一个原型系统来帮助组织离线社交活动。

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