机会社交网络下基于中介市场模型的激励机制

智能手机和其他具备短距离无线通信能力设备的普及,使利用机会社交网络进行社会性活动的组织成为了可能.然而,机会网络中的消息传递需要代理节点的支持,代理节点的不协作,会大大降低消息传递的效率.为了解决消息传递过程中的"丢包"问题,引入了具有中介的市场模型作为活动组织激励机制.系统将消息发起者、代理节点和消息接收者分别与市场交易中的卖家、中间商和买家进行类比,分析在不同社会关系与社会属性下的社会福利和收益情况.同时,提出了代理选择的"流浪者算法",选择与多个社区相遇次数较多且与各社区相遇概率较大的用户(流浪者)作为代理,并基于MIT Reality Mining数据集进行实验.以具有中介的市场模型作为激励机制,比较了该数据集下使用不同代理选择算法时形成的收益与社会福利情况.在上述算法的基础上,基于Android平台初步实现了社会性活动组织的原型系统,为活动的组织提供服务.

Incentive Mechanism for Opportunistic Social Networks: The Market Model with Intermediaries

The wide use of smart phones and other intelligent devices equipped for short-range wireless communications makes it possible for people to organize social activities via opportunistic social networks. However, message delivery can be easily disturbed due to the selfishness of nodes. This paper introduces a model of markets with intermediaries as an incentive scheme. On the basis of this model, an agent selection algorithm called "Ranger Algorithm" is proposed. Rangers refer to those users who not only have met with users in other communities for multiple times, but also have a higher probability of meeting those users. Experiments using MIT Reality Mining dataset is implemented and the effects of using market model with intermediaries as an incentive mechanism are analyzed. Results show that this model can effectively serve as an incentive mechanism to assist message delivery. In addition, this paper also finds that Ranger Algorithm outperforms other methods at improving communication performance. Based on the above work, a prototype system is built to help organize social activities.