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Effective Social Relationship Measurement and Cluster Based Routing in Mobile Opportunistic Networks

机译:移动机会网络中有效的社会关系度量和基于集群的路由

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摘要

In mobile opportunistic networks, the social relationship among nodes has an important impact on data transmission efficiency. Motivated by the strong share ability of “circles of friends” in communication networks such as Facebook, Twitter, Wechat and so on, we take a real-life example to show that social relationships among nodes consist of explicit and implicit parts. The explicit part comes from direct contact among nodes, and the implicit part can be measured through the “circles of friends”. We present the definitions of explicit and implicit social relationships between two nodes, adaptive weights of explicit and implicit parts are given according to the contact feature of nodes, and the distributed mechanism is designed to construct the “circles of friends” of nodes, which is used for the calculation of the implicit part of social relationship between nodes. Based on effective measurement of social relationships, we propose a social-based clustering and routing scheme, in which each node selects the nodes with close social relationships to form a local cluster, and the self-control method is used to keep all cluster members always having close relationships with each other. A cluster-based message forwarding mechanism is designed for opportunistic routing, in which each node only forwards the copy of the message to nodes with the destination node as a member of the local cluster. Simulation results show that the proposed social-based clustering and routing outperforms the other classic routing algorithms.
机译:在移动机会网络中,节点之间的社会关系对数据传输效率具有重要影响。受Facebook,Twitter,微信等通信网络中“朋友圈”的强大共享能力的激励,我们以一个真实的例子来说明节点之间的社会关系由显性和隐性部分组成。显性部分来自节点之间的直接接触,而隐性部分可以通过“朋友圈”来衡量。我们给出了两个节点之间显性和隐性社会关系的定义,根据节点的接触特征给出了显性和隐性部分的自适应权重,并设计了分布式机制来构造节点的“朋友圈”,即用于计算节点之间社会关系的隐性部分。基于对社会关系的有效度量,我们提出了一种基于社会的聚类和路由方案,其中每个节点选择具有紧密社会关系的节点以形成本地集群,并使用自控方法来保持所有集群成员始终彼此有密切的关系。基于集群的消息转发机制被设计用于机会路由,其中​​每个节点仅将消息的副本转发给目标节点为本地集群成员的节点。仿真结果表明,所提出的基于社交的聚类和路由性能优于其他经典的路由算法。

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