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Data Forwarding Based on Node Moving Trajectory in Mobile Social Networks

机译:移动社交网络中基于节点移动轨迹的数据转发

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With the popularization of wireless mobile device, mobile social networks (MSNets) have begun to attract more and more attention. In MSNets, mobile social users can communicate with each other by intermittent connectivity. The wireless device moving trajectory reflects the social attribute of the device carrier. In this paper, the relay selection is addressed in terms of both the absolute character and the relative character of node moving trajectory. First, the hot degree of node movement trajectory is defined based on the steady-state node distribution using a semi-markov chain. Moreover, another semi-markov chain is used to predict the future locations of a mobile user, with the predictive location nodes as basis, and the similarity of node movement trajectories is presented. Furthermore, a data forwarding based on node moving trajectory is proposed. Its main idea is to choose a node with higher hot degree of node moving trajectory and lower similarity of movement trajectories between it and a packet carrier to propagate the data packets. The simulation results show that, compared with the Spray and Wait routing and the social groups-based routing, our algorithm can outperform them in the delivery ratio and delivery delay, and apparently reduce network overhead compared with the Epidemic algorithm. Additionally, our algorithm nears the maximum delivery ratio and minimum delivery delay obtained by the Epidemic algorithm in a realistic trace data.
机译:随着无线移动设备的普及,移动社交网络(MSNets)已开始引起越来越多的关注。在MSNets中,移动社交用户可以通过间歇性连接相互通信。无线设备移动轨迹反映了设备载体的社会属性。在本文中,从节点移动轨迹的绝对特性和相对特性这两个方面来讨论继电器的选择。首先,使用半马尔可夫链基于稳态节点分布来定义节点移动轨迹的热度。此外,使用另一个半马尔可夫链以预测位置节点为基础,预测移动用户的未来位置,并给出了节点移动轨迹的相似性。此外,提出了一种基于节点移动轨迹的数据转发方法。其主要思想是选择节点移动轨迹的热点度较高,移动轨迹与包载体之间的相似度较低的节点来传播数据包。仿真结果表明,与Epidemic算法相比,与Spray和Wait路由以及基于社会群体的路由相比,我们的算法可以在传递比率和传递延迟方面胜过它们,并且明显减少了网络开销。此外,我们的算法在实际的跟踪数据中接近Epidemic算法获得的最大传递比率和最小传递延迟。

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