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Impact of Mobility Models on Social Structure Formation in Opportunistic Networks

机译:机会网络中交通模型对社会结构形成的影响

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In mobile opportunistic networks, the network topology is unpredictable and very dynamic due to the existence of the mobile nodes in the network. The nodes' mobility affect the nodes' interaction frequency. Hence, it also affecting the social structure formation between nodes. In this paper, we study the impact of different mobility models namely Random Walk, Random Waypoint, Gauss Markov and D-GM on the social structure formation in opportunistic networks. The study shows that different mobility models have different impact on the social structures formation. Based on our experimental results, the Gauss Markov model provides better social structure compared to others because it creates more opportunities for a node to interact with different nodes.
机译:在移动机会网络中,由于网络中存在移动节点,因此网络拓扑是不可预测的且非常动态。节点的移动性影响节点的交互频率。因此,它也影响节点之间的社会结构形成。在本文中,我们研究了随机游走,随机航点,高斯马尔可夫和D-GM等不同流动性模型对机会网络中社会结构形成的影响。研究表明,不同的流动模型对社会结构形成有不同的影响。根据我们的实验结果,高斯马尔可夫模型提供了比其他模型更好的社会结构,因为它为节点与不同节点交互提供了更多机会。

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