Path prediction based on second-order Markov chain for the opportunistic networks

Yubo Deng, Wei Liu, Lei Zhang, Yongping Xiong, Yunchuan Sun
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引用次数: 2

Abstract

In the opportunistic networks, nodes carry and store the data and forward it until they encounter each other. How to choose an appropriate opportunity to forward data is pivotal for nodes' routing in this type of networks. Since nodes currently will keep a regular movement state in the scene of this paper discussed, forecasting a node's moving track in the near future would be very helpful. Through this way, the node can modify the forwarding strategy in time and increase the success rate of data routing. We proposed a path prediction model based on second-order Markov chain in this paper to analyze and predict node's next path choice at an intersection depending on its statistic of history tracks. Then we use the real data from Dartmouth College to verify the model and the result shows that it is more precise than normal one step Markov models and is more reasonable than some other models such as Manhattan model.
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基于二阶马尔可夫链的机会网络路径预测
在机会网络中,节点携带和存储数据,并将其转发,直到它们彼此相遇。如何选择合适的机会进行数据转发,是这类网络中节点路由的关键。由于当前节点在本文所讨论的场景中会保持规律的运动状态,因此预测节点在不久的将来的运动轨迹将非常有帮助。通过这种方式,节点可以及时修改转发策略,提高数据路由的成功率。本文提出了一种基于二阶马尔可夫链的路径预测模型,利用节点的历史轨迹统计量来分析和预测节点在路口的下一个路径选择。然后用达特茅斯学院的实际数据对模型进行验证,结果表明该模型比一般的一步马尔可夫模型更精确,比其他一些模型如曼哈顿模型更合理。
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