Prediction method of node movement using Markov Chain in DTN

DTN에서 Markov Chain을 이용한 노드의 이동 예측 기법

  • Received : 2016.02.24
  • Accepted : 2016.03.21
  • Published : 2016.05.31


This paper describes a novel Context-awareness Markov Chain Prediction (CMCP) algorithm based on movement prediction using Markov chain in Delay Tolerant Network (DTN). The existing prediction models require additional information such as a node's schedule and delivery predictability. However, network reliability is lowered when additional information is unknown. To solve this problem, we propose a CMCP model based on node behaviour movement that can predict the mobility without requiring additional information such as a node's schedule or connectivity between nodes in periodic interval node behavior. The main contribution of this paper is the definition of approximate speed and direction for prediction scheme. The prediction of node movement forwarding path is made by manipulating the transition probability matrix based on Markov chain models including buffer availability and given interval time. We present simulation results indicating that such a scheme can be beneficial effects that increased the delivery ratio and decreased the transmission delay time of predicting movement path of the node in DTN.


Delay Tolerant Network;Prediction;Context-awareness;Markov chain


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Supported by : MSIP(Ministry of Science, ICT and Future Planning)