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.

In this paper, we propose an algorithm that select efficient relay nodes using information of network environment and nodes. The proposed algorithm can be used changeable weight factors as following network environment in node density. The routing protocols adopting store-carry-forward method are used for solving network problems occurred by unstable end-to-end connection in Delay Tolerant Networks(DTNs). Exiting DTN routing algorithms have problems that large latency and overhead because of deficiency of network informations. The proposed algorithm could be provide a solution this problems using changeable weight factor and prediction of network environment. Thus, selected relay nodes work efficiently in unstable and stressed network environment. Simulation results show that enhancement performance as overhead, delivery ratio, average latency compared to exiting DTN routing algorithm.

In this study, prediction routing algorithms are proposed to select efficient relay nodes. While most prediction algorithms assume that nodes need additional information such as node's schedule and connectivity between nodes, the network reliability is lowered when additional information is unknown. To solve this problem, this study proposes a context-aware Markov chain prediction based on the Markov chain that uses the node's movement history information without requiring additional information. The evaluation results show that the proposed scheme has competitive delivery ratio but with less average latency.Creative Commons CC-BY: This article is distributed under the terms of the Creative Commons Attribution 3.0 License

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This paper propose an improving relay node selection method for node-to-node connectivity. This concern with the mobility and analysis of deployed for masking operation using highest connectivity node. The major of Delay Tolerant Network (DTN) routing protocols make use of simple forwarding approach to transmit the message depend on the node's mobility. In this cases, the selection of the irrelevant mobile node induced the delay and packet delivery loss caused by limiting buffer size and computational power of node. Also the proposed algorithm provides the node connectivity considering the mobility and direction select the highest connectivity node from neighbor node using masking operation. From the simulation results, the proposed algorithm compared the packet delivery ratio with PROPHET and Epidemic. The proposed Enhanced Prediction-based Context-awareness Matrix(EPCM) algorithm shows an advantage packet delivery ratio even with selecting relay node according to mobility and direction.