In this paper, we propose EPCM(Efficient Prediction-based Context-awareness Matrix) algorithm analyzing connectivity by predicting cluster's context data such as velocity and direction. In the existing DTN, unrestricted relay node selection causes an increase of delay and packet loss. The overhead is occurred by limited storage and capability. Therefore, we propose the EPCM algorithm analyzing predicted context data using context matrix and adaptive revision weight, and selecting relay node by considering connectivity between cluster and base station. The proposed algorithm saves context data to the context matrix and analyzes context according to variation and predicts context data after revision from adaptive revision weight. From the simulation results, the EPCM algorithm provides the high packet delivery ratio by selecting relay node according to predicted context data matrix.키워드 : DTN, 상황인식, 연결성, 매트릭스

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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.