Context-Aware Connectivity Analysis in Delay Tolerant Networks

In this paper, we propose EPCM (Efficient Prediction-based Context-awareness Matrix) algorithm analyzing connectivity by predicting a cluster's context data such as velocity and direction. In the existing DTN, unrestricted relay node selection cause increasing delay and packet loss as well as overhead from limited storage and capability. The proposed EPCM algorithm analyzes the predicted context data using context matrix and adaptive revision weight, and selects relay node by considering connectivity between the cluster and the base station. To predict the context data, the proposed EPCM algorithm stores the context data in the context matrix at regular time and analyzes stored context data according to the variation of context data. The mobility of relay node can determine to combine the current context data value with the variation of context data. In order to eliminate the error from the irregular movement of the node, we use the adaptive revision weight to evaluate the reliability of the context data stored in the context matrix. Though the predicted context data, we know that any node can move toward the base station and can select relay node among the neighbor node to transfer the message more efficiently. We simulate the packet delivery ratio both of the proposed EPCM algorithm and PROPHET algorithm selecting relay node according to node-to-node contact. Our results of simulation demonstrate the efficiency of the EPCM algorithm that provide the higher packet delivery ratio by applying the context data such as velocity and direction in the relay node selection process. (C) 2015 The Authors. Published by Elsevier B.V.