Spatial and Temporal Correlations-Based Routing Algorithm in Intermittent Connectivity Human Social Network

Abstract

The social network formed by people is one of the key applications of Delay-Tolerant Network (DTN). Owing to its intermittent connectivity and unique human mobility patterns, how to transmit data in an effective way is a challenging problem for the social network. In this paper, we propose the idea of Trip History Model (THM) which establishes a model on a single person's mobility, and then a Spatial and Temporal Correlations-Based Routing Algorithm (STC) is proposed. In STC, the node delivery probability is calculated according to both a node's current moving prediction and its history record to give guidance for message transmission. Our simulation results show that, compared with LABEL and PROPHET algorithms, STC effectively improves the routing performance of the network.