LAUSR

One of the most challenging issues in Mobile Social Networks (MSNs) is to design a messages forwarding method that achieves high delivery and low communication overhead. Single-copy forwarding methods boasting minimum communication overhead are well-known in MSNs. However, attaining a reasonable delivery ratio by using a single-copy method is an open problem. A common way to resolve the problem is social-aware forwarding, but this faces two main weaknesses: one is that they either are unaware of community detection, or use supervised learning strategies, other is that they generally use the relay-destination contact probability for predicting future contacts without considering the contact time. In this paper, we propose community-aware forwarding (CAF) as a new single-copy forwarding method using Hidden Semi-Markov Model (HSMM) to find communities by utilizing the similarity of node contact patterns in different sojourn cycles. In this study, we train HSMM as an unsupervised algorithm to compute the node community transition and then propose a novel forwarding method that utilizes message expiration in order to make relay selection. Evaluation results confirm our superior CAF performance over the popular solutions investigated in terms of message delivery and latency.