Predicting Encounters in Opportunistic Networks Using Gaussian Process

In particular types of Delay-Tolerant Networks (DTN) such as Opportunistic Mobile Networks, node connectivity is transient, and connections are sparse and small in length. For this reason, traditional routing mechanisms are no longer suitable. Routing algorithms designed for such networks try to maximize the probability of successful message delivery. The most popular approach is to compute the probability of delivering a message using information such as node contacts and location knowledge, thus using past encounters to predict future ones. In this paper we investigate the predictability of human mobility and interactions patterns. We propose the use of supervised learning techniques together with Gaussian process modeling to predict future encounters based on historical patterns of individual nodes. We analyze their accuracy compared to previous prediction techniques, using real-world mobility data traces.