On the fly learning of mobility profiles for routing in pocket switched networks

In this paper, we propose a novel routing protocol, PRO, for profile-based routing in pocket switched networks. Differing from previous routing protocols, PRO treats node encounters as periodic patterns and uses them to predict the times of future encounters. Exploiting the regularity of human mobility profiles, PRO achieves fast (low-delivery-latency) and efficient (low-message-overhead) routing in intermittently connected pocket switched networks. PRO is self-learning, completely decentralized, and local to the nodes. Despite being simple, PRO forms a general framework, that can be easily instantiated to solve searching and querying problems in adhoc smartphone networks. We validate the performance of PRO on real world traces and compare its performance with that of previous approaches.