Efficient Neighbor Discovery in Mobile Opportunistic Networking using Mobility Awareness

To detect peers in mobile opportunistic networks, mobile devices transmit and listen for beacons ("scanning"). If networks are sparse, devices spend quite a bit of energy scanning the vicinity for possible contacts with their radios. Numerous techniques were developed to adapt the scanning intervals as a function of the observed node density. In this paper, we complement such techniques by considering that protocol exchanges between nodes require contacts of a minimal time span and infer scanning opportunities from node mobility. The adaptive beaconing presented in this paper reduces the scanning effort significantly without "losing" many contacts that last long enough to (i) fully establish an ad-hoc connection between two devices and to (ii) transfer a sizeable amount of data. We propose a theoretical model to derive connection probabilities from sojourn times in different mobility settings and evaluate the impact on energy consumption and data forwarding performance using simulations with different mobility models.