Decentralised detection of periodic encounter communities in opportunistic networks

We tackle the problem of individuals being able to self-detect the encounter communities within which they periodically occur. This has widespread applicability, not least for future communication systems where content can be locally shared via wireless opportunistic networking when devices carried by participants come into close range. In this paper, we introduce a comprehensive model and decentralised algorithm to accomplish the detection of periodic communities in opportunistic networks. To the best of our knowledge, this is the first decentralised algorithm for the detection of periodic communities. We investigate the behaviour of our approach both analytically and with real-world data.