A Human Connectivity Model for Opportunistic Mobile Systems

Opportunistic networking protocols have recently started to emerge in different contexts, ranging from vehicular communications and remote populations connectivity to wildlife monitoring. These protocols are mainly based on the ability to exploit asynchronous communication among hosts who can act as carriers for the messages which are first stored and transported, and then delivered when the destination is reached. At the heart of these protocols is the concept of hosts colocation and connectivity patterns. Often, however, the protocols are evaluated using mobility models which tend not to mirror the connectivity patterns of the domain in which the protocol needs to be applied, failing to give insight into the performance of the protocols in realistic settings. In this paper we propose a different approach: based on the assumption that opportunistic networking protocols are based on colocation (and connectivity), we present a model for connectivity patterns, which can be extracted from real data. To validate our approach, we show how we used the Dartmouth Campus traces as one of the inputs of our framework to generate connectivity traces with a similar behaviour.