The rise and fall of spatio-temporal clusters in mobile ad hoc networks

Cluster detection has been widely applied to the problem of efficient data delivery in highly dynamic mobile ad hoc networks. By grouping participants who meet most often into clusters, hierarchical structures in the network are formed which can be used to efficiently transfer data between the participants. However, data delivery algorithms which rely on clusters can be inefficient in some situations. In the case of dynamic networks formed by encounters between humans, sometimes called Pocket Switched Networks (PSNs), cluster based data delivery methods may see a drop in efficiency if obsolete cluster membership persists despite changes to behavioural patterns. Our work aims to improve the relevance of clusters to particular time frames, and thus improve the performance of cluster based data delivery algorithms in PSNs. Furthermore, we will show that by detecting spatio-temporal clusters in PSNs, we can now improve on the data delivery success rates and efficiency of data delivery algorithms which do not use clustering; something which has been difficult to demonstrate in the past.