@inproceedings { 1409953,
	abstract = {People living in urban areas spend a considerable amount of
time on public transport, for example, commuting to/from
work. During these periods, opportunities for inter-personal
networking present themselves, as many members of the
public now carry electronic devices equipped with Bluetooth
or other wireless technology. Using these devices, individuals can share content (e.g., music, news and video clips) with
fellow travellers that are on the same train or bus. Transferring media content takes time; in order to maximise the
chances of successful downloads, users should identify neighbours that possess desirable content and who will travel with
them for long-enough periods. In this paper, we propose a
user-centric prediction scheme that collects historical colocation information to determine the best content sources. The
scheme works on the assumption that people have a high
degree of regularity in their movements. We first validate
this assumption on a real dataset, that consists of traces of
people moving in a large city's mass transit system. We then
demonstrate experimentally on these traces that our prediction scheme significantly improves communication efficiency,
when compared to a memory(history)-less source selection
scheme.},
	keywords = {Bluetooth, P2P, Transport, Media},
	address = {New York, NY, USA},
	publisher = {ACM},
	doi = {http://doi.acm.org/10.1145/1409944.1409953},
	location = {San Francisco, California, USA},
	pages = {58--69},
	isbn = {978-1-60558-096-8},
	year = {2008},
	booktitle = {MobiCom '08: Proceedings of the 14th ACM international conference on Mobile computing and networking},
	title = {Media sharing based on colocation prediction in urban transport},
	author = {McNamara , Liam and Mascolo , Cecilia and Capra , Licia}
}