Media sharing based on colocation prediction in urban transport Export

@inproceedings{mcnamara:media, 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.}, address = {New York, NY, USA}, author = {Mcnamara, Liam and Mascolo, Cecilia and Capra, Licia}, booktitle = {MobiCom '08: Proceedings of the 14th ACM international conference on Mobile computing and networking}, citeulike-article-id = {3997884}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1409944.1409953}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1409944.1409953}, doi = {10.1145/1409944.1409953}, isbn = {978-1-60558-096-8}, keywords = {crawdad, crawdad\_mention, dtn, mobility, transport}, location = {San Francisco, CA, USA}, month = {September}, pages = {58--69}, posted-at = {2009-09-20 22:54:05}, priority = {2}, publisher = {ACM}, title = {Media sharing based on colocation prediction in urban transport}, url = {http://dx.doi.org/10.1145/1409944.1409953}, year = {2008} }

TY - CONF ID - mcnamara:media L3 - citeulike-article-id:3997884 N2 - 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. CY - San Francisco, CA, USA SN - 978-1-60558-096-8 AD - New York, NY, USA EP - 69 TI - Media sharing based on colocation prediction in urban transport PB - ACM T2 - MobiCom '08: Proceedings of the 14th ACM international conference on Mobile computing and networking SP - 58 KW - crawdad KW - crawdad_mention KW - dtn KW - mobility KW - transport AU - Mcnamara, Liam AU - Mascolo, Cecilia AU - Capra, Licia PY - 2008/September// UR - http://dx.doi.org/10.1145/1409944.1409953 ER -