Understanding Urban Interactions from Bluetooth Phone Contact Traces Export

@incollection{natarajan:interactions, abstract = {The increasing sophistication of mobile devices has enabled several mobile social software applications, which are based on opportunistic exchange of data amongst devices in proximity of each other. Examples include Delay Tolerant Networking (DTN) and PeopleNet. In this context, understanding user interactions is essential to designing algorithms which are efficient and enhance the user experience. In our experiment, users were handed Bluetooth enabled phones and asked to carry them all the time to log information about other devices in their proximity. Data was logged over several months, with over 350,000 contacts logged and over 10,000 unique devices discovered in this period. This paper analyzes this data by charactering the distributions of metrics such as contact time and inter-pair-contact time, and introducing several other important metrics useful for understanding user interactions. We find that most metrics follow a power law, except for inter-pair-contact time. We also look for patterns in user interactions, with the hope that these can be exploited for better algorithm design.}, author = {Natarajan, Anirudh and Motani, Mehul and Srinivasan, Vikram}, citeulike-article-id = {5313577}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-540-71617-4_12}, citeulike-linkout-1 = {http://www.springerlink.com/content/w30q50v238386x32}, doi = {10.1007/978-3-540-71617-4_12}, journal = {Passive and Active Network Measurement}, keywords = {bluetooth, cambridge\_haggle, crawdad, dtn, mobile, modelling, nus\_bluetooth, opportunistic-networks}, pages = {115--124}, posted-at = {2009-07-31 21:38:44}, priority = {2}, title = {Understanding Urban Interactions from Bluetooth Phone Contact Traces}, url = {http://dx.doi.org/10.1007/978-3-540-71617-4_12}, year = {2007} }

TY - CHAP ID - natarajan:interactions L3 - citeulike-article-id:5313577 N2 - The increasing sophistication of mobile devices has enabled several mobile social software applications, which are based on opportunistic exchange of data amongst devices in proximity of each other. Examples include Delay Tolerant Networking (DTN) and PeopleNet. In this context, understanding user interactions is essential to designing algorithms which are efficient and enhance the user experience. In our experiment, users were handed Bluetooth enabled phones and asked to carry them all the time to log information about other devices in their proximity. Data was logged over several months, with over 350,000 contacts logged and over 10,000 unique devices discovered in this period. This paper analyzes this data by charactering the distributions of metrics such as contact time and inter-pair-contact time, and introducing several other important metrics useful for understanding user interactions. We find that most metrics follow a power law, except for inter-pair-contact time. We also look for patterns in user interactions, with the hope that these can be exploited for better algorithm design. JF - Passive and Active Network Measurement EP - 124 TI - Understanding Urban Interactions from Bluetooth Phone Contact Traces SP - 115 KW - bluetooth KW - cambridge_haggle KW - crawdad KW - dtn KW - mobile KW - modelling KW - nus_bluetooth KW - opportunistic-networks AU - Natarajan, Anirudh AU - Motani, Mehul AU - Srinivasan, Vikram PY - 2007/// UR - http://dx.doi.org/10.1007/978-3-540-71617-4_12 ER -