Periodic properties of user mobility and access-point popularity Export

@article{kim:jclassify, abstract = {Understanding user mobility and its effect on access points (APs) is important in designing location-aware systems and wireless networks. Although various studies of wireless networks have provided useful insights, it is hard to apply them to other situations. Here we present a general methodology for extracting mobility information from wireless network traces, and for classifying mobile users and APs. We used the Fourier transform to reveal important periods and chose the two strongest to serve as parameters to a classification system based on Bayes' theory. Analysis of one-month traces shows that while a daily pattern is common among both users and APs, a weekly pattern is common only for APs. Analysis of one-year traces revealed that both user mobility and AP popularity depend on the academic calendar. By plotting the classes of APs on our campus map, we discovered that their periodic behavior depends on their proximity to other APs.}, author = {Kim, Minkyong and Kotz, David}, citeulike-article-id = {2820551}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/s00779-006-0093-4}, doi = {10.1007/s00779-006-0093-4}, journal = {Journal of Personal and Ubiquitous Computing}, keywords = {crawdad, dartmouth\_campus, measurement, wireless}, posted-at = {2008-05-21 17:13:08}, priority = {2}, title = {Periodic properties of user mobility and access-point popularity}, url = {http://dx.doi.org/10.1007/s00779-006-0093-4}, year = {2006} }

TY - JOUR ID - kim:jclassify L3 - citeulike-article-id:2820551 N2 - Understanding user mobility and its effect on access points (APs) is important in designing location-aware systems and wireless networks. Although various studies of wireless networks have provided useful insights, it is hard to apply them to other situations. Here we present a general methodology for extracting mobility information from wireless network traces, and for classifying mobile users and APs. We used the Fourier transform to reveal important periods and chose the two strongest to serve as parameters to a classification system based on Bayes' theory. Analysis of one-month traces shows that while a daily pattern is common among both users and APs, a weekly pattern is common only for APs. Analysis of one-year traces revealed that both user mobility and AP popularity depend on the academic calendar. By plotting the classes of APs on our campus map, we discovered that their periodic behavior depends on their proximity to other APs. JF - Journal of Personal and Ubiquitous Computing TI - Periodic properties of user mobility and access-point popularity KW - crawdad KW - dartmouth_campus KW - measurement KW - wireless AU - Kim, Minkyong AU - Kotz, David PY - 2006/// UR - http://dx.doi.org/10.1007/s00779-006-0093-4 ER -