Evaluating location predictors with extensive Wi-Fi mobility data

By Libo Song, David F. Kotz, Ravi Jain, Xiaoning He. IEEE INFOCOM 2004, 2003

321 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Epidemiology, Transportation

View Paper: https://www.semanticscholar.org/paper/7d25eff10a7fbe53dda9dae7d3ac059ad6e7ae41

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Cecilia Mascolo
2. Mirco Musolesi
3. Vania Conan
4. Jérémie Leguay
5. Timur Friedman
6. Zhili Sun
7. Arun Venkataramani
8. Aruna Devi Balasubramanian

In Epidemiology

1. Anthony J. Nicholson
2. Brian D. Noble
3. J. L. Boudec

In Transportation

1. Cecilia Mascolo
2. Mirco Musolesi
3. Salvatore Scellato
4. Andrew T. Campbell
5. Anthony J. Nicholson
6. Brian D. Noble
7. Vito Latora
8. David F. Kotz

Citations in the corpus

• Extracting a Mobility Model from Real User Traces (2006, background): “This characterization provides useful insights itself, for example, to researchers interested in predicting mobility in support of location-aware applications [1] or for network optimization [2].”
• NextPlace: A Spatio-temporal Prediction Framework for Pervasive Systems (2011, methodology): “The evaluation of prediction techniques applied to the problem of forecasting the next location (but not the arrival time to that location and the corresponding residence time) are presented in [27].”
• Predicting User-Cell Association in Cellular Networks from Tracked Data (2009, methodology): “in [12] present extensive evaluation of location predictors, using a two-year trace of over 6000 users of a Wi-Fi campus network.”
• Evaluating Mobility Pattern Space Routing for DTNs (2005, background): “in their prior work [13] on mobility prediction.”
• BreadCrumbs: forecasting mobile connectivity (2008, methodology): “Song et al [25] applied di.erent mobility prediction meth­ods to the problem of improving bandwidth provisioning and hando. for VoIP telephony.”
• Practical Routing in Delay-Tolerant Networks (2005, background): “This data is useful because while the mobility appears to be random, there are patterns that can be exploited [17].”
• DTN routing as a resource allocation problem (2007, background): “[30] suggests that meeting times between people in a busy public place follow a uniform exponential model, i.”