Extracting Localised Mobile Activity Patterns from Cumulative Mobile Spectrum RSSI

Techniques for observing the flow of people are creating new means for observing the dynamics between people and the environments they pass through. This ubiquitous connectivity can be observed and interpreted in real-time, through mobile device activity patterns. Recent research into urban analysis through the use of mobile device usage statistics has presented a need for the collection of this data independently from mobile network operators. In this paper we demonstrate that by extracting cumulative received signal strength indication (RSSI) for overall mobile device transmissions, such information can be obtained independently from network operators. We present preliminary results and suggest future applications for which this collection method may be used.

[1]  Carlo Ratti,et al.  Cellular Census: Explorations in Urban Data Collection , 2007, IEEE Pervasive Computing.

[2]  Hari Balakrishnan,et al.  6th ACM/IEEE International Conference on on Mobile Computing and Networking (ACM MOBICOM ’00) The Cricket Location-Support System , 2022 .

[3]  Mubarak Shah,et al.  Monitoring human behavior from video taken in an office environment , 2001, Image Vis. Comput..

[4]  Eyal de Lara,et al.  Accurate GSM Indoor Localization , 2005, UbiComp.

[5]  Anind K. Dey,et al.  Location-Based Services for Mobile Telephony: a Study of Users' Privacy Concerns , 2003, INTERACT.

[6]  Carlo Ratti,et al.  Mobile Landscapes: Graz in Real Time , 2007, Location Based Services and TeleCartography.

[7]  Andy Hopper,et al.  A new location technique for the active office , 1997, IEEE Wirel. Commun..

[8]  Timo Ojala,et al.  Bluetooth and WAP push based location-aware mobile advertising system , 2004, MobiSys '04.

[9]  Kostas E. Bekris,et al.  Robotics-Based Location Sensing Using Wireless Ethernet , 2005, Wirel. Networks.

[10]  Richard P. Martin,et al.  The limits of localization using signal strength: a comparative study , 2004, 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004..

[11]  Vassilis Kostakos,et al.  Instrumenting the City: Developing Methods for Observing and Understanding the Digital Cityscape , 2006, UbiComp.

[12]  Kevin W. Bowyer,et al.  Face recognition technology: security versus privacy , 2004, IEEE Technology and Society Magazine.

[13]  R. Ahas,et al.  Seasonal tourism spaces in Estonia: Case study with mobile positioning data , 2007 .

[14]  Carlo Ratti,et al.  Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis , 2006 .

[15]  Ian F. Akyildiz,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey , 2006, Comput. Networks.

[16]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[17]  Tsenka Stoyanova,et al.  Evaluation of impact factors on RSS accuracy for localization and tracking applications , 2007, MobiWac '07.

[18]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

[19]  Carlo Ratti,et al.  Real time Rome , 2006 .