SensLoc: sensing everyday places and paths using less energy

Continuously understanding a user's location context in colloquial terms and the paths that connect the locations unlocks many opportunities for emerging applications. While extensive research effort has been made on efficiently tracking a user's raw coordinates, few attempts have been made to efficiently provide everyday contextual information about these locations as places and paths. We introduce SensLoc, a practical location service to provide such contextual information, abstracting location as place visits and path travels from sensor signals. SensLoc comprises of a robust place detection algorithm, a sensitive movement detector, and an on-demand path tracker. Based on a user's mobility, SensLoc proactively controls active cycle of a GPS receiver, a WiFi scanner, and an accelerometer. Pilot studies show that SensLoc can correctly detect 94% of the place visits, track 95% of the total travel distance, and still only consume 13% of energy than algorithms that periodically collect coordinates to provide the same information.

[1]  Deborah Estrin,et al.  Using Context Annotated Mobility Profiles to Recruit Data Collectors in Participatory Sensing , 2009, LoCA.

[2]  Injong Rhee,et al.  Towards Mobile Phone Localization without War-Driving , 2010, 2010 Proceedings IEEE INFOCOM.

[3]  Mika Raento,et al.  Adaptive On-Device Location Recognition , 2004, Pervasive.

[4]  Fumihiro Kato,et al.  Exploiting Multiple Radii to Learn Significant Locations , 2005, LoCA.

[5]  Dan Frankowski,et al.  Because i carry my cell phone anyway , 2006, CHI 2006.

[6]  Romit Roy Choudhury,et al.  Micro-Blog: sharing and querying content through mobile phones and social participation , 2008, MobiSys '08.

[7]  Xing Xie,et al.  Mining user similarity based on location history , 2008, GIS '08.

[8]  Jatinder Pal Singh,et al.  Improving energy efficiency of location sensing on smartphones , 2010, MobiSys '10.

[9]  Chandramohan A. Thekkath,et al.  StarTrack: a framework for enabling track-based applications , 2009, MobiSys '09.

[10]  Mike Y. Chen,et al.  Voting with your feet : An investigative study of the relationship between place visit behavior and preference , 2006 .

[11]  John Krumm,et al.  Accuracy characterization for metropolitan-scale Wi-Fi localization , 2005, MobiSys '05.

[12]  Guang Yang,et al.  Discovering Significant Places from Mobile Phones - A Mass Market Solution , 2009, MELT.

[13]  Mikkel Baun Kjærgaard,et al.  EnTracked: energy-efficient robust position tracking for mobile devices , 2009, MobiSys '09.

[14]  A. Kansal,et al.  Energy-Accuracy Aware Localization for Mobile Devices , 2009 .

[15]  Feng Zhao,et al.  Energy-accuracy trade-off for continuous mobile device location , 2010, MobiSys '10.

[16]  Sourav Bhattacharya,et al.  Identifying Meaningful Places: The Non-parametric Way , 2009, Pervasive.

[17]  William G. Griswold,et al.  Place-Its: A Study of Location-Based Reminders on Mobile Phones , 2005, UbiComp.

[18]  W. H. Engelmann,et al.  The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants , 2001, Journal of Exposure Analysis and Environmental Epidemiology.

[19]  Yi Wang,et al.  A framework of energy efficient mobile sensing for automatic user state recognition , 2009, MobiSys '09.

[20]  Joongheon Kim,et al.  Energy-efficient rate-adaptive GPS-based positioning for smartphones , 2010, MobiSys '10.

[21]  Gaetano Borriello,et al.  Extracting places from traces of locations , 2004, MOCO.

[22]  Sunny Consolvo,et al.  Learning and Recognizing the Places We Go , 2005, UbiComp.

[23]  Kentaro Toyama,et al.  Project Lachesis: Parsing and Modeling Location Histories , 2004, GIScience.

[24]  Sivan Toledo,et al.  VTrack: accurate, energy-aware road traffic delay estimation using mobile phones , 2009, SenSys '09.

[25]  Thad Starner,et al.  Using GPS to learn significant locations and predict movement across multiple users , 2003, Personal and Ubiquitous Computing.

[26]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[27]  Henry A. Kautz,et al.  Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields , 2007, Int. J. Robotics Res..

[28]  Deborah Estrin,et al.  Discovering semantically meaningful places from pervasive RF-beacons , 2009, UbiComp.

[29]  P. Jaccard THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.1 , 1912 .

[30]  Gaetano Borriello,et al.  Extracting places from traces of locations , 2004 .

[31]  Romit Roy Choudhury,et al.  EnLoc: Energy-Efficient Localization for Mobile Phones , 2009, IEEE INFOCOM 2009.

[32]  Romit Roy Choudhury,et al.  SurroundSense: mobile phone localization via ambience fingerprinting , 2009, MobiCom '09.

[33]  Dan Frankowski,et al.  Because I carry my cell phone anyway: functional location-based reminder applications , 2006, CHI.

[34]  Wei Pan,et al.  SoundSense: scalable sound sensing for people-centric applications on mobile phones , 2009, MobiSys '09.

[35]  Marta C. González,et al.  Understanding individual human mobility patterns , 2008, Nature.

[36]  Chris Schmandt,et al.  Location-Aware Information Delivery with ComMotion , 2000, HUC.