Hybrid Battery-friendly Mobile Solution for Extracting Users’ Visited Places

Extracting and analyzing outdoor humans’ activities represent a strong support for several applications fields, ranging from traffic management to marketing and social studies. Mobile users take their devices with them everywhere which leads to an increasing availability of persons’ traces used to recognize their activities. However, mobile environment is distinguished from one to another by its resources limitations. In this paper, the authors present a novel hybrid approach that combines activity recognition and prediction algorithms in order to online recognize users’ outdoor activities without draining the mobile resources. Their approach minimizes activity computations by wisely reducing the search frequency of activities, the authors demonstrate that their proposal is capable of reducing the battery consumption up to 60% while maintaining the same accuracy as its similar.

• Record URL:
  • https://doi.org/10.1016/j.procs.2016.08.008
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S1877050916317483
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/18770509
• Supplemental Notes:
  • © 2016 Published by Elsevier B.V. 13th International Conference on Mobile Systems and Pervasive Computing (MobiSPC 2016)
• Authors:
  • Boukhechba, Mehdi
  • Bouzouane, Abdenour
  • Gaboury, Sebastien
  • Gouin-Vallerand, Charles
  • Giroux, Sylvain
  • Bouchard, Bruno
• Publication Date: 2016

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 25-32
• Serial:
  • Procedia Computer Science
  • Volume: 94
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1877-0509
  • Serial URL: http://www.sciencedirect.com/science/journal/18770509
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Activity choices; Electric batteries; Highway traffic control; Navigation systems; Smartphones
• Subject Areas: Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01610921
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 21 2016 2:41PM