Adaptive threshold triggering of GPS for long-term tracking in WSN

Llewyn Salt, Branislav Kusy, Raja Jurdak

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

1 Citation (Scopus)

Abstract

Long-term tracking is an expanding field with applications in logistics, ecology and wearable computing. The main challenge for longevity of tracking applications is the high energy consumption of GPS, which has been addressed by using low power sensors to trigger GPS activation upon detecting events of interest. While triggering can reduce power consumption, static thresholds can underperform in the longterm as context changes. This paper presents an auto-covariance based triggering algorithm that adapts trigger thresholds based on the incoming data and is effective with limited prior knowledge. We test the algorithm on empirical data from flying foxes and show that it outperforms static thresholding and existing adaptive algorithms from the literature.

Original languageEnglish
Title of host publicationProceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR 2015)
EditorsMario Köppen, Bing Xue, Hideyuki Takagi, Ajith Abraham, Azah Kamilah Muda, Kun Ma
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages308-313
Number of pages6
ISBN (Electronic)9781467393607
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015 - Fukuoka, Japan
Duration: 13 Nov 201515 Nov 2015

Other

Other7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015
CountryJapan
CityFukuoka
Period13/11/1515/11/15

Keywords

  • Adaptive Algorithms
  • Embedded Software
  • Learning systems
  • Wireless sensor networks

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  • Cite this

    Salt, L., Kusy, B., & Jurdak, R. (2015). Adaptive threshold triggering of GPS for long-term tracking in WSN. In M. Köppen, B. Xue, H. Takagi, A. Abraham, A. K. Muda, & K. Ma (Eds.), Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR 2015) (pp. 308-313). [7492827] Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/SOCPAR.2015.7492827