Sensor network topology estimation using time-series data from infrared human presence sensors

Yuta Watanabe, Satoshi Kurihara, Toshiharu Sugawara

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

7 Citations (Scopus)

Abstract

We describe a method for accurately estimating the topology of sensor networks from time-series data collected from infrared proximity sensors. Our method is a hybrid combining two different methodologies: ant colony optimization (ACO), which is an evolutionary computation algorithm; and an adjacency score, which is a novel statistical measure based on heuristic knowledge. We show that, using actual data gathered from a real-world environment, our method can estimate a sensor network topology whose accuracy is approximately 95% in our environment. This is an acceptable result for real-world sensor-network applications.

Original languageEnglish
Title of host publicationIEEE Sensors 2010 Conference, SENSORS 2010
Pages664-667
Number of pages4
DOIs
Publication statusPublished - 2010 Dec 1
Externally publishedYes
Event9th IEEE Sensors Conference 2010, SENSORS 2010 - Waikoloa, HI, United States
Duration: 2010 Nov 12010 Nov 4

Publication series

NameProceedings of IEEE Sensors

Other

Other9th IEEE Sensors Conference 2010, SENSORS 2010
CountryUnited States
CityWaikoloa, HI
Period10/11/110/11/4

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ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Watanabe, Y., Kurihara, S., & Sugawara, T. (2010). Sensor network topology estimation using time-series data from infrared human presence sensors. In IEEE Sensors 2010 Conference, SENSORS 2010 (pp. 664-667). [5690090] (Proceedings of IEEE Sensors). https://doi.org/10.1109/ICSENS.2010.5690090