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)


    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 publicationProceedings of IEEE Sensors
    Number of pages4
    Publication statusPublished - 2010
    Event9th IEEE Sensors Conference 2010, SENSORS 2010 - Waikoloa, HI
    Duration: 2010 Nov 12010 Nov 4


    Other9th IEEE Sensors Conference 2010, SENSORS 2010
    CityWaikoloa, HI


    ASJC Scopus subject areas

    • Electrical and Electronic Engineering

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