Estimation of sensor-network topology from time-series sensor data using ant colony optimization method

Kensuke Takahashi, Toshiharu Sugawara

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

    2 Citations (Scopus)

    Abstract

    We propose a method for estimating sensor network topology from only with time-series sensor data and without prior knowledge about the locations of sensors. The proposed method is based on ant colony optimization (ACO) but is further improved, compared with previous work[s], to construct a more accurate topology through an examination of the reliability of the acquired sensor data for the adjacency estimation. This reliability value is used to control the amount of pheromones deposited. We evaluate our method using actual sensor data and show that it can estimate adjacencies, in which the error rate is approximately 87% less than that of the previous method.

    Original languageEnglish
    Title of host publication2008 IEEE Swarm Intelligence Symposium, SIS 2008
    DOIs
    Publication statusPublished - 2008
    Event2008 IEEE Swarm Intelligence Symposium, SIS 2008 - St. Louis, MO
    Duration: 2008 Sep 212008 Sep 23

    Other

    Other2008 IEEE Swarm Intelligence Symposium, SIS 2008
    CitySt. Louis, MO
    Period08/9/2108/9/23

    Fingerprint

    Ant colony optimization
    Sensor networks
    Time series
    Topology
    Sensors

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computational Theory and Mathematics
    • Electrical and Electronic Engineering

    Cite this

    Estimation of sensor-network topology from time-series sensor data using ant colony optimization method. / Takahashi, Kensuke; Sugawara, Toshiharu.

    2008 IEEE Swarm Intelligence Symposium, SIS 2008. 2008. 4668278.

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

    Takahashi, K & Sugawara, T 2008, Estimation of sensor-network topology from time-series sensor data using ant colony optimization method. in 2008 IEEE Swarm Intelligence Symposium, SIS 2008., 4668278, 2008 IEEE Swarm Intelligence Symposium, SIS 2008, St. Louis, MO, 08/9/21. https://doi.org/10.1109/SIS.2008.4668278
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