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

Kensuke Takahashi; Toshiharu Sugawara

doi:10.1109/SIS.2008.4668278

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

Kensuke Takahashi^*, Toshiharu Sugawara

^*Corresponding author for this work

School of Fundamental Science and Engineering

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

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 language	English
Title of host publication	2008 IEEE Swarm Intelligence Symposium, SIS 2008
DOIs	https://doi.org/10.1109/SIS.2008.4668278
Publication status	Published - 2008 Dec 22
Event	2008 IEEE Swarm Intelligence Symposium, SIS 2008 - St. Louis, MO, United States Duration: 2008 Sept 21 → 2008 Sept 23

Publication series

Name	2008 IEEE Swarm Intelligence Symposium, SIS 2008

Conference

Conference	2008 IEEE Swarm Intelligence Symposium, SIS 2008
Country/Territory	United States
City	St. Louis, MO
Period	08/9/21 → 08/9/23

ASJC Scopus subject areas

Artificial Intelligence
Computational Theory and Mathematics
Electrical and Electronic Engineering

Access to Document

10.1109/SIS.2008.4668278

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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, 2008 IEEE Swarm Intelligence Symposium, SIS 2008, St. Louis, MO, United States, 08/9/21. https://doi.org/10.1109/SIS.2008.4668278

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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.",

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N2 - 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.

AB - 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.

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

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