Traffic deduction exploring sensor data's intra-correlations in train track monitoring WSN

Zhi Liu; Toshitaka Tsuda; Hiroshi Watanabe

doi:10.1109/ICSENS.2015.7370621

Traffic deduction exploring sensor data's intra-correlations in train track monitoring WSN

Zhi Liu, Toshitaka Tsuda, Hiroshi Watanabe

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

7 Citations (Scopus)

Abstract

WSNs are good options to help monitor the scene of interest and notify the unusual happening to control center. But sensors' high sampling rates lead to tremendous network traffic over the bandwidth-limited and energy-critical WSNs, hence how to reduce the network traffic while maintaining the unusual events monitoring function becomes important. In this paper, we investigate the intra-correlations of the data generated by each sensor at different time instances. And we propose a traffic deduction algorithm exploring the sensor data's intra-correlations which could reduce the data volume significantly and guarantee the parameters needed for unusual detection are delivered.

Original language	English
Title of host publication	2015 IEEE SENSORS - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)	9781479982028
DOIs	https://doi.org/10.1109/ICSENS.2015.7370621
Publication status	Published - 2015 Dec 31
Event	14th IEEE SENSORS - Busan, Korea, Republic of Duration: 2015 Nov 1 → 2015 Nov 4

Other

Other	14th IEEE SENSORS
Country/Territory	Korea, Republic of
City	Busan
Period	15/11/1 → 15/11/4

ASJC Scopus subject areas

Instrumentation
Electronic, Optical and Magnetic Materials
Spectroscopy
Electrical and Electronic Engineering

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10.1109/ICSENS.2015.7370621

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title = "Traffic deduction exploring sensor data's intra-correlations in train track monitoring WSN",

abstract = "WSNs are good options to help monitor the scene of interest and notify the unusual happening to control center. But sensors' high sampling rates lead to tremendous network traffic over the bandwidth-limited and energy-critical WSNs, hence how to reduce the network traffic while maintaining the unusual events monitoring function becomes important. In this paper, we investigate the intra-correlations of the data generated by each sensor at different time instances. And we propose a traffic deduction algorithm exploring the sensor data's intra-correlations which could reduce the data volume significantly and guarantee the parameters needed for unusual detection are delivered.",

author = "Zhi Liu and Toshitaka Tsuda and Hiroshi Watanabe",

year = "2015",

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isbn = "9781479982028",

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AU - Liu, Zhi

AU - Tsuda, Toshitaka

AU - Watanabe, Hiroshi

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Y1 - 2015/12/31

N2 - WSNs are good options to help monitor the scene of interest and notify the unusual happening to control center. But sensors' high sampling rates lead to tremendous network traffic over the bandwidth-limited and energy-critical WSNs, hence how to reduce the network traffic while maintaining the unusual events monitoring function becomes important. In this paper, we investigate the intra-correlations of the data generated by each sensor at different time instances. And we propose a traffic deduction algorithm exploring the sensor data's intra-correlations which could reduce the data volume significantly and guarantee the parameters needed for unusual detection are delivered.

AB - WSNs are good options to help monitor the scene of interest and notify the unusual happening to control center. But sensors' high sampling rates lead to tremendous network traffic over the bandwidth-limited and energy-critical WSNs, hence how to reduce the network traffic while maintaining the unusual events monitoring function becomes important. In this paper, we investigate the intra-correlations of the data generated by each sensor at different time instances. And we propose a traffic deduction algorithm exploring the sensor data's intra-correlations which could reduce the data volume significantly and guarantee the parameters needed for unusual detection are delivered.

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M3 - Conference contribution

AN - SCOPUS:84963627079

SN - 9781479982028

BT - 2015 IEEE SENSORS - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 14th IEEE SENSORS

Y2 - 1 November 2015 through 4 November 2015

ER -

Traffic deduction exploring sensor data's intra-correlations in train track monitoring WSN

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