Intelligent sensor hub benefits for wireless sensor networks

Stanley, Michael; Gervais-Ducouret, Stéphane; Adams, Jon T

doi:10.1109/sas.2012.6166299

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…Wireless signal transmission is also a research hotspot in current intelligent sensors [ 66 ]. In recent years, the wireless sensor network technology has been rapidly developed [ 67 ].…”

Section: Intelligent Sensor Technologymentioning

confidence: 99%

Review on the Traction System Sensor Technology of a Rail Transit Train

Feng

Liao

et al. 2017

Sensors

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The development of high-speed intelligent rail transit has increased the number of sensors applied on trains. These play an important role in train state control and monitoring. These sensors generally work in a severe environment, so the key problem for sensor data acquisition is to ensure data accuracy and reliability. In this paper, we follow the sequence of sensor signal flow, present sensor signal sensing technology, sensor data acquisition, and processing technology, as well as sensor fault diagnosis technology based on the voltage, current, speed, and temperature sensors which are commonly used in train traction systems. Finally, intelligent sensors and future research directions of rail transit train sensors are discussed.

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“…Wireless signal transmission is also a research hotspot in current intelligent sensors [ 66 ]. In recent years, the wireless sensor network technology has been rapidly developed [ 67 ].…”

Section: Intelligent Sensor Technologymentioning

confidence: 99%

Review on the Traction System Sensor Technology of a Rail Transit Train

Feng

Liao

et al. 2017

Sensors

View full text Add to dashboard Cite

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“…Detection of hubs can lead to reduced computational and/or sampling costs. For example in wireless sensor networks, the identification of hub nodes can be useful for reducing power usage and adding or removing sensors from the network [6,7]. Hub detection can also give new insights about underlying structure in the dataset.…”

Section: Introductionmentioning

confidence: 99%

Spectral Correlation Hub Screening of Multivariate Time Series

Firouzi

Wei

Hero

2015

Excursions in Harmonic Analysis, Volume 4

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Summary. This chapter discusses correlation analysis of stationary multivariate Gaussian time series in the spectral or Fourier domain. The goal is to identify the hub time series, i.e., those that are highly correlated with a specified number of other time series. We show that Fourier components of the time series at different frequencies are asymptotically statistically independent. This property permits independent correlation analysis at each frequency, alleviating the computational and statistical challenges of high-dimensional time series. To detect correlation hubs at each frequency, an existing correlation screening method is extended to the complex numbers to accommodate complex-valued Fourier components. We characterize the number of hub discoveries at specified correlation and degree thresholds in the regime of increasing dimension and fixed sample size. The theory specifies appropriate thresholds to apply to sample correlation matrices to detect hubs and also allows statistical significance to be attributed to hub discoveries. Numerical results illustrate the accuracy of the theory and the usefulness of the proposed spectral framework.

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“…Detection of hubs can lead to reduced computational and/or sampling costs. For example in wireless sensor networks, the identification of hub nodes can be useful for reducing the power usage and also for adding or removing sensors from the network [5], [6]. In addition, detecting hubs can give new insights about the underlying structure in the data-set involved.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal analysis of Gaussian WSS processes via complex correlation and partial correlation screening

Firouzi

Wei

Hero

2013

2013 IEEE Global Conference on Signal and Information Processing

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We propose a framework for spatio-temporal correlation analysis of jointly Gaussian Wide Sense Stationary (WSS) multivariate time series. The goal is to identify the hub time series, i.e., the ones that are highly correlated with a number of other time series. When the dimension of the multivariate time series and the number of time samples are relatively large, direct correlation analysis in the time domain could be computationally intractable. As an alternative, we apply the Discrete Fourier Transform to the time series and perform correlation analysis in the frequency domain. We extend the previous theory of hub screening to the complex domain to accommodate complex-valued Fourier transforms. The theory allows p-values to be assigned to time series for being a hub under the null hypothesis that the time series are independent of each other. It also specifies thresholds for which thresholded sample (partial) correlation matrices can be used to identify hubs. We then use an independence property of Gaussian WSS time series in the frequency domain to perform multiple inference for detecting hub time series. Experimental results on both synthetic data and real financial data illustrate the accuracy of our theoretical results and the usefulness of the proposed framework.

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Intelligent sensor hub benefits for wireless sensor networks

Cited by 5 publications

References 6 publications

Review on the Traction System Sensor Technology of a Rail Transit Train

Review on the Traction System Sensor Technology of a Rail Transit Train

Spectral Correlation Hub Screening of Multivariate Time Series

Spatio-temporal analysis of Gaussian WSS processes via complex correlation and partial correlation screening

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