A Blind Calibration Scheme Exploiting Mutual Calibration Relationships for a Dense Mobile Sensor Network

Lee, Byung-Tak; Son, Seung-Chul; Kang, Kyungran

doi:10.1109/jsen.2013.2297714

Cited by 29 publications

(56 citation statements)

References 20 publications

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“…(8)-so that we can use the update rules (10). F can be estimated using the update rules (9). Lastly, the optimization problem in Eq.…”

Section: Bmsc With Relaxed Rendezvousmentioning

confidence: 99%

“…In crowdsensing applications, most sensors-which are usually embedded in mobile devices-are low cost and must be remotely calibrated, as it may not be possible to request these devices to regularly go to a laboratory in order to perform sensor calibration. As a consequence, specific Blind Mobile Sensor Calibration (BMSC) methods 1 have been proposed in, e.g., [9][10][11][12]. All these methods are exploiting the rendezvous model [13] which assumes that sensors in the same spatio-temporal vicinity should acquire the same data.…”

Section: Introductionmentioning

confidence: 99%

“…All these methods are exploiting the rendezvous model [13] which assumes that sensors in the same spatio-temporal vicinity should acquire the same data. Contrary to [9], the authors in [10][11][12] consider that some of the available sensors are calibrated. In particular, the methods in [10,11] use a multi-hop calibration structure: calibrated sensors are used to calibrate uncalibrated ones which lie in the same vicinity.…”

Section: Introductionmentioning

confidence: 99%

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Blind mobile sensor calibration using an informed nonnegative matrix factorization with a relaxed rendezvous model

Dorffer

Puigt

Delmaire

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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To cite this version:Clément Dorffer, Matthieu Puigt, Gilles Delmaire, Gilles Roussel. Blind mobile sensor calibration using an informed nonnegative matrix factorization with a relaxed rendezvous model. 41st IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2016) ABSTRACTIn this paper, we consider the problem of blindly calibrating a mobile sensor network-i.e., determining the gain and the offset of each sensor-from heterogeneous observations on a defined spatial area over time. For that purpose, we previously proposed a blind sensor calibration method based on Weighted Informed Nonnegative Matrix Factorization with missing entries. It required a minimum number of rendezvous-i.e., data sensed by different sensors at almost the same time and place-which might be difficult to satisfy in practice.In this paper we relax the rendezvous requirement by using a sparse decomposition of the signal of interest with respect to a known dictionary. The calibration can thus be performed if sensors share some common support in the dictionary, and provides a consistent performance even if no sensors are in exact rendezvous.Index Terms-Blind calibration, mobile sensor networks, informed nonnegative matrix factorization, sparse data analysis

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“…(8)-so that we can use the update rules (10). F can be estimated using the update rules (9). Lastly, the optimization problem in Eq.…”

Section: Bmsc With Relaxed Rendezvousmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Blind mobile sensor calibration using an informed nonnegative matrix factorization with a relaxed rendezvous model

Dorffer

Puigt

Delmaire

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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show abstract

“…When the sensors are mobile, they can be in rendezvous, i.e., they are in the same spatio-temporal neighborhood, thus sensing the same phenomenon [6]. Such an assumption was recently used 1 in both micro- [8], [9], [10] and macro-calibration 2 [12], [13], [14]. However, in the case of fixed sensors, other assumptions are needed.…”

Section: Introductionmentioning

confidence: 99%

Outlier-robust calibration method for sensor networks

Dorffer¹,

Puigt²,

Delmaire³

et al. 2017

2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and Their Application to Mechatronics (ECMSM)

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Abstract-In this paper, we aim to blindly calibrate the responses of a sensor network whose outputs are possibly corrupted by outliers. In particular, we extend some well-known nullspacebased blind calibration approaches, proposed for fixed sensors with affine responses-i.e., with unknown gain and offset for each sensor-to that difficult case. These state-of-the-art approaches assume that the true data lie in a known lower dimensional subspace, so that in practice sensors can be calibrated by projection of the uncalibrated observations to this subspace. A robust extension was recently proposed in order to provide less sensitivity to noise. In this paper, we show that such methods (including the robust extensions) are very sensitive to outliers and we propose new extensions able to deal with such issues. For that purpose, we assume the outliers to be rare events, which can be modeled as a sparse contribution to the low-rank observed data. Using such an assumption, we separate sparse outliers from the low-rank data, so that we can perform calibration. We show that the proposed approach is able to handle up to 10% of outliers in the data without major impact on the calibration accuracy while state-of-the-art methods are already sensitive to the presence of one unique outlier.

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“…However, to make MCS applications reliable and practical, measured data from mobile device sensors should be managed in an accurate manner. However, it is clear from [12] that calibration errors are a major obstacle to the practical use of sensor-based networks. Therefore, in such networks, the precise, efficient calibration of sensors is crucial.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Sensor Calibration Scheme for Mobile Crowdsensing-based City-Scale Environmental Measurements

Son¹,

Lee²,

Ko³

et al. 2016

ETRI J

Self Cite

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In this paper, we propose a hybrid sensor calibration scheme for mobile crowdsensing applications. As the number of newly produced mobile devices containing embedded sensors continues to rise, the potential to use mobile devices as a sensor data source increases. However, because mobile device sensors are generally of a lower performance and cost than dedicated sensors, sensor calibration is crucial. To enable more accurate measurements of natural phenomena through the use of mobile device sensors, we propose a hybrid sensor calibration scheme for such sensors; the scheme makes use of mobile device sensors and existing sensing infrastructure, such as weather stations, to obtain dense data. Simulation results show that the proposed scheme supports low mean square errors. As a practical application of our proposed scheme, we built a temperature map of a city using six mobile phone sensors and six reference sensors. Thanks to the mobility of the sensors and the proposed scheme, our map presents more detailed information than infrastructure-based measurements.

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A Blind Calibration Scheme Exploiting Mutual Calibration Relationships for a Dense Mobile Sensor Network

Cited by 29 publications

References 20 publications

Blind mobile sensor calibration using an informed nonnegative matrix factorization with a relaxed rendezvous model

Blind mobile sensor calibration using an informed nonnegative matrix factorization with a relaxed rendezvous model

Outlier-robust calibration method for sensor networks

A Hybrid Sensor Calibration Scheme for Mobile Crowdsensing-based City-Scale Environmental Measurements

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