On Rendezvous in Mobile Sensing Networks

Saukh, Olga; Hasenfratz, David; Walser, Christoph; Thiele, Lothar

doi:10.1007/978-3-319-03071-5_3

Cited by 24 publications

(33 citation statements)

References 33 publications

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“…All radio signals (GPS, GSM, and WiFi) are processed by a single planar antenna, which is mounted on top of the box. While the streetcars are in operation, on average 20 h per day [7], they supply the nodes with power. During the night, typically from 1:00 AM to 5:00 AM, the streetcars are in their depots and the nodes are turned off.…”

Section: Air Quality Sensor Nodementioning

confidence: 99%

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Deriving high-resolution urban air pollution maps using mobile sensor nodes

Hasenfratz

Saukh

Walser

et al. 2015

Pervasive and Mobile Computing

Self Cite

242

160

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Section: Air Quality Sensor Nodementioning

confidence: 99%

“…Contributions and road-map. To tackle the challenges above, we propose to use a mobile measurement system [5,6,7,8]. Node mobility trades off temporal resolution against spatial resolution, enabling a high spatial resolution across large areas without the need for a huge number of fixed sensors.…”

Section: Introductionmentioning

confidence: 99%

Deriving high-resolution urban air pollution maps using mobile sensor nodes

Hasenfratz

Saukh

Walser

et al. 2015

Pervasive and Mobile Computing

Self Cite

242

160

View full text Add to dashboard Cite

“…Depending whether or not the sensors are mobile in the network, different assumptions have been proposed to solve such problem. 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].…”

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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“…Pioneering contributions to the field of online rendezvous calibration are the works of Hasenfratz et al [8] and Saukh et al [9], [10]. In [8], the authors introduce and compare three types of online algorithms (forward, backward, and instant calibration) based on least squares temporal-weighted regression of calibration tuples (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…They evaluate the performance of these algorithms by considering both simulated and real measurements, with uniformly distributed simulated rendezvous events. In [9], Saukh et al give a rigorous definition for rendezvous events and introduce the concept of rendezvous connection graphs, studying their properties in the context of sensor fault detection and sensor calibration. More recently, in [10], the authors address the problem of error accumulation in multi-hop calibration algorithms that use ordinary least squares regression, and propose the use of geometric mean regression as a better alternative to the former method.…”

Section: Introductionmentioning

confidence: 99%

Model-based rendezvous calibration of mobile sensor networks for monitoring air quality

2015

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Abstract-Mobile Wireless Sensor Networks (WSNs) hold the potential to constitute a real game changer for our understanding of urban air pollution, through a significant augmentation of spatial resolution in measurement. However, temporal drift, crosssensitivity and effects caused by varying environmental conditions (e.g., temperature) in low-cost chemical sensors (typically used in mobile WSNs) pose a tough challenge for reliable calibration. Based on state-of-the-art rendezvous calibration methods, we propose a novel model-based method for automatically estimating the baseline and gain characteristics of low-cost chemical sensors taking temporal drift and temperature dependencies of the sensors into account. The performance of our algorithm is evaluated using data gathered by our long-term mobile sensor network deployment, developed within the Nano-Tera.ch OpenSense II project 1 in Lausanne, Switzerland. We show that, in a realistic context of sparse and irregular rendezvous events, our method consistently improves rendezvous calibration performance for single-hop online calibration.

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On Rendezvous in Mobile Sensing Networks

Cited by 24 publications

References 33 publications

Deriving high-resolution urban air pollution maps using mobile sensor nodes

Deriving high-resolution urban air pollution maps using mobile sensor nodes

Outlier-robust calibration method for sensor networks

Model-based rendezvous calibration of mobile sensor networks for monitoring air quality

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