WSNs Self-Calibration Approach for Smart City Applications Leveraging Incremental Machine Learning Techniques

Rossini, Rosaria; Ferrera, Enrico; Conzon, Davide; Pastrone, Claudio

doi:10.1109/ntms.2016.7792490

Cited by 9 publications

(19 citation statements)

References 11 publications

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“…For instance, temperature sensors ( Table 2) typically follow linear response functions and have been analyzed using linear regression [6,11] for calibrating the sensors, Bayesian inference for modeling drift [43], and maximum likelihood [44], Gaussian processes [44], or kriging [45] for state-space modeling applications. Light point sensors have mainly been calibrated using splines [7,35] or support-vector regression [41] due to the presence of nonlinearities, and they have been analyzed with distributed consensus [8] to show how light sensing is affected by sensor orientation. In the case of localization applications (Table 3), the most used calibration technique is maximum a posteriori [38,39,40] although distributed consensus [52] is also used when spatial redundancy is present.…”

Section: Choosing a Modelmentioning

confidence: 99%

“…Evaluation metric Data set Application [6] Linear regression Mean error Real (thermocouple) Temperature [7] Splines/optimization Confidence interval Real (photovoltaic) Point-lights [8] Distributed consensus Mean error real Light [11] Linear regression Mean error Real Light, temp., humidity [18] Linear regression Mean & median error Real (thermistor) Temp., humidity [35] Nonlinear/splines Mean squared error Real Light [36] Hidden Markov model Recognition accuracy Real Motion (accelerometer) [41] Support vector regression (SVR) Mean squared error Real Light [43] Bayesian Root mean squared error Synthetic Temperature [44] Maximum likelihood Absolute error Synthetic Temperature [45] Kriging Root mean squared error Real Temp., humidity, light [46,47,48] Gaussian process K-L divergence Real Temperature [49] Linear/nonlinear optimization Mean absolute error Real Vibration (water flow) [50] Distributed consensus Mean squared error Synthetic Temp., humidity, sound [51] PCA + compressive sensing Mean squared error Real Temperature Table 3: Localization, synchronization, and target location applications.…”

Section: References Calibration Modelmentioning

confidence: 99%

“…In distributed calibration, a sensor node and its neighboring, or multi-hop, sensor nodes collaborate to calculate the calibration parameters [8,10,35,41,52]. There are several possibilities for building distributed system architectures.…”

Section: Mode Of Calibration Processingmentioning

confidence: 99%

“…[18] X X X X X X X X Temp., Hum. [35] X X X X X X X X Light [36] X X X X X X X Motion (accelerometer) [41] X X X X X X X Light [43] X X X X X X X Temperature [44] X X X X X X X Temperature [45] X X X X X X X X Temp. Hum., Light [46,47,48] X X X X X X X Temperature [49] X X X X X X X Vibration (Water Flow) [50] X X X X X X X Temp., Hum., Sound [51] X X X X X X X Temperature Attributes used in localization, synchronization and target detection applications [5] X X X X X X X Localization [17] X X X X X X X X X Target Detection [37] X X X X X X X Target Detection [38] X X X X X X X Localization [39,40] X X X X X X X Localization [52] X X X X X X X Localization [53,10] X X X X X X X Synchronization [54] X X X X X X X Synchronization Attributes used in air pollution and water chemistry applications [12] X X X X X X X Water Chemistry [15] X X X X X X X X X O 3 [16,27] X X X X X X X O 3 ,CO,CO 2 ,NO,NO 2 [28] X X X X X X X CO,CH 4 ,C 3 H 8 ,CeO 2 ,NiO [29] X X X X X X X O 3 ,CO [30] X X X X X X X O 3 ,CO,NO 2 [31] X X X X X X X O 3 , NO, NO 2 [32] X X X X X X X X X O 3 , CO, NO 2 [33] X X X X X X X O 3 [55] X X X X X X X Photosynthetically Active Radiation (PAR) [56] X X X X X X X Air pollution CaliBree [8] is a model-based calibration network in which ground-truth calibrated nodes inform non-calibrated nodes, using a beacon protocol, that they will participate in the calibration process.…”

Section: Consensus Algorithmsmentioning

confidence: 99%

“…Some sensors present nonlinearities, because their response function is nonlinear or because of other causes such as drift, aging, or achieving their maximum dynamic range. Some recent works including [16,27,31,41] proposed using deep learning models such as artificial neural networks or support-vector regression. These models are said to characterize better the nonlinearities than splines but at the cost of greater computational complexity.…”

Section: Open Issues In Calibration Modelsmentioning

confidence: 99%

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Self-calibration methods for uncontrolled environments in sensor networks: A reference survey

et al. 2019

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Growing progress in sensor technology has constantly expanded the number and range of low-cost, small, and portable sensors on the market, increasing the number and type of physical phenomena that can be measured with wirelessly connected sensors. Large-scale deployments of wireless sensor networks (WSN) involving hundreds or thousands of devices and limited budgets often constrain the choice of sensing hardware, which generally has reduced accuracy, precision, and reliability. Therefore, it is challenging to achieve good data quality and maintain error-free measurements during the whole system lifetime. Self-calibration or recalibration in ad hoc sensor networks to preserve data quality is essential, yet challenging, for several reasons, such as the existence of random noise and the absence of suitable general models. Calibration performed in the field, without accurate and controlled instrumentation, is said to be in an uncontrolled environment. This paper provides current and fundamental self-calibration approaches and models for wireless sensor networks in uncontrolled environments.

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