Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey

Abstract

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.

References in corpus (3)

• Self-Calibration and Biconvex Compressive Sensing
• Calibrating chemical multisensory devices for real world applications: An in-depth comparison of quantitative Machine Learning approaches
• Convex Optimization Approaches for Blind Sensor Calibration using Sparsity

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• Graph Signal Reconstruction Techniques for IoT Air Pollution Monitoring Platforms
• Heterogeneous Data Fusion Considering Spatial Correlations using Graph Convolutional Networks and its Application in Air Quality Prediction
• Gain and phase calibration of sensor arrays from ambient noise by cross-spectral measurements fitting
• MAQ-CaF: A Modular Air Quality Calibration and Forecasting method for cross-sensitive pollutants
• MTNet: A Multi-Task Neural Network for On-Field Calibration of Low-Cost Air Monitoring Sensors