Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 May 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 4; 2022: 1; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
6 papers.
- 2021Heterogeneous Data Fusion Considering Spatial Correlations using Graph Convolutional Networks and its Application in Air Quality Predictioncited by 2
- 2022Graph Signal Reconstruction Techniques for IoT Air Pollution Monitoring Platformscited by 2
- 2021MAQ-CaF: A Modular Air Quality Calibration and Forecasting method for cross-sensitive pollutantscited by 0
- 2021MTNet: A Multi-Task Neural Network for On-Field Calibration of Low-Cost Air Monitoring Sensorscited by 0
- 2021Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost Sensorscited by 0
- 2026Rotation-Invariant Multi-IMU Activity Recognition under Independent Per-Location Orientation Shiftscited by 0
- 2026Rotation-Invariant Multi-IMU Activity Recognition under Independent Per-Location Orientation Shifts16 Aug 2026
- 2022Graph Signal Reconstruction Techniques for IoT Air Pollution Monitoring Platforms2 Jan 2022
- 2021Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost Sensors14 Dec 2021
- 2021Heterogeneous Data Fusion Considering Spatial Correlations using Graph Convolutional Networks and its Application in Air Quality Prediction24 May 2021
- 2021MTNet: A Multi-Task Neural Network for On-Field Calibration of Low-Cost Air Monitoring Sensors10 May 2021
- 2021MAQ-CaF: A Modular Air Quality Calibration and Forecasting method for cross-sensitive pollutants22 Apr 2021