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

## TL;DR

This survey reviews self-calibration techniques for wireless sensor networks operating in uncontrolled environments, addressing challenges like noise and hardware limitations to ensure data quality.

## Contribution

It compiles and analyzes current self-calibration methods and models specifically designed for sensor networks in uncontrolled, real-world settings.

## Key findings

- Identifies key challenges in field calibration of sensor networks.
- Summarizes existing self-calibration approaches and their effectiveness.
- Highlights gaps and future directions in calibration research.

## 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.