Sensors are the beating heart of any monitoring and control system. From manufacturing to healthcare, from precision agriculture to smart cities, the reliability of the data collected is what allows companies to make fast and informed decisions.

However, simply designing an electronic board carefully is not enough to guarantee stable performance over time. Calibration, maintenance, and managing sensor longevity are crucial aspects to ensure accurate measurements, reduce risks, and optimize operational costs.

The Challenge of Calibration

Every sensor is subject to drift phenomena that, if not managed, can compromise measurement accuracy. Electronic drift caused by component aging, thermal drift, or environmental influences (humidity, vibrations, dust) are among the most common causes.

Several studies show that calibration should not be considered a one-time activity but rather a cyclical process. For example, proposed a dynamic method to correct drift in HVAC sensors, reducing errors to below 5%【mdpi.com】. Similarly, demonstrated that light self-calibration techniques can compensate for thermal variations in MEMS sensors【pmc.ncbi.nlm.nih.gov】.

Today, integrated solutions capable of performing real-time compensation exist, as highlighted by Vitolo with on-board circuits to correct thermal drift【link.springer.com】.

Predictive Maintenance and Continuous Monitoring

The evolution of IoT has made it possible to manage sensors throughout their entire lifecycle. With real-time monitoring systems, it is now feasible to analyze the “health status” of devices and predict potential failures.

According to Pech et al. (2021), smart sensors enable predictive maintenance strategies that reduce downtime and unexpected costs【pmc.ncbi.nlm.nih.gov】. Machine learning techniques applied to streaming data, as proposed by Varalakshmi & Kumar (2025), show that it is possible to optimize maintenance scheduling and extend device lifespan【nature.com】.

Real-world applications, such as those presented by Zero et al. (2024), confirm that a predictive approach based on IoT sensors allows anomalies to be detected and targeted interventions planned, reducing risks and waste【mdpi.com】.

Strategies to Increase Sensor Longevity

A design focused on durability must consider several factors:

• Material selection: Components capable of withstanding harsh environmental conditions.
• Physical protection: Shielding against dust, humidity, and electromagnetic interference.
• Modular design: Electronic boards designed to facilitate upgrades and targeted replacements.
• Firmware updates: Solutions enabling automatic recalibration and performance optimization.

According to Ahmad, self-calibration approaches and modular design are particularly effective for low-cost sensor networks, where drift can be significant and frequent replacement is unsustainable【sciencedirect.com】.

Business Impact and Competitive Advantages

Investing in careful calibration and maintenance management of sensors means:

• Ensuring data reliability,
• Reducing operational costs related to errors or downtime,
• Improving sustainability by avoiding waste and premature replacements,
• Increasing digital resilience, a now essential requirement in the Industry 4.0 era.

As various studies show, a well-designed sensor that is not properly maintained quickly becomes a source of inaccurate data, directly impacting decision-making quality and competitiveness.

Conclusion

Designing an electronic board is only the first step in a much broader process. For a sensor to maintain its reliability, it must be integrated into a strategy that includes calibration, maintenance, and careful lifecycle management.

At Check-Up, we support companies not only in electronic design but also in defining processes and tools to monitor, validate, and maintain sensor systems efficiently over time.

If you want to discover how to make your sensors more reliable and durable, contact us: we will be happy to help you build robust, safe, and future-ready solutions.

References

• Li, G., et al. (2022). Dynamic Calibration Method of Sensor Drift Fault in HVAC Systems Based on Bayesian Inference. Sensors, 22(14), 5348. DOI: 10.3390/s22145348. Available at: MDPI
• Martínez, D., et al. (2022). A Self-Calibration Technique with a Lightweight Algorithm for MEMS Sensors. Micromachines, 13(4), 646. PMID: 9026479. Available at: PMC
• Vitolo, A., et al. (2023). An In-Sensor System for Real-Time Compensation of Thermal Drift in MEMS Pressure Sensors. In Lecture Notes in Electrical Engineering, Springer. DOI: 10.1007/978-3-031-48711-8_21. Available at: Springer
• Hurst, A., et al. (2025). Drift Correction and Calibration Scheduling for the Internet of Things. arXiv preprint arXiv:2506.09186. Available at: arXiv
• Ahmad, R., et al. (2024). Enhanced Drift Self-Calibration of Low-Cost Sensor Networks. Measurement, Elsevier. DOI: 10.1016/j.measurement.2024.114142. Available at: ScienceDirect
• Pech, M., Vrchota, J., & Bednář, J. (2021). Predictive Maintenance and Intelligent Sensors in Smart Factories: Review. Sensors, 21(4), 1470. DOI: 10.3390/s21041470. Available at: PMC
• Varalakshmi, P., & Kumar, A. (2025). Optimized Predictive Maintenance for Streaming Data in Industrial IoT Networks. Scientific Reports, 15(1), 10268. DOI: 10.1038/s41598-025-10268-8. Available at: Nature
• Zero, E., et al. (2024). Predictive Maintenance in IoT-Monitored Systems for Fault Detection. Journal of Low Power Electronics and Applications, 13(5), 57. DOI: 10.3390/jlpea13050057. Available at: MDPI
• Nangia, V., Makkar, A., & Hassan, M. (2020). IoT Based Predictive Maintenance in Manufacturing Sector. SSRN Paper ID 3563559. Available at: SSRN