Е-Публикации
Технически университет - София

Abstract: The popularity of smart sensors and the Internet of Things (IoT) is growing in various fields and applications. They collect data from the real world and transfer it to networks. However, deploying IoT in real world applications can be challenging due to their limited resources and the often-associated unsatisfactory data quality. In this paper, an error-minimization approach is proposed based on the use of Allan deviation to model basic noise components of low-cost sensors stochastically. This paper elucidates the methodologies and practices for measuring and interpreting Allan deviation specifically for low-cost temperature sensors. It emphasizes the accurate extraction of key noise components, such as White noise and bias instability (BI), and their significance in practical applications. The study underscores Allan deviation as the fundamental stochastic limit for sensor performance amidst larger deterministic errors. Detailed guidelines for the measurement setup are provided.

References

1. J. Guruprakash et al., "A Framework for Platform-Agnostic Blockchain and IoT-Based Insurance System," IEEE Access, vol. 12, pp. 64079-64102, 2024.
2. Dimitrov K., Valkovski T, "Using optical acoustic sensors for smart city applications," International Conference on Creative Business for Smart and Sustainable Growth, CreBUS 2019, art. no. 8840100, DOI: 10.1109/CREBUS.2019.8840100, 2019.
3. Z. Li, K. Liu, and Y. E. A. Su, "Adaptive resource allocation algorithm for internet of things with bandwidth constraint," Trans. Tianjin Univ., vol. 18, p. 253-258, 2012.
4. X. Liu and O. Baiocchi, "A comparison of the definitions for smart sensors, smart objects, and Things in IoT," in IEEE 7th Annual Information Technology Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, 2016.
5. E. Oriwoh and M. Conrad, "Things' in the Internet of Things: towards a definition," International Journal of Internet of Things, vol. 4, no. 1, pp. 1-5, 2015.
6. C. Liu, K. Wu and J. Pei, "An energy-efficient data collection framework for wireless sensor networks by exploiting spatiotemporal correlation," IEEE Trans. Parallel Distrib. Syst., vol. 18, p. 1010-1023, 2007.
7. M. M. Hossain, M. Fotouhi and R. Hasan, "Towards an Analysis of Security Issues, Challenges, and Open Problems in the Internet of Things," New York, NY, 2015.
8. S. Zahoor and R. Mir, "Resource management in pervasive Internet of Things: A survey," Journal of King Saud University - Computer and Information Sciences, 2018.
9. M. Marinov, N. Nikolov, S. Dimitrov, T. Todorov, Y. Stoyanova, and G. Nikolov, "Linear Interval Approximation for Smart Sensors and IoT Devices," Sensors, vol. 22, no. 3, February 1, 2022.
10. I.-M. Choi, S.-Y. Woo and Y.-K. Kim, "Evaluation of high temperature pressure sensors," Review of Scientific Instruments, vol. 82, no. 3, 2011.
11. M. Tian, X. Zou and F. Weng, "Use of Allan Deviation for Characterizing Satellite Microwave Sounder Noise Equivalent Differential Temperature (NEDT)," IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 12, pp. 2477-2480, 2015.
12. M. El-Diasty, A. El-Rabbany, and S. Pagiatakis, "Temperature variation effects on stochastic characteristics for low-cost MEMS-based inertial sensor error," Measurement Science and Technology, vol. 18, no. 11, pp. 3321-3328, 2007.
13. I. Demanega, I. Mujan, B. Singer, A. Anđelković, F. Babich and D. Licina, "Performance assessment of low-cost environmental monitors and single sensors under variable indoor air quality and thermal conditions," Building and Environment, vol. 187, 2021.
14. B. Mobaraki, S. Komarizadehasl, F. Castilla Pascual and J. Lozano-Galant, "Application of Low-Cost Sensors for Accurate Ambient Temperature Monitoring," Buildings, vol. 12, no. 1411, 2022.
15. Y. Li, H. Li, J. Huang, C. Fang, M. Liu, C. Huang and G. Zeng, "High-precision temperature sensor based on weak measurement," Opt. Express, vol. 27, pp. 21455-21462, 2019.
16. Y. Xianjun and L. Cuimei, "Development of high-precision temperature measurement system based on ARM," in 2009 9th International Conference on Electronic Measurement & Instruments, Beijing, China, 2009.
17. C. N. Lawrence and J. P. Darry II, "Characterization of Ring Laser Gyro Performance Using the Allan Variance Method," Engineering Notes, Journal of Guidance, Control, and Dynamics, vol. 20, no. 1, pp. 211-214, January-February, 1997.
18. N. El-Sheimy, H. Hou and X. Niu, "Analysis and Modeling of Inertial Sensors Using Allan Variance," IEEE Transactions on Instrumentation and Measurement, vol. 57, no. 1, pp. 140-149, 2008.
19. IEEE, IEEE standard specification format guide and test procedure for single-axis interferometric fiber optic gyros, IEEE Std 952-1997 (R2008), 2008.
20. T. Hiller et al., "Practical Approaches to Allan Deviation Analysis of Low-Cost MEMS Inertial Sensors," in 2023 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL), Lihue, HI, USA, 2023.
21. L. Blocher et al., "Purely Inertial Navigation with a Low-Cost MEMS Sensor Array," in 2021 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL), Kailua-Kona, HI, USA, 2021.
22. T. Hiller, L. Blocher, M. Vujadinović, Z. Péntek, A. Buhmann and H. Roth, "Analysis and Compensation of Cross-Axis Sensitivity in Low-Cost MEMS Inertial Sensors," in 2021 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL), Kailua-Kona, HI, USA, 2021.
23. Phidgets Inc., "Allan Deviation Guide," 13 July 2023. [Online]. Available: https://www.phidgets.com/docs/Allan_Deviation_Guide. [Accessed 10 August 2024].
24. Ideal Vacuum Products, LLC, "Ideal Vacuum Cube User Manual," Doc. Ref. IdealCUBE-6X121416, 12142016 - V 1.1, 2016.
25. JULABO GmbH, "JULABO HE-4 Heating Circulator," Product data sheet HE-4.

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