Predictive Reliability Engineering for IoT Devices Using Deep Learning-Driven Telemetry Analytics and Observability-First SRE Methodologies
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V6I6P105Keywords:
Predictive Maintenance, Deep Learning, Telemetry Analytics, Observability, Site Reliability Engineering (SRE), Anomaly Detection, LSTM, Reliability EngineeringAbstract
The emerging need to build infrastructure networks that are highly reliable and self-monitoring and resistant to failure has enhanced the development of Internet of Things (IoT) systems and devices. Conventional reliability engineering techniques, mainly rule based, reactive, and threshold driven, have difficulty in handling the scale, heterogeneity and real time variability of the contemporary IoT telemetry streams. In order to overcome the barriers, this paper suggests a predictive reliability engineering system that combines deep learning-based telemetry analytics with an observability-first Site Reliability Engineering (SRE) approach. It sends high-velocity device operating metrics, system logs, and sensor measurements through the framework and uses state of the art neural networks such as LSTM and Transformer based sequence models to predict anomalies, forecast failures and estimate device health scores. A layer of observability-first SRE has standardized SLIs/SLOs, is driven by automatic error-budget policy, and coordinates self-healing executive functions via event-driven remediation. Experimental results show that early-failure detectors based on experimentation do markedly better than classical statistical baselines in accuracy of early-failure detection, anomaly recall and prediction lead time. Deep learning structures and SRE governance with Mean Time to Recovery (MTTR) and uptime improvements, and operational resilience across large-scale internet of things applications means faster recovery and better operational availability. The suggested methodology proposes a non-dwarfed, data-driven roadmap towards the attainment of predictive, autonomous, and reliability-focused operations of IoXT.
References
[1] Lee, I., & Lee, K. (2015). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business horizons, 58(4), 431-440.
[2] Ammar, M., Russello, G., & Crispo, B. (2018). Internet of Things: A survey on the security of IoT frameworks. Journal of information security and Applications, 38, 8-27.
[3] Wen, L., Li, X., Gao, L., & Zhang, Y. (2017). A new convolutional neural network-based data-driven fault diagnosis method. IEEE transactions on industrial electronics, 65(7), 5990-5998.
[4] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
[5] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
[6] Lim, B., & Zohren, S. (2021). Time-series forecasting with deep learning: a survey. Philosophical transactions of the royal society a: mathematical, physical and engineering sciences, 379(2194).
[7] Hu, Z., Bai, Z., Yang, Y., Zheng, Z., Bian, K., & Song, L. (2018). UAV aided aerial-ground IoT for air quality sensing in smart city: Architecture, technologies and implementation. arXiv. https://arxiv.org/abs/1809.03746
[8] Beyer, B., Jones, C., Petoff, J., & Murphy, N. R. (2016). Site reliability engineering: how Google runs production systems. " O'Reilly Media, Inc.".
[9] Kolter, J. Z., & Maloof, M. A. (2004, August). Learning to detect malicious executables in the wild. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 470-478).
[10] Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2), 1-19.
[11] Warden, P., & Situnayake, D. (2019). Tinyml: Machine learning with tensorflow lite on arduino and ultra-low-power microcontrollers. O'Reilly Media.
[12] Ghourab, E. M., Azab, M., Rizk, M., & Mokhtar, A. (2017, October). Security versus reliability study for power-limited mobile IoT devices. In 2017 8th IEEE annual information technology, electronics and mobile communication conference (IEMCON) (pp. 430-438). IEEE.
[13] Zhu, T., Ran, Y., Zhou, X., & Wen, Y. (2019). A survey of predictive maintenance: Systems, purposes and approaches. arXiv. https://arxiv.org/abs/1912.07383
[14] Sgambelluri, A., Paolucci, F., Giorgetti, A., Scano, D., & Cugini, F. (2020, July). Exploiting telemetry in multi-layer networks. In 2020 22nd International Conference on Transparent Optical Networks (ICTON) (pp. 1-4). IEEE.
[15] Beyer, B., Murphy, N. R., Rensin, D. K., Kawahara, K., & Thorne, S. (2018). The site reliability workbook: practical ways to implement SRE. "O'Reilly Media, Inc.".
[16] Sivanathan, A., Gharakheili, H. H., & Sivaraman, V. (2020). Managing IoT cyber-security using programmable telemetry and machine learning. IEEE Transactions on Network and Service Management, 17(1), 60-74.
[17] Ateeq, M., Ishmanov, F., Afzal, M. K., & Naeem, M. (2019). Multi-parametric analysis of reliability and energy consumption in IoT: A deep learning approach. Sensors, 19(2), 309.
[18] Pang, J., Liu, D., Peng, Y., & Peng, X. (2017). Anomaly detection based on uncertainty fusion for univariate monitoring series. Measurement, 95, 280-292.
[19] Kamat, P., & Sugandhi, R. (2020). Anomaly detection for predictive maintenance in Industry 4.0: A survey. E3S Web of Conferences, 170, 02007. https://doi.org/10.1051/e3sconf/202017002007
[20] Sivanathan, A. (2020). IoT behavioral monitoring via network traffic analysis. arXiv preprint arXiv:2001.10632.
[21] Maheshwari, S., Raychaudhuri, D., Seskar, I., & Bronzino, F. (2018, October). Scalability and performance evaluation of edge cloud systems for latency constrained applications. In 2018 IEEE/ACM Symposium on Edge Computing (SEC) (pp. 286-299). IEEE.
[22] Avancini, D. B., Rodrigues, J. J., Martins, S. G., Rabêlo, R. A., Al-Muhtadi, J., & Solic, P. (2019). Energy meters evolution in smart grids: A review. Journal of cleaner production, 217, 702-715.
[23] Zhang, W., Yang, D., & Wang, H. (2019). Data-driven methods for predictive maintenance of industrial equipment: A survey. IEEE systems journal, 13(3), 2213-2227.
