An Intelligent Monitoring Framework for Distributed Resource Systems Using Hybrid Machine Learning and IoT

Jude Eseoghene Agamugoro1; Lucky Oghenerovwo Akpore2; Henry Omuan Atafo

doi:10.56201/ijemt.vol.12.no2.2026.pg185.197

📄 Download Paper

An Intelligent Monitoring Framework for Distributed Resource Systems Using Hybrid Machine Learning and IoT

Jude Eseoghene Agamugoro1, Lucky Oghenerovwo Akpore2, Henry Omuan Atafo

Abstract

The decentralized nature of resource structure, notably such as store facilities (e.g., stores), environmental reservoirs and infrastructure devices, commonly does not operate engaged monitoring that may lead to unnoticed degradation and threatening situations [1], [2]. A generalized Internet of Things – Artificial intelligence (AI) Infrastructure for predictive tracking of typical distributed instinctive resource environments is proposed in this paper. The architecture introduced has integrated multivariate sensors, edge cloud communication and hybrid machine learning methodologies, adaptive persistence mechanisms with a clear objective to strengths real time detection and early risk identification [3], [4]. It also presents a decision tree learning & recurrent neural network-based hybrid that can be useful for non- linear trends in sensor data and time variations [5], [6]. The framework is adaptable across different areas like keeping an eye on environmental storage, managing resources that take a lot of manpower, and building resilience in infrastructure systems

References

[1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless sensor networks: A survey,” Computer Networks, vol. 38, no. 4, pp. 393–422, 2002. [2] H. Karl and A. Willig, Protocols and Architectures for Wireless Sensor Networks. Chichester, U.K.: Wiley, 2005. [3] L. Da Xu, W. He, and S. Li, “Internet of Things in industries: A survey,” IEEE Transactions on Industrial Informatics, vol. 10, no. 4, pp. 2233–2243, 2014. [4] S. Li, L. Da Xu, and S. Zhao, “The Internet of Things: A survey,” Information Systems Frontiers, vol. 17, no. 2, pp. 243–259, 2015. [5] T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2016, pp. 785–794. [6] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997. [7] M. Halfawy and A. Hengmeechai, “Integrated infrastructure asset management: A framework,” Journal of Infrastructure Systems, vol. 14, no. 3, pp. 186–196, 2008. [8] J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, “Internet of Things : A vision, architectural elements, and future directions,” Future Generation Computer Systems, vol. 29, no. 7, pp. 1645–1660, 2013. [9] D. Bandyopadhyay and J. Sen, “Internet of Things: Applications and challenges in technology and standardization,” Wireless Personal Communications, vol. 58, no. 1, pp. 49–69, 2011. [10] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, pp. 436–444, 2015. [11] N. Nikolenko, “Synthetic data for deep learning,” ACM Computing Surveys, vol. 54, no. 3, pp. 1–38, 2021. [12] Z.-H. Zhou, Ensemble Methods: Foundations and Algorithms. Boca Raton, FL, USA: CRC Press, 2012. [13] V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM Computing Surveys, vol. 41, no. 3, 2009. [14] W. Ye, J. Heidemann, and D. Estrin, “An energy-efficient MAC protocol for wireless sensor networks,” in Proc. IEEE INFOCOM, 2002. [15] E. A. Elsayed, Reliability Engineering, 2nd ed. Hoboken, NJ, USA: Wiley, 2012.

An Intelligent Monitoring Framework for Distributed Resource Systems Using Hybrid Machine Learning and IoT

Abstract

References

More Articles from INTERNATIONAL JOURNAL OF ENGINEERING AND MODERN TECHNOLOGY