Machine learning for intelligent optimization of remote laboratory resources

Elmissaoui, Taoufik and Chebil, Jalel and Habaebi, Mohamed Hadi and Taher, Jamel Bel Hadj (2026) Machine learning for intelligent optimization of remote laboratory resources. In: 2026 International Wireless Communications and Mobile Computing (IWCMC), 1-6 June 2026, Wuzhou, China.

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Abstract

Remote laboratory systems (RLS) are a key component of Education 4.0 technologies that span flexibility, accessibility, and experiential learning. Interoperability, reliability, and user experience continue to be targeted to position the technologies with more robust integration between software, hardware, and networking into future engineering education. For remote labs, efficient reservation systems are essential to ensure smooth resource allocation, low costs, and maintained compatibility. We propose a machine-learningbased approach to optimize the reservation process. We assess and compare several predictive models — Random Forest, Long Short-Term Memory (LSTM) networks, and Reinforcement Learning (RL) — against an XGBoost-based framework. Results demonstrate that XGBoost provides superior predictive accuracy and computational efficiency for scheduling tasks, using a threshold-based decision logic that categorizes requests into approved, moderate-risk, and rejected states, thereby reducing scheduling conflicts and equipment idle time. An integrated feedback loop for periodic model retraining ensures continuous model refinement, further increasing the reliability and scalability of RLS in challenging academic settings.

Item Type:	Proceeding Paper (Plenary Papers)
Uncontrolled Keywords:	Machine-learning, Remote-lab, Reinforcement Learning, Resources management
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering Kulliyyah of Engineering
Depositing User:	Dr. Mohamed Hadi Habaebi
Date Deposited:	20 Jul 2026 17:14
Last Update:	20 Jul 2026 17:14
Queue Number:	2026-07-Q4019
URI:	http://irep.iium.edu.my/id/eprint/129884

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