Machine Learning-Based Intelligent Resource Allocation for Smart Electronic Systems

Abstract

The rapid growth of smart electronic systems, driven by the convergence of the Internet of Things (IoT), edge computing, cyber-physical systems, and intelligent automation, has intensified the need for efficient resource allocation mechanisms. Conventional static and rule-based approaches are increasingly inadequate for handling dynamic workloads, heterogeneous devices, and stringent quality-of-service requirements. Machine learning (ML) techniques offer adaptive, data-driven solutions capable of optimizing resource utilization, minimizing energy consumption, and improving system responsiveness in complex operational environments. This paper presents a comprehensive framework for machine learning-based intelligent resource allocation in smart electronic systems. The proposed architecture integrates data acquisition, feature extraction, predictive analytics, and reinforcement learning mechanisms to dynamically allocate computational, communication, and energy resources according to real-time system conditions. Mathematical formulations are developed to model resource optimization objectives involving energy efficiency, latency, throughput, and reliability. A hybrid learning strategy combining supervised prediction and reinforcement learning-based decision-making is introduced to support adaptive resource management. Simulation-based evaluations demonstrate significant improvements in resource utilization, energy savings, and service quality compared with conventional allocation techniques. The findings indicate that machine learning-driven resource management provides a scalable and robust foundation for future smart electronic infrastructures, including industrial automation, intelligent transportation, smart healthcare, and next-generation cyber-physical ecosystems.