Advanced Autonomous Computing Models for Smart Systems

Eleanor Hughes

Advanced Autonomous Computing Models for Smart Systems

Author(s)	Dr. Eleanor Hughes
Country	United Kingdom
Abstract	Advanced autonomous computing models are becoming increasingly important for the development of smart systems capable of sensing, analyzing, learning, and responding to changing environmental and operational conditions with limited human intervention. The integration of artificial intelligence, machine learning, edge computing, cloud computing, Internet of Things technologies, distributed computing, and intelligent automation provides a technological foundation for autonomous decision-making. This study examines advanced autonomous computing models for smart systems through a conceptual and literature-based research approach. The study focuses on autonomous sensing, distributed intelligence, adaptive computation, edge intelligence, predictive analytics, intelligent resource allocation, and automated decision-making. The analysis indicates that autonomous computing models can improve responsiveness, scalability, operational efficiency, and adaptability across smart infrastructure, transportation, manufacturing, healthcare, energy, agriculture, and urban systems. Edge-based intelligence can reduce latency by processing information closer to data sources, while cloud platforms provide scalable computational resources for complex analytical tasks. Distributed and collaborative computing models can further improve system resilience and support decentralized decision-making. However, autonomous computing introduces challenges related to cybersecurity, privacy, explainability, interoperability, computational resource requirements, reliability, and human oversight. The study emphasizes that autonomous smart systems should incorporate continuous monitoring, adaptive learning, secure communication, transparent decision-making, and appropriate human supervision. Future research should investigate energy-efficient autonomous computing, federated intelligence, explainable autonomous decision-making, self-healing architectures, intelligent digital twins, and resilient decentralized systems. The study concludes that advanced autonomous computing models can provide a strong foundation for next-generation smart systems when computational intelligence is integrated with secure, reliable, scalable, and responsible system architectures.
Keywords	Autonomous Computing, Smart Systems, Artificial Intelligence, Edge Computing, Machine Learning, Internet of Things, Distributed Computing, Intelligent Automation, Autonomous Decision-Making, Smart Infrastructure
Field	Engineering
Published In	Volume 5, Issue 6, November-December 2023
Published On	2023-12-28

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American International Journal of Advanced Research and Development

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Advanced Autonomous Computing Models for Smart Systems

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