Intelligent Edge Computing: AI-Powered Optimization for Smart IoT System

Authors

  • Benisha Chris A Assistant Professor, Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 602105, India.
  • D. Kanchana Assistant Professor, Department of Computer Science and Applications, SRM Institute of Science and Technology, Ramapuram campus, Chennai, Tamil Nadu, India.
  • U. Harita Assistant Professor, Department of Computer Science and Engineering , Koneru Lakshmaiah Education Foundation,Guntur Andhra Pradesh, India.
  • Sankar Ganesh S Associate Professor, Department of Electronics and communication Engineering, RVS College of Engineering & Technology (Autonomous) Coimbatore, Tamil Nadu, India
  • Ananda Babu T Assistant professor, Department of Computer Science and Engineering Aditya university,Surampalem.
  • B. Venkataramanaiah Assistant professor department of ECE, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology,Chennai, India,

DOI:

https://doi.org/10.22399/ijcesen.1529

Keywords:

Edge Computing, IoT, Artificial Intelligence, Deep Reinforcement learning, Federated Learning, Real-Time Optimization

Abstract

In the era of rapidly evolving Internet of Things (IoT) ecosystems, the convergence of Artificial Intelligence (AI) with Edge Computing has emerged as a transformative paradigm to meet the stringent requirements of low latency, reduced bandwidth usage, and enhanced data privacy. This study presents an Intelligent Edge Computing (IEC) framework powered by AI-based optimization techniques designed specifically for smart IoT systems operating in real-time environments. The proposed system utilizes lightweight Deep Reinforcement Learning (DRL) for dynamic task offloading and scheduling, and a Swarm Intelligence-based Resource Allocation (SIRA) algorithm to optimize energy consumption and computational load across edge nodes. Additionally, the system leverages Federated Learning (FL) for decentralized model training while maintaining data security and minimizing transmission overhead.

 

Experimental evaluations conducted using the iFogSim simulator across smart home, industrial automation, and healthcare monitoring scenarios demonstrate the effectiveness of the IEC framework. Key results include a 32.8% reduction in average task latency, 27.4% improvement in energy efficiency, and 22.5% increase in task success rate compared to traditional cloud-based architectures. The IEC framework also achieved 94.6% model accuracy using FL with minimal privacy leakage.

These results affirm that AI-powered edge optimization can significantly enhance the performance and scalability of smart IoT systems while ensuring sustainable and secure operations..

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Published

2025-04-13

How to Cite

A, B. C., D. Kanchana, U. Harita, S, S. G., Ananda Babu T, & B. Venkataramanaiah. (2025). Intelligent Edge Computing: AI-Powered Optimization for Smart IoT System. International Journal of Computational and Experimental Science and Engineering, 11(2). https://doi.org/10.22399/ijcesen.1529

Issue

Vol. 11 No. 2 (2025): IJCESEN

Section

Research Article

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