Intelligent Edge Computing Models for IoT Applications

Michael J. Anderson

Intelligent Edge Computing Models for IoT Applications

Author(s)	Dr. Michael J. Anderson
Country	United States
Abstract	Intelligent edge computing has emerged as an important computing paradigm for Internet of Things applications by bringing processing, analytics, and decision-making capabilities closer to connected devices. Conventional cloud-centric architectures can experience communication latency, bandwidth requirements, and data-transfer challenges when large numbers of IoT devices continuously generate information. Edge computing addresses these limitations by enabling localized processing through edge nodes, gateways, and distributed computing resources. This study examines intelligent edge computing models for IoT applications, focusing on distributed processing, edge-cloud collaboration, artificial intelligence, resource management, latency reduction, data analytics, and intelligent decision-making. A qualitative descriptive and conceptual research design was adopted using secondary academic literature. The analysis indicates that intelligent edge models can improve responsiveness by processing time-sensitive IoT information closer to data sources while reducing unnecessary communication with centralized cloud platforms. Artificial intelligence can further enhance edge environments through local prediction, anomaly detection, classification, and adaptive resource allocation. Edge-cloud collaboration can provide a balance between low-latency local processing and large-scale cloud computation. However, challenges remain concerning limited computational resources, energy consumption, security, privacy, heterogeneous hardware, scalability, task scheduling, and interoperability. The study concludes that intelligent edge computing can provide an effective foundation for future IoT applications when distributed intelligence, efficient resource management, secure communication, and adaptive edge-cloud coordination are integrated. Future research should emphasize lightweight AI models, energy-efficient edge architectures, federated learning, autonomous resource management, security-aware orchestration, and scalable edge-cloud frameworks.
Keywords	Edge Computing, Intelligent Edge Computing, Internet of Things, Edge Intelligence, Artificial Intelligence, Edge-Cloud Collaboration, Distributed Computing, IoT Applications, Resource Management, Low-Latency Computing.
Field	Engineering
Published In	Volume 4, Issue 5, September-October 2022
Published On	2022-09-19

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American International Journal of Advanced Research and Development

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Intelligent Edge Computing Models for IoT Applications

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