Low-Latency Fog Computing for Healthcare IoT: Nature inspired Approaches to Resource Optimization

Author: Jinal Mehta, Ankur Goswami, Monil S. Shah
Published Online: January 5, 2026
Abstract
References

The swift incorporation of the Internet of Things (IoT) in healthcare has led to continuous data generation, demanding low-latency processing and optimal resource use. The central research problem is that conventional cloud computing, due to its centralized structure and high communication delay, fails to achieve the real-time requirements of critical healthcare applications. By bringing calculation closer to IoT devices, fog computing overcomes this constraint and supports time-sensitive applications like wearable health tracking, remote monitoring, and emergency treatment. With a focus on Artificial Intelligence (AI) and Machine Learning (ML) methodologies, this survey examines recent developments in fog based resource management for healthcare IoT. It looks at techniques for load balancing, intelligent scheduling, task offloading, and dynamic provisioning in fog situations with limited resources. The comparative evaluation of state-of-the-art strategies reveals that AI-driven frameworks achieve significant improvements in latency reduction, energy efficiency, throughput, and overall Quality of Service (QoS). These findings highlight the growing relevance of intelligent solutions for addressing the complexity of decentralized fog systems. This work's main contribution is a structured classification of AI enabled fog computing approaches tailored specifically for healthcare IoT applications. Beyond summarizing existing methods, the review identifies key research issues like scalability, energy-aware scheduling, and adaptive real-time processing that remain open for exploration. By aligning emerging AI-driven techniques with performance parameters, this study offers a comprehensive foundation for experimenters and professionals seeking to design next-generation healthcare frameworks that are both intelligent and resilient.

Keywords: Internet of Things (IoT) , Healthcare IoT (HIoT), Quality of Service (QoS), Artificial Intelligence (AI), Machine Learning (ML), Fog Computing Resource Management (FRM), Fog computing, Resource Allocation, Nature Inspired Algorithm.
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