Optimization-based hybrid offloading framework for IoMT in edge-cloud healthcare systems

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-09-26 DOI:10.1016/j.future.2025.108163

Sheharyar Khan, Shijun Liu, Li Pan, Guangxu Mei

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引用次数: 0

Abstract

The Internet of Medical Things (IoMT) produces substantial amounts of real-time data from devices like ECG and EEG monitors, presenting significant issues in latency, energy efficiency, and resource allocation. Traditional offloading methods often fail to satisfy the low-latency and high-reliability requirements of modern healthcare systems. To address these limitations, this study presents a hybrid computing framework that integrates edge and cloud resources to facilitate efficient and scalable data processing. The proposed system integrates Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Graph Neural Networks (GNNs) to enhance task offloading, reduce latency, and optimize resource utilization. Experimental findings indicate that the approach significantly enhances system performance, minimizes energy consumption, and ensures consistent connectivity among diverse IoMT devices. The framework enables adaptable and efficient real-time processing, thereby enhancing advanced healthcare systems and optimizing both clinical decision-making and patient outcomes.

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边缘云医疗保健系统中基于优化的IoMT混合卸载框架

医疗物联网（IoMT）从ECG和EEG监视器等设备产生大量实时数据，在延迟、能源效率和资源分配方面存在重大问题。传统的卸载方法往往不能满足现代医疗保健系统的低延迟和高可靠性要求。为了解决这些限制，本研究提出了一个混合计算框架，该框架集成了边缘和云资源，以促进高效和可扩展的数据处理。该系统集成了遗传算法（GA）、粒子群优化（PSO）和图神经网络（gnn），以增强任务卸载、降低延迟和优化资源利用率。实验结果表明，该方法显著提高了系统性能，最大限度地降低了能耗，并确保了不同IoMT设备之间的一致连接。该框架支持适应性强且高效的实时处理，从而增强了先进的医疗保健系统，并优化了临床决策和患者结果。

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来源期刊

Future Generation Computer Systems-The International Journal of Escience 工程技术-计算机：理论方法

CiteScore

19.90

自引率

2.70%

发文量

376

审稿时长

10.6 months

期刊介绍： Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.