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雾云物联网系统中智能家居任务的动态多准则调度算法

Dynamic multi-criteria scheduling algorithm for smart home tasks in fog-cloud IoT systems.

作者信息

Bhakhar Ruchika, Chhillar Rajender Singh

机构信息

Department of computer science and applications, Maharshi Dayanand University, Rohtak, India.

出版信息

Sci Rep. 2024 Dec 2;14(1):29957. doi: 10.1038/s41598-024-81055-0.

DOI:10.1038/s41598-024-81055-0
PMID:39622969
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11612154/
Abstract

The proliferation of Internet of Things (IoT) devices in smart homes has created a demand for efficient computational task management across complex networks. This paper introduces the Dynamic Multi-Criteria Scheduling (DMCS) algorithm, designed to enhance task scheduling in fog-cloud computing environments for smart home applications. DMCS dynamically allocates tasks based on criteria such as computational complexity, urgency, and data size, ensuring that time-sensitive tasks are processed swiftly on fog nodes while resource-intensive computations are handled by cloud data centers. The implementation of DMCS demonstrates significant improvements over conventional scheduling algorithms, reducing makespan, operational costs, and energy consumption. By effectively balancing immediate and delayed task execution, DMCS enhances system responsiveness and overall computational efficiency in smart home environments. However, DMCS also faces limitations, including computational overhead and scalability issues in larger networks. Future research will focus on integrating advanced machine learning algorithms to refine task classification, enhancing security measures, and expanding the framework's applicability to various computing environments. Ultimately, DMCS aims to provide a robust and adaptive scheduling solution capable of meeting the complex requirements of modern IoT ecosystems and improving the efficiency of smart homes.

摘要

智能家居中物联网（IoT）设备的激增，引发了对跨复杂网络进行高效计算任务管理的需求。本文介绍了动态多标准调度（DMCS）算法，该算法旨在增强智能家居应用中雾计算-云计算环境下的任务调度。DMCS根据计算复杂度、紧急程度和数据大小等标准动态分配任务，确保对时间敏感的任务在雾节点上迅速处理，而资源密集型计算则由云数据中心处理。DMCS的实施表明，与传统调度算法相比有显著改进，减少了完工时间、运营成本和能源消耗。通过有效平衡即时和延迟任务执行，DMCS提高了智能家居环境中的系统响应能力和整体计算效率。然而，DMCS也面临局限性，包括大型网络中的计算开销和可扩展性问题。未来的研究将集中于集成先进的机器学习算法以优化任务分类、加强安全措施，并扩大该框架对各种计算环境的适用性。最终，DMCS旨在提供一种强大且自适应的调度解决方案，能够满足现代物联网生态系统的复杂需求并提高智能家居的效率。

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