发布/订阅边缘智能:系统回顾和未来展望

IF 30.4 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Alaa Saleh, Sasu Tarkoma, Susanna Pirttikangas, Lauri Lovén
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引用次数: 0

摘要

物联网的指数级增长导致数十亿设备产生大量数据流。然而,物联网设备的资源有限性,加上交互式应用的兴起,需要在设备-边缘-云连续体之间进行高效处理。由于其可伸缩性和灵活性,发布/订阅(pub/sub)模型已成为首选范例。这篇综述批判性地分析了pub/sub系统,检查了它们的体系结构、安全机制、协议和实际实现。与之前的调查不同,我们探索了它们在EdgeOps和实时应用中的作用,确定了关键的限制和增强的机会。此外,我们强调了未被探索的挑战,如自适应消息路由,并讨论了人工智能驱动的发布/订阅框架如何提高系统自主性和响应性。该调查为推进发布/订阅机制以满足交互式分布式计算环境的需求提供了路线图。此外,我们建议整合代理AI以增强适应性和自主性。我们进一步确定了有可能塑造pub/sub模式未来的几个关键挑战,并将其作为重要的开放问题进行进一步探索。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Publish/Subscribe for Edge Intelligence: Systematic Review and Future Prospects
The exponential growth of IoT has led to billions of devices generating massive data streams. However, the resource-limited nature of IoT devices, coupled with the rise of interactive applications, requires efficient processing across the Device-Edge-Cloud continuum. The publish/subscribe (pub/sub) model has emerged as a preferred paradigm due to its scalability and flexibility. This review critically analyzes pub/sub systems, examining their architectures, security mechanisms, protocols, and real-world implementations. Unlike previous surveys, we explore their role in EdgeOps and real-time applications, identifying key limitations and opportunities for enhancement. Furthermore, we highlight underexplored challenges, such as adaptive message routing, and discuss how AI-driven pub/sub frameworks could improve system autonomy and responsiveness. This survey provides a roadmap for advancing pub/sub mechanisms to meet the demands of interactive distributed computing environments. Additionally, we propose the integration of agentic AI to enhance adaptability and autonomy. We further identify several key challenges that have the potential to shape the future of the pub/sub model, and we present these as important open issues for further exploration.
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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