工业物联网应用URLLC的人工智能性能评估：回顾、开放挑战与未来方向

IF 2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication Pub Date : 2025-06-14 DOI:10.1016/j.phycom.2025.102712

Abdinasir Hirsi , Lukman Audah , Adeb Salh , Mohammed Alhartomi , Zhili Sun , Ahmed Hammoodi , Salman Ahmed

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引用次数: 0

摘要

第五代/第六代（5G/6G）超可靠低延迟通信（URLLC）与工业物联网（IIoT）应用的集成正在彻底改变工业4.0，并通过人工智能（AI）模拟提高工业物联网性能。通过利用人工智能技术、优化数据处理和实时决策，可以有效解决工业物联网设备对低延迟和高可靠性的关键需求。传统方法可以达到一定程度的效率和安全性，但人工智能在决策、安全、质量预测和员工采用方面提供了重大改进。将人工智能集成到工业物联网应用中可以增强工业工作流程，同时带来机遇和挑战。机器学习（ML）和深度学习（DL）算法使工业应用程序能够高效智能地运行。本文概述了工业物联网设备与可采用人工智能算法的主要研究领域（如工业物联网应用中的故障诊断、智能异常检测、边缘计算、网络性能和入侵检测系统）之间可靠和低延迟通信链路的要求。特别关注人工智能技术在提高工业物联网系统性能和效率方面的作用，突出其优势，应用和挑战。讨论了当前工业物联网中人工智能的最新挑战和未来方向，为进一步研究提供了见解。进一步研究的潜在领域包括开发新技术，集成5G/6G技术，自主决策，自我优化，解决关键任务应用，以及将人工智能处理转移到边缘。这项全面的审查将使人工智能和工业物联网领域的学者、研究人员、专业人士以及寻求利用人工智能技术提高工业物联网性能和效率的行业受益。

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Artificial intelligence performance evaluation for URLLC of industrial IoT applications: A review, open challenges and future directions

The integration of fifth-generation/sixth-generation (5G/6G) ultra-reliable low-latency communication (URLLC) with industrial Internet of Things (IIoT) applications is revolutionizing Industry 4.0, and enhancing IIoT performance through artificial intelligence (AI) simulations. The critical need for low latency and high reliability in IIoT devices can be effectively addressed by leveraging AI techniques, optimizing data processing, and making decisions in real time. Traditional methods achieve some level of efficiency and safety, but AI offers significant improvements in decision-making, safety, quality prediction, and employee adoption. Integrating AI into IIoT applications enhances industrial workflows, while presenting opportunities and challenges. Machine learning (ML) and deep learning (DL) algorithms enable industrial applications to operate efficiently and intelligently. This paper outlines the requirements for reliable and low-latency communication links between IIoT devices and primary research areas where AI algorithms can be employed, such as fault diagnosis, intelligent anomaly detection, edge computing, network performance, and intrusion detection systems in IIoT applications. Special attention is paid to the role of AI techniques in enhancing IIoT system performance and efficiency, highlighting its advantages, applications, and challenges. The current state-of-the-art challenges and future directions of AI in IIoTs are discussed, providing insights for further research. Potential areas for further research include developing new techniques, integrating 5G/6G technologies, autonomous decision-making, self-optimization, addressing mission-critical applications, and shifting AI processing to the edge. This comprehensive review will benefit academics, researchers, professionals in AI and IIoT, and industries seeking to leverage AI technologies to enhance IIoT performance and efficiency.

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来源期刊

Physical Communication ENGINEERING, ELECTRICAL & ELECTRONICTELECO-TELECOMMUNICATIONS

CiteScore

5.00

自引率

9.10%

发文量

212

审稿时长

55 days

期刊介绍： PHYCOM: Physical Communication is an international and archival journal providing complete coverage of all topics of interest to those involved in all aspects of physical layer communications. Theoretical research contributions presenting new techniques, concepts or analyses, applied contributions reporting on experiences and experiments, and tutorials are published. Topics of interest include but are not limited to: Physical layer issues of Wireless Local Area Networks, WiMAX, Wireless Mesh Networks, Sensor and Ad Hoc Networks, PCS Systems; Radio access protocols and algorithms for the physical layer; Spread Spectrum Communications; Channel Modeling; Detection and Estimation; Modulation and Coding; Multiplexing and Carrier Techniques; Broadband Wireless Communications; Wireless Personal Communications; Multi-user Detection; Signal Separation and Interference rejection: Multimedia Communications over Wireless; DSP Applications to Wireless Systems; Experimental and Prototype Results; Multiple Access Techniques; Space-time Processing; Synchronization Techniques; Error Control Techniques; Cryptography; Software Radios; Tracking; Resource Allocation and Inference Management; Multi-rate and Multi-carrier Communications; Cross layer Design and Optimization; Propagation and Channel Characterization; OFDM Systems; MIMO Systems; Ultra-Wideband Communications; Cognitive Radio System Architectures; Platforms and Hardware Implementations for the Support of Cognitive, Radio Systems; Cognitive Radio Resource Management and Dynamic Spectrum Sharing.