The potential for the wide-scale acceptance of the Industrial IoT is limited by a lack of automation, real-time monitoring, and connectedness. However, the future communication trend towards 5G is expected to bring greater benefits to IIoT infrastructures in terms of high-speed transmission and ultra-low latency. Furthermore, with emerging techniques such as millimeter-wave (mmWave), massive multiple-input multiple-output (MIMO), and machine-to-machine (M2M) communications, the coupling of IIoT and 5G will advance profoundly. Despite these advantages, 5G-envisioned IIoT ecosystems are expected to face other potential concerns such as trust, security, and privacy. Apart from this, the challenges related to data storage and processing and computational complexities will also draw significant attention. To address the above-mentioned challenges, it's important to analyze data in real-time. In this direction, transfer learning (TL) can be a revolutionary breakthrough. TL fosters greater explorations and experimentations, leading to innovations and greater productivity.

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客座社论：5G辅助工业物联网迁移学习专题

工业物联网被广泛接受的潜力受到缺乏自动化、实时监控和连接性的限制。然而，未来5G的通信趋势有望在高速传输和超低延迟方面为工业物联网基础设施带来更大的好处。此外，随着毫米波 (mmWave)、大规模多输入多输出 (MIMO) 和机器对机器 (M2M) 通信等新兴技术的发展，IIoT 和 5G 的耦合将得到深刻推进。尽管有这些优势，但 5G 设想的 IIoT 生态系统预计将面临其他潜在问题，例如信任、安全和隐私。除此之外，与数据存储和处理以及计算复杂性相关的挑战也将引起人们的极大关注。为应对上述挑战，实时分析数据很重要。在这个方向上，迁移学习 (TL) 可以成为革命性的突破。TL 促进更大的探索和实验，从而带来创新和更高的生产力。