Guest Editorial: Special Section on Transfer Learning for 5G-Aided Industrial Internet of Things

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.