Guest Editorial Introduction to the Special Section on Computational Intelligence and Advanced Learning for Next-Generation Industrial IoT

The rapid development of real-time Industrial Internet-of-Things (IIoT) applications including green infrastructure, smart grids, smart city, intelligent transport networks, etc. enables green communication between tens of billions of end devices such as wearable devices and sensors. As a result, a tremendous amount of data is generated from massively distributed sources, which require computational intelligence techniques to fulfill high computing and communication demand that frequently exceeds energy consumption. Many emerging IIoT applications including remote surgery, machine monitoring and control, fault detection, and healthcare generate delay-sensitive tasks, which require timely processing with minimum delay. Besides, according to the energy consumption formulation, the required energy consumption for processing real-time tasks on remote computing devices should be the accumulation of data transmission time, transmission power, and processing capacity. Thereby, the energy emission rate can be controlled by balancing the trade-off between the transmission power and transmission time. IIoT covers a broad domain of real-time IIoT applications and refers to the combination of IoT technologies and computational intelligence techniques for processing real-time data with minimum delay. In addition, energy-efficient communication and computation of the real-time IIoT applications target to increase efficiency, automation, and productivity.