The development of many new technologies and products is shaping a new industrial Internet of Things, from software and hardware innovation to intelligent wearable devices that enhancehuman sensory abilities, all of which have the potential to impact production systems and processes through improved data-driven intelligence. The Industrial Internet of Things is transforming traditional linear manufacturing into dynamic, interconnected systems, helping factories unleash their potential for more efficient, high-yield, and proactive operation. We need to pay attention to the trends presented by the Internet of Things and keep up with them.

The Six Trends of Industrial Internet of Things

Artificial Intelligence (AI): Artificial intelligence is a computer simulation of human intelligence processes, which comprehensively utilizes multidimensional data to construct a data model and make accurate control and prediction. More and more enterprises are deploying artificial intelligence to analyze IIoT data, track device usage, improve workflow, simplify logistics, enhancesecurity, and achieve higher overall efficiency in all aspects of operation.

Data augmentation and virtual reality: The emergence of cloud computing greatly enhances the efficiency and ability of enterprises to work in the cloud. The long distance between cloud computing and IoT devices can lead to propagation and transmission delays. The large amount of computing load on a single cloud server can also bring processing and queuing delays. In contrast, fog dispersion computing pushes data and intelligence to analysis platforms located (or close to) data sources. Edge computing is one of them. Fog pushes intelligence to the edge ofthe network to achieve real-time device control, security and management. This is a transition from centralization to centralization.

Big data analysis: As the amount of data generated by devices continues to grow exponentially, big data storage and analysis are helping to understand it and provide valuable insights. Recent innovations in big data analysis and new machine learning algorithms have made real-time analysis solutions possible to compare historical trends with forward-looking predictions, so that frontline managers can more accurately predict future performance.

Digital twin: applied to the industrial Internet of Things, digital twin refers to the mapping of real devices or factories in virtual space. By acting as a virtual helper of physical systems, digital twin allows users to access the structure, context, and behavior of machines and processes, providing a window for understanding past, present, and potential future states and conditions.

Industrial Internet of Things: Industrial Internet of Things is a constantly evolving, broader, and more comprehensive concept of the Internet of Things. In the early days, it was just the networking between devices. With the development of technology, more entities participated, including the network connection of personnel, processes, data, and things in the system, rather than just the Internet of Things referring to physical objects or devices on more centralized platforms.

Device network security: The increase in wireless connections in industrial facilities has expanded the attack surface of network threats. In addition, industrial Internet of Things components and devices such as industrial routers and industrial Ethernet switches are usually not protected by network security like other network tools, making them vulnerable to attacks. The losses borne by enterprises will be unpredictable, so data security will be increasingly valued.