■ Perhaps that's why it's so important for organizations to be able to survive the massive digital transformation brought about by Industry 4.0 without critical help from IIoT. The combination of these two technologies, Artificial Intelligence (AI) and IIoT, can effectively manage and fully utilize the massive amounts of data generated during digital production, taking industrial process control to a whole new level.
4 Must-Have Capabilities for IIoT Data Management
With the penetration of the wave of digitalization in the industrial field, big data has become the entrance to industrial digitalization. According to IDC, the global data volume reached 42ZB in 2019 and is expected to reach 163ZB in 2022, with a compound annual growth rate of 57%. And the application scenarios of industrial data in the industrial field are also increasing, and the statistics of Saidi Intelligence indicate that China's industrial big data market will be about 14.69 billion yuan in 2019, and it is expected to maintain a high growth rate of more than 30% in the future. That said, when organizations start working on deploying IIoT in their industrial systems, one of the first challenges they face is how to retrieve data from the IIoT system and make it available for real-time analysis and decision-making in the manufacturing process. To ensure that data management solutions are IIoT-ready, here are 4 features to focus on:
Versatile connectivity to handle a variety of data. There are various standards for IoT systems that produce data that needs to adhere to various protocols such as MQTT, OPC, AMQP, etc. In addition, most IoT data exists in semi-structured or unstructured formats. Therefore, the data management system must be able to connect to all systems and adhere to various protocols in order to be able to receive data from these systems. At the same time, the solution has to support both structured and unstructured data.
Rich edge processing capabilities. A good data management solution should be able to filter out error logs from systems, and it should also be able to enrich the data with metadata, such as timestamps or static text, to support better data analysis.
Big data processing and machine learning capabilities. Since the amount of IoT data is very large, it is important that the system maintains ultra-low latency when performing real-time data analytics so that data can be processed in real-time.
Real-time monitoring capabilities. Acquisition and processing of IoT data is an ongoing process, so data management solutions should provide real-time monitoring through visualization to show the status of the process in terms of performance and throughput at any given time.
How does Artificial Intelligence impact the Industrial IoT?
Before discussing this topic, let's take a look at what expert research organizations have to say about the future of both technologies, AI and IoT: According to Markets&Markets, AI will be a $190 billion industry by 2025.IDC, on the other hand, believes that 40% of digital transformation initiatives in 2019 are driven by AI. Business Insider predicts that there will be more than 64 billion IoT devices by 2025, up from about 10 billion in 2018. As a result, McKinsey gives this prediction that by 2025, IoT has the potential to generate between $4 trillion and $11 trillion in economic value.
From the above figures, it is clear that AI and IoT, two technological concepts that have been around for decades, are re-emerging at the right time and place, they are disrupting traditional industry norms and are set to spark a digital revolution that will take the traditional industrial revolution of the 18th century into the 21st century with Industry 4.0.With the incorporation of AI, the performance of the Industrial IoT is set to be greatly enhanced.
Artificial intelligence is becoming the brain of industrial intelligence
After sufficient development of basic elements such as data, algorithms and arithmetic power, artificial intelligence has a foundation for realization. At the same time, the development of artificial intelligence also brings good opportunities for the development of manufacturing industry, and comprehensively improves the level of industrial manufacturing from multiple dimensions. At present, artificial intelligence has been used in a number of application scenarios in the industrial field, such as industrial visual inspection in intelligent production scenarios and predictive maintenance in the field of equipment management. During predictive maintenance, using existing data, AI algorithms can determine when to implement preventive measures before a machine needs repair. Computer vision for visual inspection is also a key technology that can reduce costs and increase efficiency; when provided with the right training data and hardware, machine learning (ML) algorithms can be more accurate and effective than humans in visual inspection, and is already being used by BMW, for example, to ensure quality control of its automotive parts. Globally, manufacturing companies are increasingly focusing on improving the efficiency of machinery and systems and reducing production costs. As semiconductor technology advances and affordable sensors and processors become more widely available, IIoT adoption will continue to grow. According to an analysis by Grand View Research, the global IIoT market will be approximately $216.13 billion in 2020. Now that the industrial sector is accelerating towards smart and autonomous industrial processes, data collection from IoT devices is reaching an unprecedented scale. When big data, AI, and IoT come together, it creates a range of opportunities for advanced IoT data analytics solutions. In the process, artificial intelligence, especially deep/machine learning technologies, provide a powerful support for managing and analyzing massive amounts of sensory data.
A report by research firm MobiDev predicts that by 2025, AI and IoT will be worth more than $26 billion. They also demonstrated that AI improves the efficiency of IoT data by 25% and industry analytics by 42%, and that it plays an important role both at the center of the IoT and in the edge network. For example, on an assembly line in a factory, quality control can be performed through the use of AI visual inspections, which can effectively reduce the rate of manufacturing defects during the manufacturing process.
AI + IIoT Solutions
Influenced by a number of favorable factors such as advances in semiconductor and electronic device technology, increased use of cloud computing platforms, IPv6 standardization, and government support for IIoT-related R&D activities, the IIoT solutions and market incorporating AI are growing rapidly, and according to a new market research report by Markets&Markets, the size of the global IIoT market is projected to to grow from $76.7 billion in 2021 to $106.1 billion in 2026, and by 2026, AI revenue in this segment is expected to reach $16.7 billion.
Under this megatrend, major technology vendors will also already be working hard to promote AI + IIoT solutions with innovative technologies and products.
Conclusion
Artificial intelligence has the ability to manage itself and its applications independently and intelligently. Among the technological breakthroughs in the past decade or so, almost nothing has reached the level of impact that AI combined with the Industrial Internet of Things (IoT) has had on the industrial sector. According to Deloitte's statistical surveys and forecasts, the application of AI in China's manufacturing sector is very promising, with applications expected to be around 25.22 billion yuan in 2020, and to reach 205.76 billion yuan by 2025, at a compounded growth rate of more than 40%. By integrating AI algorithms into industrial IoT infrastructures, entire machinery and equipment can be trained and automated for intelligent factory management and operation. Maybe we can't see widespread AI+IIoT applications yet, but I believe that in a few years, AI and IoT will become more and more prevalent in the industrial sector.