The Fourth Industrial Revolution known as Industrie 4.0 involves big data, automation tools, and controllers in robotics. They make it possible for any manufacturing company to operate and produce efficiently. Future industry 4.0 market growth will be remarkable due to increased government investment, industrial automation artificial intelligence (AI), and the Internet of Things (IoT). However, the two primary obstacles to the market’s progress are the installation of state-of-the-art robotic and data analytics equipment, which calls for a sizable initial investment and a team of skilled employees.
The demand for the worldwide Industry 4.0 market is expected to rise at a compound annual growth rate (CAGR) of 17% from 2020 to 2026, from USD$70 billion in 2019 to USD$210 billion, according to a new study report.
AIoT: The most cutting-edge and revolutionary technologies
Artificial Intelligence (AI), and the Internet of Things (IoT) are innovative and advanced technologies pushing the growth of various industries. AIoT (the integration of AI and the IoT) allows machines to communicate better and industries to analyze patterns, trends, and correlations among connected devices.
Through machine learning and intelligent decision-making, the integration of AI into IoT systems has greatly increased the efficiency of IoT devices. Edge computing, sophisticated SoCs, apps, and software connected to IoT devices are some of the components that make up the AI package in an integrated AIoT system.
A report from Statista says that by 2025, there will be over 75 billion IoT devices on the market, more than cloud infrastructures can handle. As more devices are added, the IoT network will be required to solve the latency issues by continuously sending data to and from the cloud.
The key technologies in AIoT
- Artificial Intelligence
Whereas deep learning models require less power to run, they are becoming more accurate and efficient. Artificial Intelligence improves Internet of Things devices in two ways. Firstly, IoT devices and intelligent sensors make the telemetry data process considerably more efficient. Second, for sophisticated and mission-critical applications, AI enables batch (Big Data) and stream (real-time) processing at the edge.
- Big Data
The amount of Big Data being collected as it moves between devices and networks has increased dramatically due to the spread of connected IoT. Because IoT devices create so much data, AI experts are developing increasingly sophisticated Deep Learning models to be integrated into AIoT devices.
Furthermore, by collecting, filtering, processing, and analyzing data at the edge before delivering the most crucial information to the cloud, AIoT devices reduce workloads at the cloud.
- Hardware Accelerators
The ability of IoT devices to quickly run AI, Deep Learning, and Machine Learning models is being pushed ahead by more potent computer processors, such as CPUs and GPUs from Intel, AMD, NVIDIA, and Qualcomm.
Additionally, computer manufacturers are creating more CPUs with an AI focus. These processors, which can execute AI models rapidly and efficiently, include Intel’s Movidius VPUs and Google’s TPUs. Manufacturers can create AIoT devices that are more powerful and small as machine learning technology develops.
- 5G Network
The next generation of wireless communication offers 100 times faster speed than 4G and 100 fold in connected devices. AIoT applications will be much more powerful, mobile, and efficient.
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The benefits of AIoT in Industry 4.0
The combination of IoT Consulting services in artificial intelligence, and the Internet of Things provides certain advantages in Industry 4. 0 —including:
- Complete autonomous environment
AIoT systems aid in monitoring operations from start to finish. These systems offer a glimpse into everyday processes. Despite the vast quantity of data being created, AI aids IIoT systems in establishing a platform for quicker operations and seamless communication. The fast and direct response can increase production efficiency by reducing human involvement.
- Predictive maintenance
The AIoT system observes important machine data and enables machine learning to identify the specific issue and its root cause. AI implementation in Industry 4.0 can be beneficial in forecasting machine breakdowns. Machine Learning-driven systems can identify recurring patterns that result in failures and alert maintenance teams to conduct inspections.
An integrated system can initiate maintenance beforehand and commence troubleshooting processes. Organizations can transition from schedule-based maintenance to conditions-based maintenance, ultimately leading to decreased repairs and restoration needs.
- Scalability and efficiency
Numerous businesses employ integrated AIoT systems to discover patterns, trends, and insights. AI provides cutting-edge data analytics that businesses can utilize to control their output levels and improve their operational effectiveness and overall output.
An IoT device gathers data in massive quantities. Utilizing AI assists in condensing the data and delivering in-depth information to other devices within the network. AIoT can handle large amounts of data and enhance the scalability of an IoT network.
See also: Harnessing big data for precision and efficiency in supply chain management
Applications of AIoT in Industry 4.0
Industry 4.0 utilizes IoT. A term that refers to the fourth industrial revolution. This revolution harnesses digital technology and connectivity to enhance quality and productivity. It is worth knowing that smart factories incorporate robotics, AI, autonomous vehicle, big data analytics, cloud technologies, and blockchain.
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- Smart grids: IoT development services are used in smart grids to monitor the energy consumption of each household. The data is transmitted to a main server that the utility company can log into. Therefore, they can offer tailored rates based on the requirements of individual households.
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- Manufacturing: Companies can utilize the Industrial Internet to enhance the effectiveness of their production procedures by gathering live data from the field and examining it with tools such as machine learning algorithms or deep neural networks. This allows them to enhance their supply chain process by lowering stock levels and boosting throughput times.
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- Smart thermostats: These devices can be set up in homes or offices to automatically adjust temperature settings depending on occupancy patterns, energy usage, and other variables. Smart thermostats can gather data from sensors inside a home or office, like motion or temperature sensors, to learn user preferences through Wi-Fi or Bluetooth connections.
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- Digital twins: Digital Twins are virtual copies of engineering objects. They assist in running simulations before reproducing the real product. Companies utilize digital twins to simulate the operational environment before manufacturing new engine components or wind turbines for testing purposes. AIoT systems aid in reducing mistakes and reaching conditions similar to the actual working atmosphere.
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- Edge computing: The AI systems connected to a central hub conduct intelligent predictive analysis and alert the system to any discrepancies. Edge computing AI systems start rapid data gathering. Users can make decisions locally instead of transferring data to the cloud. IIoT gathers information in an AIoT setup, while AI offers decision-making capabilities at the edge.
IoT and AI technologies are merging to produce innovative products and services. With the expansion of technologies such as edge computing and blockchain, the integration with IoT enables the development of new applications. The utilization of both IoT and blockchain together is exemplified in the smart contract solution. These technologies are on the verge of offering fresh benefits that will simplify the process of developing a connected, intelligent, and smart environment.
