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The industrial application of the Internet of Things refers to Industrial IoT. The interconnection of sensors, Instruments and other devices networks together is known as IIoT. This interconnection of various sensors which are network together allows data collection and exchange of data between two industrial bodies.

Industrial Internet of Things

The main aim of IoT (Internet of Things) is:

  • To globally connect smart “things” and “objects”
  • Object uniquely identified
  • Interoperability among the objects

The Industrial Internet of Things (IIoT) is a type of technological advancement to modify the various existing industrial system and auto-link or links to the automation systems in the industry with enterprises, planning and life cycles.

Also, Industrial IoT interlinked with Artificial Intelligence and cloud computing (smart cloud) like google labs for cloud computing (Qwiklabs – Google Cloud).


IIoT includes
1. Artificial Intelligence
2. Machine Learning
3. Big Data Technologies
4. Machine to Machine interaction (M2M)
5. Automation
6. Mechatronics

The concept of IIoT is the collection of real-time data with various sensors and this data is used by the industry or users in desired and various ways. It is based on “Wrap and Re-uses” approach, rather than “Rip and Replace” approach.


There are mainly four revolutions occurred in the industries

  • 1st Industrial Revolution: Mechanized Production
  • 2nd Industrial Revolution: Mass Production
  • 3rd Industrial Revolution: Internet evolution and automation
  • 4th Industrial Revolution: IIoT (Industrial Internet of Things)

Overview (IIoT)

IIoT is enabled by cybersecurity, cloud computing, Mobile technologies, M2M, Advanced Robotics (Mechatronics), 3D Printing etc. Five most important one defined below:

Cyber-physical Systems– CPS is the basic technology for IoT and IIoT. A CPS is a system in which mechanism is controlled by a computer-based algorithm. In this system physical and software components are deeply interconnected and able to operate different spatial and temporal scales. Examples- Smart Grid, Autonomous automobile systems, Medical Monitoring, Robotics system, Astronautics and Mechatronics automation.

Cloud Computing– With cloud computing resources and data can be uploaded to a remote server in real-time and retrieved from the internet to use in the desired way by the user or the industry. Files/Data can be kept on cloud-based storage, not on the local storage facilities.

Edge Computing– Edge computing is a distributed computing technique in which data storage facility closer to the location where it needed. It is mainly referred to as decentralization data processing at the edge of the network. The more the edge-plus-cloud the more the productivity, products.

Big-Data Analytics– Big data analysis is the process of examining large and varied data sets, Data management systems, data mining of big data.

Artificial Intelligence and Machine Learning– Artificial intelligence is much complicated as the human brain itself. It’s an idea of computer science to replicate the human brain. The term “artificial intelligence” is often used to describe machines that mimic “cognitive” functions that humans associate with the human mind, such as “learning” and “problem- solving”.

Architecture

IIoT architecture is a layered module architecture of digital technology mainly conceived many modular architectures.

Content Layer – User Interface Device or GUI (Screens, Smart Glasses etc.)
Service Layer – Applications, software to analyze data and transform it into information
Network Layer – Communication Protocols, Wi-Fi, Zigbee, Smart cloud, cloud computing.
Device Layer – Hardware: CPS, Machines, Sensors

Abhishek Tyagi

Abhishek Tyagi

Currently, A Mechatronics Engineer, Machine learning and deep learning enthusiast. And love to research on various topics.

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