Top 5 Technologies that Enable IIoT and Data Science

Introduction

For many years now, data science has been a hot topic. We all encounter examples of data science's application to the Internet of Things (IoT) daily in our personal and professional lives. The Industrial Internet of Things, or IIoT, is the application of IoT technologies to manufacturing and industrial settings. Data Science has a unique place in this field.


What is IoT?

The network of physical objects known as the Internet of things (IoT) is equipped with electronics, software, sensors, and network connectivity, allowing them to gather, share, and communicate data.


Consumer goods such as connected cars, home automation, wearable tech, connected health devices, and appliances with monitoring capabilities make up a growing portion of IoT devices. The information gathered from these IoT sectors and their activities are essential for businesses and society. For instance, this technology will be used by the medical and healthcare sectors to track and assess patient health. Additionally, transportation technology will require the integration of communication, control, and information processing across the systems.


What is IIOT?

The connectivity between sensors, instruments, and other devices used in manufacturing and energy management is referred to as the Industrial Internet of Things (IIOT). In other words, these gadgets assist with data analysis, operational technology, places, and people by being connected to industrial equipment. As a result, it includes the manufacturing, agricultural, and maritime sectors.


This technology aims to analyze, monitor, and automate the streamlining process to improve it and cut production costs. Cyber-physical systems, Cloud computing, Edge computing, big data, artificial intelligence, and machine learning are some of the technologies that make IIoT possible. We now reach the point where data science is applied.





How is Data Science applied here?

The industrial Internet of things' system configuration is divided into three categories: first, the cloud, where all data is stored, transformed, and analyzed. The second is the network, which is where the devices communicate with one another, and the third is the edge, which manages every machine.


Data science is applied cyclically to address problems. It calls for taking action, comprehending the business need, visualizing the data for any issues, and putting a machine learning model into place.


  • Cyber-physical

A computer system in which the mechanism is managed or controlled by a computer-based algorithm is referred to as a cyber-physical or intelligent system. In these systems, hardware and software are tightly integrated, enabling communication and cloud data sharing.


Data science is a tremendous asset for finding anomalies, performance problems, or cost-saving detections in the algorithm and the machine. For instance, when keeping track of a cyber-physical process, the algorithm will log data from the hardware and modify its behavior as necessary. However, the hardware or software occasionally veers off course and causes issues throughout the supply chain.


  • Artificial intelligence

In short, artificial intelligence, also known as AI, is the study of intelligent programming machines to behave like people. A key component of AI called machine learning, or ML, seeks to predict outcomes accurately without explicit programming. IIOT uses AI and machine learning to forecast machinery's performance thanks to big data. Check out Learnbay’s trending Artificial Intelligence Course in Pune.


  • Cloud Computing

Cloud computing is known as the on-demand availability of computer system resources for storage and processing power without the need to own the hardware. For complex analytics, big data mining, cutting-edge visualization, and long-term data storage, cloud computing is helpful in the IIOT. Furthermore, its data centralization is one of its key benefits. For instance, gathering all the data on a single server in a windmill field would be simpler than downloading it individually from each turbine.


Traditional data storage involves pushing data to a server and having a client pull it back; cloud computing is not appropriate for real-time data where timing is crucial in a supply chain. The Cloud Computing system, on the other hand, is ideal for more complicated analyses that need a lot of computing power. Predictive maintenance, for instance, uses cloud computing to determine when a machine needs to be repaired by the business.


  • Edge Computing

A distributed computing paradigm known as "edge computing" brings computer data storage closer to the point of need. As opposed to cloud computing, edge computing refers to decentralized data processing at the network's edge.


Real-time data analysis and machinery control are the edge's main benefits. It permits constant data flow, pre-processing/filtering, device-to-device communication, and basic data visualization and analytics. Accessing an edge computing device can quickly diagnose the machinery and ascertain its functionality and efficiency. For instance, a cloud-based solution can more effectively deliver the crucial data required to diagnose the turbine in a windmill field without depending on erratic cellular communications.


  • Big Data

Big data is analyzing a large amount of data that requires sufficient computing power, whereas cloud computing deals with computing power and storage. Big data is currently used to describe advanced analysis, predictive analysis, or behavior analysis that uses knowledge gleaned from big data.


Because of all the devices connected to the machinery in the industrial environment, big data computing is essential (visual sensors, heat sensors, communication protocols, WIFI, Bluetooth, etc.). It became simpler to forecast the evolution of the machinery (machine failure, maintenance analysis) and its efficiency by analyzing the data provided by the captor (productivity, expected productivity).


Conclusion

Data is produced across various industries daily, particularly in the IoT and industrial settings. Therefore, better tools and technologies are required for businesses and professionals to find, gather, and analyze every insight and anomaly of each process. Thanks to technologies like data science and machine learning, we can now make the most of the data collected by industrial devices.


We examine the ideas behind IoT and IIoT in this article and how data from these concepts can be used to make predictions and identify problems more accurately. Check out India’s top data science course in Pune, crafted in partnership with IBM for working professionals of domains.

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