Artificial intelligence at the Edge and its role as an enabler of industrial AIoT applications

The advent of the Industrial Internet of Things (IIoT) has enabled a wide range of companies to collect massive amounts of data from previously unexplored sources and explore new avenues to improve productivity.

By obtaining data on the performance and environment of field equipment and machinery, organizations now have even more information at their disposal to make informed business decisions. This massive amount of data means that most of this information remains unanalyzed. As a result, companies and industry experts are turning to artificial intelligence and machine learning solutions for IIoT applications to gain a holistic view and make smarter decisions faster.

The marriage of AI with IoT, also called Artificial Intelligence of Things (AIoT), offers capabilities beyond the individual adoption of either technology.

AIoT offers a way for networks of connected devices to collect large amounts of data from the physical world and, with programmable intelligence, learn to reason and process this data into information and knowledge, and then act on it as humans do.

The Edge presents new challenges in the way the industry produces compute. While traditional data centers and cloud computing have historically been relied upon to process data generated outside of data centers, the need for real-time value at the point of generation requires the presence of computing resources at the Edge, which has led to the emergence of edge computing. The Edge is the point of intersection of the physical and digital worlds. It is the point at which data is generated, gathered and processed to create new value.

More than 50% of IIoT data remains unanalyzed

The staggering number of industrial devices connecting to the Internet continues to grow year after year and is expected to reach 41.6 billion endpoints by 2025. Imagine the enormous amount of data generated by all these devices.

In fact, manually analyzing the information generated by all the sensors on a manufacturing assembly line could take a lifetime. It is no wonder that less than half of an organization's structured data is actively used in decision making.

This inability of humans to analyze all the data we produce is precisely why companies are looking for ways to incorporate artificial intelligence and machine learning into their IIoT applications.

In each of the industrial applications, IAoT offers the possibility to reduce labor costs, reduce human error, and optimising preventive maintenance.

What exactly do we mean by AI and how does it fit into the IIoT?

"Artificial Intelligence of Things" (AIoT) refers to the adoption of AI technologies in Internet of Things (IoT) applications in order to improve operational efficiency, human-machine interactions, and data analysis and management.

AI is a field of science that studies how to build intelligent programs and machines to solve problems traditionally performed by human intelligence. It also includes machine learning (ML) which is a specific subset of AI that allows systems to automatically learn and improve through experience without being programmed to do so, such as through various algorithms and neural networks. Another related term is "deep learning," which is a subset of machine learning in which multilayer neural networks learn from large amounts of data.

Since AI is such a broad discipline, the following discussion focuses on how AI, along with ML, are used for classification and recognition in industrial applications.

From reading data in remote monitoring to preventive maintenance, Artificial Intelligence and Machine Learning are unleashing greater productivity and efficiency in industrial applications.

Artificial Intelligence at the Edge

As mentioned before, the proliferation of IIoT systems generates lot of data. To send all this raw data to a public cloud or private server either for storage or processing would require considerable bandwidth, availability and power consumption.

In many industrial applications, especially in highly distributed systems located in remote areas, it is not feasible to constantly send large amounts of data to a central server. Even if a system had sufficient bandwidth and infrastructure, it would be incredibly expensive to deploy and maintain and would still have very high latency when transmitting and analyzing that data. Mission-critical industrial applications must be able to analyze raw data as quickly as possible.

To reduce latency, reduce data communication and storage costs, and increase network availability, IIoT applications are moving AI and machine learning capabilities to the edge of the network (Edge) to enable more powerful preprocessing capabilities directly at the very point where that data is generated.

Progress in Edge processing power has enabled IIoT applications to take advantage of AI decision-making capabilities in remote locations. In fact, by connecting end devices to edge computers equipped with powerful local processors, companies no longer need to send data to the cloud. AIoT essentially enables AI inference in the field, as opposed to sending raw data to the cloud for processing and analysis. To effectively run AI models and algorithms,  AIoT applications require a reliable platform on the Edge.‍

Related reading: AIoT: the perfect fusion between the Internet of Things and Artificial Intelligence.

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Barbara, the Cybersecure Industrial Edge Platform

While an increasing number of IoT use cases demand a higher degree of edge processing, solutions at the edge are still grappling with the challenges of secure connectivity and application management. This is where Barbara comes in: our edge node platform designed with security by design, enables one-click, centralized deployment, management and configuration of applications on nodes.

Wth Barbara Edge Platform companies can deploy artificial intelligence applications from a centralized point. In addition,  we can deploy more than 5 different applications in each edge node, from different authors and also  communicate one node with another so that we avoid centralized infrastructures with higher costs and security risks.

We are moving from a model where intelligence was stored in the industrial equipment, in the hardware, to a model where intelligence resides in the software.

If you like to learn more on how to implement AI at the Edge, get in touch.

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