AIoT: The perfect combination of iot and artificial intelligence

Is the Internet of Things Nothing without Big Data

Think of the Iiot as the nervous system of a company: it’s a network of sensors that collect valuable information from all corners of the production plant and store it in repositories for data analysis and exploitation. This network is necessary to measure and acquire data to make informed decisions. But what happens next? What should we do with all this data? We always talk about making good decisions based on reliable information, but while it may sound obvious, achieving that goal isn’t always easy. In this article, we’ll go beyond iot to focus on data and how it can be leveraged through AIoT and data analytics.

Ai can act as the brain of the subject of an IoT solution, synthesizing new algorithms to react and even proactively solve potential problems. Click on a tweet

We’ll talk specifically about the analysis phase, the process of first turning data into information and then into knowledge (sometimes called business logic). Ultimately, however, we won’t stray from the core theme of iot, because iot without big data makes no sense to us.

Big Data and data analytics

In recent decades, especially in the 10s, we have witnessed an influx of massive amounts of data (both structured and unstructured) generated by ubiquitous digital technologies. In the special circumstances of the industrial world, making the most of and making the most of this vast amount of information is essential to success.

This need to process business data has given rise to the interchangeable terms “big data,” “data science,” and “data analytics,” which we can collectively refer to as the process followed to examine data captured by a network of devices, with the goal of revealing confusing trends, patterns, or correlations. The fundamental goal is to use new knowledge to improve the business.

Because it is a recently created term, there are different definitions of big data. One provided by Gartner Outlines three key aspects: data volume, diversity, and speed of data capture. These are often referred to as 3V, although other definitions extend this to include 5V, increasing the authenticity of the data and the value it brings to the business.

However, we believe that there is not much point in theoretical discussion of what is big data and what is not, because big data analysis and processing are already applicable to a wide range of the industrial world due to the ubiquity of data collection devices.

Internet of Things and Big Data

How do iot and Big Data relate to each other? The main point of connection is usually the database. In general, we can say that the work of the Internet of Things ends in that database; In other words, the goal of iot is to dump all acquired data into a common repository in a more or less orderly manner. The big data domain first accesses this repository to manipulate the acquired data and get the information it needs.

In any case, it’s useful to visualize iot big data analytics as a toolbox. Depending on the type of information and knowledge we want to extract from the data, we will extract one tool or another from the data. Many of these tools come in the form of traditional algorithms, as well as improvements or adaptations of these algorithms, with very similar statistical and algebraic principles. These algorithms were not invented in this century, surprising many who wonder why they are more relevant now than before.

The quick answer is that the amount of data available is now much greater than when the algorithms in question were first conceived, but more importantly, the computing power of today’s machines allows for the use of these technologies on a much larger scale, providing new uses for old methods.

But we don’t want to give the impression that everything has already been invented and that current trends in data analytics aren’t bringing anything new to the table; In fact, quite the opposite. The data ecosystem is extensive and has witnessed significant innovation in recent years.

One of the fastest growing areas is artificial intelligence. It is arguably not a recent invention, as the phenomenon was discussed as far back as 1956. However, artificial intelligence is such a broad concept and its implications are so widespread that it is often considered a discipline in its own right. However, the reality is that in some ways it plays an integral role in big data and data analytics. It’s another tool already included in our metaphor toolbox, but found a natural evolution of AIoT.

AIoT: Artificial Intelligence for the Internet of Things

The exponential growth in the volume of data requires new methods of analysis. In this context, artificial intelligence becomes particularly important. According to Forbes, the two big trends dominating the technology industry are the Internet of Things (IoT) and artificial intelligence.

The Internet of Things and artificial intelligence are two independent technologies that greatly influence each other. While iot can be thought of as a digital nervous system, AI is equally an advanced brain that can make decisions that control the entire system. IBM says the true potential of iot can only be realized with the introduction of AIoT.

But what is AI and how is it different from traditional algorithms?

When machines mimic human cognitive functions, we usually talk about artificial intelligence. That is, it solves problems in the same way that humans do, or assumes that machines can find new ways to make sense of data. The strength of AI is its ability to generate new algorithms to solve complex problems – and this is key – independently of programmer input. As such, we can think of AI in general, and machine learning in particular (which is the part of AI with the greatest projected growth potential) as algorithms that invent algorithms.

Edge AI and cloud AI

The combination of IoT and AI has brought us to the concept of AIoT (Artificial Intelligence for the Internet of Things), intelligent, connected systems that are able to make decisions on their own, evaluate the results of those decisions, and improve over time.

This combination can be done in a number of ways, two of which we’d like to highlight:

On the one hand, we can continue to conceptualize AI as a centralized system that can handle all impulses and make decisions. In this case, we are referring to a system in the cloud that centrally receives all telemetry data and acts accordingly. This will be called Cloud AI (artificial intelligence in the cloud).
On the other hand, we must also talk about a very important part of our metaphorical nervous system: reflexes. Reflexes are autonomous decisions made by the nervous system without having to send all the information to the central processing unit (brain). These decisions are made on the periphery, close to the source of the data. This is called edge AI (artificial intelligence at the edge).

Use cases for edge AI and cloud AI

Cloud AI provides a comprehensive analysis process that takes the entire system into account, while Edge AI gives us fast response and autonomy. But like the human body, the two responses are not mutually exclusive and can actually complement each other.

For example, a water control system could close a valve at the site as soon as a leak is detected to prevent significant water loss, while sending a notification to a central system where higher-level decisions can be made, such as opening alternative valves that direct water through another loop.

The possibilities are endless and can go beyond this simplified example of reactive maintenance, where complex systems are able to predict the events that are likely to occur, enabling the possibility of predictive maintenance.

Another example of AIoT data analytics can be found in the smart grid, where we have smart devices at the edge analyzing the current at each node and making load balancing decisions locally, while sending all this data to the cloud for analysis to generate a more comprehensive national energy strategy. Macro-level analysis will allow load-balancing decisions to be made at the regional level and even to reduce or increase electricity production by shutting down hydropower plants or starting the power purchase process from neighboring countries.