After the great wave of IoT (Internet of Things) invaded homes, industries and other sectors, the advancement of this technology presents us with a new concept: AIoT, the Artificial Intelligence of things. This new technology represents the fusion of the Internet of things with Artificial Intelligence (AI).
IoT collects, stores and processes data that is extracted by sensors and transducers in an environment — a residence, factory, farm, etc. This data, processed locally or sent to a central system, is used as a basis for decision-making in these scenarios.
Artificial Intelligence, in turn, provides the capabilities of algorithms that seek to reproduce human reasoning, content production, and decision-making skills. AIoT combines information gathered through IoT with Artificial Intelligence.
Uniting IoT and AI, AIoT allows decisions to be made by the very devices that make up the IoT network. That is, decisions are made at the end end of the system, at the edge, implementing what is now known as Edge Computing.
Thus, with AIoT, the function of acting on the collected data also becomes part of the system, without or with little human interaction. In addition, devices come to have the ability to learn over time and in this way improve their activities in the process in which they are involved.
All this has only become possible with the technological evolution of recent years, which has brought greater processing power, miniaturization of circuits and cost reduction in microelectronics. Today, we can already find ready-made and cheap boards, with the processing capacity necessary to perform AI tasks — such as nVidia Jetson, Raspberry Pi — and modules specific to AIoT, such as the dipped M1W board.
New technologies will also work to aid improvements in AIoT. 5G technology, for example, promises higher speed and extremely low latency in device connections to the Internet, enabling near real-time data processing. She will be a great ally of AIoT, which, in addition to hardware equipment, also requires connection to work — so that the devices on the network can communicate with each other and receive external commands.
Benefits of AIoT
The benefits of AIoT begin with the increased agility of industrial processes, in which decision-making is no longer centralized and can be done locally, on the machine itself, without the need to send the data for remote processing or a human operator to make the decisions.
With AIoT, the machine can learn and act, sending external data only to report the progress of activities. Processes become more agile and the machines themselves can improve their performance during normal operation and achieve their maximum throughput.
Thus, the need for human intervention is reduced and the process begins to have fewer failures. Predictive maintenance-which uses data extracted from machines in a factory to predict future failures — also wins with AIoT, as machines predict when they might fail or need adjustments.
AIoT in practice
Since each industry has its own problems, challenges and needs, there is no “silver bullet”, a ready-made solution that meets every case. Thus, when starting a project to implement AIoT in a business, it is necessary to conduct a case study. This study should identify the best way to distribute sensors in equipment, control the manufacturing process through an IoT device and add Artificial intelligence algorithms — which, over time, learn to improve productivity.
An example of AIoT’s application in manufacturing is the replacement of Automated Guided Vehicles (AGVs) robots with fully autonomous robots. AGVs follow a pre-defined route and have no autonomy to decide how to deal with obstacles on the route. When they encounter problems, they stop and wait for the route to become free. Autonomous robots do not need predetermined routes, only a map of the factory and the loading and unloading points. With this information, they can get around and carry out their activities.
In this way, in case of unexpected obstacles on the route, an autonomous robot can not only follow the predetermined route, but also make decisions, diverting and resuming the route to follow. Over time, he can also learn how to improve his route, calculating the best path, gaining speed and delivering results more efficiently.
The implementation of object detection by cameras is also an application of AIoT. In this type of solution, the camera not only captures images, but processes them and sorts the objects identified on the device itself (to which the camera is connected). By connecting these AIoT cameras to other devices such as robots, we can automate tasks and decrease human interaction, saving time and agility in the processes involved.
An example is screwing robots that, aided by an AIOT device, can identify which product has entered its assembly area and change its tools — screwing tip — without human assistance. With proper training, the system can even dispense with programming the bolting points, identifying the holes automatically.
With the fusion between the Internet of things and the artificial intelligence generated by AIoT, we will have smarter machines and manufacturing systems. They will be able to take care of themselves and learn to improve the way they perform their tasks, increasing efficiency and gaining in production.
By reducing the need for supervision and decision-making by humans, we also gain the reduction of human failure rate and personnel cost. Reactive maintenance tends to decrease as machines start to monitor themselves, indicating possible failures in advance (predictive maintenance).
In this way, AIoT presents itself as a new step of the technological revolution that we are living. Just like IoT and AI, it is designed to be used in conjunction with other techniques and technologies, so it is ideal for advancing any type of business — as it makes equipment increasingly intelligent, adaptive and versatile. In this way, AIoT brings new possibilities and gains to different sectors, with many applications still to be discovered.