AIoT: Improved decision making with AI + IoT

The internet of things (IoT) is a system of interconnected devices and sensors that collect and share data. The data is then analyzed to improve efficiency and optimize performance. The IoT has the potential to revolutionize the way we live and work. One of the key components of the IoT is artificial intelligence (AI).

AI enables devices to interact with each other and with humans to make decisions based on data. For example, AI can be used to control a home’s temperature, lighting, and security system. AI can also be used to monitor and optimize industrial processes. The IoT is still in its early stages, but it is clear that AI will play a major role in its development. As the IoT grows, so will the demand for AI. The combination of the IoT and AI has the potential to change the world as we know it.

AI + IoT

In recent years, there has been an explosion in the number of Internet of Things (IoT or Internet of Things) devices in all fields. From medical devices or home and building automation to industrial automation! As we say, they range from wearable devices and sensors to medical monitors, all connected, allowing the data collected to be massive (Big Data).

According to the big data report, It is estimated that by 2025, there will be more than 65 billion connected IoT devices generating close to 90 zettabytes (ZB) of data.

A key driver of this growth is wireless connectivity. This allows “things” to be connected to the Internet and to each other. This hyper-connectivity has many advantages, such as automated control, easy communication between devices and the exchange of large amounts of data.

Artificial intelligence (AI) is the next logical step to make IoT even more useful. Intelligence can be embedded in connected devices to enable them to not only collect and share data but also analyze it, learn from it, make decisions and act on them, without human intervention.

A combination of AI and IoT ( AIoT or Artificial Intelligence of Things ) uses “intelligent” devices that learn from the data generated and use this information to make autonomous decisions.

Additionally, new AI technologies are enabling intelligence at the edge and significantly reducing the need for and costs associated with cloud analytics. In this way, AIoT allows computing to get closer to the data. Likewise, AI technologies, running on edge devices, can automatically process and analyze data generated by sensors and other IoT devices, such as temperature, pressure, humidity, vibration, or sound, and use this information to make decisions and trigger actions.

Edge AI

Initially, AI applications ran primarily in the cloud due to the complexity of machine learning models. However, there are some applications that cannot run in the cloud due to lack of connectivity or bandwidth or when the application is such that it requires models to run on the device itself.

These could be applications that need a fast real-time operation, which prevents the use of the cloud due to its latency. Thus, other examples of this type of application are virtual assistants, industrial control, facial recognition or medical devices that need fast responses in real-time and cannot tolerate the latency of the connection to the cloud.

In addition, there may be security and privacy or data protection concerns, leading to the need to store and process data on the local device.

Therefore, AI at the edge ( Edge AI ) brings undoubted advantages. Among them: are autonomy, lower latency, lower power, lower bandwidth requirement, lower costs and higher security. All this makes it more attractive for new applications and emerging use cases.

AI finds use in many IoT applications, such as vibration analysis, speech processing, image classification, and computer vision, which need a combination of DSP computing power and inference using machine learning.

Advantages of AIoT

The use of AI together with IoT technology entails a series of advantages, which are:

• Increased Operational Efficiency – AIoT can process and detect patterns in real-time operational data and can use that data to establish real-time operational conditions, which result in optimal business outcomes. Production processes can be optimized and workflow improved, improving efficiency and reducing operating costs.
• Improved Risk Management: Risks can be identified and these insights used to increase safety, reduce losses, and make better-informed business decisions. For example, predict mechanical failures in airlines or detect safety risks in production plants.
• New products and services: AI has opened up new technologies that didn’t exist before, such as voice recognition, facial recognition, and predictive analytics. These capabilities can be used in many applications, such as the use of robots in delivery services, disaster search and rescue operations, voice-based virtual assistants, and predictive maintenance for vehicles or buildings.
• Reducing Unplanned Downtime – In manufacturing, unplanned machinery downtime as a result of an equipment breakdown can be very detrimental to business. AIoT helps predict failures by being able to schedule proactive maintenance that reduces the incidence and costs of downtime.
• Enhanced User Experience – In the retail environment, AIoT helps personalize the shopping experience and provides personalized recommendations based on customer demographics and behavior.
• Reduced Product Costs – Using Edge AI helps reduce the volume of data that needs to be transferred to the cloud, thereby reducing costs related to cloud connectivity and services.

AIoT applications

The main fields of application that AIoT technology can have are the following:

• Agriculture: Used to create intelligent systems that adjust parameters based on weather conditions, water usage, temperature, and crop/soil conditions.
• Robots: Robots used in manufacturing, package/food delivery, or search and rescue operations in disaster zones use AI to detect complex environments and adapt their responses accordingly.
• Industrial automation: AI machine vision enables improved quality control on the assembly line and helps with anomaly detection. It also helps in the predictive maintenance of machinery.
• Autonomous Vehicles – Autonomous vehicles can monitor traffic, weather, and road conditions, or predict pedestrian behavior and act accordingly.
• Building/Home Automation: Facilitate the transformation of buildings to be energy efficient by adjusting lighting and climate control based on usage and user preference data. It also improves predictive maintenance and enables automated access control.
• Smart cities: Creating more efficient cities and improving public services such as waste management, parking management, traffic management and smart lighting.
• Transport and logistics: Improve fleet management thanks to predictive maintenance, with real-time monitoring of the fleet and proactive maintenance of vehicles.
• Retail management: Improved data analysis for business decision-making and recommendations that improve the user experience.
• Health: There are various applications, such as detecting and diagnosing diseases by analyzing image data, remote monitoring of patient information, and generating alerts when abnormalities are detected.

AI, the future of IoT

AIoT is enabling new applications and use cases within the Internet of Things and will bring it to its full potential.

The applications are immense and are found in markets as diverse as smart cities, industrial automation, medicine, agriculture or transportation. The future is very promising, and more and more manufacturers will make AIoT a significant investment area.