IoT systems excel at gathering information from the physical world
Sensors can measure temperature, vibration, pressure, movement, location and equipment status, RFID offers identification, and GPS, BLE and UWB can offer various kinds of location information.
However, the real-world problem of connected devices is
how to take all that data and create value from it.
This is where AIoT, or Artificial Intelligence + IoT, comes in.
IoT Can Gather. AI Can Understand
An example IoT architecture might look something like this:
Physical Device
↓
Sensor / Identifier
↓
Connectivity
↓
Data Platform
↓
Dashboard / Application
This architecture could help create a powerful visibility layer. However, actual human analysis might be necessary to make sense of all that data.
By adding an AI layer, the architecture changes:
Physical Assets
↓
Sensors & Identifiers
↓
Connectivity
↓
Data
↓
AI / ML
↓
Insights
↓
Action
The AI can analyze historical and current information to find patterns, irregularities, relationships or trends that might be important to business.
For example, consider an industrial machine that generates vibrations and temperatures. While a single reading might not be noteworthy, an AI model could identify a set of circumstances in which action may be required.
It's important to note that the AI layer builds on the previous layers of sensing and connectivity.
The Importance of the Pipeline
The creation of an AIoT application is not simply a matter of attaching a sensor and an AI model.
The performance of the entire pipeline is crucial, and a realistic implementation may require addressing issues of
device / asset identification, sensor collection, network connectivity, data ingestion, storage, formatting / normalization, processing, AI/ML inference, alerts or suggestions and operational system integration.
Context is important when applying AI to a physical asset. Having a temperature reading is much more valuable with information about what asset generated it, its location and operational condition, and how that reading compares to historical data.
Otherwise, there isn't enough information for the model to generate meaningful insights or suggestions.
AIoT Goes Beyond Predictive Maintenance
While predictive maintenance is often seen as the ultimate expression of AIoT, the concept encompasses other capabilities as well.
These include
anomaly detection (detecting irregularities in equipment or processes), forecasting (predicting demand, production or other requirements), optimization (finding new opportunities for improvement), asset visibility (tracking the location and movement of physical assets) and decision support (providing information to improve the speed or accuracy of decisions).
These capabilities can be applied in manufacturing, logistics and transportation, construction, energy, mining and numerous other industries. Various technologies including RFID, BLE, UWB, GPS, computer vision and industrial sensors can be employed to create a solution.
Looking at Physical AI
AIoT is closely related to the concept of Physical AI: systems that operate in the physical world rather than purely in the digital world.
This has importance on an architectural level, since such a system would need a reliable method of sending signals from the physical world to the digital realm (primarily through sensors) and potentially applying digital-domain processing to the physical world.
This article goes into further detail about how an AIoT system can be architected
to take advantage of different layers, from sensing to machine learning to operations.
Understanding AIoT: A Simple Framework
A simple way of looking at AIoT is the following chain:
Connect → Collect → Analyze → Understand → Act
IoT enables the first step, connecting to the physical world, while data platforms perform the organization and storage functions. AI supports analysis and pattern recognition, and applications and people can perform actions based on the information.
The engineering challenge is creating an effective end-to-end system in which all these parts work together reliably.
AIoT is about making these capabilities accessible to organizations: It's less about "sticking an AI model on a device" and more about building a complete system in which information from the physical world can be turned into valuable insights.
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