AI has moved beyond software applications. It is increasingly interconnected with sensors, machines, embedded devices, vehicles and industrial infrastructure.
The combination of AI and IoT is usually referred to as AIoT.
It seems quite straightforward; Connect Devices, Collect data, Apply AI. However, building a useful AIoT solution requires more than just plugging an AI model into an IoT platform:
It's about reliably providing the path from physical signals to actionable intelligence.
What is AIoT?
IoT deals with connecting physical devices with networks.
AI looks for patterns in data, derives predictions or can aid decision-making.
AIoT integrates these functionalities as followed:
Physical Environment Data Acquisition Sensors Connectivity Data Analysis AI Insight Action
For example, sensors embedded in manufacturing machines will monitor data, like temperature, vibration, pressure, status, etc. This data would be processed and analyzed to identify anomalous trends, aid in decision making for maintenance, etc.
Key Architectural Layers of an AIoT System
An AIoT architecture can be generically categorized into multiple layers:
- Physical Assets
The entire premise rests on the physical entities and the context they operate in.
This includes:
Manufacturing machines
Cars, bikes, trucks
Industrial equipment
Buildings
Stores and inventory
Infrastructure networks (pipelines, power grids)
Sensors acquire data from these entities to represent real-world states.
- Sensors and Embedded Devices
Sensors generate data, while embedded devices (like a micro-controller) can take the raw data, do any filtering, processing and transmit it.
This can look like:
Sensor-> MicroController->Local Processing->Network
Local processing would avoid sending huge amounts of data to the cloud,but could have limitations.
- Connectivity
IoT devices need to communicate with other entities reliably.
The type of connectivity technology adopted would very much depend upon the application, its range, bandwidth, latency, power and reliability requirements.
This could mean drastically different approaches for a factory, a warehouse, a vehicle or a remote monitoring system.
- Edge & Cloud Computing
AIoT can distribute computing between the edge devices and the centralized cloud infrastructure.
A very basic diagram will look like this:
Sensor-> Embedded Device->Edge Gateway->Cloud->AI
Edge computing could be very handy where very low latency is required, where connectivity to the cloud is intermittent or where the sheer volume of sensor data is too large to transmit. The cloud serves as a platform for larger scale storage, analytics, model management and global monitoring.
- AI & Data Analysis
As with traditional AI, once data is collected and prepared, one applies AI techniques for identifying interesting patterns within the data, like:
Anomaly detection
Predictive maintenance
Forecasting
Monitoring (e.g. Equipment, environment)
Pattern recognition
Operational analytics
Optimization etc.
The correct AI technique will depend on the specific operational question and data.
Predictive Maintenance Example
Let's think of a manufacturing machine fitted with sensors to monitor temperature and vibration.
The following process could occur:
Machine->Sensors->Embedded Device->Edge Gateway->Data Platform->AI model->Alert
The AI model would examine current values compared to historically logged patterns of operation and could determine if unusual behavior indicative of a future failure is being observed. The AI model would generate an alert to the maintenance team to investigate further. Importantly, AI doesn't replace the expertise of the engineer but rather adds to the information available so that they may focus their attention effectively.
AIoT for asset tracking
Visibility across physical assets can also be significantly enhanced with AIoT.
A tracking device can report on where its asset is, and what state it is in. This could be combined with other operational data to see where the assets are, where they are travelling, and if they are actually being utilized for their purpose. This can be particularly useful in warehousing, manufacturing and logistics.
Importance of Data Quality
A fundamental premise is that an AI model is only as good as the data fed to it.
Poor sensor readings, incomplete values in data, inappropriate timestamps, connectivity failures or incorrect device labeling can all lead to poor insights. It means that AIoT is not merely an issue involving the AI model alone but is a complex systems-engineering exercise which will involve both hardware (sensors, edge devices) connectivity, data infrastructures, software applications, security, and AI.
Start with the problem
Donβt ask: βwhat AI model should we applyβ?
Instead, you should ask: 'what is the operational problem we want to solve?'
Only after you understand that problem, you may wish to consider: what data do we need to gather, which sensors should collect it, where should this processing happen, what AI technique is most appropriate, and how are users supposed to get insights and take action.
Future of AIoT
AIoT can transform the physical world. It's moving us from a world where devices were connected to where the environment around us becomes intelligent. We can see the fusion of IoT which provides connectivity, embedded systems and software connecting to hardware, Edge & Cloud providing distributed computing power, and AI providing intelligence, all working to revolutionize how we conduct operations in the real world.
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Conclusion
The real value of AIoT stems not just from collect ing or processing data with AI but also from bridging that intelligence back to the real world in an actionable way.
Sense-> Connect-> Process-> Analyze-> Act
This is the core principle of AIoT where the physical world interfaces with the digital domain to create intelligence.
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