Internet of Things (IoT) has linked millions of devices within factories, warehouses, hospitals, and supply chains. There are sensors collecting data about devices, inventories, temperatures, locations, and many other operational parameters.
However, there is an issue with such a setup:
Just collecting the data doesn't mean you are able to understand it.
While companies may gather information via dashboards, they depend on people to analyze the information to determine trends, find anomalies, and decide on actions. In this case, Artificial Intelligence of Things (AIoT) becomes important.
AIoT doesn't just monitor the systems. It lets systems understand the data, forecast future events, and provide intelligent decision making.
What Is AIoT?
AIoT is the combination of two technologies:
Internet of Things (IoT): Links physical devices and collects data in real time.
Artificial Intelligence (AI): Analyzes data to identify patterns, forecast future events, and make decisions.
It means that IoT can be seen as data collection, while AI is decision-making.
Examples of AIoT Application
Predictive Maintenance
Measurements of vibration, temperature, pressure, and operating time from industrial equipment are obtained.
The signals are analyzed through machine learning techniques to determine the abnormality leading to malfunctioning of machines.
Beneficial aspects include:
- Minimal down time
- Reduced maintenance costs
- Longevity of machines
Smart Asset Management
Big warehouses usually have trouble tracking down equipment effectively.
By integrating RFID, GPS, BLE, or UWB systems together with artificial intelligence, one can:
- Monitor assets
- Forecast usage trends
- Prevent loss of equipment
- Increase efficiency
Optimization of Inventory
Data about inventory is continuously being generated at the warehouse.
Artificial intelligence algorithms can spot:
- Variations in demand
- Risk of overstocking
- Low stock issues
- Patterns of buying
This will allow for better planning of inventory.
Workplace Safety
With the use of wearable devices and environment sensors, dangerous situations like:
- Access to restricted areas
- Unacceptable temperatures
- Worker exhaustion
- Machinery malfunction
Can be detected and addressed.
Technical Challenges
Setting up an AIoT solution is not just about installing sensors.
Typical technical challenges include:
- Data Quality
- Machine learning models need high-quality data.
- Missing values, sensor drift, and inconsistent timestamps can negatively affect model performance.
- Scalability
Industrial solutions may require thousands of devices to be connected.
They need to be able to process:
- High-frequency data ingestion
- Distributed processing
- Scalable storage
- Event processing
- Edge Computing
Transferring data from every single sensor to the cloud is not always convenient.
With Edge AI, you can perform inference closer to devices, decreasing:
- Network latency
- Bandwidth requirements
- Latency of responses
Security
Every connected device is an additional point of vulnerability.
Recommended practices include:
- Device authentication
- Encryption of all communications
- Firmware updates
- Access control
- Monitoring
AIoT Is Problem-Solving
There is a myth that every AIoT implementation starts with selecting technology.
Actually, successful implementations start with problems and difficulties that need to be solved.
For instance:
- Why do we have unexpected equipment failures?
- Why are we having problems locating our assets?
- Why is our inventory inaccurate?
- What is causing operational inefficiencies?
AIoT solutions have to improve operations – not because of technology itself.
Where the Industry is Headed
With better-performing AI models and more advanced edge devices, AIoT platforms are able to make decisions in real time.
This may result in:
- Autonomous industrial systems
- Intelligent robots
- Digital twins empowered by AI
- Predictive supply chains
- Manufacturing operations optimized through self-organization
Those who are developing cloud solutions, working with embedded devices, using machine learning algorithms and distributed architecture will find more and more need to work with AIoT.
For those who are interested in how AI, IoT and venture development are used for industrial innovations, Aperture Venture Studio can provide some perspectives and practical use cases here.
Final Thoughts
IoT provided visibility for industries. AI provided them intelligence. AIoT allows moving from monitoring to prediction, optimization and automation.
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