From IoT Data to AIoT Intelligence: What's Context Got To Do With It?
IoT (Internet of Things) has allowed the connection of physical assets with digital ones and enabled continuous data gathering from it.
Sensors can track its temperature, vibrations, location, energy use, status, and other different variables that can be collected and stored, shared, monitored in real-time, and even analyzed.
But gathering data from these connected devices is only half of the puzzle.
How can these data points be turned into usable information?
This is where AIoT, Artificial Intelligence of Things, comes to play.
While IoT is focused on connecting devices and getting data from it, AIoT provides interpretation to these data points
A basic IoT architecture is usually represented by:
Physical asset Sensors Connectivity Data platform Monitoring
In AIoT it now gains a layer of intelligence in the mix:
Physical asset Sensors Connected data Context AI analysis Decision or action
The difference here is not only a matter of adding an AI layer on top of the IoT architecture.
Context is also a factor that can help analyze the data and create valuable insights from it.
If a machine's vibration rate (data point) changes in a certain period higher, an IoT sensor can acknowledge and alert about it but it's the analysis that puts this into perspective: is it an anomaly? Is it a normal deviation? Does it require action?
Contextual layers can bring:
Which asset generated it
Current conditions
How historical data compares to it
The maintenance schedule of the asset
If the production was taking place during the reading
And other readings coming from different sensors
These layers of information can be combined to provide additional valuable data points to create patterns and identify anomalies.
How can context be helpful?
It might not be straightforward to rely on a single data point.
The same variable can mean many different things (temperature) depending on the asset, environment, state of the machine, the production being held, and others.
This is especially relevant in industrial and logistics environments where many pieces take part in a single production line: conditions can change, failures occur, and different factors contribute to them.
That is why AIoT goes beyond being just data analysis: it creates a loop that makes a system where information, context, intelligence, and operational factors can be combined.
Sense Connect Understand Act Learn
The AIoT loop can be broken down into these five steps.
1. Sense
Sensors, cameras, machines, vehicles, RFID scans, and other devices, both physical and digital, can gather information.
2. Connect
The data can be shared among devices (edge systems) or the cloud.
3. Understand
Analyze the data to recognize patterns, anomalies, correlations, or other similarities.
4. Act
Make decisions or support action based on the data.
5. Learn
Further decisions or actions can generate more data for further analysis.
In which areas can AIoT be applied?
This kind of system can be seen in many fields in the physical world:
In manufacturing sense
In logistics and transportation
In civil engineering and construction
In infrastructure and urban mobility
In healthcare
In field service and fleets
In industrial inspections and robotics
And in asset and equipment monitoring: this is where IoT meets AI, and where ApertureVentureStudio https://apertureventurestudio.com/ focuses its work.
This mix of artificial intelligence, internet of things, industrial systems, and venture building is their field of expertise.
AIoT and Physical AI
Another aspect of AIoT is its connection with the growing interest in the concept of Physical AI: instead of being limited to software and data, AIoT is connected with physical machines and systems.
As Physical AI looks at machines, robots, equipment, and vehicles, the quality of information is paramount.
A good AIoT system is essential for a Physical AI to be successful: in addition to having the best algorithms, a physical system also needs sensors, connectivity, contextual and historical data, good architecture, and ways to operate and integrate with existing machinery.
More connections equals more complexity
The future of IoT has to move from quantity to quality: while most systems have been focused on mere connectivity, the next step is about analyzing them.
A good AIoT architecture needs to have that in mind: for devices to be connected, for data to be contextualized, for intelligence to be created, and for decision-making and action to take place.
Without enough contextual layers, even the best data analysis will be limited: in other words, not every connected asset will have the same value for AIoT.
After all, the future belongs to systems that can connect and operate with existing equipment, understands it, and allows for the best decision-making possible.
Top comments (0)