IoT has enabled connecting physical assets to digital systems and to collect data from the real-world.
A machine can report its status, a sensor may collect temperature, a vehicle can provide location and so on. A production system can capture events as they may occur.
However, the data collected brings another question:
How can you extract value from what connected devices report?
This is where Artificial Intelligence (AI) and IoT come into play, in what is sometimes called AIoT.
The Data Flow of AIoT
At a high level, an AIoT environment can be seen as:
Physical Assets -> Sensors -> Connectivity -> Data -> AI/ML -> Insights -> Actions
Where each part has a difference responsibility.
Physical assets are the machines, vehicles, equipment or other objects being observed.
Sensors capture information from the physical world, such as temperature, vibration, movement, location, pressure or others.
Connectivity sends and receives information between nodes in the environment, including gateways, platforms and others.
The Data represents the information that can be stored and processed.
AI and ML can process the information to discover patterns, anomalies, connections and other cues which can be useful.
Finally, the insights can be processed to take actions.
An important note is that IoT provides the connection, and the data, and AI can process this data to gain information and perform valuable actions.
IoT Data: Just Not Enough Information?
An organization may have thousands of sensors, capturing tens of thousands of data points every day.
However, it may also have isolated systems, where one database or application contains the equipment information, a different one tracks location and another one has production records.
This can make it challenging for an organization to capture a full view of the business operations.
This is where AIoT helps, by connecting the information about the physical world with actionable analytics.
For example, if equipment captures vibrations and temperatures while other systems track the operational status of machines: the combination of this information can provide better contextual information about changes and patterns in the equipment.
The objective is not to gather all information, but to leverage the most valuable data points.
AIoT in Different Industries
The same concept can be applied to different industries and different types of physical assets.
For example, in manufacturing, connected machines and production systems can provide data which, when processed by an AIoT approach, can analyze patterns about operations and equipment.
In logistics and transportation, connected vehicles can report location and movement data which can be combined with other information from these assets to understand their operations.
In the energy sector, connected equipment can capture information about its operation and allow to monitor and process patterns and anomalies.
In construction, connected equipment can help track materials, machinery and other factors which can enable a better understanding of activities in complex worksites.
Other industries and applications can follow the same patterns: connect physical devices, capture the data, process it to gain insights and use them to make decisions.
The main differences are in how the physical assets are connected, how they capture the data, how this information is processed, stored and analyzed, which specific AI/ML approaches are better suited to analyze them and what UIs or APIs should be built to make them actionable.
The Challenge for Developers and Engineers: Spanning Multiple Layers
An important point about AIoT is that it requires interaction at multiple layers in an environment.
A physical asset, using sensors and gates, can be connected through a network, have its data ingested and processed, have APIs built, be connected to different apps or systems and have ML models analyzing it.
Essentially, a complete AIoT environment can span multiple stacks, from the physical devices to the applications and analytics taking place.
As such, while implementing a single solution at a certain layer (such as database optimization or analytics) may provide some value, the real power is in taking into account the end-to-end information and providing actionables across an entire stack.
From Simply Collecting to Taking Action
The point of AIoT is essentially "bridging the gap" between the physical and digital world, and more specifically, to allow the information which comes from connected devices to be used to create valuable analytics.
More specifically, while collecting this information is important, the emphasis is on ensuring that it is used in a meaningful way.
As physical devices become more prevalent and common in industrial environments, this connection between IoT data and AI analytics will become more important.
For developers, engineers, and companies looking to explore this space, Aperture Venture Studio provides additional information about AIoT, AI, IoT and technology innovation.
The best AIoT implementations are not about how much data gets captured, but how the best information from connected devices gets processed to make a difference.
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