Picture a factory with a malfunctioning machine. The machine generates different temperatures. Production is slowing, and technicians have to determine what is happening: Is there a need for repair?
Without the right systems in place, the process involves going through numerous reports before deciding. With the right mix of Internet of Things, or IoT, and artificial intelligence, or AI, we can observe processes, detect changes, and make recommendations.
This is where the concept of artificial intelligence of things, or AIoT, comes into play.
Instead of looking at how IoT and AI can exist separately, let’s connect them to observe and identify factors affecting our operations.
What Is AIoT?
IoT technology enables us to connect objects, such as machinery, sensors, vehicles, and robots, into an information generating machine.
Using them, we can collect data about location, condition, movement, or activity of physical assets.
AI algorithms, in turn, can analyze this information to detect patterns, identify outliers, draw conclusions, and make recommendations.
Combined, they create a powerful AIoT concept, which allows us to see beyond the data and understand the situation.
Using AIoT, we can, for example, collect, organize, and evaluate data to transform a current process into a sequence of informed decisions. We might proceed as follows:
IoT sensors generate information about temperature, pressure, and vibration.
Connectivity layer, perhaps utilizing edge computing, allows us to receive these readings
An AIoT application interprets data and detects irregularities.
As a next step, it provides actionable recommendations.
A human supervisor checks what has occurred and decides what to do.
The point is not that we use AI to analyze every connected device. Instead, we want to tackle a single issue that can benefit from both data and an informed recommendation.
Let’s review how practical AIoT can potentially be by looking at its architecture. We’ll examine how to best implement such a system.
Physical World To AIoT Architecture, How It Works
A functional AIoT application usually covers several steps, beginning with our physical assets
As we connect our physical assets with sensors and identification tools, the system is ready for processing.
1. Physical Assets And Data Collection
Every system needs physical assets to observe. These may include vehicles, robots, machinery, tools, and materials.
Sensors collect quantitative data, while identifiers help track inventory, materials, or goods. They can include thermal sensing, vibration analysis, RFID tags, and barcode labels.
Camera imagery or video surveillance can add more insight, depending on our purposes. It is crucial to recognize that the quality of information affects our results.
Incorrect sensor data, missing information, and poor recording conditions degrade our outputs. We must ensure consistent data quality, sensor accuracy, and proper recording settings across locations and equipment.
2. Connectivity And Edge Processing
The next step focuses on how we collect and process this information. Our data transmission and processing capabilities affect how we design the rest of the application.
Industrial applications often use a mix of connectivity solutions, including cabling, Wi-Fi, cellular, gateway connections, and more.
Likewise, cloud and edge processing methods will differ. Some operations are better suited for a system running locally in proximity to the data source, while others benefit from centralization.
A reliable connection is required for transmitting readings and recommendations over these systems. Finally, decisions can be made and actions executed.
3. AI And Machine Learning
Artificial intelligence can help us recognize patterns, detect deviations, and find valuable information. Using the information, we can, for example, create an electronic version of a production flow diagram.
It becomes easier to see which processes generate the most bottlenecks, how to improve robotic movements, or how to adjust supply chains. We can detect irregularities and recommend actions:
Detecting when equipment needs maintenance
Assess if products meet the required standard
Forecast how much raw material we will need
Determine the cause-effect relationship in observed production
We must evaluate our application, data, and potential results realistically. No matter how much training our AI model has, we should not consider its output as a fact.
4. The Final Process, Generating Insights
The application needs to transform ambiguous data into specific recommendations. A human operator receives it and takes action, perhaps observing the machine and adjusting the settings.
We might automate decisions and execution of certain commands, depending on our needs. It is critical to consider what the appropriate permissions, restrictions, tests, and operators are before proceeding with physical AI.
Now, let’s look at how we could use AIoT in practice.
Predictive Maintenance In A Factory Using AIoT Use Case
An application for predictive maintenance in a factory could look like this:
A machine runs for a few hours a day. We want to ensure there are no failures and disruptions to the production line.
Following the AIoT architecture above, we could, for example:
Step 1: collect data.
Sensors observe machine vibrations, temperatures, and activity. The application collects additional information about the machine’s condition.
We could include previous maintenance records for better context.
*Step 2: we establish a baseline by comparing our averages to the current values.
*
Engineers could review what values are acceptable during specific production processes.
Step 3: An AIoT algorithm detects outliers after establishing a baseline.
A maintenance worker reviews the data and determines if there is a need for maintenance.
Step 4: Our final step generates recommendations for the worker. They could include recent readings and observations.
Based on their evaluation, the worker can perform a full maintenance procedure.
It is the application’s ability to collect data and recommend the most probable causes of deterioration that makes the system valuable.
A single temperature reading would not be enough to suggest that the machine needs maintenance.
How Can Edge And Cloud Computing Combine Into AIoT?
The application has several capabilities. It can process and analyze the data as described above.
Cloud computing can play a role at this stage by providing resources for storing information, running analytics and recommendation algorithms, and analyzing patterns with machine learning models.
This is where cloud and edge computing come together.
Edge computing in AIoT can help if:
The application requires fast processing speeds (low latency).
The operation environment is highly distributed.
The process is critical and requires continuous performance.
Applications need to operate independently of the cloud.
Both edge and cloud computing have their place in AIoT. Usually, there is no reason to choose one exclusively over the other. Rather, we design the application so that its overall architecture best suits our needs.
It is one thing to build an impressive application. But how can we ensure that our approach will work when we implement it?
What Are The Challenges Of Implementing AIoT Systems?
Creating an application involves more than simply connecting a few sensors. Let’s examine practical challenges of implementing AIoT applications, such as the following:
1. Data Quality
Sensors have limitations. Their data may be incomplete, erroneous, misleading, or irrelevant to our needs.
Before continuing, we must assess quality ratings, potential errors, and variances.
2. System Integration
Modern, sophisticated manufacturing applications require multiple interconnected software systems. These can range from production control and robotics to inventory and enterprise resource planning tools.
Integration requires careful coordination and consideration of data, processes, and authorization.
3. Security and Access
An IoT-enabled system adds additional software and hardware to the current infrastructure. It introduces additional opportunities for infiltration and abuse.
Correct device permissions, network segmentation, data encryption, and protection are essential.
4. Model Performance
If the model does not deliver consistently, it can slow our processes. A functioning AI model requires training, evaluation, and ongoing maintenance.
5. Interpretation and Execution
A working system is great. But unless our application translates insights into clear recommendations and execution instructions, the model will not improve our processes.
A dashboard containing dozens of alerts means little unless someone knows what to do with this information.
A system application can notify, recommend, and execute commands. However, it does not know what to do in a specific case without human review.
Now that we can envision the necessary architecture, let’s turn our attention to practical application.
How Should We Approach Development Of AIoT Architectures?
Organizations considering investing in this space should start the process by examining a single system.
For example, we might want to:
Increase efficiency by reducing downtime during production.
Improve inventory management by reducing losses and inaccuracies.
We should then determine which physical assets we need to monitor and identify.
This can involve sensors, GPS trackers, identifiers, and even video surveillance. Now is the time to ensure we receive high-quality data.
We must perform a verification procedure and evaluate potential weaknesses before progressing. With these components, we can then design a narrow, focused application. We are not trying to build a fully automated, end-to-end application at this stage.
We then perform a test run and compare our results to the baseline: our current system or a similar process executed manually.
We can determine what actions to take at this stage. Perhaps AIoT helps us schedule and prioritize maintenance tasks.
We must establish a procedure, designate personnel, and provide permissions and requirements for execution. In the long term, we want to ensure that our application continues to generate value and that adjustments are made as needed.
A working AIoT solution can transform an end into an informed start.
What Is The Difference Between AIoT And Physical AI?
While AIoT deals primarily with data analysis and information processing, physical AI is about linking AI technologies with physical objects.
This connection enables systems to perform a wider range of tasks, from controlling robotics to regulating heating and air conditioning systems.
In essence, AIoT describes how we process data about our physical world. In contrast, Physical AI represents how we can bring AI into the physical world by applying it to objects and processes.
We can see the difference in information processing and decision-making: AIoT systems are designed to generate useful information by detecting what has happened.
Physical AI systems make decisions based on information about the physical world.
Physical AI systems, unlike AIoT systems, extend into the physical world and have an effect on it. Decisions and instructions require consideration of authorization, personnel, and processes.
For companies considering how such capabilities can apply to their operations, Aperture Venture Studio works to identify companies at the intersection of AI and IoT, with a specific emphasis on physical objects and industrial operations.
Final Notes About AIoT And Applications
AIoT is best applied when we combine high-quality information about the physical world with specific recommendations that lead to decision-making.
We can leverage sensors for data gathering, connect them, and apply artificial intelligence.
Finally, application software can transform this information into recommendations. By doing so for a particular system, we can fine-tune processes, make decisions, and continuously improve.
The aim is to create a system that best meets our needs with regard to the process, existing technologies, and the operating environment.
Discussion: If we were to develop such a solution within a factory or warehouse, what would we begin with – predictive maintenance, asset tracking, quality control, or inventory?
Do you think AIoT is valuable, and can it be implemented successfully? Does a factory need to connect everything available, or are there systems that should be left alone?
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