Engineering a Reliable AIoT Solution

Artificial Intelligence and the Internet of Things (aka AIoT) have become increasingly relevant for solutions interacting with the physical world.

However, connecting a set of sensors to an AI model is not sufficient to create a solution that will provide tangible value. A reliable engineering pipeline is needed to move from physical data to actions in the physical world.

1. Start With the Physical Environment

Any AIoT solution starts with something happening in the physical environment.

A machine experiences vibrations. Production line changes its speed. A sensor detects some temperature changes. An asset moves to different places.

All those physical factors are captured by sensors and turned into digital data.

Common sensor inputs are the following:

• Temperature
• Vibration
• Pressure
• Location
• Energy consumption
• Status of equipment

The quality of initial data becomes crucial at this point since any other stage relies on it.

2. Clean and Preprocess Data Before Using It In Machine Learning

Raw data coming from sensors is rarely ready to be used in machine learning.

The dataset may contain gaps, noise, duplicates, and other inconsistencies.

Processing layer allows cleaning, filtering, normalizing and structuring data before passing it to AI.

This step is sometimes less glamorous than developing an AI model itself but becomes fundamental for reliable AI systems.

3. Choose the Right Place for Processing Layer

There are three types of data processing architectures used by AIoT solutions:

• edge computing,
• cloud computing,
• hybrid approach which combines both.

Edge processing is needed when applications require low latency, local decision making, or work in case of disconnected network.

Cloud infrastructure provides advantages for large scale storage, central analytics, model training, and monitoring across different sites.

So hybrid approach can be reasonable:

Sensors → Edge processing → AI → Cloud analytics → Application

The choice should be made based on application requirements, not because all tasks have to be done in the cloud.

4. Tailor AI for a Specific Use Case

AI should help answer an important question about operations.

Examples include:

• Does the equipment exhibit abnormal behavior?
• Are there signs that a part will require maintenance?
• Did production quality change?
• Is the asset underutilized?
• Did the environment exceed expectations?

A smaller and specific model addressing a well-defined problem may be more valuable than the larger system without a purpose.

5. Turn Predictions into Decisions and Actions

The prediction made by an AI model does not carry much meaning if there are no clear actions based on it.

An anomaly detection system might raise an alarm, trigger the creation of an inspection ticket, or perform some other action depending on the use case.

In this way, the following cycle is formed:

Data → Intelligence → Decision → Action

The last step should be taken into account during design rather than being an afterthought.

6. Prepare for Physical World

An engineering solution for physical world has to face challenges which might be hard to predict and address during the development process.

The AIoT system might deal with:

• Inconsistencies in network availability
• Degradation of the sensors
• Limitations of the hardware
• Variable working conditions
• Unforeseen events
• Security concerns

As such, testing should go beyond evaluation of model accuracy.

Besides, engineers should take into account reliability, latencies, resources consumption, behavior under failure, and performance in realistic conditions.

Problem-First AIoT

A general guideline is to start from the problem, not the technology itself.

What decision should be improved? What information is missing to make it better? What outcome should prove the improvement?

Otherwise, the team might fall into a trap of using AI or IoT just because they are available.

Aperture Venture Studio looks at AI + IoT ventures centered around real-world challenges and physical-world applications.

Conclusion

A reliable AIoT system cannot be just a machine learning algorithm plugged into sensors.

It is an engineering pipeline:

Physical Environment → Sensors → Data → Infrastructure → AI → Decision → Action

By designing each step around a well-defined operational objective, AIoT can go beyond experiments and become a practical way to tackle real-world problems.