I still remember a meeting with a group of lecturers, where I asked a simple question.
“Why do we really need an IoT Lab in universities?”
I paused for a moment.
Because deep down, I already knew the answer.
Most universities already have something they proudly call an IoT lab. Rows of ESP32 boards. Arduino kits. LEDs are blinking happily. LCD screens displaying temperature values. Students smile because something lights up.
And yet, something feels incomplete.
This is not IoT. This is just the beginning.
This blog is not meant to criticise universities. I have spent years inside them. I was once a lecturer designing syllabuses, labs, and assessments. This comes from care. From concern. From watching students graduate with confidence in embedded programming but struggle the moment systems become real, connected, and dependent upon.
This reflection is based on a recent lecture I delivered on the need to establish a proper IoT Lab in universities, one that reflects how systems are actually built, deployed, and trusted today.
Embedded Systems Taught Us How to Build Devices
Let me be very clear.
Embedded systems are important. They are foundational.
Students need to learn how to program microcontrollers. They need to understand sensors, actuators, interrupts, memory, and power consumption. All of that matters.
An embedded system is usually a standalone device. It senses something. It controls something. It logs data into local storage.
There is nothing wrong with that.
In fact, embedded systems are still used today in places with no connectivity. Remote areas. Harsh environments. Offline conditions.
But here is the problem.
Once the data is captured, someone has to physically go to the site, connect a laptop, download the data, return with it, and process it manually.
I have seen this happen too many times.
Two technicians. One vehicle. Hours of work. Just to retrieve data that could have been transmitted automatically.
I always ask myself… why are we still doing this in 2026?
Connectivity Changes Everything
The moment a device is connected to the internet, everything changes.
Data no longer waits for humans to come and collect it.
It flows.
It moves.
It becomes alive.
This is the moment embedded systems evolve into the Internet of Things.
Now we can monitor systems remotely.
Now we can detect failures early.
Now we can see battery levels dropping before devices die silently.
Now we can act before complaints arrive.
And yet, most university labs stop just before this moment.
Students are taught how to blink LEDs, but not how to send data reliably.
They learn how to display values, but not how to secure data in transit.
They build devices, but not systems.
And systems are what the real world depends on.
A Real IoT Lab Must Teach Technology Layers

IoT is not a single skill. It is a stack.
In my lecture, I stressed that a proper IoT Lab must expose students to multiple technology layers, not in theory, but through hands-on work.
Layer 1: Hardware and Firmware
This is where universities are already strong.
Sensors. Controllers. Actuators. Firmware logic. Power management.
Students should continue learning this well.
But they must also understand that this is just one layer.
Layer 2: Connectivity and Protocols
This is where gaps start to appear.
Students must learn how data travels.
Wi-Fi. Cellular. LPWAN.
Bluetooth. ZigBee. RFID.
MQTT. CoAP. REST. HTTP.
LoRa. NB-IoT. Sigfox.
Not as a list to memorise.
But as choices with consequences.
Which protocol suits low power?
Which network works for long range?
What happens when connectivity drops?
Without this understanding, troubleshooting becomes guesswork.
Layer 3: Platform and Middleware
This is the heart of IoT.
Devices do not talk directly to dashboards. They talk to platforms.
An IoT platform manages devices.
Authenticates them.
Stores data.
Provides APIs.
Handles scale.
This is where students should learn about device identities, data ingestion, databases, and analytics pipelines.
This is also where they start to understand why platforms like FAVORIOT exist in the first place.
Not to replace learning.
But to enable it.
Layer 4: Analytics and Visualisation
Dashboards are not the end goal.
They are the beginning of understanding.
Students should learn how data evolves from descriptive charts to deeper insights.
They should see patterns.
Spot anomalies.
Ask better questions.
This prepares them for real projects, not demos.
Security Must Exist Across All Layers
Security cannot be an afterthought.
Devices must be authenticated.
Data must be encrypted.
Platforms must be protected.
Applications must be hardened.
Most labs barely touch this.
And yet, this is where real systems fail.
When Systems Break, Knowledge Is Tested
I often tell students this.
The real test of IoT knowledge is not when everything works.
It is when something breaks.
Data stops arriving.
Dashboards go blank.
Alerts do not trigger.
At that moment, students panic if they only know how to code LEDs.
But students who understand layers start asking better questions.
Is it the device?
Is it the network?
Is it the platform?
Is it the visualisation?
This is the mindset a real IoT Lab must build.
Research, AI, and the Future of IoT Labs
Universities are not just about projects. They are about research.
To do meaningful research, students need data. Lots of it. Clean data. Continuous data.
IoT Labs enable this.
Once data flows reliably, students can apply machine learning.
They can explore pattern recognition.
They can experiment with predictive models.
Today, this also means understanding edge AI.
Inference running on devices.
Decisions made locally.
Latency reduced.
Systems are becoming smarter as they operate.
This is where IoT Labs naturally evolve into AIoT Labs.
And this is where universities must go.
This Is a Call to Universities, Lecturers, and Policymakers
If we want graduates who can build real systems, not just academic projects, we must change how we teach IoT.
IoT Labs must move beyond embedded programming.
They must teach architecture, trade-offs, and responsibility.
They must reflect how systems are deployed outside campus walls.
I believe universities can do this.
I believe lecturers want this.
I believe students deserve this.
But it requires intention.
It requires investment.
It requires collaboration with the industry.
It requires courage to redesign labs that have been unchanged for years.
If you are a lecturer, start asking what your lab is missing.
If you are a dean, ask whether your graduates can troubleshoot real systems.
If you are a policymaker, ask whether our talent pipeline matches national ambitions.
And if you are a student reading this, ask yourself one question.
Am I learning how to build a device… or how to build a system people can trust?
I would love to hear your thoughts, experiences, and struggles in building or teaching IoT.
Drop a comment. Let’s talk.
