Author: BleuIO

Bluetooth Low Energy (BLE) devices are widely used for environmental monitoring, but getting their data into the cloud often requires complex SDKs, gateways, or proprietary platforms. In this tutorial, we demonstrate a simple and flexible alternative: sending BLE advertisement data directly to Arduino Cloud using BleuIO as a USB BLE gateway.

In this project, we build a lightweight data pipeline where a HibouAir air quality sensor broadcasts environmental data over BLE advertisements, BleuIO scans and captures that data, a Python script decodes the values, and the results are sent straight to Arduino Cloud for storage and visualization — all using free tools.

This project is designed as a showcase example to illustrate how BLE development and cloud integration can be done quickly and transparently, without BLE SDKs or embedded firmware development.

Why Arduino Cloud?

Arduino Cloud offers a convenient and reliable platform for storing and visualizing IoT data without the need to build and maintain a custom backend. Although it is often associated with Arduino hardware, the platform supports Manual Devices, which makes it suitable for gateway-based solutions where data originates from external devices such as BLE sensors. In this project, Arduino Cloud serves as a secure endpoint where decoded air quality data can be published using standard MQTT communication. Its integrated dashboards allow developers to quickly visualize sensor data, making it especially useful for prototyping, demonstrations, and proof-of-concept projects that require minimal setup.

Hardware and Software Requirements

Hardware

• HibouAir CO2 air quality monitor
• BleuIO Bluetooth Low Energy USB dongle

Software

• Python 3.9 or later
• pyserial Python library
• arduino-iot-cloud Python library
• Arduino Cloud

No embedded programming or BLE SDKs are required.

How the System Works

The HibouAir device periodically broadcasts air quality data within its BLE advertisement payload. BleuIO continuously scans for nearby BLE advertisements and filters packets that match a specific device identifier. When a matching advertisement is detected, the Python gateway script extracts the raw data and applies decoding logic to convert the hexadecimal values into human-readable measurements. These decoded values are then published to Arduino Cloud using authenticated MQTT communication. The entire process runs continuously, enabling real-time data updates without establishing a persistent BLE connection to the sensor.

Arduino Cloud Setup (Step by Step)

Step 1: Create or Log In to Arduino Cloud

Go to:
https://app.arduino.cc/dashboards

Create a free account or log in to your existing one. After login Arduino Cloud will generate

• Device ID
• Secret Key

Save these securely — they will be used in the Python script.

Step 2: Create a Device

Navigate to:
https://app.arduino.cc/devices

• Click Add Device from left menu
• Choose Manual Device
• Name the device HibouAir

Step 3: Create a Thing

When prompted after creating device, create a new Thing and name it HibouAir Thing.

Step 4: Add Cloud Variables

Add the following variables to the Thing:

Variable Name	Type	Description
co2_ppm	int	CO₂ concentration (ppm)
temperature_c	float	Temperature in °C
humidity_rh	float	Relative humidity (%)

Step 5: Create a Dashboard

Go back to Dashboards and create a new dashboard.

Add widgets such as:

• Value widget for CO2
• Gauge widget for temperature
• Chart widget for humidity over time

Your cloud setup is now complete.

Project Source Code

Clone or download the project from GitHub:

https://github.com/smart-sensor-devices-ab/bleuio-to-arduino-cloud

Configure secrets.py

Update the following values:

DEVICE_ID = "YOUR_DEVICE_ID"
SECRET_KEY = "YOUR_SECRET_KEY"
SERIAL_PORT = "/dev/tty.usbmodemXXXX"

Make sure the serial port matches where BleuIO is connected.

Configure gateway.py

In gateway.py, update the scan command:

SCAN_CMD = "AT+FINDSCANDATA=220069=3"

In this example, 220069 is the HibouAir board ID used in the BLE advertisement.
If your HibouAir device uses a different ID, update this value accordingly.

Running the Project

Once the Arduino Cloud configuration and local script setup are complete, running the project requires only a single command.

python3 gateway.py

When the gateway script is executed, BleuIO is placed into dual-role mode and begins scanning for BLE advertisements that match the specified HibouAir board identifier. As advertisement packets are received, the script decodes the sensor values and immediately publishes them to Arduino Cloud. Within moments, the dashboard begins displaying live air quality data. This continuous loop allows the system to operate as a real-time BLE-to-cloud gateway with minimal overhead.

Customizing the Dashboard

Arduino Cloud dashboards can be customized to present air quality data in a way that best fits the user’s needs. Live values can be displayed using numeric widgets, gauges can be used to visualize ranges such as CO2 concentration or temperature, and chart widgets can be added to show trends over time. By arranging and configuring these widgets, users can create a clear and informative interface for monitoring indoor air quality. This flexibility makes the dashboard suitable not only for development and testing, but also for presentations and live demonstrations.

This project demonstrates how BLE advertisement data can be efficiently captured and delivered to the cloud using a minimal and transparent approach. By combining HibouAir sensors, BleuIO, a simple Python gateway, and Arduino Cloud, it is possible to create a complete end-to-end monitoring solution without relying on complex SDKs or embedded firmware development. While this tutorial focuses on air quality data, the same method can be extended to other BLE-based sensors and cloud platforms. As a showcase example, it highlights the flexibility of BleuIO as a BLE development tool and provides a solid foundation for developers who want to build and expand their own BLE-enabled cloud solutions.

In the earlier parts of this series, we combined the Adafruit Feather RP2040 with the BleuIO USB dongle to build different BLE applications: setting up the RP2040 as a USB host, reading sensor data, advertising measurements and handling secure connections.

In this Part 5, we take the next step and create a simple two-way communication setup. Instead of only broadcasting data, we let a Python script running on your computer talk to the BleuIO dongle connected to the Feather RP2040 and control its LED in real time. At the same time, the Feather responds over the Serial Port Service (SPS), echoing messages back so you can see exactly what was sent on both sides.

This project is a good starting point if you want to remotely control devices, test custom BLE command protocols or build interactive demos using BleuIO and RP2040.

What This Project Does

Arduino project on Adafruit Feather RP2040

On the hardware side, the Adafruit Feather RP2040 is configured as a USB host for the BleuIO dongle, using the same TinyUSB and Pico PIO USB approach as in Part 1 of the series. When the board starts, it initializes the USB host stack, detects the BleuIO dongle and sends a short sequence of AT commands. These commands disable echo, ask the dongle for its own MAC address, set a friendly advertising name (BleuIO Arduino Example) and start BLE advertising. After that, the sketch simply listens for BLE connection events and SPS messages. Depending on what text message it receives over SPS, it either echoes the message back or sends a command to change the LED behaviour on the dongle.

Python script on the computer

On the computer, a Python script acts as the BLE central. It uses the MAC address printed by the Feather’s serial output to connect to the advertising BleuIO dongle. Once connected, it sends text commands over SPS such as ALERT, NORMAL or OFF, and reads back whatever the Feather sends in response. When the Python script sends one of these special words, the Feather generates BLEU AT commands to control the dongle’s LED; for any other text, it just echoes the message. This creates a complete round-trip: you type in Python, the message travels over BLE to the RP2040 and BleuIO, and a response comes back the same way.

Requirements

Hardware

• Adafruit Feather RP2040 board
• BleuIO Pro – Bluetooth Low Energy USB dongle
• USB cable for the Feather
• USB-A port or hub for the BleuIO dongle
• Computer running Windows, macOS or Linux

Software

• Arduino IDE with RP2040 board support installed
• Adafruit TinyUSB Library
• Pico PIO USB Library
• ArduinoJson library
• Python 3.x and the Python project that connects to BleuIO over SPS

If you already followed Part 1, your RP2040 USB host environment and board configuration should be ready to use.

Source Code on GitHub

You can find the complete source code for this project — both the Arduino sketch and the Python script — in our public GitHub repository: bleuio_arduino_message_transfer_example. Visit the repository at:

https://github.com/smart-sensor-devices-ab/bleuio_arduino_message_transfer_example

Feel free to clone or download the repo to get started quickly. All necessary files — including the .ino, helper headers, and the Python script — are included, so you can replicate the example or adapt it for your own project.

Recap: Preparing the Feather RP2040 as a USB Host

To quickly recap the setup from the earlier article: you install the Raspberry Pi RP2040 board package in the Arduino IDE, select the Feather RP2040 board, and install the Adafruit TinyUSB and Pico PIO USB libraries. You then make sure the CPU speed is set to 120 MHz or 240 MHz, since Pico PIO USB requires a clock that is a multiple of 120 MHz.

Uploading the Arduino Sketch

1. Open the bleuio_arduino_connect_example.ino and usbh_helper.h in the same Arduino sketch folder.
2. Select Adafruit Feather RP2040 (or your RP2040 board) under Tools → Board.

1. Choose the correct COM port for the Feather.
2. Click Upload.

After upload:

1. Open Serial Monitor at 9600 baud.
2. You should see something like:

Connect test v1.0
Core1 setup to run TinyUSB host with pio-usb
SerialHost is connected to a new CDC device. Idx: 0

BleuIO response:
{"own_mac_addr":"xx:xx:xx:xx:xx:xx"}
----

3. Every 10 seconds (based on ALIVE_TIME) you’ll see an update:

H:M:S - 0:0:10
own_mac_addr: xx:xx:xx:xx:xx:xx
Not connected!

Initially it will say Not connected! because no BLE central is connected yet.

The Python Script (BLE Central)

The Python script acts as a BLE central that connects to the advertising BleuIO dongle and uses the Serial Port Service (SPS).

A typical flow in the Python script is:

1. Read the MAC address printed by the Arduino Serial Monitor (own_mac_addr).
2. Use the BleuIO Python library (or BLE stack) to connect to that address.
3. Once connected, send plain text messages over SPS:
   • "ALERT"
   • "NORMAL"
   • "OFF"
   • Or any other text.

On the Python side you’ll see:

• Connection success message.
• Any SPS response sent from the RP2040 (e.g. [RP2040] Alert command Received: [...] or [RP2040] Echo: ...).

On the Arduino Serial Monitor you’ll see:

Connected!
SPS Received!
BleuIO response:
{"type":"SPS","evt":{"len":5,"ascii":"ALERT"}}
----
Sending command: AT+SPSSEND=[RP2040] Alert command Received: [ALERT]

And the LED on the BleuIO dongle will react according to the command:

• ALERT → Blink pattern (AT+LED=T=100=100).
• NORMAL → Toggle LED (AT+LED=T).
• OFF → Turn LED off (AT+LED=0).
• Any other message → Just an echo, no LED change.

Where to Go Next

This example completes the journey from simple advertising to full two-way communication between a computer application and a BleuIO dongle hosted by an Adafruit Feather RP2040. With this pattern in place, you can replace the LED commands with your own device protocol, combine it with the sensor examples from Part 2 and Part 4, or feed the exchanged messages into larger systems for logging, dashboards or control logic. Because the communication relies on the standard Serial Port Service and BleuIO AT commands, the same structure can be reused for many other projects where a PC, an embedded board and a BLE device need to work together.

Bluetooth Low Energy (BLE) has become a key technology in modern wireless applications—from IoT devices and sensors to wearables, smart tools, and more. While BLE development can traditionally require time, experience, and familiarity with complex protocols, BleuIO dramatically simplifies the process.

BleuIO is a powerful USB BLE dongle designed to help developers of all levels build BLE applications quickly and efficiently. With straightforward AT commands, intuitive documentation, and cross-platform flexibility, it allows users to prototype and develop BLE solutions without the usual learning curve.

But now, with the rapid growth of AI tools such as ChatGPT and Gemini, the development workflow becomes even smoother. AI can help generate ready-to-run scripts, automate coding tasks, and speed up BLE experiments—making the combination of BleuIO + AI incredibly valuable for developers.

Common Challenges in BLE Development

Developing Bluetooth Low Energy applications often requires a solid understanding of BLE protocols and command structures, which can be intimidating for beginners. Developers must also write code that interfaces correctly with hardware such as dongles or embedded devices, and this process can involve repetitive boilerplate code—especially when handling tasks like scanning, connecting, and transferring data. Another common challenge is ensuring that code works consistently across different languages and platforms. These factors can slow down development and create barriers for those who simply want to prototype or test BLE functionality quickly.

How BleuIO and AI Solve These Problems

BleuIO addresses many of these challenges by offering straightforward AT commands that simplify common BLE operations. When paired with modern AI tools, the development process becomes even more efficient. AI systems can read the BleuIO AT Command List and instantly generate complete scripts that integrate these commands correctly, significantly speeding up prototyping. This eliminates the need for manually writing repetitive code, allowing developers to focus on their application rather than the setup. Because BleuIO works seamlessly with Python, JavaScript, C#, Node.js, and many other environments, developers can choose the language they prefer. Even newcomers can get started easily, as AI-generated scripts help bridge any knowledge gaps and provide a smooth entry point into BLE development.

Example: Using ChatGPT and Gemini to Generate a BLE Scan Script

To demonstrate how effectively BleuIO and AI work together, we created a simple test scenario. We began by downloading the BleuIO AT Command List PDF from the Getting Started guide and then asked both ChatGPT and Gemini to generate a Python script that communicates with the BleuIO BLE USB dongle. The script needed to use the correct AT commands, include the appropriate COM port, and perform a scan for nearby BLE devices lasting five seconds. After generating the scripts, we ran them to compare the output produced by the two AI tools.

Video Demonstration

You can watch the full demonstration below, where we walk through the entire process—from downloading the command list to generating and testing the scripts:

This example demonstrates just how powerful the combination of BleuIO and modern AI tools can be. By letting AI generate boilerplate code and BLE scripts, you can focus on building features, testing ideas, or integrating wireless communication into your products.

BleuIO already makes BLE development easy—but with AI, it becomes even more efficient, accessible, and developer-friendly.

Outdoor air quality is a major focus in Europe in 2025, with tighter standards placing greater emphasis on fine particulate matter—especially PM2.5. Elevated PM levels are linked to asthma, reduced cognitive performance, and increased cardiovascular risk, making reliable monitoring essential. This project demonstrates a simple, browser-based way to visualize PM1.0, PM2.5, and PM10 in real time—supporting better ventilation decisions and aligning with evolving EU air-quality expectations.

What you’ll build

A single HTML file styled with Tailwind CSS that:

• Puts BleuIO in a central scanning role
• Periodically runs a targeted scan for your HibouAir Board ID
• Decodes PM1.0 / PM2.5 / PM10 from the manufacturer data inside BLE advertisements
• Maps the values to three horizontal bars (default display windows: PM1.0/PM2.5 → 0–150 µg/m³, PM10 → 0–200 µg/m³)
• Shows a high particulate banner when any value exceeds your thresholds

Source code: https://github.com/smart-sensor-devices-ab/pm-monitor-bleuio
Live demo: https://smart-sensor-devices-ab.github.io/pm-monitor-bleuio/

Hardware & software

• BleuIO USB BLE dongle
• HibouAir air quality monitor with particle sensor

How it works

HibouAir broadcast short advertisement packets that includes real-time air quality data. We can read them without pairing.

Scan cadence. The dongle sends:

• AT+CENTRAL once to enter scanning mode
• AT+FINDSCANDATA=<BOARD_ID>=3 every cycle to run a 3-second targeted scan
• It reads lines until BleuIO prints SCAN COMPLETE, then waits and repeats

Decoding. HibouAir advertises a compact environmental frame beginning with the marker 5B 07 05. PM values are 16-bit little-endian fields. In this build we anchor to the marker and read:

• PM1.0 (raw ÷ 10 → µg/m³)
• PM2.5 (raw ÷ 10 → µg/m³)
• PM10 (raw ÷ 10 → µg/m³)

UI behavior. Each metric drives a bar that fills left-to-right as the value rises within its display window. Thresholds are configurable (defaults: PM1.0 1, PM2.5 2, PM10 5 µg/m³). If any metric is at or above its threshold, the page shows “High particulate levels detected.”

Customize & extend

You can tailor this monitor to your space and workflow in several practical ways. If you anticipate larger spikes, widen the display windows—for example, expand PM2.5 to 0–200 µg/m³—to keep the bar responsive at higher ranges. For lightweight analytics, append readings to a CSV file or store them in IndexedDB to explore trends over hours or days. If you’re tracking multiple HibouAir units, build a wallboard that scans a list of Board IDs and renders compact tiles for each sensor in a single view. To act on thresholds, add automation hooks that trigger a webhook or drive a fan/relay from a companion script when levels rise. Finally, pair this particulate display with your existing CO₂ or Noise monitors to create a more complete picture of indoor conditions and ventilation effectiveness.

Output

In the video , the session starts at 0.0 µg/m³ across PM1.0/PM2.5/PM10. To demonstrate responsiveness, we briefly spray aerosol near the HibouAir device. Within seconds, the bars respond and the page displays “High particulate levels detected.” After stopping the aerosol and allowing air to clear, values decay back down, the bars recede, and the banner disappears. This sequence illustrates typical behavior you’ll see during quick particulate events (e.g., cleaning sprays, dust disturbances, smoke from cooking) and their recovery.

This project turns HibouAir’s BLE adverts into a clear view of PM1.0, PM2.5, and PM10 using a BleuIO dongle. In minutes, you get live bars, thresholds, and a simple alert that makes particulate spikes obvious. It’s easy to tune—adjust display windows, tweak thresholds, and adapt the layout for different rooms. As EU air-quality expectations tighten, this lightweight monitor helps you spot issues and validate ventilation quickly. From here, you can add data export, multi-device dashboards, or pair it with your CO2 monitor for a fuller picture.