DEV Community: Bleuio tech

Scanning BLE Devices with C++ and Boost Using the BleuIO Dongle

Bleuio tech — Mon, 23 Mar 2026 10:00:03 +0000

Bluetooth Low Energy (BLE) has become one of the most widely used wireless technologies for IoT devices, sensors, wearables, and industrial monitoring systems. Developers working with embedded systems, automation platforms, and hardware integration often rely on C++ because of its performance, low-level hardware access, and portability.

In this tutorial, we will create a simple command-line BLE scanning application using C++. The program connects to the BleuIO USB dongle through a serial port and sends AT commands to control Bluetooth operations. After starting the program, the user enters the number of seconds to scan, and the application instructs the BleuIO dongle to perform a BLE scan and print the detected devices directly in the terminal. This example demonstrates the basic workflow of communicating with BleuIO from a C++ application.

Why C++ and Boost Are Commonly Used for Bluetooth Development

C++ is widely used in Bluetooth and embedded development because it provides high performance and direct access to hardware interfaces such as serial communication. Many IoT gateways, embedded systems, and industrial applications rely on C++ to interact with sensors and wireless devices. To simplify development, developers often use the Boost libraries, which extend the C++ standard library with reliable cross-platform tools. In this tutorial we use Boost.Asio, which provides a portable and efficient way to handle serial communication and asynchronous input/output across different operating systems.

Requirements

Before starting this project, you should have the following:

A computer running macOS, Windows, or Linux
A BleuIO USB dongle
A C++ compiler
Boost libraries installed
Basic knowledge of C++ and terminal commands

Installing the Required Tools

macOS Setup

First install Xcode Command Line Tools, which provide the C++ compiler.

xcode-select --install

Next install Homebrew if it is not already installed.

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

Then install Boost:

brew install boost

You can verify the installation using:

brew --prefix boost

Windows Setup

On Windows you will need:

Visual Studio or MSVC compiler
Boost libraries

Steps:

Install Visual Studio Community Edition
Enable Desktop development with C++
Download Boost from https://www.boost.org
Extract Boost and configure it for your project.

Alternatively, Boost can be installed using vcpkg.

Have a look into the getting started guide on boost office page

https://www.boost.org/doc/user-guide/getting-started.html

Understanding How the Script Works

The example script uses Boost.Asio serial communication to interact with the BleuIO dongle.

The application works in several stages.

Serial Connection

The program opens a serial port connected to the BleuIO dongle.

serial_.open(port_name);

The serial port parameters are configured to match BleuIO’s default UART settings.

serial_.set_option(serial_port_base::baud_rate(57600));

serial_.set_option(serial_port_base::character_size(8));

serial_.set_option(serial_port_base::parity(serial_port_base::parity::none));

serial_.set_option(serial_port_base::stop_bits(serial_port_base::stop_bits::one));

serial_.set_option(serial_port_base::flow_control(serial_port_base::flow_control::none));

Asynchronous Serial Reader

The script uses an asynchronous reader to continuously listen for responses from the BleuIO dongle.

serial_.async_read_some(...)

Whenever the dongle sends data, the program prints the received information to the terminal.

This allows us to see scan results in real time.

Sending AT Commands

Commands are sent to BleuIO using the sendCommand() function.

bleuio.sendCommand("AT+CENTRAL");

The command is written to the serial port followed by a carriage return and newline.

Setting Central Role

BLE devices can operate in different roles.

Before scanning, the BleuIO dongle must be set to central mode.

bleuio.sendCommand("AT+CENTRAL");

Starting a BLE Scan

The scan command is then issued.

AT+GAPSCAN=

For example:

AT+GAPSCAN=5

This instructs the BleuIO dongle to scan for nearby BLE devices for five seconds.

The dongle returns advertising data for detected devices during the scan.

Full Source Code

You can find the full source code on GitHub.

GitHub repository

https://github.com/smart-sensor-devices-ab/bleuio-cpp-boost

The repository contains the complete C++ script used in this tutorial.

How to Run the Script

First compile the program.

clang++ -std=c++17 main.cpp -I$(brew --prefix boost)/include -o bleuio_scan

After compilation, run the program:

./bleuio_scan

The program will ask for the scan duration.

Example:

Enter scan duration in seconds: 5

The script will then:

Connect to the BleuIO serial port,Put the dongle into central mode,Start scanning for BLE devices,Print scan results in the terminal

Example output may look like this:

Locating the BleuIO Serial Port

Before running the program, you need to identify the serial port where the BleuIO dongle is connected.

On macOS, you can list available serial devices using the terminal command:

ls /dev/cu.*

The BleuIO device will typically appear with a name similar to:

/dev/cu.usbmodemXXXXXXXX

This value can then be used in the script as the serial port path.

On Windows, the serial port can be identified through Device Manager. After plugging in the BleuIO dongle, open Device Manager and expand the Ports (COM & LPT) section. The device will appear as a USB serial device with a COM port number, such as COM17.

Expanding This Example

The script in this tutorial is a basic example showing how to communicate with the BleuIO dongle using C++ and Boost.Asio. Although it only performs BLE scanning, the same approach can be used to send any AT command supported by BleuIO. Developers can extend this example to connect to devices, read GATT characteristics, parse advertisement data, or integrate BLE functionality into larger applications such as IoT gateways, monitoring tools, or automation systems.

Integrating BleuIO with Teensy 4.1 – Scanning and Decoding HibouAir Sensor Data (Part 2)

Bleuio tech — Mon, 23 Mar 2026 09:58:29 +0000

In the previous project, we focused on getting Teensy 4.1 working as a USB Host for the BleuIO. The goal was simple: remove the PC from the equation and prove that a microcontroller could directly control BleuIO and communicate over BLE using AT commands.

This project builds on that foundation and does something practical with it. Instead of manually sending commands and observing responses, we now create a complete scanner that automatically detects nearby HibouAir sensors, reads their BLE advertisement data, decodes it, and prints meaningful environmental values in real time.

At this point, the system stops being a connectivity demo and becomes an actual application.

Hardware Requirements

Teensy 4.1
BleuIO
HibouAir Air quality monitoring sensor
USB cable
USB TYPE A pcb adaptor
Arduino IDE 2.x
ArduinoJson library (v7.x or compatible)

Software Requirements

Install ArduinoJson

This project uses ArduinoJson to parse scan results from BleuIO.

In Arduino IDE:

Open Library Manager
Search for arduinojson
Install version 7.x or compatible

ArduinoJson is required to deserialize the JSON scan data received from BleuIO.

How it Works

The architecture remains the same as in Part 1, but now it is used with purpose.

Teensy operates in USB Host mode and communicates directly with BleuIO. BleuIO handles all Bluetooth Low Energy scanning internally and outputs scan results as structured JSON strings over USB serial. Teensy receives those strings, parses the JSON content, extracts the manufacturer-specific payload, and decodes it into usable values.

Conceptually, the flow looks like this:

Teensy 4.1 (USB Host + Application Logic)

↓

BleuIO (BLE Scanning Engine)

↓

BLE Advertisement Data (JSON)

↓

HibouAir Decoder

↓

Readable Environmental Measurements

The important thing to notice here is that Teensy never deals with BLE packets directly. There is no radio handling, no GAP or GATT management, and no BLE stack integration. Everything related to Bluetooth stays inside BleuIO. The microcontroller simply receives structured scan results and processes them like any other data stream.

Automatic Startup and Scanning

When the firmware starts, it configures BleuIO automatically. It disables command echo, enables verbose mode, and then sends a filtered scan command:

AT+FINDSCANDATA=FF5B07

This tells BleuIO to report only devices containing the HibouAir manufacturer identifier. From that moment, scan results begin arriving continuously as JSON lines.

Each line contains fields such as the device address and a data field containing the manufacturer payload in hex format. That hex string is where the sensor readings are encoded.

Parsing the JSON Data

Since scan data arrives asynchronously, the project includes a small USB serial line reader. It buffers incoming characters until a newline is detected, ensuring that we always attempt to parse complete JSON messages.

The ArduinoJson library is used to deserialize each line into a JsonDocument. Once deserialized, we check that the expected scan fields are present. If so, we extract the hex-encoded manufacturer payload and pass it to the HibouAir decoder.

At this stage, the data is still raw — just a long hex string representing packed bytes from the BLE advertisement.

Decoding the HibouAir Advertisement Payload

The core of this project is the HibouAir structure. Instead of manually extracting bytes in the main loop, the decoding logic is encapsulated in a dedicated class.

The constructor receives the JSON document, extracts the data field, and interprets the hex string as a packed binary structure. Using offsetof() ensures that the correct byte offsets are used, and helper functions convert the hex pairs into integers. Because the BLE advertisement uses little-endian ordering, some fields require byte swapping before they become meaningful.

Once decoded, the class provides clean accessor functions such as:

getTemp()
getHum()
getBar()
getCo2()
getPM2_5()

These functions already return properly scaled values. For example, temperature is divided by 10 to convert from raw integer format to degrees Celsius.

This separation keeps the application logic simple. The main loop only needs to create a HibouAir object and call show_sensor() to print the values.

Example Output

When running the project with a nearby HibouAir sensor, the Serial Monitor shows structured environmental readings like this:

Sensor ID: 22008C

Light: 14 Lux

Pressure: 1007.3 hPA

Temperature: 22.9 C

Humidity: 14.1 %rh

CO2: 508 ppm

For particulate matter devices, additional values appear:

PM 1.0: 0.0 ug/m3

PM 2.5: 1.2 ug/m3

PM 10: 2.5 ug/m3

This output is generated directly from BLE advertisements without establishing a connection to the sensor. The sensors simply broadcast their measurements, and the system passively collects and decodes them.

GitHub Repository

The complete source code for this project is available here:

https://github.com/smart-sensor-devices-ab/bleuio-teensy-hibouair-scanner

You can clone the repository, install the ArduinoJson library through the Arduino IDE Library Manager, upload the sketch to Teensy 4.1, and run it immediately. The code is modular and organized so you can reuse the USB line reader, the HibouAir decoder, or the scanning logic in your own applications.

With this foundation in place, several natural extensions become possible. You could store measurements on an SD card, publish them via MQTT, expose them through a REST interface, or even build a complete air-quality gateway. The BLE side does not need to change.

Integrating BleuIO with Teensy 4.1 for Seamless BLE Applications

Bleuio tech — Mon, 16 Feb 2026 12:57:50 +0000

In this project, we will build a high-performance embedded Bluetooth Low Energy (BLE) host system using the Teensy 4.1 microcontroller and the BleuIO USB BLE dongle. The goal is to turn the Teensy into a USB Host controller that communicates directly with BleuIO through a simple Arduino sketch, allowing us to create BLE applications without implementing a full BLE stack on the microcontroller.

By the end of this project, you will have a fully functional embedded BLE platform where Teensy 4.1 acts as the application controller and BleuIO handles all Bluetooth Low Energy communication internally. You will be able to send AT commands from Teensy to scan for BLE devices, connect to them, read characteristics, and build your own BLE-based solutions. More importantly, you will gain a reusable architecture that can serve as the foundation for industrial gateways, IoT devices, monitoring systems, or custom embedded products.

Why We Chose Teensy 4.1

The Teensy 4.1 is built around the powerful NXP i.MX RT1062 ARM Cortex-M7 processor running at 600 MHz. This makes it one of the fastest microcontrollers compatible with the Arduino ecosystem. Its high clock speed, large memory capacity, and hardware floating point support allow it to handle complex logic, real-time data processing, and communication tasks with ease.

What makes Teensy 4.1 particularly ideal for this project is its USB Host capability. Since BleuIO is a USB device, it requires a host controller to operate independently of a PC. Teensy 4.1 provides exactly that. It allows us to connect BleuIO directly to the microcontroller and build a fully standalone BLE system. The board’s performance headroom ensures stable communication, fast response handling, and scalability for advanced applications.

Rather than choosing a minimal low-power MCU, we selected Teensy 4.1 because it bridges the gap between traditional microcontrollers and more complex application processors. It gives developers flexibility, speed, and reliability in embedded BLE projects.

Project Requirements

To build this project, you will need:

Teensy 4.1 microcontroller board
BleuIO – Bluetooth Low Energy USB Dongle
Arduino IDE 2.x (2.0.4 or later recommended)
Teensy board support package (Teensyduino)
USB cable for programming

Project Architecture Overview

The system architecture is straightforward. Teensy 4.1 operates as a USB Host and communicates directly with the BleuIO dongle over serial. BleuIO then manages all Bluetooth Low Energy communication with nearby BLE devices. This separation of responsibilities simplifies development significantly. Teensy focuses on application logic, while BleuIO handles the BLE stack internally.

Teensy 4.1 (USB Host)
        ↓
BleuIO USB Dongle
        ↓
BLE Devices

How the Project Works – Step-by-Step

Step 1: Install Arduino IDE and Teensy Support

First download the Arduino 2.x.x IDE from Arduino’s website. All versions 2.0.4 and later are supported. Versions 2.3.0 or later are recommended, due to improvements in Boards Manager.

To install Teensy on Arduino IDE 2.x, click File > Preferences (on MacOS, click Arduino IDE > Settings).

In the main Arduino window, open Boards Manager by clicking the left-side board icon, search for “teensy”, and click “Install”.

Step 2: Configure USB Type

Teensy supports multiple USB configurations. Under Tools → USB Type, select the appropriate mode so the board can manage serial communication while operating with USB Host functionality. Teensyduino is also compatible with many Arduino libraries.

Step 3: Upload the Sketch

The source code for this project, USBtoUSBHostSerial.ino sketch, is publicly available on GitHub: https://github.com/smart-sensor-devices-ab/bleuio_Teensy_host

Upload the provided USBtoUSBHostSerial.ino sketch to the Teensy 4.1 using the Arduino IDE. This sketch initializes the USB Host interface and establishes a communication bridge between the Teensy and the BleuIO dongle. Once programmed, the Teensy essentially becomes a serial terminal for BleuIO, allowing you to type AT commands through the Arduino Serial Monitor and receive responses in real time.

Instead of embedding complex BLE logic in the microcontroller firmware, the sketch focuses on maintaining stable USB communication and forwarding user commands to the dongle. BleuIO processes these commands internally and returns structured responses. This design keeps the firmware clean, modular, and easy to expand.

Why This Approach Is Powerful

Developing BLE applications directly on microcontrollers traditionally requires integrating a BLE stack, managing connection states, handling security layers, parsing protocol events, and debugging complex timing issues. This process can be time-consuming and hardware-dependent. Each microcontroller family often requires a different SDK, stack configuration, and maintenance strategy.

By using BleuIO, this complexity is dramatically reduced. BLE functionality is abstracted behind a simple AT command interface. The microcontroller does not need to manage low-level BLE operations. Instead, it communicates using straightforward serial commands while BleuIO takes care of scanning, connecting, reading characteristics, and maintaining protocol compliance internally. This modular architecture makes the system portable across hardware platforms and reduces firmware complexity, development time, and maintenance effort.

GitHub Repository

The source code for this project, USBtoUSBHostSerial.ino sketch, is publicly available on GitHub:

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

This project is shared as a public example to demonstrate how BleuIO can be integrated into high-performance embedded systems. It is intentionally simple in structure so that developers can clearly understand the architecture and reuse it in their own applications.

By combining the computational power of Teensy 4.1 with the modular BLE capabilities of BleuIO, we created a clean and scalable embedded architecture. This project highlights how BLE integration does not need to be complicated. With the right approach, developers can focus on innovation and application logic rather than low-level protocol management.

Developing a Desktop BLE Air-Quality Application with Rust, Dioxus, and BleuIO

Bleuio tech — Mon, 02 Feb 2026 13:40:14 +0000

Bluetooth Low Energy is often associated with mobile apps, embedded firmware, or cloud gateways. In practice, however, BLE is equally powerful on the desktop. With the right tools, a desktop application can scan, decode, and visualize BLE sensor data in real time—without relying on browsers or mobile platforms.

In this tutorial, we demonstrate how BleuIO can be used as a flexible BLE interface for desktop applications written in Rust, using the Dioxus framework. The result is a native desktop application that scans nearby HibouAir sensors, decodes their BLE advertisement data, and presents air-quality metrics in a clean, responsive user interface.

This project is intended as a practical example that shows how BleuIO fits naturally into modern desktop development workflows—regardless of programming language.

What We Are Building

The application is a native desktop air-quality dashboard. It connects to a BleuIO USB dongle over a serial port, puts the dongle into BLE scanning mode, and continuously listens for BLE advertisements from nearby HibouAir sensors. These advertisements contain manufacturer-specific data with environmental measurements such as CO2 concentration, particulate matter, temperature, humidity, pressure, VOC levels, and ambient light.

The desktop application decodes this data locally and displays it in real time. Each detected sensor is shown as its own panel, with a clear header identifying the device type and a structured content area showing the latest measurements.

The entire solution runs locally on the user’s computer. There is no cloud dependency, no browser runtime, and no mobile device involved.

Why Rust and Dioxus?

Rust has become increasingly popular for system-level and desktop applications because it combines performance, memory safety, and strong tooling. For BLE applications that involve continuous serial communication and real-time data processing, these properties are particularly valuable.

Dioxus is a Rust UI framework inspired by modern component-based design. It allows developers to build native desktop interfaces using a declarative approach while still compiling to a true desktop binary. In this project, Dioxus is used to render the dashboard, manage state updates as sensor data arrives, and keep the UI responsive even during continuous BLE scanning.

The combination of Rust, Dioxus, and BleuIO demonstrates that desktop BLE applications do not need to rely on platform-specific SDKs or heavyweight frameworks.

Requirements

To run this project, the following hardware and software are required:

Hardware

BleuIO USB BLE dongle
HibouAir air-quality sensor

Software

Rust toolchain (stable) https://www.rust-lang.org/tools/install
Dioxus CLI https://dioxuslabs.com/learn/0.5/getting_started
A supported desktop operating system (macOS, Linux, or Windows)

No proprietary SDKs or BLE drivers are required. BleuIO communicates using standard AT commands over a serial interface, making the setup lightweight and portable.

How the Project Works Internally

When the application starts, it first searches for a connected BleuIO dongle by scanning available serial ports and matching the dongle’s USB vendor and product IDs. Once the correct device is found, the application opens the serial port and initializes the dongle by disabling command echo and enabling verbose mode.

After initialization, the application instructs BleuIO to start scanning for BLE advertisements that match HibouAir’s manufacturer identifier. BleuIO then streams scan results back to the application as JSON-formatted lines over the serial connection.

Each incoming scan packet is parsed and inspected. The application locates the BLE manufacturer-specific data, verifies that it belongs to HibouAir, and decodes the payload into a structured Rust data type. To ensure stable and predictable readings, only the full advertisement format used by HibouAir beacon type 0x05 is processed. Partial or auxiliary beacon formats are ignored in this example project.

Decoded sensor data is stored in memory and immediately reflected in the user interface. As new advertisements arrive, the corresponding sensor panel updates automatically.

Project Structure

The source code is organized into clear, functional modules. UI components are separated from BLE logic and data models, making the project easy to understand and extend. The main application entry point configures the desktop window and mounts the dashboard component. BLE communication is encapsulated in a dedicated hook that runs asynchronously and feeds decoded sensor data into the UI layer.

src/
├── components/
│   ├── dashboard.rs
│   ├── sensor_panel.rs
│   └── mod.rs
├── hooks/
│   ├── use_bleuio.rs
│   └── mod.rs
├── models/
│   ├── bleuio.rs
│   ├── hibouair.rs
│   ├── sensor_data.rs
│   └── mod.rs
├── main.rs
assets/
├── main.css
├── tailwind.css
└── favicon.ico

This structure mirrors how larger Rust desktop applications are typically organized, and it provides a solid foundation for adding features such as data logging, historical charts, filtering, or export functionality.

Source Code and How to Run the Project

The complete source code for this project is publicly available on GitHub:

GitHub repository:

https://github.com/smart-sensor-devices-ab/hibouair-bleuio-rust-readltime-desktop

After cloning the repository, the application can be run in development mode using the Dioxus CLI. This launches the desktop window and enables hot reloading, which is useful when experimenting with UI changes or decoding logic. The project can also be built and run using standard Cargo commands, producing a native desktop binary.

Because the code is open and self-contained, developers are free to study it, modify it, or reuse parts of it in their own BLE-based desktop applications.

The complete instruction on how to run this project is available on the Readme file.

Application Output

When running, the application displays a dashboard with one panel per detected HibouAir sensor. Each panel includes a colored header identifying the sensor type, followed by a white content area showing live air-quality measurements. Values update continuously as new BLE advertisements are received, providing an immediate view of the surrounding environment.

A screenshot of the running application is shown below to illustrate the final result.

This project is intentionally kept simple. It is not a finished product, but rather an educational example that demonstrates how BleuIO can be used with Rust and Dioxus to build a native desktop BLE application. The source code is public, easy to follow, and designed to be extended.

Building a BLE Device Detection Web App with Phoenix and BleuIO

Bleuio tech — Wed, 31 Dec 2025 11:14:12 +0000

In this tutorial, we build a simple but practical web application that demonstrates how BleuIO can be integrated with a modern web framework to interact with Bluetooth Low Energy (BLE) devices in real time. The goal of this project is not to create a full-featured product, but to provide a clear example that developers can learn from and extend for their own use cases.

The application connects to a BleuIO USB dongle via a serial port, enables verbose mode, performs a BLE scan, and detects whether a specific nearby BLE device is present. If the target device is found, the application displays its MAC address. This approach is particularly useful for scenarios such as proximity detection, presence monitoring, or gateway-style BLE integrations.

The complete source code for this project is provided separately so developers can easily clone, modify, and build upon it.

Why Phoenix Framework?

Phoenix is a web framework written in Elixir and built on top of the Erlang/OTP ecosystem. It is designed for applications that require high concurrency, real-time updates, and long-running background processes. These characteristics make Phoenix particularly suitable for hardware-integrated applications where continuous communication with external devices is required.

In this project, Phoenix allows the BleuIO serial connection to remain open while BLE scan results are streamed to the web interface. The framework handles process supervision, message passing, and real-time UI updates in a clean and reliable way, without the need for complex front-end JavaScript logic.

How This Differs from a JavaScript-Based Web App

This project does not use the Web Serial API. Web Serial allows browser-based JavaScript applications to access serial devices, but it is primarily intended for interactive, user-driven scenarios. It requires explicit user permission, depends on browser support, and is not suitable for unattended or always-on systems.

By contrast, this Phoenix-based approach keeps all BLE logic on the backend. The web interface simply reflects the current state of the system and allows the user to trigger actions such as connecting or rescanning. This separation makes the application easier to extend, easier to deploy, and more suitable for real-world integrations where reliability and continuous operation are important.

Requirements

To follow this tutorial, you will need:

A computer
A BleuIO USB dongle
A Close beacon
Elixir and Phoenix installed on your system

How the Application Works

The application follows a straightforward process. The user enters the serial port name where the BleuIO dongle is connected and initiates the connection from the web interface. Once connected, the application sends an AT command to enable verbose mode, making the responses easier to read and parse.

After verbose mode is enabled, the application starts a timed BLE scan. As scan results arrive from BleuIO, they are analyzed in real time. If a BLE device advertising the configured target name is detected, the application extracts its MAC address and updates the interface accordingly. The user can repeat the scan at any time using the rescan option.

All serial communication and BLE processing run in the background, while the web interface updates automatically based on events generated by the backend.

Running the Application

Installing Phoenix

Phoenix can be installed on macOS using standard tooling for Elixir. Once Elixir is installed, Phoenix can be added using the official project generator. Detailed installation steps are included in the project documentation.

Running the App

Download the source code from https://github.com/smart-sensor-devices-ab/bleuio-phoenix-erlang

After downloading the source code:

Install dependencies
Start the Phoenix server
Open the application in a browser
Enter the BleuIO serial port
Click Connect
View scan results and detected devices

Follow README.md for more details

The application runs locally and does not require any cloud services.

Configuring the Serial Port and Target Device

Two key parameters are intentionally easy to modify:

Serial Port

The serial port used by BleuIO can be updated either through the web interface or in the configuration file (config>runtime.exs). This allows the project to run on different machines without code changes.

The serial port name depends on the operating system being used. On macOS, BleuIO typically appears as a device starting with /dev/cu.usbmodem. On Linux systems, the dongle is commonly available as /dev/ttyUSB0 or /dev/ttyACM0, depending on the system configuration. On Windows, BleuIO appears as a COM port, such as COM3 or COM5.

Target BLE Device

The application looks for a specific BLE device name during scanning. This name is defined as a constant in the backend code on lib>bleuio_phx>bleuio_worker.ex and is matched case-insensitively.

Proximity Detection and CloseBeacon Use Case

In the example implementation, the application detects a device advertising the name closebeacon.com. This makes the project suitable for proximity detection use cases, such as presence awareness, zone monitoring, or asset tracking.

BLE beacons like CloseBeacon are commonly used in proximity-based systems, and this project demonstrates how BleuIO can serve as a reliable BLE scanning interface for such applications. More information about CloseBeacon can be found at https://www.closebeacon.com/.

How This Project Helps Developers

This tutorial is intended as an example project rather than a finished solution. It shows how BleuIO can be integrated with a backend web framework to handle BLE scanning in a clean and scalable way. Developers can use this project as a reference to understand the overall architecture and then build their own custom logic on top of it.

The complete source code is provided so that anyone interested can explore the implementation details, experiment with different configurations, and adapt the project for their own BLE-enabled applications.

By combining BleuIO with Phoenix, developers can build BLE-enabled web applications that are not limited by browser APIs or client-side constraints. This example demonstrates a backend-first approach to BLE scanning that is well suited for gateways, monitoring tools, and proximity detection systems.

Sending BLE Air Quality Data to Arduino Cloud Using BleuIO

Bleuio tech — Wed, 17 Dec 2025 10:03:34 +0000

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

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.

Integrating BleuIO with Adafruit Feather RP2040 for Seamless BLE Applications – Part 5 (Two-Way Communication)

Bleuio tech — Fri, 05 Dec 2025 15:13:58 +0000

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

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

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

After upload:

Open Serial Monitor at 9600 baud.
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"}
----

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:

Read the MAC address printed by the Arduino Serial Monitor (own_mac_addr).
Use the BleuIO Python library (or BLE stack) to connect to that address.
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.

How AI Makes BLE Development Even Easier with BleuIO

Bleuio tech — Tue, 02 Dec 2025 10:04:11 +0000

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:

https://www.youtube.com/watch?v=8wegP7O5Xls

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.

Ambient-Adaptive Particulate Monitor (PM1.0 / PM2.5 / PM10) with BleuIO & HibouAir

Bleuio tech — Fri, 31 Oct 2025 10:31:40 +0000

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](https://github.com/smart-sensor-devices-ab/pm-monitor-bleuio)

Live demo: [https://smart-sensor-devices-ab.github.io/pm-monitor-bleuio/](https://smart-sensor-devices-ab.github.io/pm-monitor-bleuio/)

Hardware & software

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

https://www.youtube.com/watch?v=WUZL5VnDtfE

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.

Real-Time BLE Air Quality Monitoring with BleuIO and Adafruit IO

Bleuio tech — Mon, 22 Sep 2025 09:44:13 +0000

This project shows how to turn a BleuIO USB dongle into a tiny gateway that streams live air-quality data from a HibouAir sensor straight to Adafruit IO. The script listens for Bluetooth Low Energy (BLE) advertising packets, decodes CO2, temperature, and humidity, and posts fresh readings to your Adafruit IO feeds every few seconds. The result is a clean, shareable dashboard that updates in real time—perfect for demos, labs, offices, classrooms, and proofs of concept.

What is Adafruit IO—and why pair it with BleuIO?

Adafruit IO is a cloud platform for makers and developers that lets you collect, visualize, and route device data using a simple REST API or MQTT. You don’t need any Adafruit hardware; if you can make an HTTPS request, you can send data. BleuIO fits in beautifully here: the dongle handles the BLE side—scanning and parsing sensor frames—while a short Python script formats those values and pushes them to Adafruit IO. In practice that means you can take any BLE-advertising sensor, translate its packets into numbers, and land them on an IoT-friendly dashboard without servers or containers.

Requirements

To complete this project, you will need:

BleuIO BLE USB Dongle – acts as the BLE central device to capture advertising packets.
HibouAir Air quality monitor – broadcasts environmental data such as CO2, temperature, and humidity.
Python libraries – install them with: pip install pyserial requests
Adafruit IO account – free to sign up at io.adafruit.com.
Adafruit IO Key – available under your account’s “My Key” page for authentication.

How it works

When you start the script, it opens the BleuIO serial port and switches the dongle into central role the very first time the program runs. From then on it repeatedly performs a short BLE scan that filters for HibouAir advertising frames. The scanner always picks the latest matching packet and decodes the fields we care about: CO2 (ppm), temperature (°C), and humidity (%rH). The script then posts each value to its own Adafruit IO feed over HTTPS. Because Adafruit IO is designed for live IoT data, your dashboard widgets update as soon as new points arrive. The loop cadence is configurable (10 seconds by default), which keeps you comfortably under Adafruit IO’s free-tier request limits.

The code (key points)

The script is intentionally small and readable. It opens the serial device (for example /dev/cu.usbmodemXXXX on macOS or COM7 on Windows), sends the BleuIO commands to scan for a few seconds, and parses the returned “Device Data [ADV]” lines.

A compact decoder extracts CO2, temperature, and humidity from the HibouAir manufacturer data, including the byte order and scaling.

To make the setup painless, credentials are read from variables (AIO_USER, AIO_KEY) and feed names default to co2, temperature, and humidity. Each value is sent to the REST endpoint /api/v2/{username}/feeds/{feed_key}/data with a simple JSON body {"value": <number>}.

The script includes gentle sanity checks (for example, temperature range and humidity bounds) to ignore any malformed frames, and it prints a concise log line each time it pushes fresh data.

Here is the GitHub link with the full source so you can clone and run it as-is or adapt it to other sensors.

How to run the code

Before running, set your serial port and Adafruit IO credentials.

On macOS you can list ports with ls /dev/cu.usbmodem*;

on Windows use Device Manager to find the COM number. Update username and AIO key information, then run the script.

The program will put BleuIO into central mode on first launch and, every cycle, will scan, decode, and push CO2, temperature, and humidity to the three feeds.

If you see an HTTP 401 error, double-check the AIO key; a 404 usually means a feed name typo. If the script can’t open the serial port, confirm the path and that no other program is holding it open.

Creating Adafruit IO feeds, key, and dashboard

Log in to Adafruit IO and create three feeds named co2, temperature, and humidity. Your AIO Key is available under your account’s “My Key” page; copy it and keep it private. With feeds in place, open the Dashboards section and create a new dashboard for this project (for example, “HibouAir Live”). Add a few blocks: a gauge or line chart for CO₂ (with a range that makes sense for your space), another gauge or slide for temperature, and a slide or line chart for humidity so you can see the trend over time. Each block points to its corresponding feed. As the script posts to those feeds, the blocks will animate and refresh automatically. You can reorder blocks, tweak colors and ranges, and share a read-only link if you want others to watch along.

Output

Once everything is connected, the dashboard shows a live CO2 number in gauge an line chart, an updating temperature value, and a humidity box that advances with each new reading. The values move in near real time as the script cycles, and any spikes or changes in air quality appear immediately.

Use cases

Real-time air-quality dashboards are useful in far more places than a lab bench. Facility manager can watch CO2 levels across meeting rooms to optimize ventilation; schools and libraries can surface temperature and humidity alongside occupancy schedules; small manufacturers can keep an eye on comfort and safety in production spaces; and hobbyists can monitor their home offices or studios. Because the pipeline is “BLE sensor → BleuIO → HTTPS → Adafruit IO,” you can swap HibouAir for other BLE advertisers and reuse the same approach to visualize anything from soil moisture to ambient light.

This project highlights how quickly you can go from BLE broadcast to live cloud dashboard with BleuIO and Adafruit IO. There’s no server to maintain, no container to deploy—just a tiny USB dongle, an air quality monitoring device like HibouAir, a short Python script, and a few clicks on the Adafruit IO site. The result is a shareable, real-time view of your environment that’s easy to extend, brand, and automate.

Automating BLE Development with BleuIO: Run Without a Host Computer

Bleuio tech — Wed, 17 Sep 2025 12:29:17 +0000

When working with Bluetooth Low Energy (BLE) devices, most developers expect to rely on a computer or microcontroller to send commands. BleuIO changes that expectation. More than a USB dongle, BleuIO is now capable of running standalone BLE automation — no host computer required.

This means you can plug BleuIO into any power source (USB adapter, hub, or power bank) and let it execute predefined tasks completely on its own.

Why Standalone Automation Matters

Traditional BLE prototyping involves:

Writing custom firmware
Using a host PC or MCU to send AT commands
Managing drivers, code, and extra hardware

With BleuIO, you can store AT commands inside the dongle itself. Once powered, the device automatically runs those commands in sequence or in a loop. No drivers. No host system. Just power → BLE.

This reduces development overhead, speeds up prototyping, and makes deployment more reliable.

Quick Example: A Beacon That Starts on Power-Up

Want to turn your BleuIO into a beacon without writing a single line of host code? Just set the auto execution list:

AT+AUTOEXEC=AT+ADVSTART

Now, every time you power the dongle, it will begin advertising as a BLE beacon automatically.

Example: A Loop That Changes Beacon Data

You can also create dynamic advertising by alternating the beacon name every 10 seconds.

AT+AUTOEXECLOOP=START
AT+AUTOEXEC=AT+ADVDATA=04:09:42:6C:65:75:49:4F   // Name: BleuIO
AT+AUTOEXEC=AT+ADVSTART
AT+AUTOEXEC=AT+WAIT=10
AT+AUTOEXEC=AT+ADVDATA=04:09:43:41:54            // Name: CAT
AT+AUTOEXEC=AT+ADVSTART
AT+AUTOEXECLOOP=END

Now BleuIO will broadcast as “BleuIO,” switch to “CAT” after 10 seconds, and repeat endlessly — all without a host device.

What You Can Build With This

Smart beacons that rotate data for marketing or tracking
Sensor broadcasting devices that push data to nearby scanners
Device-to-device automation where dongles communicate directly
Prototypes that don’t need PCs or microcontrollers to run

Learn More

If you’re new to BleuIO, check out the Getting Started guide and AT command reference.

BleuIO is more than a dongle — it’s your plug-and-play BLE automation engine.

Ready to cut out the middleman? Try running BleuIO without a host and see how much simpler BLE development can be.

Understanding How BLE Devices Connect (and How to Simplify It with BleuIO)

Bleuio tech — Thu, 11 Sep 2025 12:12:31 +0000

Bluetooth Low Energy (BLE) has become one of the most widely adopted wireless technologies for IoT and embedded systems. Whether it’s fitness trackers, smart lighting, or industrial sensors, BLE devices connect seamlessly to phones, gateways, and computers. But under the hood, how does this connection process really work? And how can developers make it easier to test and prototype?

How BLE Connections Work

The BLE protocol is designed around a central–peripheral model. A peripheral (such as a sensor or beacon) broadcasts advertisement packets. These packets announce that the device is available and can contain details like its name, supported services, or other custom data.

On the other side, a central device (like a smartphone, PC, or USB dongle) listens for these advertisements by scanning the radio environment. When it detects a device of interest, the central can request a connection. Once established, the two devices exchange data using services and characteristics. This structured approach ensures BLE stays flexible enough to support anything from simple temperature sensors to secure data transfer.

Why BLE Can Be Tricky for Developers

Although the connection model sounds simple, the implementation is often challenging. Each operating system provides its own Bluetooth API, and working with these APIs can require deep knowledge of the Bluetooth stack. Developers need to manage device discovery, connection, bonding, and data exchange, all while ensuring security and cross-platform compatibility.

This complexity makes rapid prototyping harder, especially for developers who want to test ideas quickly or build cross-platform apps without rewriting code for every environment.

BleuIO: A Developer-Friendly Shortcut

BleuIO is a Bluetooth Low Energy USB dongle that removes much of this complexity. Instead of diving into low-level code, you can use simple AT commands to handle BLE connections. For example:

Scan for devices with AT+GAPSCAN
Connect to a target device with AT+GAPCONNECT followed by its address

Once connected, you can easily exchange data, test different services, or even automate workflows using BleuIO’s Python and JavaScript libraries.

This means you can focus on building your application logic while letting BleuIO handle the BLE details.

Hands-On Examples

The BleuIO blog features practical tutorials to help developers experiment with BLE connections. A few examples include:

Each tutorial shows how quickly you can go from scanning to connecting and exchanging data, without worrying about OS-level complexities.

Understanding how BLE devices connect is key for anyone working in IoT, embedded systems, or mobile development. But instead of spending weeks learning the internals of Bluetooth stacks, you can use tools like BleuIO to speed up development. With simple AT commands like AT+GAPSCAN and AT+GAPCONNECT, you can connect devices in seconds and focus on building features that matter.

DEV Community: Bleuio tech

Scanning BLE Devices with C++ and Boost Using the BleuIO Dongle

Why C++ and Boost Are Commonly Used for Bluetooth Development

Requirements

Installing the Required Tools

macOS Setup

Windows Setup

Understanding How the Script Works

Serial Connection

Asynchronous Serial Reader

Sending AT Commands

Setting Central Role

Starting a BLE Scan

Full Source Code

How to Run the Script

Locating the BleuIO Serial Port

Expanding This Example

Integrating BleuIO with Teensy 4.1 – Scanning and Decoding HibouAir Sensor Data (Part 2)

Hardware Requirements

Software Requirements

Install ArduinoJson

How it Works

Automatic Startup and Scanning

Parsing the JSON Data

Decoding the HibouAir Advertisement Payload

Example Output

GitHub Repository

Integrating BleuIO with Teensy 4.1 for Seamless BLE Applications

Why We Chose Teensy 4.1

Project Requirements

Project Architecture Overview

How the Project Works – Step-by-Step

Step 1: Install Arduino IDE and Teensy Support

Step 2: Configure USB Type

Step 3: Upload the Sketch

Why This Approach Is Powerful

GitHub Repository

Developing a Desktop BLE Air-Quality Application with Rust, Dioxus, and BleuIO

What We Are Building

Why Rust and Dioxus?

Requirements

Hardware

Software

How the Project Works Internally

Project Structure

Source Code and How to Run the Project

Application Output

Building a BLE Device Detection Web App with Phoenix and BleuIO

Why Phoenix Framework?

How This Differs from a JavaScript-Based Web App

Requirements

How the Application Works

Running the Application

Installing Phoenix

Running the App

Configuring the Serial Port and Target Device

Serial Port

Target BLE Device

Proximity Detection and CloseBeacon Use Case

How This Project Helps Developers

Sending BLE Air Quality Data to Arduino Cloud Using BleuIO

Why Arduino Cloud?

Hardware and Software Requirements

Hardware

Software

How the System Works

Arduino Cloud Setup (Step by Step)

Step 1: Create or Log In to Arduino Cloud

Step 2: Create a Device

Step 3: Create a Thing

Step 4: Add Cloud Variables

Step 5: Create a Dashboard

Project Source Code

Configure secrets.py

Configure gateway.py

Running the Project

Customizing the Dashboard

Integrating BleuIO with Adafruit Feather RP2040 for Seamless BLE Applications – Part 5 (Two-Way Communication)

What This Project Does

Arduino project on Adafruit Feather RP2040

Configure `secrets.py`

Configure `gateway.py`