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bE-More combined an Arduino light-sensing demo, IoT telemetry and locally run AI analysis to show how a workplace lighting system might avoid unnecessary use. Project lead Giovanni Zanotti says his team shared the win at its school’s Project Day 2025; the project was a miniature office demonstration, not a measured office deployment.
What bE-More demonstrated
bE-More was presented as a semi-automatic system for monitoring workplace energy use and controlling lighting. Zanotti’s account says the team began the project in February 2025 for a school showcase called Project Day, where bE-More shared the win with another project, VIPRA. That outcome is reported by Zanotti; no independent school or organizer confirmation is established.
The physical build was a miniature office diorama rather than a live workplace installation. It included workspace LEDs, an indicator LED, a buzzer, two buttons for manual or autonomous control, and a photoresistor. The intended sustainability benefit was to reduce unnecessary lighting when natural light was sufficient, but the project sources do not report measured energy savings or emissions reductions.
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How the system was put together
The design joined four layers: a desktop controller, an IoT platform, an Arduino edge device, and a local analysis service. The project’s descriptions outline the following roles:
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- Controller: A Java 23 desktop application with a WebView dashboard and management of the AI process lifecycle.
- IoT platform: ThingsBoard handled telemetry and dashboards. The Arduino communicated with it over MQTT; the article also describes a ThingsBoard PostgreSQL database queried by the analysis middleware.
- Edge device: An Arduino UNO R4 read the light sensor and applied deterministic lighting automation.
- Local analysis: A Python service connected project data to Ollama running Mistral:7b, with the analysis intended to surface insights and anomalies in the telemetry.
This division matters: the light-control decision was a rule running on the Arduino, while the AI layer analyzed data. The available descriptions do not establish that AI directly controlled the lights.
How the light-control rule worked
The photoresistor was connected to analog pin A3. In the repository’s documented AUTO behavior, a reading above 450 switches the demonstration’s lights off. The project sources do not specify the threshold’s units or sensor calibration, so 450 should be treated as a value for this particular prototype—not a universal brightness setting.
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The two control buttons allowed the demonstrator to choose between manual and autonomous operation. A real installation would need carefully defined override behavior and tested sensor placement: readings depend on the sensor, its position, and surrounding light, none of which the sources quantify for a deployed office.
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Zanotti describes privacy as a motivation for using local analysis. Running Ollama and Mistral:7b on a local machine can avoid sending the analysis prompt to a hosted model provider. That describes the intended inference path, not a guarantee that every component or deployment keeps all data on a private network.
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The article describes MQTT telemetry, HTTP dashboard delivery, and HTTPS as future work for the initial web server. Local model inference alone does not secure telemetry, dashboards, databases, backups, or other network traffic. The project materials do not establish a security review or prove that all data stayed local under every configuration.
What a reproduction would require
The project-specific setup notes identify Java JDK 23, ThingsBoard Community Edition configured locally, Ollama with Mistral, and the hardware sketch. These are the project’s stated prerequisites, not independently tested compatibility guidance.
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- An Arduino UNO R4 matching the project’s code and wiring
- A photoresistor and the wiring needed to read it on an analog input
- Workspace LEDs, an indicator LED, two buttons, a buzzer, and basic prototyping components
- A computer for the Java controller, ThingsBoard, Python middleware, and local Ollama model
The project descriptions differ in how specifically they identify Wi-Fi functionality. Verify the exact UNO R4 variant against the sketch and intended network setup before buying a board. Zanotti names an ESP32 as a possible future direction, but the sources do not show that an ESP32 version was built or tested.
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bE-More demonstrates an architecture for combining a simple sensor-based lighting rule, IoT telemetry, dashboards, and local AI analysis in a school project. Its energy-saving rationale is plausible as a design goal, but the published project descriptions provide no electricity readings, baseline comparison, operating duration, quantified savings, or carbon calculation. They therefore do not establish how much energy the prototype—or an office based on it—would save.
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- Know your air – An Alexa air quality monitor that makes it easy to understand what’s in your indoor air.
- Track and measure – Our indoor air quality monitor keeps tabs on 5 key factors: particulate matter (PM 2.5), volatile organic compounds (VOCs), carbon monoxide (CO), humidity, and temperature.
- Stay informed – Get an indication of current indoor air quality from the color-coded LED, and detailed information and an easy-to-understand air quality score in the Alexa app.
- Real-time alerts - Get notifications on your phone or announcements on Echo devices when Alexa detects poor indoor air quality.
The sources also do not establish field deployment, model accuracy, comparative performance, or independent contest verification. Those boundaries distinguish a useful demonstration from evidence that the system is ready for commercial use or has delivered a measured environmental result.
Future directions proposed by the author
Zanotti lists several ideas for further development: migrating the web server to HTTPS; improving prompts or using a newer or custom model; separating sensor and actuator roles across devices; exploring an ESP32 for a more deployment-oriented build; and adding an intermediary MQTT broker such as Mosquitto for bidirectional communication. These are proposed next steps, not features the sources establish as implemented.
Quick Recap
Sources
- Giovanni Zanotti’s account of bE-More on DEV Community, published September 24, 2026.
- GiZano’s bE-More GitHub repository, which documents the project architecture, hardware logic, and setup. Repository content may change.
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