Faculté des sciences et de la technologie
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Item A IoT-Enabled Electric Scooter Rental System with Docking Stations RAFEEQ(université de bordj bou arreridj, 2026-06-29) Mohamed Bouhalli; Mohamed Benrekia; Yassine Cherratrban mobility in Algerian cities suffers from a structural void in the one-to-three-kilometre trip range: public buses are too rigid, taxis too expensive, private cars too inefficient, and walking too constrained by climate and infrastructure. This thesis presents the design, im- plementation, and validation of Rafeeq, a smart electric scooter rental platform conceived to address this First and Last Mile problem in the Algerian institutional, economic, and cultural context. The platform integrates four interoperable subsystems. A connected fleet of Xiaomi M365 electric scooters is instrumented with a Raspberry Pi 3 Model B+ onboard computer paired with a SIM800L 2G GPRS modem for cellular uplink, and communicates with the scooter’s electronic speed controller through the Bluetooth Low Energy Nordic UART ser- vice. A network of physical docking stations equipped with ESP32 microcontrollers drives 12 V electromechanical Solenoid locks through TIP122 Darlington power stages, with full electrical protection including flyback diodes and LM7805 regulated rails. A cloud backend implemented in FastAPI and deployed on Render exposes thirty-two REST endpoints and maintains real-time WebSocket gateways with both the IoT endpoints and the mobile clients; user data is persisted in a PostgreSQL 16 database with strict ACID guarantees on all wallet operations. A cross-platform Flutter mobile application covers sixteen user screens across the full rental journey, with full right-to-left Arabic localisation, French, and English support. The system was validated through a six-family experimental campaign covering elec- tronic bench tests, BLE communication, mechanical lock characterisation, end-to-end latency measurement, backend load tests, and mobile user trials. The prototype achieves an end-to- end unlock latency of 2.83 seconds against a 3-second target, a Solenoid holding force of 73 N against a 50-N target, an onboarding completion time of 78 seconds against a 90-second target, and a station continuous power consumption of 84 W against a 120-W ceiling. The validation demonstrates that an integrated, locally developed smart-mobility platform can technically address the First and Last Mile problem in the Algerian context, and positions Rafeeq as a credible candidate for a subsequent pilot deployment at the University Mohamed El Bachir El Ibrahimi campus.Item Conception d'un réseau des capteurs intelligents pour la détection des gaz toxiques CO/Butane GASGUARD HOME (GGH)(Faculté des sciences et de la technologie, 2026-06-30) FETHALLAH Abderrahim; ABLAOUI Ziad; BENMALEK Hocine; ATOUI Mohamed Lamine; CHETIOUI MehdiCe mémoire présente la conception et la réalisation d’un système intelligent de détection des fuites de gaz domestiques, dénommé Gas Guard Home (Gas Guard Home). Basé sur une architecture distribuée IoT utilisant des microcontrôleurs ESP32- S3 et ESP32-C3 Super Mini, le système intègre des capteurs MQ-4 (méthane) et MQ-7 (monoxyde de carbone), une communication Radio-Fréquence 433 MHz entre noeuds secondaires et noeud central, ainsi qu’un mécanisme d’alerte instantanée via un Bot Telegram. En cas de détection d’une concentration anormale, le système active automatiquement une alarme sonore et une électrovanne d’arrêt de gaz, assurant ainsi une protection efficace des occupants.Item VIoT Solution: Computer Vision and IoT Integration in Monitoring and Inspection for Energy Conservation(Faculté des sciences et de la technologie, 2026-04-09) Seniguer AbderraoufThis thesis presents the design and development of a lightweight vision-based device that integrates Computer Vision (CV) and the Internet of Things (IoT) for real-time assessment of indoor illuminance distribution in buildings. The proposed system is intended for integration into daylight harvesting systems to enhance energy efficiency and improve occupants’ visual comfort. Unlike traditional approaches that rely on single photosensors, sensor grids, or High Dynamic Range (HDR) imaging, the proposed method uses visual information captured by a camera combined with machine learning techniques to estimate spatial illuminance levels across the observed scene. A dedicated data collection process was conducted using a controlled experimental setup, producing a dataset of approximately 12,000 samples covering illuminance levels from 0 to 2000 lux. From captured images, small pixel regions were extracted and associated with ground-truth measurements obtained from a calibrated lux meter. The illuminance mapping process is based on a two-stage machine learning pipeline composed of a Random Forest classifier and a Multilayer Perceptron regressor. This architecture enables accurate illuminance estimation while maintaining low computational complexity, allowing the entire system to operate directly on an embedded Raspberry Pi platform without cloud processing. In addition to illuminance estimation, the system incorporates a visual discomfort detection module for glare analysis. The device communicates with a web-based monitoring application through an IoT network, enabling real-time visualization and remote interaction. Experimental validation conducted in laboratory and real classroom environments demonstrated a Mean Absolute Error Percentage of approximately 8.7% when compared to reference illuminance sensors. The main contribution of this work lies in proposing a practical and deployable alternative to HDR imaging and photosensor grids for indoor illuminance mapping, combining computational efficiency, real-time performance, and IoT integration for smart building applications.Item Système de Sécurité biométrique pour une Smart Home / M2M-IoT(Faculté des sciences et de la technologie, 2025-06-30) Dahmouni Bessam; Semouma Ahmed WailCe projet vise à concevoir un système de sécurité biométrique intelligent pour les maisons connectées, en s’appuyant sur l’intégration des technologies émergentes telles que l’IoT (Internet des Objets) et le M2M (Machine-to-Machine). il repose sur l’utilisation de plusieurs composants électroniques interconnectés (capteur d’empreintes AS608, Arduino, clavier matriciel, capteur PIR, GSM SIM800L, servo-moteur, etc.) pour permettre une authentification sécurisée, une détection de mouvement, et l’envoi de notifications en temps réel (SMS, appels). Le système peut également être contrôlé à distance via SMS. Ce projet met en oeuvre à la fois des connaissances théoriques et pratiques, en combinant programmation embarquée, électronique et communication mobile pour renforcer la sécurité des habitations intelligentes.