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Multi-sensor pattern recognition and real-time data processing for autonomous smart waste management
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Urban waste management systems require intelligent monitoring solutions that can process multi-modal sensor data in real-time while operating autonomously. This paper presents RAICYCLE, a comprehensive smart waste management system that integrates advanced pattern recognition techniques with real-time operating systems for autonomous urban deployment. The system employs eight BME688 environmental sensors with distinct heater profiles (50°C to 350°C) for volatile organic compound (VOC) pattern classification, combined with VL53L0X Time-of-Flight sensors and GPS tracking. The embedded architecture utilizes FreeRTOS dual-core task scheduling on ESP32 microcontrollers, enabling concurrent sensor data processing, LoRaWAN communication, and system monitoring. Data serialization through Protocol Buffers achieves 70% payload reduction compared to JSON formats, while kinetic energy harvesting from container lid movements enables autonomous operation. The system demonstrates effective real-time processing of 32 dimensional feature vectors for waste classification and environmental monitoring in urban deployments.
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Jvarsheishvili, Mariam; Ibrahim, Ahmad; Ahmadi, Mahdia; Igrejas, Getúlio; Soares, Caio; Izidorio, Felipe; Lopes, Rui Pedro; Rodrigues, Pedro João (2025). Multi-sensor pattern recognition and real-time data processing for autonomous smart waste management. In RECPAD 2025 - 31st Portuguese Conference on Pattern Recognition. Aveiro, Portugal