LPWAN Smart Waste Bin With On-Device AI Trained on Synthetic Data

Abstract

The construction industry is a major contributor to global solid waste, yet circular economy initiatives are often impeded by inefficient logistics and improper sorting. Existing monitoring solutions, typically reliant on single-point distance sensors, lack the granularity to identify waste composition, which is essential for effective valorization. This work proposes an energy-efficient, image-based smart bin system enabled by Low Power Wide Area Networks (LPWAN) that utilizes on-device Artificial Intelligence (AI) to simultaneously estimate fill levels and classify waste materials. To address the scarcity of labeled field data, a synthetic data generation strategy using generative AI was employed to create photorealistic training datasets. A lightweight MobileNetV2 model was optimized via quantization and deployed on an ESP32 microcontroller. The system architecture prioritizes energy conservation by performing inference at the edge and transmitting only compact results, reserving full image transmission for a closed-loop active learning pipeline. Energy profiling demonstrated that on-device inference drastically reduces active radio time compared to raw image streaming, significantly extending battery life. The work validates the feasibility of Edge AI for scalable construction and demolition waste monitoring and highlights the potential of synthetic data to overcome data scarcity bottlenecks.