<![CDATA[Purpose – Despite advances in IoT-enabled waste monitoring, existing solutions generally fail to integrate real-time bin status information with adaptive route optimization, resulting in inefficient collection operations. This study aims to design and implement an integrated system that leverages real-time waste data to facilitate intelligent, data-driven route optimization for improved waste collection operations.Methods –This study presents an ESP32-based smart waste system using reed switch event-driven control and deep-sleep mode for energy efficiency. Waste levels were estimated using the arithmetic mean fusion of four VL53L0X sensors. A cloud-based MQTT-over-TLS architecture enables secure real-time communication, whereas a priority-based nearest-neighbor routing algorithm is evaluated across 150 nodes.Findings – The results demonstrate that the proposed system provides accurate waste-level estimation with a mean error of 1.98%, significantly reduces energy consumption by 90.9% through deep-sleep operation, and supports near-real-time communication with an average latency of 4.66 s. Moreover, the priority-based route optimization strategy decreased the travel distance by 42.7%, ensured the immediate servicing of all full-status bins, and maintained operational feasibility within a fleet capacity of 2,700 L.Research implications – The evaluation results demonstrate the feasibility of integrating real-time monitoring and adaptive route optimization for smart waste management. Future research should extend the validation to large-scale real-world deployments and incorporate road network-based routing models to enhance operational realism and optimization accuracy.Originality – This study proposes an integrated smart waste platform that combines energy-efficient event-driven sensing, dynamic priority-based nearest-neighbor routing, and hardware-assisted digital twin validation for scalable and cost-effective waste management evaluation.]]>
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