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An AI- and IoT-Enabled Smart Waste Management Framework for Real-Time Waste Classification and Collection Optimization

Year: 2026
Type: article
Abstract: The amount and variety of municipal solid trash have expanded due to rapid urbanization and shifting consumption habits, making it challenging for traditional systems to collect and segregate waste in a timely manner. When other containers are getting close to overflowing before the next scheduled visit, fixed collection schedules may cause needless journeys to partially filled bins. In this paper, a framework that combines image- based trash classification, real-time smart-bin monitoring, overflow-risk prediction, priority scoring, and dynamic collection-route optimization with AI and IoT capabilities is proposed. While IoT sensors offer operational data like fill level and optional temperature or weight, a deep-learning classifier is utilized to detect trash categories from photos. To calculate overflow risk, machine-learning models use both recent and past sensor readings.The decision engine then assigns priorities to a route-optimization module based on the urgency of the bins. As complementing computer-vision benchmarks, TrashNet and TACO are suggested, with the former offering photos for controlled classification and the latter offering litter in contextual settings [5], [6]. Results presented in the literature are just used as benchmarks; the suggested system is not claimed to have any artificial experimental precision. For assessing categorization quality, prediction performance, route efficiency, service reliability, and deployment cost, the framework offers a repeatable experimental design. In order to transition waste management from a fixed, reactive process to a predictive and adaptive system, the study offers an end-to-end design.
DOI: 10.5281/zenodo.23457667
Language: English
Open Access status: diamond
OpenAlex ID: W7216767176
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