Cloud-IoT and AI-based Intelligent Waste Monitoring and Collection System for Sustainable Urban Development
Bibliographic record
Abstract
Urban waste management has increasing problems due to rapid population increase and ineffective collection processes, resulting in overflow, environmental contamination, and decreasing resources. The proposed study handles these issues by offering an intelligent Cloud-IoT and AI-driven garbage monitoring and collecting system aimed at sustainable urban growth. The system features intelligent bins outfitted with sensors that relay real-time data to cloud platforms, where AI algorithms categorise trash types, predict waste generation, and dynamically optimise collection routes. The suggested framework utilises deep learning techniques, including CNNs and LSTMs, for enhanced accuracy and adaptability, in addition to previous study that depends on traditional machine learning algorithms. Experimental results indicate a classification accuracy of 97.8%, precision of 97.4%, and a R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score of 0.94 in trash forecasting, outperforming baseline methods such as SVM and Random Forest. Route optimisation decreases collecting time by 28%, so improving operating efficiency and reducing carbon emissions. This integrated strategy enhances trash management efficiency while advancing environmental sustainability objectives, providing a scalable and economical solution for smart cities. The research's practical consequences highlight its capacity to revolutionise municipal waste management, promoting cleaner and healthier urban settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".