IoT-Based Smart Waste Management System: A Solution for Urban Sustainability
Bibliographic record
Abstract
Abstract: Environmental waste is still a debacle in our daily lives as well as for the world. Most waste management systems do not have monitoring functionalities, which results in inefficient collection routes, higher processing costs, and environmental damage. These figures only continue to grow. In modern metropolises that are home to most population growth, people are increasingly turning to outdated systems that can no longer handle the amount of waste being produced. These systems then turn out to be too costly, which becomes a major and unsolved problem in the long run. This paper presents a secure, low-cost IoT smart waste system that integrates five sensors, HC-SR04 ultrasonic (fill-level), HX711 load-cell (weight), DHT22 (temperature/-humidity), MQ-135 gas (air quality) and Ublox NEO-6M GPS, around an Arduino-ESP8266 core and Firebase cloud analytics. A 30-day field trial on 12 municipal bins achieved 85% fill-level accuracy, < 3% mean absolute error in weight, and 100% detection of hazardous temperature (≥ 40°C) or humidity (≥ 70% RH). GPS-guided routing cut truck mileage by 20 % and CO₂ emissions by 18% versus fixed schedules (p < 0.01, paired-sample t-test). These results confirm that multi-sensor IoT retrofits can reduce operational costs while improving public-health safeguards, providing a replicable blueprint for sustainable smart-city waste infrastructure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".