Smart Waste Management Systems: An IoT and AI-Driven Approach for Urban Sanitation
Bibliographic record
Abstract
The explosive rise in city dwellers has created a growing scenario of waste management, presenting tremendous environmental, logistical, and health dilemmas to the urban regime. This study focuses on how a more innovative waste management system could be designed and implemented by integrating the IoT devices and Artificial Intelligence (AI) technologies to increase efficiency, transparency, and sustainability of a city's sanitation. The smart bins that would have been introduced in the proposed system would have incorporated IoT and sensors to help understand the current levels of waste, as well as the temperature and humidity of each bin. The transmission of these data is facilitated by low-power, wide-area network technologies such as LoRaWAN and NB-IoT to a centralized waste management system. Such AI algorithms include linear regression, random forest, and A* search, which analyze past and real-time data to refine waste collection schedules, estimate fill levels, and create smart route plans. Moreover, AI models that rely on computer vision can automate the segregation of waste into categories of allowable, organic, and non-recyclable materials with minimal human input. To ensure accountability and track waste throughout its lifecycle, blockchain technology is incorporated, providing tamper-proof records from collection to final disposal. This essay illustrates how the combination of IoT, AI, and blockchain has the potential to reinvent one of the oldest premises of waste management into a data-driven, flexible, and ecologically conscious system that embraces the circular economy concept. Pilot implementation has demonstrated how operational costs are reduced, the recycling rate is increased, and how fuel usage and emissions are significantly decreased.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".