Trajectory-Assisted Municipal Agent Mobility: A Sensor-Driven Smart Waste Management System
Why this work is in the frame
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Bibliographic record
Abstract
Ubiquity, heterogeneity and dense deployment of sensors have yielded the Internet of Things (IoT) concept, which is an integral component of various smart spaces including smart cities. Applications and services in a smart city ecosystem aim at minimizing the cost and maximizing the quality of living. Among these services, waste management is a unique service that covers both aspects. To this end, in this paper, we propose a WSN-driven system for smart waste management in urban areas. In our proposed framework, the waste bins are equipped with sensors that continuously monitor the waste level and trigger alarms that are wirelessly communicated to a cloud platform to actuate the municipal agents, i.e., waste collection trucks. We formulate an Integer Linear Programming (ILP) model to find the best set of trajectory-truck with the objectives of minimum cost or minimum delay. In order for the trajectory assistance to work in real time, we propose three heuristics, one of which is a greedy one. Through simulations, we show that the ILP formulation can provide a baseline reference to the heuristics, whereas the non-greedy heuristics can significantly outperform the greedy approach regarding cost and delay under moderate waste accumulation scenarios.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it