Bus‐Based Sensor Deployment for Intelligent Sensing Coverage and k‐Hop Calibration
Bibliographic record
Abstract
ABSTRACT Drive‐by sensing is a promising concept that employs public transport as a mobile sensing platform to achieve high spatio‐temporal coverage for urban sensing tasks. At the same time, the low‐cost nature of mobile IoT sensors necessitates their more frequent calibration to ensure data accuracy and reliability. Manual or lab‐based calibration of a large number of mobile sensors may no longer be feasible and thus new approaches for automatic calibration are needed. Most prior work on optimal mobile sensor deployment focuses on coverage aspect without considering the sensor calibration. In this study, we present a joint approach for optimising the placement of bus‐based sensors for maximising the total unique sensing area and combining the optimal reference sensors geo‐placement for maximising k‐hop calibrate requirements on the selected routes. A metric‐based system developed in our model uses geographical set operations which includes both spatial and temporal joins to quantify the contribution of each bus route and rank them accordingly. We formulate the coverage optimisation problem as a mixed integer linear program (MILP) solve it with a greedy algorithm, and demonstrate this method’s potential using real‐world bus‐transit data from Toronto, Canada and Manchester, UK. Our approach involves a metric‐based system which quantifies each bus route unique coverage contribution for determining an optimal set of bus routes and bus stops for bus‐based and reference sensor deployment, to minimise sensor network costs and maximise spatio‐temporal coverage. The comparison with a random baseline algorithm indicates that our method outperforms in terms of deployment and coverage efficiency. Our results also include the potential of our weighted method in improving drive‐by sensing for air quality monitoring by comparing it with a separate benchmark scheme with different criteria.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 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".