Vehicular Traffic Monitoring Using Bluetooth Scanning Over a Wireless Sensor Network
Bibliographic record
Abstract
The ubiquitous nature of Bluetooth equipped devices has made it opportunistic to scavenge information that can be repurposed for applications other than initially intended. One such opportunity is in vehicular traffic monitoring, whereby sampling of Bluetooth radios serve as proxies for vehicles and consequently for traffic density and flow. This paper discusses a complete data collection system developed at the University of Manitoba that utilizes a variety of wireless networking technologies and devices to collect inferred traffic data at an intersection along a major thoroughfare in an urban setting. Specifically, a wireless sensor network of slave probes was designed and implemented with the objective to collect Bluetooth device information for this purpose. To facilitate easy setup and a long battery life, a solar-powered probe design was investigated. Data from each slave probe is communicated to a master node through XBee communication, where it is stored on a secure digital (SD) memory card before being transmitted to a central server every five minutes over a global system for mobile communications (GSM) cellular network. The server parses the data received and stores it in a database. Consumer and corporate websites may then access this database to display archived data or current data in real-time to various users.
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 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 | 0.000 | 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 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".