Energy Efficient Bike-Share Tracking System with BLE Beacons and LoRa Technology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Around the world, vast improvements in public transportation methods in urban environments have been made. However, in densely populated areas, the bicycle remains a very useful means of transportation. Its small size and minimal environmental impact are the critical factors that maintain its relevance. Moreover, the advancement of connected devices and sharing-based services have allowed private vendors to develop bike-sharing programs, giving millions access to bike transportation around the globe. These bike-sharing programs rely on the user to check out and return the bike to a designated bike-holding station. With the growth of Internet of Things (IoT) services and wirelessly connected devices, there is a major benefit in enabling vendors to track their bicycle assets. Satellite navigation has come a long way, however, it requires a large power overhead. This paper proposes an energy-efficient bicycle tracking system that utilizes bicycle powered Bluetooth Low Energy (BLE) beacons and Long Range (LoRa) type base-stations in order to track and maintain a real-time location-based inventory of all assets. The BLE beacons are used to track individual bicycle assets based on Received Signal Strength Indicator (RSSI) proximity and the LoRa base stations exploit longer range communication capabilities to transmit asset location information between each other, for added management capabilities. Preliminary proximity estimations using BLE beacons in an urban outdoor environment show promising results with proximity accuracy consistently under 2 meters.
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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 | 0.000 | 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