A Survey on Energy Efficient Wireless Sensor Networks for Bicycle Performance Monitoring Application
Bibliographic record
Abstract
Wireless sensor networks (WSNs) have greatly advanced in the past few decades and are now widely used, especially for remote monitoring; the list of potential uses seems endless. Three types of wireless sensor technologies (Bluetooth, ZigBee, and ANT) have been used to monitor the biomechanical and physiological activities of bicycles and cyclists, respectively. However, the wireless monitoring of these activities has faced some challenges. The aim of this paper is to highlight various methodologies for monitoring cycling to provide an effective and efficient way to overcome the various challenges and limitations of sports cycling using wireless sensor interfaces. Several design criteria were reviewed and compared with different solutions for the implementation of current WSN research, such as low power consumption, long distance communications, small size, and light weight. Conclusions were drawn after observing the example of an advanced and adaptive network technology (ANT) network highlighting reduced power consumption and prolonged battery life. The power saving achieved in the slave node was 88–95% compared to the similar ANT protocol used in the medical rehabilitation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".