A Survey on Energy Efficient Wireless Sensor Networks for Bicycle Performance Monitoring Application
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
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.
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.
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