Energy Efficient Low Latency Routing Design for Target Tracking Applications of Wireless Sensor Network
Why this work is in the frame
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Bibliographic record
Abstract
Target tracking is the greatest important applications in Wireless Sensor Networks (WSNs). The wireless sensor network applications have been increasing since the IoT has been established. Most of the applications have various kind of sensors to transmit the information from one source to another. The basic operation of a wireless sensor network is to sense the data, collect the data and transmit the data from time to time whenever the base station requires the data for evaluation. Improving the reliability, performance for the collection of the data is the main role of the wireless sensor device. Moreover, the objective of the wireless sensor network device is to minimize the latency and improve the energy efficiency in order to provide more reliability is a major performance metric for provisioning WSNs. In this paper, we have presented an Energy Efficient Low Latency Routing (EELLR) design for Target Tracking (TT) Applications of Wireless Sensor Network. This model provides reliability and has a better performance in terms of communication overhead, energy efficiency and packet processing latency reduction when compared with the existing routing-based models.
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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 | 0.001 | 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 | 0.001 | 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