Energy Efficient Routing Design for Target Tracking in Wireless Sensor Network
Bibliographic record
Abstract
The wireless sensor networks (WSNs) provides an advance way for connection of various applications. Most of the applications use the wireless sensor network to transmit the information from the device to the main base station. The main operation of the wireless sensor network is to first sense the data using the sensor, then collect the data and finally transmit to the required base station. The data is transmitted in a timely manner such that the other wireless sensor network transmitting the information should not have any problem. During the transmission of the data the main objective of the wireless sensor network device is to provide reliability to the information which is being sent with less latency and to reduce the energy consumption in order to increase the lifespan of the wireless sensor network. Therefore, this paper presents an Energy Efficient Routing for the Target Tracking (EER-TT) in the wireless sensor network which provides reliability, less latency and reduces the energy consumption during the transmission of the information to the base station. Proposed model also provides a cluster selection method for the routing of the wireless sensor network devices. The results attained show that our model EER-TT shows better results when compared with the existing routing-based models.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".