Performance evaluation of lifetime-aware routing in Wireless Sensor Networks with practical design considerations
Bibliographic record
Abstract
Energy resource management is considered essential in battery-operated Wireless Sensor Networks (WSNs). Radio communication circuitry is a significant power consumer in a WSN. Therefore, it is desired to design an efficient routing algorithm which conserves the battery power as much as possible, resulting in a longer network lifetime. Lifetime-aware routing protocols were suggested to distribute the flow of information through different routes. These works were mainly based on simplified assumptions which ignored practical considerations and assumed perfect knowledge of the channel conditions and the ability to adjust the transmission power accordingly. In this work, the practical case of a constant transmission level is applied to the Flow Augmentation algorithm and the lifetime performance of the proposed modified Flow Augmentation algorithm called Constant Transmission Power Flow Augmentation (CTPFA) is evaluated and compared with the original Flow Augmentation. The CTPFA analysis proves that lifetime of a WSN with nodes that have constant transmission power decreases significantly due to the excess transmission power used. Methods to improve the lifetime performance are discussed.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".