Deployment-based Solution for Prolonging Network Lifetime in Sensor Networks
Bibliographic record
Abstract
Enhancing sensor network lifetime is an important research topic for wireless sensor networks. In this paper, we introduce a solution for prolonging the lifetime of sensor networks that is based on a deployment strategy. In our proposal, data traffic is directed away from the network center toward the network peripheral where sinks would be initially deployed. Sinks stay stationary while collecting the data reports that travel over the network perimeter toward them. Eventually perimeter nodes would be exposed to a peeling phenomenonwhich results in partitioning one or more sinks from their one-hop neighbors. The partitioned sinks move discrete steps following the direction of the progressive peeling. The mechanism maintains the network connectivity and delays the occurrence of partition. The performance of the proposed protocol is evaluated using intensive simulations. The results show the efficiency (in terms of both reliability and connectivity) of our deployment strategy with the associated data collection protocol.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".