Achieving Reliability Over Cluster-Based Wireless Sensor Networks Using Backup Cluster Heads
Bibliographic record
Abstract
Wireless Sensor Networks (WSN) are becoming a viable tool for many monitoring applications. These applications may be of critical nature where the transportation of the information of events from the region of interest to some base station is crucial, where the data loss can not be tolerated. In the cluster-based two-tier WSN, where cluster-head nodes gather data from sensors in their clusters and then transmit to base station, when cluster head nodes start to die, the coverage of those clusters is lost and it leaves the region unmonitored. Even if the cluster heads are rotated and reassigned after some time, until the next rotation that cluster in question will be out of cluster head and will lose coverage. A lot of information is lost which is sensed and sent by the sensor nodes of the cluster to the dead cluster head. We propose here to select backup cluster heads (BCHs), for those cluster heads which are close to deplete their energy. The cluster head, when about to die, sends an SOS with the gathered information until then, to the respective BCH which takes over the responsibility and continues to work as a new cluster head. To evaluate the effect and results of BCH we used LEACH-C protocol and compared the data loss ratio with and without a BCH.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".