Reliability model for extending cluster lifetime using Backup Cluster Heads in cluster-based Wireless Sensor Networks
Bibliographic record
Abstract
In cluster-based two-tier Wireless Sensor Networks (WSNs), the cluster-head nodes (CHs) gather data from sensors and then transmit to the base station. When these cluster head nodes start to die, the coverage of the respective clusters is lost and it leaves the region unmonitored. Even if the CHs are rotated and reassigned after some time, until the next rotation that cluster in question will be out of cluster head, causing a loss of information and loss of coverage. To select a Backup Cluster Head (BCH) is suggested for those CHs which are close to deplete their energy [1]. When the CH dies, BCH takes over the responsibility and continues to work as a new cluster head. In this paper we present an analytical model of cluster reliability in cluster-based WSN using BCH, based on Markov chain model. We use non-homogeneous Markov process, along with Forward Chapman-Kolmogorov equations to illustrate the cluster monitoring period in a finite three state space model. We test the accuracy of the model by applying the probabilities of failure of CH and BCH nodes, for a fixed number of sensor nodes in a cluster. The results show that the presented model is able to match the behaviour of the cluster state transition accurately and validates the simulation results and analysis published in [1].
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".