k-Connectivity in Wireless Sensor Networks: Overview and Future Research Directions
Bibliographic record
Abstract
$k$-connectivity is a strong notion of robust connectivity. Indeed, in a$k$-connected network, each node has k disjoint paths to all the other nodes in the network. Therefore, even in the case of$\mathrm{k}-1$node/link failure(s), a k-connected wireless sensor network (WSN) remains connected because each node still has, at least, one path to the rest of the surviving nodes. Networks with higher$k$values are, typically, more reliable and fault tolerant than those with lower k values. In this study, we present a systematic and dedicated overview of WSN k-connectivity problem. We, first, outline the k-connectivity detection problem (i.e., determining the k value of a network). Second, we explore the k-connected network deployment problem. Third, we dissect the restoration problem that addresses the rehabilitation of a deteriorated network to restore its original k value. Built upon the provided foundations, we identify and discuss a rich set of important and promising open research problems along with pointers to possible solution approaches.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.016 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".