Efficient Algorithms for Connected Dominating Sets in Ad Hoc Networks
Bibliographic record
Abstract
A Connected Dominating Set (CDS) can be used as a routing backbone in ad hoc networks and a data gathering/dissemination infrastructure in sensor networks. This virtual backbone can efficiently narrow down the search space for a route to the nodes in the CDS and thus be used by any routing protocol. Ideally, the backbone should constitute the smallest percentage of nodes in the network. However, finding a Minimum CDS (MCDS) is an NP-hard problem. In this paper, we propose an efficient distributed algorithm to construct a CDS in general graphs. The time and message complexity of our algorithm is linear in the number of nodes and degree of the network. Extensive simulations on Unit Disk Graphs (UDGs) show that this algorithm outperforms the distributed algorithms proposed in terms of the size of the CDS. We also present a local implementation of our algorithm in location-aware UDGs. Our algorithm provides the flexibility to arbitrarily adjust the tradeoff between the degree of locality and the size of the generated CDS.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".