On Constructing Interference-Aware k-Fault Resistant Topologies for Wireless Ad hoc Networks
Bibliographic record
Abstract
The most fundamental problem in wireless ad-hoc networks is to determine a connected communication subgraph that satisfies desirable topological properties by assigning appropriate transmission power to each node. Some of the properties considered by a vast majority of researchers include minimum-energy, fault tolerance, minimum interference, and bounded node degree. However preserving two or more combination of these properties at the same time is harder to achieve and often overlooked by the research community. In this paper, we propose a topology control algorithm that preserves connectivity and combines two other important properties, e.g, (a) minimum interference, and (b) fault tolerance. Interestingly, these two properties create a fundamental tradeoff by running against each other. In one end, interference can be reduced by dropping links that create high interference. On the other end, dropping too many links make a network more susceptible to node failure/departure. Thus dropping high interference links while keeping the network significantly connected is an important goal to achieve. We achieve such goal by formulating the problem of constructing minimum interference path preserving and fault tolerant wireless ad hoc networks under the same framework and then provide algorithms, both centralized and distributed with local information, to solve the problem. Moreover, for the first time in literature, we conceive the concept of fault tolerant interference spanner and provide a local algorithm to construct such spanner of a communication graph. Key words: graph theory, network topology, interference, fault tolerance, wireless ad hoc networks, interference-aware topology, stretch factor, sparse topology. 1
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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 | 0.005 |
| 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 | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".