Performance evaluation of MANETs virtual backbone formation algorithms
Bibliographic record
Abstract
Mobile Ad hoc networks (MANETs) are gaining increased interest as the technology that potentially will make mobile computing a tangible reality. The self-control, selforganization, topology dynamism, and bandwidth limitation of the wireless communication channel make MANETs' implementation a challenging task. The Connected Dominating Set (CDS), a.k.a. virtual backbone or spine, has been proposed to facilitate routing, broadcasting, and establishing a dynamic infrastructure for distributed location databases in MANETs. Minimizing the CDS cardinality simplifies the MANET's abstracted topology and allows for using shorter routes. Since finding the minimum size CDS (MCDS) is NP-complete, approximation algorithms and heuristics are used to tackle this problem. It has been reported that localized CDS creation algorithms run fast and generate light signaling overhead; examples of these algorithms are Wu and Li algorithm and its Stojmenovic variant, the MPR algorithm, and Alzoubi algorithm. Some theoretical performance analysis of these algorithms is presented in the literature; however, this analysis has not been validated across any physical or at least simulation-based measures. Moreover, the presented theoretical performance analysis has overlooked the cost of maintaining the CDS in the presence of topology changes. In this paper, a simulation-based evaluation of the performance of these algorithms is conducted using the ns-2 network simulator. Moreover, the effects of mobility rates and network size on the performance of each algorithm is investigated.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".