Performance Enhancement of Routing Protocols in Mobile Ad hoc Networks
Bibliographic record
Abstract
This article proposes to use virtual backbone structure to handle control messages in ad hoc networks. This structure is effective in reducing the overhead of disseminating control information. In the first part, the approach to build the virtual backbone on the setup phase is presented. The construction of backbone is based on the Minimum Connected Dominating Set (MCDS). The novelty is in the way on finding the MCDS. A Linear Programming approach is used to build a Minimum Dominating Set (MDS). Then, a spanning tree algorithm is applied to provide the MCDS. A theoretical analysis based on probabilistic approach is developed to evaluate the size of MCDS. Different techniques of diffusion in ad hoc networks are presented and compared. The flooding technique is simple and efficient, but it is expensive in term of bandwidth consumption and causes broadcast storm problem. Simulation results show that technique using virtual backbone performs flooding and it is compared to MPR (Multipoint Relay). The second part of this article presents a distributed procedure to maintain the backbone when the mobility of terminals is introduced. A maintenance procedure will be executed by the node which changes its position. This procedure is distributed and guarantees the node connectivity to the backbone. The authors believe that the maintenance of the backbone with small size will be more effective. Simulation results show the performance of this procedure when mobility and scalability are considered.
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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 | 0.002 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".