Bibliographic record
Abstract
Mobile Ad hoc NETworks (MANETs) are networks that do not need to be pre-configured. They are composed of transient nodes connected through wireless interfaces. Due to their flexibility, the ease to build and the associated low cost, they are gaining more and more momentum. They are also seen as part of the fourth generation wireless networks. New applications, such as conferencing, are emerging for such networks. Conferencing enables a set of applications such as audio/video conferencing, debating, distance-learning and multi-party gaming. The implementation of conferencing in MANETs is not an easy task due to scarce network resources, heterogeneous devices, frequently changing topology and unstable wireless connections. It challenges each technical aspect of conferencing: signaling, media handling and conference control. Signaling is the control component of conferencing. It handles the session initiation, modification and termination. In this work, we focus on signaling for conferencing in MANETs. Two types of MANETs are considered: standalone MANETs and integrated MANETs/3G networks. Background information is provided, requirements are derived and the state of the art, including signaling protocols such as SIP and H.323, are reviewed. Since there is no existing solution that meets all of the derived requirements, we propose a novel cluster-based signaling architecture that meets the requirements of signaling for standalone MANETs. The clusters are application-layer clusters that are dynamically created and deleted for a conference. We also propose a signaling architectures for integrated MANETs/3G networks. The solution is based on conference gateways. We implement the architectures using SIP extensions. Experimental results are obtained from prototypes and OPNET based simulations. In the prototype, we built the signaling system on a small scale network using IEEE 802.11 ad hoc settings. In the OPNET simulation, we use MANET features. From experiments, we found that clustering is a very promising approach for solving signaling problems in MANETs. Being aware of several performance issues of our signaling systems, we further propose optimization schemes that are based on cross-layer design. We also implement some of these schemes and apply them to our signaling systems. The evaluation shows that the schemes significantly improve the signaling performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".