An Overlay Network for a SIP Servlet-Based Service Execution Environment in Stand Alone MANETs
Bibliographic record
Abstract
Mobile ad hoc networks (MANETs) are an example of new networks that are gaining more and more momentum because of their flexibility and easy deployment. Service execution is an important phase in the service life cycle. SIP servlets are a promising service provisioning framework for MANETs. However, MANETs are very challenging because of their characteristics (e.g. node heterogeneity, no permanent centralized entity, transient links). Using SIP servlets in MANETs requires distributing the servlet engine first, due to resource constraints. As the distributed components need to interact, a cooperation scheme including self-organization and self-recovery is needed. Overlay networks are an attractive approach to cooperation because of their scalability and flexibility. In this paper we propose an overlay network for a SIP servlets-based service execution environment in MANETs. We assume a servlet engine distribution scheme that we have proposed in previous work. The architecture of the overlay network and the related procedures are presented. Results of a formal validation using Promela/SPIN are discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".