The DBF-based semantic service discovery for mobile ad hoc networks
Bibliographic record
Abstract
Service discovery is a significant area of research in mobile ad hoc networks, where mobile nodes are allowed to exchange services and utilize each other's services. Furthermore, in ad hoc environments, the service discovery process is enhanced by making use of semantic descriptions of services. Semantic-based user requests are used to carry out discovery process to satisfy the requester's service requirements. In this paper, we exploit the OWL-DL to describe services and the dynamic bloom filter (DBF) to improve the discovery process. The integration of the DBF and distributed service directories, and the utilization of the hybrid service matchmaking approach provide an adaptive, flexible and efficient discovery of services. Simulations were carried out to evaluate a DBF-based service discovery model in terms of the service discovery overhead, the Dynamic Bloom filter's efficiency, the network scalability, the service discovery success ratio and the average response time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".