Proceedings of the 2nd ACM international workshop on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks
Bibliographic record
Abstract
Wireless ad hoc, sensor, along with ubiquitous networks have recently witnessed a dramatic growth, and this trend is likely to continue for the foreseeable future. However, as such networks become more widespread and complex, performance modeling and evaluation will play a crucial part in their design process to ensure their successful deployment and exploitation in practice.In this context, the Second International Workshop on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks (PE-WASUN) aims at bringing together scientists, engineers, and practitioners to share and exchange their experience, discuss challenges, and report state-of-the-art and in-progress research on all aspects of wireless ad hoc, sensor, and ubiquitous networks, with a specific emphasis on their performance evaluation and analysis. The workshop is held in conjunction with the 8th ACM/IEEE International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM), and takes place in Montreal, one of the most dynamic towns in the world.The general interest in the topics of the workshop is testified by the number of submissions received: 99 papers form major research groups worldwide. After a careful review process, 33 papers were accepted for regular presentations at the workshop, which represents a 31% acceptance rate. Moreover, following recommendations from the TPC members, 8 papers were accepted as short presentations and 12 as posters. We believe that the range of topics in these papers provide an interesting and complete view of the state-of-the-art in the field of performance evaluation of wireless ad hoc, sensor, and ubiquitous networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.009 |
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".