Call for Papers: Special issue on security challenges in emerging and next‐generation wireless communication networks (security and communication networks)
Bibliographic record
Abstract
Wireless network technologies are undergoing rapid advancements. Researchers are currently envisioning different attractive properties of wireless systems such as the ability to self-organize, self-configure, self-heal, self-manage and self-maintain. Different wireless networks having the potential to offer cost-effective home and enterprise access networking solutions are being researched. Concepts such as dynamic spectrum access, convergence, unified network architectures and seamless service access in heterogeneous networks are gaining widespread popularity. Technologies such as Wireless Mesh Networks (WMNs), WiFi, WiMAX, Bluetooth, ZigBee, RFID, IEEE 802.20, IEEE 802.22 and software defined radio are becoming increasingly popular. Even though these technologies hold great promises for our future, there are several research challenges that need to be addressed. A significant portion of these research challenges are attributed to security and privacy issues in these kinds of networks. This Special Issue aims to publish high quality research papers related to the recent advances in the security and privacy of different emerging and next-generation network technologies. Topics of interest include, but are not limited to, the security and privacy issues and challenges in the following:
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.224 | 0.193 |
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".