Resource Management Based on Security Satisfaction Ratio with Fairness-Aware in Two-Way Relay Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Information security has been received more and more attention for next-generation wireless sensor networks. In this paper, we consider the problem of resource management based on security satisfaction ratio with fairness-aware in two-way relay networks. Multiple source nodes exchange information with the help of relay node in the presence of an eavesdropper, and diverse security requirements are taken into account with coexistence of security users and normal users. The joint problem of power allocation, and subchannel pairing and allocation aims to maximize the security satisfaction ratio for legitimate users subject to limited power and subchannel constraints. We model the security resource management problem as a mixed integer programming problem, which is decomposed into three subproblems, distributed power allocation, distributed subchannel allocation, and distributed subchannel pairing, and then solved it in constraint particle swarm optimization (CPSO), binary CPSO (B_CPSO), and classic Hungarian algorithm (CHA) method, respectively. Moreover, a suboptimal subchannel pairing algorithm is proposed to reduce the computational complexity compared with the CHA. Simulations are conducted to evaluate the effectiveness of the proposed algorithms.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it