Secure Multipath Routing Algorithm for Device-to-Device Communications for Public Safety over LTE Heterogeneous Networks
Bibliographic record
Abstract
This article proposes a new approach for secure communications Device-to-Device (D2D) if unable to apply network coding transmissions within LTE small cells. Our new algorithm called Secure Load Balancing Selective Ad hoc On-demand Multipath Distance Vector (LBS-AOMDV) is based on a multipath coded information transmissions, Data Splitting and Data Shuffling schemes. The objective of this study is to reduce the impact of confidentiality attack within Wireless Mesh Networks (WMN) by preventing eavesdroppers to obtain significant information from those transmitted by legal users, while ensuring a high level of quality of Service (QoS) for transmitted traffic. The simulation results show that Secure LBS-AOMDV increases the level of security in the wireless network compared to the LBS-AOMDV approach without adding traffic control.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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".