A Survey of Secure Routing Protocols in Multi-Hop Cellular Networks
Why this work is in the frame
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Bibliographic record
Abstract
Multi-hop networks are expected to be an important part of 5G mobile systems. In a multi-hop cellular network (MCN), the nodes assist each other in relaying packets towards their destinations. The structures and operations of multi-hop networks make them particularly vulnerable to certain types of attack. Hence, security measures to counter these attacks are necessary. In this paper, we provide an overview of the secure routing protocols for multi-hop networks and classify them into MCN Type-1 and Type-0 categories for device-to-device communications, and the Internet-of-Things category for machine-to-machine communications. Our focus is on the applied cryptographic techniques and the security mechanisms in secure routing protocols. We propose an evaluation framework incorporating different security aspects, vulnerabilities, and levels of deployability to compare a number of secure routing protocols. Moreover, we review the secure routing paths in software-defined networking as a solution for existing challenges in multi-hop networks. Some open research problems are highlighted as possible directions for future studies.
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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 | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.006 | 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 it