A Survey of Secure Routing Protocols in Multi-Hop Cellular Networks
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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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".