Modeling and Managing the Trust for Wireless and Mobile Ad Hoc 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
With the growing popularity of wireless mobile ad hoc networks (MANETs), many security concerns have arisen from MANETs especially in that misbehaving nodes pose a major threat during the construction of a trusted network. A reputation-based trust system can track the behavior of nodes and thereby proceed by rewarding well-behaving nodes and punishing misbehaving ones. However, existing techniques are usually either energy-consuming or complicated since the relevant reputation information is propagated throughout the network. In this paper, we propose a novel trust computation and management system, called TOMS, which not only establishes the new concepts of trust and community but also includes both the trust computation model and trust management mechanism. Using the results of extensive simulations, we highlight the effectiveness and efficiency of our trust system in comparison to other trust schemes in traditional protocols. Thus, TOMS is shown to be dynamic, distributed, and efficient, as well as sensitive to suspicious behaviors and peers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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