Replica Dissemination and Update Strategies in Cluster‐Based 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
A mobile ad hoc network (MANET) is a collection of wireless mobile nodes that forms a temporary network without the aid of a fixed communication infrastructure. Since every node can be mobile and network topology changes can occur frequently, node disconnection is a common mode of operation in MANETs. Providing reliable data access and message delivery is a challenge in this dynamic network environment. Caching and replica allocation within the network can improve data accessibility by storing the data and accessing them locally. However, maintaining data consistency among replicas becomes a challenging problem. Hence, balancing data accessibility and consistency is an important step toward data management in MANETs. In this paper, we propose a replica‐based data‐storage mechanism and undelivered‐message queue schemes to provide reliable data storage and dissemination. We also propose replica update strategies to maintain data consistency while improving data accessibility. These solutions are based on a clustered MANET where nodes in the network are divided into small groups that are suitable for localized data management. The goal is to reduce communication overhead, support localized computation, and enhance scalability. A simulation environment was built using an NS‐2 network simulator to evaluate the performance of the proposed schemes. The results show that our schemes distribute replicas effectively, provide high data accessibility rates and maintain consistency.
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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 | 0.001 | 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 | 0.001 | 0.004 |
| 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