Efficient and guaranteed service coverage in partitionable mobile ad-hoc networks
Why this work is in the frame
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Bibliographic record
Abstract
In wireless ad-hoc networks, the network topology changes dynamically and unpredictably due to node mobility. Such topological dynamics are further exacerbated by the natural grouping behavior in the mobile user's movement, which leads to frequent network partitioning. Network partitioning poses significant challenges to the provisioning of centralized services in ad-hoc networks, since partitioning disconnects many mobile users from the central server. We propose a collection of novel run-time algorithms that adaptively ensure the centralized service is available to all mobile nodes during network partitioning, while minimizing the number of servers required. The network-wide service coverage is achieved by partition prediction and service replication on the servers, and assisted by distributed service selection on regular mobile nodes. Simulation results show that our algorithm efficiently achieves guaranteed service coverage to all nodes. To the best of our knowledge, there have been no similar approaches that use partition prediction to provision centralized services adaptively in partitionable mobile ad-hoc networks.
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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 | 0.001 |
| 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