Efficient and guaranteed service coverage in partitionable mobile ad-hoc networks
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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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".