A scalable location management scheme in mobile ad-hoc networks
Bibliographic record
Abstract
In ad-hoc networks, geographical routing protocols take advantage of location information so that stateless and efficient routing is feasible. However such routing protocols are heavily dependent on the existence of scalable location management services. We present a novel scheme to perform scalable location management. With any location management schemes, a specific node, A, in the network trusts a small subset of nodes, namely its location servers, and periodically updates them with its location. Our approach adopts a similar strategy, but a different and original approach to select such location servers. First, we present a selection algorithm used to designate location servers of a node by its identifier. Second, we propose a hierarchical addressing model for mobile ad-hoc networks, where node locations could be represented at different accuracy levels. With this approach, different location servers may carry location information of different levels of accuracy and only a small set of location servers needs to be updated when the node moves. Through rigorous theoretical analysis, we are able to show that the control message overhead is bounded under our scheme. Finally, simulation results are presented to demonstrate the performance of our location management scheme.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".