Routing and Traversal via Location Awareness in Ad Hoc Networks
Bibliographic record
Abstract
We survey some recent results that make use of location awareness of the hosts of an ad-hoc network in order to provide for efficient information dissemination. We explore several new methodologies for constructing hop- and geometric-spanners in a distributed manner, discuss advantages and disadvantages of preprocessing the network topology, and outline several algorithms for efficient traversal and route discovery in ad-hoc networks. 1 Challenges in Ad-Hoc Networking The current rapid growth in the spread of wireless devices of ever increasing miniaturization and computing power has greatly influenced the development of ad-hoc networking. Ad-hoc networks are wireless, self-organizing systems formed by co-operating nodes within communication range of each other that form temporary networks with a dynamic decentralized topology. It is desired to make a variety of services available (e.g., internet, GPS, service discovery) in such environments and our expectation is for a seamless and ubiquitous integration of the new wireless devices with the existing wired communication infrastructure. At the same time we anticipate the development of new wireless services that will provide solutions to a variety of communication
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".