A cross-layer approach to service discovery and selection in MANETs
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
When a service is offered by multiple servers in a mobile ad hoc network (MANETs), the manner in which clients and servers are paired together, referred to as service selection, is crucial to network performance. Good service selection groups clients with nearby servers, localizing communication, which in turn reduces inter-node interference and allows for multiple concurrent transmissions in different parts of the network. Although much previous research has concentrated on service discovery in MANETs, not much effort has gone into understanding the effects of service selection. This paper demonstrates that service selection in MANETs has profound implications for network performance. Specifically, we show that effective service selection can improve network throughput by up to 400%. We show that to maximize performance service selection decisions need to be continuously reassessed to offset the effects of topology changes. We argue that effective service selection in MANETs requires a cross-layer approach that integrates service discovery and selection functionality with network ad hoc routing mechanisms. The cross-layer approach leverages existing routing traffic and allows clients to switch to better servers as network topology changes.
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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 | 0.001 |
| 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