A Mobile-Directory Approach to Service Discovery in Wireless Ad Hoc Networks
Bibliographic record
Abstract
We present the Service Directory Placement Algorithm (SDPA), a directory-placement scheme that leverages the performance of existing service discovery protocols over wireless ad-hoc networks. SDPA promotes the deployment of a nomadic service directory, whose current location in the network varies according to the dynamics of service-discovery queries driven by the users' applications and partial knowledge of the network's topology. SDPA is based on a heuristic approach, whose performance is optimized by formulating the directory-placement problem as a Semi-Markov Decision Process solved by means of a reinforcement-learning technique known as Q-Learning. Performance evaluations obtained through computer simulations of networks with up to 45 hosts moving at pedestrian walking speeds equal to or slower than 2 m/s reveal average bandwidth savings close to 50% over a default broadcast approach for service discovery once an efficient directory-placement policy is found.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".