A new approach to service discovery in wireless mobile ad hoc networks
Bibliographic record
Abstract
Service discovery is essential for many wireless applications, yet it is more difficult to achieve in Mobile Ad Hoc Networks (MANETS) than in both wired and traditional wireless networks due to the lack of central control. In addition, the heterogeneity, mobility and limited energy of the mobile devices precludes the use of traditional service discovery protocols. This paper presents HESED, a fundamentally different service discovery protocol based on multicast query and multicast reply. Clients multicast service queries and matching servers multicast their response to all nodes. The service information is cached by all and may be used in place of future queries. HESED also eliminates the effect of asymmetric links, providing reliability for its forwarding algorithms. The packet complexity of HESED is shown to be O(N) for N-node MANETs, as opposed to O(N) for traditional service discovery schemes. Simulation results show that HESED significantly outperforms a Traditional On-demand Service Discovery (TOSD) algorithm.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".