Hop count optimal position-based packet routing algorithms for ad hoc wireless networks with a realistic physical Layer
Bibliographic record
Abstract
Existing routing and broadcasting protocols for ad hoc networks assume an ideal physical layer model. We apply the log-normal shadow fading model to represent a realistic physical layer and use the probability p(x) for receiving a packet successfully as a function of distance x between two nodes. We define the transmission radius R as the distance at which p(R)=0.5. We propose a medium access control layer protocol, where receiver node acknowledges packet to sender node u times, where u*p(x)/spl ap/1. We derived an approximation for p(x) to reduce computation time. It can be used as the weight in the optimal shortest hop count routing scheme. We then study the optimal packet forwarding distance to minimize the hop count, and show that it is approximately 0.73R (for power attenuation degree 2). A hop count optimal, greedy, localized routing algorithm [referred as ideal hop count routing (IHCR)] for ad hoc wireless networks is then presented. We present another algorithm called expected progress routing with acknowledgment (referred as aEPR) for ad hoc wireless networks. Two variants of aEPR algorithm, namely, aEPR-1 and aEPR-u are also presented. Next, we propose projection progress scheme, and its two variants, 1-Projection and u-Projection. Iterative versions of aEPR and projection progress attempt to improve their performance. We then propose tR-greedy routing scheme, where packet is forwarded to neighbor closest to destination, among neighbors that are within distance tR. All described schemes are implemented, and their performances are evaluated and compared.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".