A Cross-Layer Design for Passive Forwarding Node Selection in Wireless Ad Hoc Networks
Bibliographic record
Abstract
In this paper, we propose a cross layer design concept that improves network performances in terms of delay and throughput by minimizing control overhead packets in wireless ad hoc network. The design is based on the observation that when a shared-channel wireless network has sufficient number of nodes only a few of them need to participate in packet forwarding operation in order to maintain active connections of the network. A hierarchy among the network nodes is created by classifying network nodes as mobile node (MN) and forwarding node (MN). FNs route the packets and MNs host the applications. A FN selection algorithm is presented in this paper which is based on the information content in on demand route discovery packet and the lower layer channel information such as contention level estimation at the medium access control (MAC) layer. Such provision of forming hierarchy among network nodes significantly reduces overhead control packets and hence improve network performances. We modify dynamic source routing (DSK) protocol to implement our algorithm called hierarchical dynamic source routing (HDSR) by a network simulator (network simulator-2 of University of California). Our simulation results show HDSR reduces control overhead packets per data packet and average delay per data packet up to 85% and 50% respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".