Toward a Comprehensive Model for Performance Analysis of Opportunistic Routing in Wireless Mesh Networks
Bibliographic record
Abstract
Opportunistic routing (OR) is a promising paradigm that has been proposed for wireless mesh networks. This routing paradigm takes advantage of the broadcast nature of the wireless medium to increase the reliability of transmissions in multihop wireless networks. The selection of a set of candidates involves satisfying the basic requirements of the model, in which packets are forwarded toward the destination. In OR, if one of the selected candidates does not receive the packet, another candidate might be able to continue forwarding the packet. The decision of which forwarder to choose is made by coordination between candidates that have successfully received the transmitted packet. In this paper, we propose a discrete-time Markov chain as a general model for OR and demonstrate how it can be used to evaluate the performance of OR protocols. We also review three well-known OR protocols that we have selected as a study case. Our study demonstrates how our model facilitates better understanding of the combination of a number of candidates and retransmissions and their significant contributions to the successful delivery of data packets. Thus, this shows that our model can help in the design of future OR protocols and efficient candidate selection algorithms.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".