A Markov Model for Batch-Based Opportunistic Routing in Multi-Hop Wireless Mesh Networks
Bibliographic record
Abstract
Opportunistic routing is a promising technique that has been proposed for wireless mesh networks. It achieves significant performance gains under unstable wireless links since it can take advantage of the broadcast nature of the wireless medium. Many protocols of opportunistic routing have been proposed to increase transmission reliability and network throughput. Instead of using a dedicated next hop, opportunistic routing can consider multiple downstream nodes as potential forwarders. The decision of which nodes being chosen to forward packets is made by the coordination schema. Although the coordination helps opportunistic routing to deliver opportunistic gains, it requires a batch-based schedule to minimize the cost, which may prevent spatial channel reuse and thus under utilize wireless media. In this paper, we explain the fundamental idea of opportunistic routing and propose a discrete-time Markov chain as a general model to map the transmission process. It demonstrates how to map batch-based packet transmissions in the network with state transitions in a Markov chain. Our model considers the pipelined data transfer and evaluates opportunistic routing under different wireless networks in terms of the expected number of transmissions and time slots. It shows both the pros and cons of opportunistic routing and thus helps in the design of future protocols of opportunistic routing.
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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 | 0.004 |
| 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 | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".