Opportunistic Routing in Wireless Multi-hop Networks: A Tutorial
Bibliographic record
Abstract
Opportunistic Routing (OR) [1] is a new promising paradigm, which has been proposed as a way to increase the performance of wireless networks by exploiting its broadcast nature. It benefits from the broadcast characteristic of wireless mediums to improve the network performance. In OR, instead of pre-selecting a single specific node to be the next-hop as a forwarder for a packet, multiple nodes, usually called a Candidate Set, can potentially be selected as the next-hop forwarder. Hence, each node in OR can use different potential paths to send packets toward the destination. This is different from the traditional unipath routing which selects one next-hop forwarder before starting the transmission [2], [3]. By using OR, for each packet a dynamic route toward the destination is built, this is done according to the condition of the wireless links at the moment when the packet is being transmitted. In OR when a candidate receives a packet, it coordinate with the other candidates to decide which of them must forward the packet and which one must discard it. This tutorial gives a comprehensive review of the important issues and different aspects of the state of the art of OR. It will enable the attendees to understand the OR concept and to make contributions on their own.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".