Dominating sets and neighbor elimination-based broadcasting algorithms in wireless networks
Bibliographic record
Abstract
In a multihop wireless network, each node has a transmission radius and is able to send a message to all of its neighbors that are located within the radius. In a broadcasting task, a source node sends the same message to all the nodes in the network. In this paper, we propose to significantly reduce or eliminate the communication overhead of a broadcasting task by applying the concept of localized dominating sets. Their maintenance does not require any communication overhead in addition to maintaining positions of neighboring nodes. Retransmissions by only internal nodes in a dominating set is sufficient for reliable broadcasting. Existing dominating sets are improved by using node degrees instead of their ids as primary keys. We also propose to eliminate neighbors that already received the message and rebroadcast only if the list of neighbors that might need the message is nonempty. A retransmission after negative acknowledgements scheme is also described. The important features of the proposed algorithms are their reliability (reaching all nodes in the absence of message collisions), significant rebroadcast savings, and their localized and parameterless behavior. The reduction in communication overhead for the broadcasting task is measured experimentally. Dominating set based broadcasting, enhanced by a neighbor elimination scheme and highest degree key, provides reliable broadcast with /spl les/53 percent of node retransmissions (on random unit graphs with 100 nodes) for all average degrees d. Critical d is around 4, with <48 percent for /spl les/3, /spl les/40 percent for d/spl ges/10, and /spl les/20 percent for d/spl ges/25. The proposed methods are better than existing ones in all considered aspects: reliability, rebroadcast savings, and maintenance communication overhead. In particular, the cluster structure is inefficient for broadcasting because of considerable communication overhead for maintaining the structure and is also inferior in terms of rebroadcast savings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".