Parameterless Broadcasting in Static to Highly Mobile Wireless Ad Hoc, Sensor and Actuator Networks
Bibliographic record
Abstract
In a broadcasting task, source node wants to send the same message to all the other nodes in the network. Existing solutions range from connected dominating set (CDS) based for static networks, to blind flooding for moderate mobility, to hyperflooding for highly mobile and frequently partitioned networks. The only existing protocol for all scenarios is based on some threshold parameters (which may be expensive to gather) to locally select between these three solution approaches. Here we propose a new protocol, which adjusts itself to any mobility scenario without using any parameter. Unlike existing methods for highly mobile scenarios, in proposed method, two nodes do not transmit every time they discover each other as new neighbors. Each node maintains a list of two hop neighbors by periodically exchanging 'hello' messages, and decides whether or not it is in CDS. Upon receipt of the first copy of message intended for broadcasting, it selects a waiting timeout and constructs two lists of neighbors: neighbors that received the same message and neighbors that did not receive it. Nodes not in CDS select longer timeouts than nodes in CDS. These lists are updated upon receipt of further copies of same packet. When timeout expires, node retransmits if the list of neighbors in need of message is nonempty. 'Hello' messages received while waiting, or after timeout expiration may revise all lists (and CDS status) and consequently the need to retransmit. This provides a seamless transition of protocol behavior from static to highly mobile scenarios. Our protocol is compared to existing solutions. It was shown to be superior to all of them in number of retransmissions and reliability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".