Distributed Stable Multi-Source Dynamic Broadcasting for Wireless Multi-Hop Networks Under SINR-Based Adversarial Channel Jamming
Bibliographic record
Abstract
Disseminating continuous packet flows injected at multiple location-random source nodes to all network nodes, known as the multi-source dynamic global broadcast problem, is a fundamental building block for wireless multi-hop networks to run smoothly and efficiently. Previous studies on dynamic global broadcast all assume reliable communications. However, in realistic wireless networks, there exist unpredictable transmission failures caused by the randomized signal interference from uncorrelated wireless networks sharing the same spectrum or even malicious attackers. In this paper, by integrating the Signal-to-Interference-plus-Noise-Ratio (SINR) model, multi-channel communication mode, and randomized malicious channel jamming controlled by an adaptive adversary, we present an SINR-based adversarial channel jamming model to capture the unpredictable transmission failures in a wireless multi-hop network. We first propose a distributed Jamming-resilient Multi-source Static Broadcast (JMSB) algorithm based on random channel selection and message transmissions for multi-hop wireless networks under the above SINR-based adversarial channel jamming model. We then propose a distributed stable Jamming-resilient Multi-source Dynamic Broadcast (JMDB) algorithm which iterates JMSB repeatedly and efficiently in a two-stage manner. We derive the maximum supportable broadcast throughput of JMDB under the stability guarantee, i.e., the expected boundedness on the queue length of each network node and expected broadcast latency for each injected packet. Simulation results shows the stability and throughput efficiency of our proposed JMDB algorithm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".