Hybrid Data Dissemination Approach for Sensor Network with Multiple Cooperative Sinks
Bibliographic record
Abstract
In this paper, we introduce a hybrid scalable approach for data-gathering and dissemination in sensor networks. The approach maintains the desirable on-demand communication feature of the pull technique by synchronizing its underling push and pull components. At the same time, scalability is achieved by allowing sinks to cooperatively embed virtual network sectorizing. The mechanism has two phases. First, virtual boards are constructed to form virtual closed sectors where queries are announced. Second, nodes located outside these boards initiate local bridges to carry their active events to the first board they encounter. Bridges are only created when boards are constructed. We validate our proposed solution with a simple analytical model and extensive software simulation. The initial investigation shows that our integrated protocol promises an efficient and scalable paradigm for data-gathering and dissemination in sensor networks that reduces the communication cost and leads to load balancing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".