Hybrid PUSH-PULL for Data Diffusion in Sensor Networks without Location Information
Bibliographic record
Abstract
PUSH and PULL are two common data dissemination algorithms for data-centric sensor networks. The two algorithms work well with only a few sources or a few sinks, respectively; however, when there are many sources and many sinks, both of them become inefficient. In this paper, we propose a novel location-oblivious hybrid PUSH-PULL data diffusion (LOHD) algorithm, which suits a wide range of networks and source/sink settings. Different from existing hybrid approaches, LOHD does not rely on any location information; it adaptively selects an ultra-node through a well-controlled flooding and the ultra-node maintains the gradients from sources to sinks. It then incorporates enhanced PUSH and PULL to distribute messages along the gradients instead of flooding. We model and analyze the algorithms and perform extensive simulations. The results show that LOHD remarkably outperforms both PUSH and PULL, particularly when the number of sources and sinks increases. We also show that the overhead well resists to such increase, suggesting LOHD is highly scalable.
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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 | 0.000 | 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 | 0.002 |
| 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".