Topology-based on-board data dissemination approach for sensor network
Bibliographic record
Abstract
In a previous paper, we introduced a hybrid scalable approach for data-gathering and dissemination in sensor networks we called On Board Data Dissemination OBDD [6]. The approach was validated and implemented on well-structured network topologies where virtual boards carry the queries are constructed guided by well-defined trajectories. In this paper we extend our previous work to include networks with irregular topologies. In such networks boards guided by well-defined trajectories are no longer enough to construct the dissemination structure. We have adapted and modified an underlying topology detection algorithm to fit our protocol. The resulting topology-based on-board data dissemination TOBDD efficiently works with any network topology. It also maintains the desirable features of OBDD: although the approach adapts both pull and push strategies, it synchronizes both phases to maintain the communications mainly on-demand and to prohibit problems that could be inherited from the push strategy. Both analysis and simulation show that TOBDD 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 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) | 0.001 | 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 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".