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 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 | 0.002 | 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".