Efficient Data Harvesting for Tracing Phenomena in Sensor Networks
Bibliographic record
Abstract
Many publish/subscribe systems have been built using wireless sensor networks, WSNs, deployed for real-world environmental data collection, security monitoring, and object tracking. However, research efforts on WSN-based publish/subscribe systems have largely focused on routing algorithms leaving data management issues mostly untouched. This paper considers a publish/subscribe system built on top of a sensor network that monitors the occurrences of phenomena. In quest for explanations to the occurrence of a phenomenon, a subscriber poses one-time queries to the sensor network for sensor readings taken seconds or minutes before the reported phenomenon occurred. These types of queries cannot be satisfied by subscriptions since subscriptions are only effective in delivering streams of new phenomena. To efficiently answer such queries, it is imperative that a data farm of sensor readings be cultivated within WSNs. This paper proposes a new algorithm for archiving sensor readings on data farm that leverages the non-volatile memory of sensor nodes in the network. The proposed algorithm takes advantage of the memory space on nodes that have low probabilities of detecting phenomena. By running an extensive set of simulation experiments, the performance results show that the proposed algorithm can provide 32.9% memory gain and 81.8% low communication overhead when compared to an approach in which nodes use only their own physical memory
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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 | 0.004 |
| 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 | 0.001 |
| 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".