Efficient Aggregation Processing in the Presence of Duplicately Detected Objects in WSNs
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Bibliographic record
Abstract
Wireless sensor networks (WSNs) have received increasing attention in the past decades. Owing to an enhancement of MEMS technology, various types of sensors such as motion detectors, infrared radiation detectors, ultrasonic sensors (sonar), and magnetometers can detect the objects within a certain range. Under such an environment, an object without an identifier can be detected by several sensor nodes. However, existing studies for query processing in WSNs simply assume that the sensing regions of sensors are disjoint. Thus, for query aggregation processing, effective deduplication is vital. In this paper, we propose an approximate but effective aggregate query processing algorithm, called DE-Duplication on the Least Common Ancestor<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:msup><mml:mrow/><mml:mrow><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:math> (abbreviated as DELCA<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:msup><mml:mrow/><mml:mrow><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:math>). In contrast to most existing studies, since we assume that each object does not have a unique identifier, we perform deduplication based on similarity. To recognize the duplicately detected events earlier, we utilize the locality-sensitive hashing (LSH) technique. In addition, since the similarity measures are not generally transitive, we adapt three duplicate semantics. In our experiments, by using a transmission cost model, we demonstrate that our proposed technique is energy-efficient. We also show the accuracy of our proposed technique.
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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 | 0.001 |
| 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 it