Energy-Efficient Distributed Spatial Joint Processing in Wireless Sensor Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the area of wireless sensor networks, the efficient spatial query processing based on the locations of sensor nodes is required. Especially, spatial queries on two sensor networks need a distributed spatial join processing among the sensor networks. Because the distributed spatial join processing causes lots of wireless transmissions in accessing sensor nodes of two sensor networks, our goal of this paper is to reduce the wireless transmissions for the energy efficiency of sensor nodes. In this paper, we propose an energy-efficient distributed spatial join algorithm on two heterogeneous sensor networks, which performs in-network spatial join processing. To optimize the in-network processing, we also propose a Grid-based Rectangle tree (GR-tree) and a grid-based approximation function. The GR-tree reduces the wireless transmissions by supporting a distributed spatial search for sensor nodes. The grid-based approximation function reduces the wireless transmissions by reducing the volume of spatial query objects which should be pushed down to sensor nodes. Finally, we compare naïve and existing approaches through extensive experiments and clarify our approach's distinguished features.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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