A framework for spatio-temporal query processing over wireless sensor networks
Bibliographic record
Abstract
Wireless sensor networks consist of nodes with the ability to measure, store, and process data, as well as to communicate wirelessly with nodes located in their wireless range. Users can issue queries over the network, e.g., retrieve information from nodes within a specified region, in applications such as environmental monitoring. Since the sensors have typically only a limited power supply, energy-efficient processing of the queries over the network is an important issue. In this paper, we introduce a general framework for distributed processing of spatio-temporal queries in a sensor network that has two main phases: (1) routing the query to the spatial area specified in the query; (2) collecting and processing the information from the nodes relevant to the query. Within this framework, different algorithms can be designed independently for each of the two phases. We also propose novel algorithms for this framework, one for the first phase and two for the second phase. In an extensive experimental evaluation we study the performance of these algorithms in terms of energy consumption, under varying conditions. The results allow us to recommend the most energy efficient solution, given a network and a spatiotemporal query.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".