HARVEST: A Task-objective Efficient Data Collection Scheme in Wireless Sensor and Actor Networks
Bibliographic record
Abstract
In this paper, we study data fusion in Wireless Sensor and Actor Networks (WSANs). We propose an efficient data collection scheme, called HARVEST to collect data with an application-oriented Mobile Actor (MA). An MA is capable of saving energy of each sensor node and performing advanced computation functions based on the requests of various applications. We consider data collection application in agriculture scenario. An MA determines the next migration based on the uncertainty of sensory data provided by all n-hop neighbor nodes in HARVEST. The uncertainty is important for users to know unexpected events happened in a farmland, which they cannot cope with in advance. HARVEST, not only referring sensory data on immediate neighbors but also involving n-hop neighbors, leads total optimization of an MA's itinerary. The performance of HARVEST is evaluated by simulations of frost prediction which is a real-world application. It is shown that the total execution time of the MA can be reduced significantly while the efficiency of searching interests is maintained.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".