Continuous data collection in wireless sensor networks
Bibliographic record
Abstract
Recently, it has come to be generally believed by academia and industry alike that the sensor network will have a key role to extend the reachability of the next generation Internet.A key characteristic of this network is that there is no single node in the network that is powerful enough to perform the assigned tasks.An application should be served via the cooperation of several nodes or even the entire network.The network serves as an information base, and is data driven, as opposed to a provider for the point-to-point connection.The main challenge of this network is huge information organization, including information storage, searching and retrieval, especially in a continuous way.There are many specific and interrelated problems.We list a few examples.First, data accuracy: the correctness of the sensor network to represent the properties of the sensor field.Second, data search and retrieval delay; while low delay is always preferred, various applications have different delay constraints.Third, overhead; low transmission overhead is often the main consideration in system design, as it is directly related to the usage of energy, the most severely limited resource for sensors.In this thesis, we first discuss load balanced sensor coverage, which provides a lower layer support for long run sensor data collection.We then concentrate on how to balance the parameters in data collection of the sensor networks, so that the user queries and applications can be satisfied with reasonable delay and low overhead.Based on different application specifics, we try to use a smaller number of sensors, less number of transmissions by exploring historical and topological information, coding techniques and data distribution information.Our analysis and experimental results show that our architecture and algorithms provide both theoretical and practical insights for sensor network design and deployment.iii iv To my parents I would first like to
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".