Extending the Lifetime of Wireless Sensor Networks with Spatial Data Aggregation
Bibliographic record
Abstract
In this thesis, we propose mechanisms to extend the lifetime of wireless sensor networks. In-network data aggregation is considered on both tree-based and flow-based routing protocols during the process of data collection to reduce redundant transmissions. In the flow-based data collection design, we introduce the concept of flow loss multiplier to express the impact of data aggregation over correlated data. The application has the freedom to set the flow loss multiplier to reflect its specific knowledge of correlation. We also introduce traffic balancing as a complementary technique to data aggregation. It helps avoid exhausting the energy of any sensor node while leaving large amounts of energy at other nodes. In tree-based data collection schemes, we adjust the tree structure judiciously to balance energy consumption before any node's failure due to total residual energy depletion. In flow-based schemes, after aggregation, data flows are split and the fragments are spread to increase network lifetime. We investigate the impact of performing greedily data aggregation at the "best" aggregation site regardless of its location, the results of our analysis show that only applying 2-way data aggregation may limit the ability to explore more complex aggregation possibilities. To address this problem, we propose an aggressive data aggregation for a specified application, contour map reconstruction. Based on the simulation results, our aggregation scheme is shown to be able to eliminate large volume of contour data and retain satisfying data accuracy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".