Data Aggregation Using Distributed Lossy Source Coding in Wireless Sensor Networks
Bibliographic record
Abstract
In this paper, we study the application of distributed lossy source coding for data aggregation in cluster-based wireless sensor networks (WSNs). We consider a clustered lossy coding (CLC) problem, which aims to select a set of disjoint clusters to cover the whole network such that the total rate of encoded data generated by all clusters or nodes in the network is minimized, given the spatial correlation structure of the network and a couple of total and individual distortion constraints. To solve this problem, we first prove that the overall optimization problem can be decoupled into two independent optimization problems: an optimal clustering problem and an optimal distortion allocation problem. The first problem aims at constructing a clustered hierarchy to minimize the global network entropy without considering distortion allocation, while the second problem aims to optimally allocate a distortion to each sensor node under the given distortion constraints without considering node clustering. We then present a distributed optimal-compression clustering protocol to solve the first problem and use Lagrange multipliers to solve the second problem.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".