Resilient Aggregation in Simple Linear Sensor Networks.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A sensor network is a network comprised of many small, wireless, resourcelimited nodes that sense data about their environment and report readings to a base station. One technique to conserve power in a sensor network is to aggregate sensor readings hop-by-hop as they travel towards a base station, thereby reducing the total number of messages required to collect each sensor reading. In an adversarial setting, the ability of a malicious node to alter this aggregate total must be limited. We present three aggregation protocols inspired by three natural key pre-distribution schemes for linear networks. Assuming no more than k consecutive nodes are malicious, each of these protocols limits the capability of a malicious node to altering the aggregate total by at most a single valid sensor reading. Additionally, our protocols are able to detect malicious behavior as it occurs, allowing the protocol to be aborted early, thereby conserving energy in the remaining nodes. A rigorous proof of security is also given for each protocol.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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 it