Linear approaches to resilient aggregation in sensor networks
Bibliographic record
Abstract
Abstract A sensor network is a network comprised of many small, wireless, resource-limited 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 given for each protocol. We then demonstrate how to extend our linear protocols to tree-based topologies, thereby allowing linear-based approaches to be applied in a much wider range of network topologies.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".