A Secure Query Protocol for Multi-layer Wireless Sensor Networks Based on Internet of Things
Bibliographic record
Abstract
This paper puts forward a secure query protocol for wide-range multi-layer WSNs based on the Internet of Things (IoT), which protects data transmission from three aspects: network security, attack mode and privacy protection. Drawing the merits from the Range Doppler (R-D) algorithm and Z-O encoding, our protocol only needs to compare the perceptual data nodes with the radius of the query interval once, which greatly improves the query efficiency. The embedded Z-O codes can transform the comparison into solving the intersection of the two sets. In addition, the hash-based message authentication code (HMAC) was introduced to achieve unidirectional protection of the original data. The simulation results show that our protocol outperforms the traditional protocols in query time, coding length and bucket mechanism, and enjoys high power efficiency, low storage cost and good query accuracy.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".