Joint data aggregation and encryption using Slepian‐Wolf coding for clustered wireless sensor networks
Bibliographic record
Abstract
Abstract This paper proposes a joint data aggregation and encryption scheme using Slepian‐Wolf coding for efficient and secured data transmission in clustered wireless sensor networks (WSNs). We first consider the optimal intra‐cluster rate allocation problem in using Slepian‐Wolf coding for data aggregation, which aims at finding a rate allocation subject to Slepian‐Wolf theorem such that the total energy consumed by all sensor nodes in a cluster for sending encoded data is minimized. Based on the properties of Slepian‐Wolf coding with optimal intra‐cluster rate allocation, a novel encryption mechanism, called spatially selective encryption, is then proposed for data encryption within a single cluster. This encryption mechanism only requires a cluster head to encrypt its data while allowing all its cluster members to send their data without performing any encryption. In this way, the data from all cluster members can be protected as long as the data of the cluster head (calledvirtual key) is protected. This can significantly reduce the energy consumption for performing data encryption. Furthermore, an energy‐efficient key establishment protocol is also proposed to securely and efficiently establish the key used for encrypting the data of a cluster head. Simulation results show that the joint data aggregation and encryption scheme can significantly improve energy efficiency in data transmission while providing a high level of data security. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".