Energy efficiency of encryption schemes applied to wireless sensor networks
Bibliographic record
Abstract
ABSTRACT In this paper, we focus on the energy efficiency of secure communication in wireless sensor networks (WSNs). Our research considers link layer security of WSNs, investigating both the ciphers and the cryptographic implementation schemes, including aspects such as the cipher mode of operation and the establishment of initialization vectors (IVs). We evaluate the computational energy efficiency of different symmetric key ciphers considering both the algorithm characteristics and the effect of channel quality on cipher synchronization. Results show that the computational energy cost of block ciphers is less than that of stream ciphers when data are encrypted and transmitted through a noisy channel. We further investigate different factors affecting the communication energy cost of link layer cryptographic schemes, such as the size of payload, the mode of operation applied to a cipher, the distribution of the IV, and the quality of the communication channel. A comprehensive performance comparison of different cryptographic schemes is undertaken by developing an energy analysis model of secure data transmission at the link layer. This model is constructed considering various factors affecting both the computational cost and communication cost, and its appropriateness is verified by simulation results. In conclusion, we recommend using a block cipher instead of a stream cipher to encrypt data for WSN applications and using a cipher feedback scheme for the cipher operation, thereby achieving energy efficiency without compromising the security in WSNs. Copyright © 2011 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".