A lightweight key management scheme based on an Adelson‐Velskii and Landis tree and elliptic curve cryptography for wireless sensor networks
Bibliographic record
Abstract
Abstract Wireless sensor networks are increasingly used in most varied fields such as environment, health, and military. Often, information transmitted on these networks requires encryption to maintain confidentiality, integrity, and non‐repudiation. But encryption techniques used to encrypt data on wired networks are not suitable for sensor networks that consist of small nodes equipped with limited resources. In this paper; we propose a security method for wireless sensor networks that provides good protection while taking into account the limited resources of the sensors. This method is based on an effective key management scheme with a minimum storage of keys. It is based on the combination and improvement of two approaches already proposed by the research community: cryptography based on elliptic curves and key management based on an Adelson‐Velskii and Landis tree. Compared with RECC ‘a routing‐driven elliptic curve cryptography based key management scheme for heterogeneous sensor networks’ and CECKM ‘high‐effect key management associated with secure data transmission approaches in sensor networks using a hierarchical‐based cluster elliptic curve key agreement’, two methods based on Diffie–Hellman elliptic curve cryptography method, our method reduces energy consumption, storage memory, and extends the lifetime of the sensor network. Our simulation results illustrate that our approach saves significant time and memory and reduces the number of exchanged packets during keys installation phase. Also, it requires fewer processing operations and maintains the scalability of the network. Concurrency and Computation: Practice and Experience, 2013.© 2013 Wiley Periodicals, Inc.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".