A Key Management Scheme for Cluster Based Wireless Sensor Networks
Bibliographic record
Abstract
Key management is a major challenge to achieve security in wireless sensor networks. In most of the schemes presented for key management in wireless sensor networks, it is assumed that the sensor nodes have the same capability. The recent research has shown that the survivability of the network can be improved if sensor nodes are grouped in clusters in which a powerful cluster head assigned. However, to gain advantages of clustering in order to find an efficient key management scheme needs more research. In this paper, we investigate the key management in cluster-based wireless sensor networks using both private and public key cryptography. Our goal is to introduce a platform in which public key cryptography is used to establish a secure link between sensor nodes and gateways. Instead of pre-loading a large number of keys into the sensor nodes, each node requests a session key from the gateway to establish a secure link with its neighbors after clustering phase. The security analysis and performance evaluation show that the proposed scheme has significant saving in storage space, transmission overhead, and perfect resilience against node capture.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".