Key Agreement Scheme for Authorization and Authentication of WSN in IoT-5G Using Elliptic Curve Cryptography
Bibliographic record
Abstract
The successful deployment of the Internet of Things (IoT) heavily relies on the integration of Wireless Sensor Networks (WSN) with 5 th Generation (5G). However, this integration presents data security challenges during continuous data transactions in WSN. Thus, to provide secured data transfer from any location in WSN, a secured data transmission framework using Public Private and Session-based Elliptic Curve Cryptography (PPSECC) and One Sample Median Vigenere Cipher-based Diffie-Hellman (OSMVC-DH) is proposed. First, the node is registered and then authenticated regarding the node's checksum. Subsequently, Geography and Energy Aware Routing (GEAR) is employed for routing, and the optimal routes are selected using the Triangle Walk strategy-based Coati Optimization Algorithm (TW-COA). The data from sensed nodes are encrypted using PPSECC, based on a Session Key (SK) generated using the OSMVC-DH technique. The encrypted data that transmits through the selected paths is changed into a hashcode using Separate Chaining-based Secure Hash Algorithm 512 (SC-SHA-512). At the receiver end, the hashcode-matched data is decrypted in the server. Hence, the proposed model authorized the user by generating the hashcode in 313ms and secured the data with 98% Security Level and 1137ms Encryption Time, thus showing better performance than existing models.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".