Research on Secure Communication Mechanisms in Wireless Sensor Networks for Industrial Control Systems
Bibliographic record
Abstract
In wireless sensor networks in industrial control systems, wireless communication security is challenged due to the broadcast nature of the wireless channel, where information is more easily eavesdropped by illegal nodes on the network.The article establishes a secure communication system based on ZigBee wireless communication technology applied to wireless sensor networks in industrial control systems.In order to improve the secure communication performance of wireless sensor networks, this paper combines the Merkle tree with the Tesla protocol to establish a key management scheme for wireless communication.Then from the node trust degree, the node two-way authentication mechanism for data transmission is constructed by combining the digital signature algorithm.For the effectiveness of the secure communication mechanism of wireless sensor networks, this paper carries out data analysis through performance testing.The key management scheme takes about 17.37 s and 3.24 s to add and revoke a key, respectively, and the local optimal value of user time consumption is 7.26 s when the connectivity frequency is 12 min and the revocation threshold is 60.The average value of the node bidirectional authentication mechanism can reach 96.17% for the accuracy of identifying the malicious nodes in the wireless sensor network, and the bit error rate is lower than 0.5 % for the communication transmission with the mesh topology.The bit error rate is less than 0.1%.The introduction of Merkle tree and digital signature algorithms into the construction of secure communication mechanisms in wireless sensor networks can significantly improve the data transmission security performance of industrial control systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".