An Internet of Things‐Based Wireless Sensor Network Secure Routing and Monitoring System Using Deep Learning With Hybrid Optimization
Bibliographic record
Abstract
ABSTRACT As the Internet of Things (IoT) drives global smart networks, secure, efficient, and resilient wireless sensor networks (WSNs) are critical. The existing methods often fail to balance energy efficiency, real‐time adaptability, and robust protection against advanced threats. This manuscript introduces an innovative approach to enhancing security and efficiency in IoT‐driven WSNs by proposing an adaptive energy‐efficient balanced uneven clustering (AEBUC) routing protocol integrated with attention‐guided generative adversarial networks (AG‐GAN). Addressing critical gaps in current research, the AEBUC protocol efficiently monitors sensor nodes, identifying potential adversaries while a path‐oriented data encryption model strengthens security by selecting sensor guard nodes. The use of AG‐GAN optimizes the selection of sensor monitor nodes and determines the most secure routes for encrypted data transmission. Furthermore, the improved border collie optimization (IBCO) algorithm fine‐tunes AG‐GAN's weight parameters, ensuring optimal performance. Implemented in Python and evaluated against key performance indicators such as network lifetime (NL), packet delivery ratio (PDR), throughput, delay, and encryption time (ET), the proposed model achieves 92% higher PDR, 14.4 s lower delay, and 6 s lower ET. The significance of this work lies in its comprehensive solution, combining adaptive clustering with advanced GAN‐based security, making a substantial impact on the reliability and safety of WSNs in IoT environments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".