QoS-Aware Resilient Routing Protocols Leveraging Cosine Similarity-Centric Convolutional Neural Network for WSN-Assisted IoT Using Clustering Techniques
Bibliographic record
Abstract
The Wireless Sensor Networks integrated with Internet of Things (WSN-IoT) serve as the backbones of such applications as smart cities, health monitoring, and environmental monitoring requiring high efficiency and secure communication for Quality of Service (QoS). However, an important challenge has been in routing in dynamic WSN-IoT systems ensuring energy-efficient resilient and QoS-aware operations. This research addresses the issues of hotspot formation, energy depletion, and secure data transmission in clustered WSN-IoT networks. The requirement for robust scalable routing protocols able to maintain QoS requirements even in dynamic environments drives the motivation to develop new protocols. The proposed “QoS-Aware Resilient Routing Protocols leveraging Cosine Similarity-Centric Convolutional Neural Network for WSN-assisted IoT Using Clustering Techniques” (Ski-CSC2-A2O), leverages a Bi-Concentric Hexagonal network structure with mobile sink assistance for energy-efficient data collection. The Skill Optimization Algorithm optimizes clustering which results in balanced power consumption while identifying optimal Cluster Heads. Secure and QoS-aware routing is achieved through a Cosine SimilarityCentric Convolutional Neural Network, while network parameters are fine-tuned using Aphid Ant Optimization. Simulation results demonstrate the effectiveness of the proposed protocol, achieving a Packet Delivery of 99.3%, throughput of 99.6%, and an extended network lifetime surpassing 99.4% of baseline approaches, even with large packet sizes and increased transmission rounds. The Ski-CSC2-A2O protocol provides WSN-IoT systems with highly efficient secure solutions through its capabilities to ensure energy efficiency robust performance QoS while functioning ideally in real-time IoT applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 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".