Enhancing IoT-WSN Security and Efficiency Using Trust-GAEP-Net: A Hybrid Deep Learning and Optimization Approach
Received: 19 April 2025 | Revised: 4 June 2025 | Accepted: 15 June 2025 | Online: 6 October 2025
Corresponding author: Pidugu Purushotham
Abstract
The Internet of Things (IoT)-based Wireless Sensor Networks (WSNs) are critical for real-time data collection and communication but face significant challenges in security, energy efficiency, and trust-based routing. Ensuring secure and optimized data transmission while minimizing energy consumption is essential for network reliability and longevity. To address these challenges, this study presents the Trust-GAEP-Net model, which combines Graph Neural Networks (GNN) for trust evaluation, AlexNet for deep feature extraction, and Enhanced Particle Swarm Optimization (EPSO) for secure and energy-efficient routing. The model dynamically detects malicious nodes using GNN-based trust graphs, classifies nodes as trusted, semi-trusted, or malicious using AlexNet-based feature extraction, and optimizes routing by choosing the most trustworthy and energy-efficient paths through EPSO. Extensive simulations show that Trust-GAEP-Net achieves a Packet Delivery Ratio (PDR) of 98.9%, ensuring high data transmission reliability. Experimental results show that Trust-GAEP-Net achieves a detection accuracy of 99.1%, reduces average latency to 85 ms, and lowers total energy consumption by 16%, making it a robust and energy-efficient solution for dynamic IoT-WSN environments. These enhancements establish Trust-GAEP-Net as a cutting-edge and efficient security solution that ensures robust, adaptive, and energy-efficient network performance in dynamic IoT environments.
Keywords:
IoT, energy, trust, packets, security, optimization, detectionDownloads
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