TrustRIDR-Net: A Hybrid Trust-Aware Routing Framework Using RFO and DRL for Scalable IoT Networks - Shafiuddin, Shaik; Krishna, Konda Hari (Engineering, Technology & Applied Science Research, 2025)
This paper presents TrustRIDR-Net, a novel hybrid trust-based routing framework for Internet of Things (IoT) networks that intelligently integrates biologically-inspired optimization and adaptive machine learning for secure, energy-efficient, and scalable communication. TrustRIDR-Net achieves a throughput of 17,560 kbps, a Packet Delivery Ratio (PDR) of 98.9%, and reduces energy consumption to 68%, significantly outperforming state-of-the-art models. The proposed model combines Rider Foraging Optimization (RFO) for trust-aware clustering and Deep Reinforcement Learning (DRL) for dynamic routing decisions, while incorporating a comprehensive trust metric based on seven behavioral and energy-related factors: Direct Trust, Indirect Trust, Forwarding Rate, Integrity, Availability, Consistency, and Energy Trust. The model dynamically computes trust scores and routing policies, while ensuring energy-aware transmission using a scalable power control formula. Extensive simulations conducted in a 200×200 m virtual IoT environment with 100 nodes and a centrally placed base station showed that TrustRIDR-Net outperformed existing models such as LSTM, GRU, LEACH, BFA, LOA, and AODV-based techniques. These results validate TrustRIDR-Net's capacity to support resilient, intelligent communication in large-scale, trust-sensitive IoT networks, setting the stage for its deployment in critical applications such as smart cities, healthcare monitoring, and industrial automation.