An Adaptive Fuzzy Trust-Based Framework for Secure RPL Routing in IoT Networks

Abstract

Internet of Things (IoT) networks based on the Routing Protocol for Low-Power and Lossy Networks (RPL) are highly susceptible to malicious node attacks, which can significantly degrade network performance and reliability. This paper proposes an Adaptive Fuzzy Trust–Based RPL mechanism that dynamically evaluates node behavior using a multi-dimensional fuzzified trust assessment model incorporating packet forwarding ratio, energy consumption patterns, control message behavior, and cooperative trust feedback. By leveraging fuzzy logic, the proposed approach effectively handles uncertainty and imprecision in trust evaluation under dynamic network conditions. An adaptive fuzzy thresholding mechanism analyzes global fuzzy trust distributions to accurately detect and classify malicious nodes. Extensive simulations conducted in the Cooja simulator on Contiki OS demonstrate substantial improvements in Packet Delivery Ratio, End-to-End delay, Throughput, and Power Efficiency compared to existing trust-based RPL schemes. Results show significant gains in Packet Delivery Ratio (PDR), End-to-End Delay, Throughput, and Power Efficiency compared to existing RPL-based trust models. Specifically, the fuzzy trust framework achieves up to 25% improvement in PDR, 18% reduction in End-to-End Delay, 20% improvement in Throughput, and 15% improvement in Power Efficiency.