Assessing Trust-Based Routing Algorithms for Robust Performance Against Anomalies in IoT Networks: A Comprehensive Framework and Evaluation

Abstract

The Internet of Things (IoT) has revolutionized connectivity by enabling billions of resource-constrained devices to communicate and share data. However, the inherent security vulnerabilities in IoT routing protocols, particularly the de facto Routing Protocol for Low Power and Lossy Networks (RPL), expose networks to various malicious attacks including blackhole, selective forwarding, and sinkhole attacks. Trust-based routing algorithms have emerged as a promising paradigm to address these security challenges by incorporating trust metrics into routing decisions. This paper presents a comprehensive evaluation framework for assessing trust-based routing algorithms in IoT networks, analyzing their effectiveness against various anomalies and security threats. Through systematic analysis of 300+ research papers published between 2013-2023 and development of standardized performance matrices, we establish a rigorous methodology for evaluating trust-based approaches. Our framework encompasses multi-dimensional performance assessment including security effectiveness, network performance, trust-specific metrics, and resource efficiency. The evaluation reveals that state-of-the-art algorithms like SMTrust, FDTM-RPL, and MRTS demonstrate significant improvements in attack detection (85-96% ADR) while maintaining acceptable performance overhead (1.2-15% energy increase). We provide comprehensive performance matrices for five major attack categories and establish benchmarks for algorithm comparison. This research contributes a standardized framework that enables fair comparison of trust-based routing algorithms and guides future research directions in secure IoT communications.