Multi-Attack Resilient Trust-Embedded Routing Architecture for Energy-Aware IOT Wireless Sensor Networks

Abstract

The growth of wireless sensor networks that are enabled with IoT technologies has increased the demand for routing methods that are secure, low-power, and can scale through dynamic and adversarial environments. Many traditional routing protocols are limited to a single-metric optimization strategy or a static trust model which creates problems with energy imbalance, improper selection of nodes, and vulnerability to coordinated attacks. This study proposes a Hybrid Trust-Metaheuristic Secure Routing Framework that includes an adaptive trust fusion approach to build trust among network nodes, a multi-objective clustering paradigm for optimization of cluster head selection, evolutionary based routing mechanisms and a multi-layered defense against a variety of attack types as components to create unified architecture for routing in WSNs. The framework uses dynamic trust models to evaluate the reliability of each node; employs multiple fitness criteria to optimize the decision-making process for both clustering and routing; and employs a continual monitoring process to evaluate and isolate malicious nodes within the wireless sensor network. By conducting simulations of large-scale IoT-enabled WSN scenarios (100-500 nodes), it can be demonstrated that compared to current methods, improvements were found in the areas of PDR (3 - 5%), Network Lifetime (12 - 18%), Energy Efficiency (10 - 15%) and Attack Detection Accuracy (4-6%). The proposed architecture provides for balanced energy use throughout the WSN, reduced latency and increased security against attacks, and improved communication performance. Thus, the proposed routing framework is a significant enhancement for the deployment of secure and sustainable large-scale IoT-enabled WSNs.