Secure IoT Routing Through Manifold Criterion Trust Evaluation using Ant Colony Optimization

Abstract

The paper presents a simplified yet innovative computational framework to enable secure routing for sensors within a vast and dynamic Internet of Things (IoT) environment. In the proposed design methodology, a unique trust evaluation scheme utilizing a modified version of Ant Colony Optimization (ACO) is introduced. This scheme formulates a manifold criterion for secure data transmission, optimizing the sensor's residual energy and trust score. A distinctive pheromone management is devised using trust score and residual energy. Concurrently, several attributes are employed for constraint modeling to determine a secure data transmission path among the IoT sensors. Moreover, the trust model introduces a dualtiered system of primary and secondary trust evaluations, enhancing reliability towards securing trusted nodes and alleviating trust-based discrepancies. The comprehensive implementation of the proposed integrates mathematical modeling, leveraging a streamlined bioinspired approach of the revised ACO using crowding distance. Quantitative results demonstrate that our approach yields a 35% improvement in throughput, an 89% reduction in delay, a 54% decrease in energy consumption, and a 73% enhancement in processing speed compared to prevailing secure routing protocols. Additionally, the model introduces an efficient asynchronous updating rule for local and global pheromones, ensuring greater trust in secure data propagation in IoT.