The proliferation of the Social Internet of Things (SIoT) necessitates robust and scalable trust management systems to ensure secure and reliable interactions among heterogeneous devices. However, existing trust management models often lack scalability for large SIoT environments. To address this, a lightweight trust evaluation model for SIoT, referred to as Micro-Moment (MMTE), is presented here. MMTE evaluates trust based on concise, context specific, repetitive, and high-frequency interactions, termed micro-moments among SIoT devices. The MMTE model is evaluated using the Lysis dataset, which is extracted from a real SIoT environment, and demonstrates superior resource efficiency compared to existing SIoT trust models with significantly lower CPU time, memory, and disk usage. MMTE’s linear complexity and simple design make it more resource efficient and scalable than other lightweight trust models, especially when processing large-scale data in heterogeneous SIoT networks. Moreover, MMTE accurately distinguishes 99.35% of malicious nodes in a simulated smart home environment. Furthermore, a numerical comparison clearly demonstrates that MMTE outperforms existing and recently published trust models in terms of classifying malicious and benign nodes. To enhance scalability, the concept of trust spheres is introduced, and devices with similar trust scores are grouped to streamline processing and storage demands. Sphere Anchors manage the trust spheres and efficiently distribute computational tasks and optimize storage through an adaptive storage strategy. The trust spheres also efficiently manage increasing network sizes, maintaining linear processing times as the traffic load increases, and also outperform existing models in terms of average propagation times. MMTE and trust spheres together provide a robust, scalable, and lightweight solution for trust management in SIoT networks.
Mustafa et al. (Sat,) studied this question.