Document Type : Original Article

Authors

• Elham Moeinaddini ¹
• Eslam Nazemi ²

¹ Department of Computer Engineering, University of Jiroft, Jiroft, Iran

² Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran

Abstract

The Social Internet of Things (SIoT) enhances device interoperability through social relationships but introduces significant trust management challenges in dynamic, resource-constrained environments. Existing machine learning-based trust models often rely on static training data or centralized architectures, limiting their adaptability and scalability in real-world SIoT deployments. This paper proposes GossipTrust, a novel decentralized trust model that integrates fog computing with adaptive control mechanisms for robust trust management. Our approach deploys machine learning models on fog nodes to enable distributed trust computation while minimizing resource constraints on individual devices. The core innovation combines a MAPE-K (Monitor, Analyze, Plan, Execute, and Knowledge) control loop for real-time attack detection with a gossip learning protocol that allows fog nodes to collaboratively refine trust models through peer-to-peer updates without central coordination. Experimental results demonstrate that GossipTrust significantly outperforms baseline models, achieving 13% higher accuracy, 14% higher success rate, 10% higher F-measure, and 12% lower loss across various trust attack scenarios. The proposed solution effectively addresses key SIoT challenges, including scalability, adaptability, and resource efficiency.

Keywords

• Trustworthiness
• Self-adaptation
• Social Internet of Things
• scalability
• resource efficiency
• Machine Learning

Main Subjects

• Security and Privacy