A new approach on self-adaptive trust management for social Internet of Things

The emergence of the Social Internet of Things (SIoT) represents a significant evolution of the traditional Internet of Things (IoT) paradigm. While earlier IoT systems prioritized the integration of intelligent devices, the SIoT paradigm shifts the focus toward enabling social interaction and collaboration among connected entities. Within the SIoT, various nodes can autonomously establish social connections to access the desired services. Given the dynamic and decentralized nature of this open environment, effective trust management becomes crucial. In recent years, machine learning (ML) techniques have made significant progress in enhancing trust computing within the SIoT ecosystem. However, challenges such as scalability, dynamic behavior, and device resource limitations continue to pose difficulties. This study introduces SATM-SIoT, a decentralized self-adaptive trust management model for SIoT that integrates the MAPE-K (Monitor, Analyze, Plan, Execute, Knowledge) control loop. By adjusting thresholds based on perceived hostile behavior, this model effectively identifies malicious devices, enhancing adaptivity in trust management. To address resource limitations and ensure scalability, our model assigns trust evaluation tasks to Fog nodes, leveraging their distributed computational power. We utilize ML techniques, specifically multi-layer perceptron (MLP), to analyze device behavior and assess trustworthiness. Federated learning (FL) enhances local ML models, enabling collaborative learning and incremental updates based on new data to adapt to the dynamics of SIoT. The primary goal of SATM-SIoT is to improve overall trustworthiness and accurately detect malicious devices while tackling challenges related to scalability, dynamic behavior, and resource constraints. We conducted experiments on a simulated SIoT network to validate our model. The results show that SATM-SIoT effectively identifies almost all malicious devices in a SIoT network. Utilizing the FL technique and MAPE-K loop led to an average increase in Success Rate of 7% and 9%, respectively.