MB-Presents

The novel paradigm Social Internet of Things (SIoT) improves the network navigability, identifies suitable service providers, and addresses scalability concerns. Ensuring trustworthy collaborations among devices is a key aspect in SIoT and can be realized through trust recommendations. However, the outcome of trust recommendations depends on multiple factors related to the context-dependent nature of SIoT and practical constraints brought by the devices and networks embedded in the SIoT. While the existing literature has proposed numerous trust recommendation models to assess the trustworthiness of devices in various scenarios, researchers have not sufficiently examined the required features for trust recommendations in the SIoT. Consequently, trust recommendation models may inaccurately assess the true risk of device interactions. In this literature survey, we investigate the context-dependent features and recommendation methods used for the SIoT using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. We propose a novel taxonomy to categorize trust recommendation models according to their input features and design. Our findings reveal limited attention is given to the context-dependent features, constraints of the information environment, and limited inference capabilities that impede more precise trust recommendations. Finally, we present the research gaps and outline future directions to enable trustworthy inter-domain operations within the SIoT.

The Social Internet of Things (SIoT) enables cross-organizational collaboration for various industrial applications. However, evaluating trust models within such environments remains challenging due to context-dependent dynamics in SIoT environments. Existing evaluation platforms often rely on overly domain-specific or generic datasets, overlooking the inherent uncertainty and dynamicity of real-world SIoT settings. Additionally, there is a lack of practical platforms to assess the feasibility and effectiveness of trust models across diverse scenarios. In this study, we present the Realistic Trust Model Evaluation Platform for the Social Internet of Things (REACT-SIoT) to rigorously assess trust models in SIoT environments, thereby facilitating trustworthy collaboration for sustainable IoT transformations. REACT-SIoT addresses 21 identified requirements essential for simulating a realistic SIoT environment, including categories of heterogeneity, dynamicity, incompleteness, uncertainty, interdependency, and authentic real- world dynamics. We developed a configurable evaluation procedure that mitigates dataset bias and supports the assessment of both existing and newly developed trust models under various scenario-dependent settings. A real-world example demonstrates the platform’s capability to satisfy these requirements effectively. Our analysis reveals that REACT-SIoT meets all defined requirements and outperforms existing evaluation environments based on accuracy, trust convergence, and robustness criteria. The platform has been successfully applied to existing trust models, showcasing its applicability and enabling comparative assessments that were previously constrained by disparate evaluation settings and datasets. In conclusion, REACT-SIoT offers a highly- adaptable evaluation framework that ensures unbiased and comprehensive trust model assessments in SIoT environments. This platform bridges a critical gap in trust evaluation research, enabling the comparison and validation of trust models across diverse, realistic scenarios, thereby supporting the development of more resilient and trustworthy collaborative SIoT systems.