2022/05/04 by Subhash Sagar, Sagar, Subhash, Adnan Mahmood +7
Computer Science · Social Sciences · #Access Control and Trust #Cloud Data Security Solutions #FOS: Computer and information sciences #Privacy-Preserving Technologies in Data #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2205.03226
openalex publication_date 2022/05/04 · openalex created_date 2022/05/11 · openalex updated_date 2026/07/28
The recent emergence of the promising paradigm of the Social Internet of Things (SIoT) is a result of an intelligent amalgamation of the social networking concepts with the Internet of Things (IoT) objects (also referred to as "things") in an attempt to unravel the challenges of network discovery, navigability, and service composition. This is realized by facilitating the IoT objects to socialize with one another, i.e., similar to the social interactions amongst the human beings. A fundamental issue that mandates careful attention is to thus establish, and over time, maintain trustworthy relationships amongst these IoT objects. Therefore, a trust framework for SIoT must include object-object interactions, the aspects of social relationships, credible recommendations, etc., however, the existing literature has only focused on some aspects of trust by primarily relying on the conventional approaches that govern linear relationships between input and output. In this paper, an artificial neural network-based trust framework, Trust-SIoT, has been envisaged for identifying the complex non-linear relationships between input and output in a bid to classify the trustworthy objects. Moreover, Trust-SIoT has been designed for capturing a number of key trust metrics as input, i.e., direct trust by integrating both current and past interactions, reliability, and benevolence of an object, credible recommendations, and the degree of relationship by employing a knowledge graph embedding. Finally, we have performed extensive experiments to evaluate the performance of Trust-SIoT vis-a-vis state-of-the-art heuristics on two real-world datasets. The results demonstrate that Trust-SIoT achieves a higher F1 and lower MAE and MSE scores.