Towards Trustworthy Object Classification in the SIoT Network

A fundamental issue that mandates careful attention in SIoT 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 chapter, 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 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 knowledge graph embedding. Finally, we have performed extensive experiments to evaluate the performance of Trust–SIoT vis-á-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.