MASA: Multi-Agent Subjectivity Alignment for Trustworthy Internet of Things

The vastly diverse and increasingly autonomous Internet of Things (IoT) devices stress trust management as a critical requirement of IoT. This paper addresses subjectivity as an important issue in trust management for IoT. Subjectivity means that the information provided by each autonomous IoT device, represented by an agent, is likely to have been influenced by the device's individual preference, which can be misleading in trust evaluation. In this paper, we seek to align the potentially subjective information with the information seeker's own subjectivity so that the acquired second-hand information is more useful and personalized. Accordingly, we propose a multiagent subjectivity alignment (MASA) mechanism, which models the subjectivity using a regression technique and exchanges the models among agents as the input to an alignment process. This mechanism substantially counteracts biases incurred by different agents and improves the accuracy of second-hand information fusion as demonstrated by our simulations. In addition, we also conduct experiments using a real-world dataset (MovieLens) which further validates the efficacy of MASA.