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One rapidly growing application of Internet of Things (IoT) is the protection of public health and well-being through enabling environmental monitoring services. In particular, an IoT-enabled health/accessibility monitoring service can be consulted by its users to query about the status of different areas so as to optimize their trip throughout a geographic region. Given the high cost associated with a vast deployment of totally trusted information sources, the IoT-enabled monitoring services also subsist on citizen engagement and on (possibly untrusted) users sensing apparatus for data collection. However, trust management becomes a key factor in the success of such services because they might be misled by malicious users through altered or fake sensor data. In this paper, we consider a monitoring service and propose a hybrid entity/data trust computation scheme which relies on Bayesian learning to score the users (as data reporters), and on Dempster–Shafer theory (DST) for data fusion and for the computation of the trustworthiness of the data itself. In order to provide resiliency against behavioral changes, the probability masses used in DST are dynamically updated using the freshly estimated user scores as well as the contextual properties associated with the reported data. We conduct simulation experiments to evaluate the performance of our scheme. Compared to prior work, the results demonstrate superior performance in terms of accuracy and resilience against malicious behavior.

A trust management scheme for IoT-enabled environmental health/accessibility monitoring services