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One rapidly growing application of Internet of Things (IoT) is the protectionof public health and well-being through enabling environmental monitoringservices. In particular, an IoT-enabled health/accessibility monitoring service(HAMS) can be consulted by its users to query about the status of differentareas so as to optimize their trip throughout a geographic region. Given thehigh cost associated with a vast deployment of totally trusted informationsources, the IoT-enabled monitoring services also subsist on citizen engagementand on (possibly untrusted) users' sensing apparatus for data collection.However, trust management becomes a key factor in the success of such servicesbecause they might be misled by malicious users through altered or fake sensordata. In this paper, we consider a monitoring service, and propose a hybridentity/data trust computation scheme which relies on Bayesian learning to scorethe users (as data reporters), and on Dempster-Shafer theory (DST) for datafusion and for the computation of the trustworthiness of the data itself. Inorder to provide resiliency against behavioral changes, the probability massesused in DST are dynamically updated using the freshly estimated user scores aswell as the contextual properties associated with the reported data. We conductsimulation experiments to evaluate the performance of our scheme. Compared toprior work, the results demonstrate superior performance in terms of accuracyand resilience against malicious behavior.

A Trust Management Scheme for IoT-Enabled Environmental Health/Accessibility Monitoring Services