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This paper focuses on the trustworthiness of data gathered from different sources, including crowdsensing and crowdsourcing, in pervasive systems. The specific focus is on mPASS (mobile Pervasive Accessibility Social Sensing), a system devoted to support mobile users with accessibility needs in a smart city context. mPASS is in charge of collecting data about urban and architectural barriers and facilities, with the aim of providing mobile users with personalized paths, during their movement, computed on the basis of their preferences and accessibility needs. A trustworthiness model is presented that combines three sources of information, i.e., crowdsensed data, crowdsourced data and authoritative data. Simulations results witness the feasibility of our approach.
Prandi et al. (Sun,) studied this question.