Mobile crowdsensing (MCS) has gained significant attention in recent years and has
become an appealing paradigm for urban sensing. For data collection, MCS systems rely on …

In mobile crowd-sensing systems, the value of crowd-sensed big data can be increased by
incentivizing the users appropriately. Since data acquisition is participatory, crowd-sensing …

Recently, a stochastic data-driven framework was introduced for forecasting uncertain
multiscale hydrological and water resources processes (eg, streamflow, urban water …

Sparse mobile crowdsensing (Sparse MCS), a new paradigm for large-scale fine-grained
urban monitoring applications, collects sensing data from relatively few areas and infers …

With the popularization of intelligent terminals, crowdsensing has become increasingly
prominent because of its advantages, such as low cost, high convenience, and fast speed in …

Parametric Budyko method has been widely used to assess water availability under
changing environment. Its single parameter (w) controlling the shape of the Budyko curve, is …

Deep neural networks (DNNs) require a large amount of manually labeled training data to
make significant achievements. However, manual labeling is laborious and costly. In this …

This paper examines the opportunities and the economic benefits of exploiting publicly-
sourced datasets of road surface quality. Crowdsourcing and crowdsensing initiatives …

Mobile crowdsensing (MCS) is a paradigm that exploits the presence of a crowd of moving
human participants to acquire, or generate, data from their environment. As a part of the …

Multivariate spectrophotometric procedures are widely used in drug analysis to quantify one
or more substances in a sample. Even if the analytical procedure is validated, there is …