The Experience Sampling Method (ESM) is used by scientists from various disciplines to
gather insights into the intra-psychic elements of human life. Researchers have used the …

Mobile crowdsensing is a burgeoning concept that allows smart cities to leverage the
sensing power and ubiquitous nature of mobile devices in order to capture and map …

Task allocation is a major challenge in Mobile Crowd Sensing (MCS). While previous task
allocation approaches follow either the opportunistic or participatory mode, this paper …

Effectively monitoring urban air quality, and analyzing the source terms of the main
atmospheric pollutants is important for public authorities to take air quality management …

Mobile crowd sensing (MCS) is a relatively new paradigm for collecting real-time and
location-dependent urban sensing data. Given its applications, it is crucial to optimize the …

Background Mobile data collection with smartphones—which belongs to the methodological
family of ambulatory assessment, ecological momentary assessment, and experience …

Sparse Mobile Crowdsensing is an emerging paradigm for data collection with budgets and
workers' limitations' which recruits workers to sense a part of spatio-temporal data and infer …

The paper presents experiences from the rapid introduction and deployment of a data
crowdsourcing and data sharing system, motivated by an urgent civic need arising due to …

Federated learning involves training statistical models over remote devices such as mobile
phones while keeping data localized. Training in heterogeneous and potentially massive …

Recently, mobile applications are widely used by smartphone owners. The understanding of
application usage can help us make prediction on its development tendency and meanwhile …