Crowdsensing has received widespread attention in recent years. It is extensively employed
in smart cities and intelligent transportation systems. This paper comprehensively surveys …

Abstract Mobile Crowd Sensing (MCS) has been recognized as a promising param to
construct numerous applications by employing enormous workers to perceive and collect …

Abstract Graph Neural Networks (GNNs) have emerged recently as a powerful way of
dealing with non-Euclidean data on graphs, such as social networks and citation networks …

The recruitment of trustworthy and high-quality workers is an important research issue for
MCS. Previous studies either assume that the qualities of workers are known in advance, or …

Mobile crowd sensing (MCS) is a cloud–edge–terminal collaboration model that relies on
edge terminal devices, or “workers,” to sense data and build applications for cloud-hosted …

Collaborative software crowdsourcing (CSC) is now a vital part of the technological
workforce due to the rising need for software solutions in an industry 5.0 smart city that …

Task assignment is a key issue in Mobile Crowd Sensing, which affects the quality and cost
of tasks. Most of the existing works mainly consider the scenario where workers can …

Adopting Internet-of-things (IoT) in large-scale environments such as smart cities raises
compatibility and trustworthiness challenges, hindering conventional service discovery and …

The data content privacy protection and data accuracy are two important research issues in
Mobile Crowdsensing (MCS). However, current researches have rarely been able to satisfy …

Mobile crowdsourcing (MCS) is a promising way to monitor urban-scale data by leveraging
the crowds' power and has attracted much attention recently. How to recruit suitable workers …