Smart Cities sensing is an emerging paradigm to facilitate the transition into smart city
services. The advent of the Internet of Things (IoT) and the widespread use of mobile …

Data volume grows explosively with the proliferation of powerful smartphones and
innovative mobile applications. The ability to accurately and extensively monitor and …

With the rapid digitalization of various industries, mobile crowdsensing (MCS), an intelligent
data collection and processing paradigm of the industrial Internet of Things, has provided a …

Promising federated learning coupled with Mobile Edge Computing (MEC) is considered as
one of the most promising solutions to the AI-driven service provision. Plenty of studies focus …

Location information of workers are usually required for optimal task allocation in mobile
crowdsensing, which however raises severe concerns of location privacy leakage. Although …

Mobile crowdsensing has shown great potential in addressing large-scale data sensing
problems by allocating sensing tasks to pervasive mobile users. The mobile users will …

Mobile crowdsensing engages a crowd of individuals to use their mobile devices to
cooperatively collect data about social events and phenomena for customers with common …

Providing appropriate monetary rewards is an efficient way for mobile crowdsensing to
motivate the participation of task participants. However, a monetary incentive mechanism is …

Federated learning is a promising distributed machine learning paradigm that has been
playing a significant role in providing privacy-preserving learning solutions. However …

Due to the limited battery and computing resource, offloading unmanned aerial vehicles
(UAVs)'computation tasks to ground infrastructure, eg, vehicles, is a fundamental framework …