Cyber–Physical convergence, the fast expansion of the Internet at its edge, and tighter
interactions between human users and their personal mobile devices push towardan …

Mobile CrowdSensing (MCS) is an emerging paradigm in the distributed acquisition of smart
city and Internet of Things (IoT) data. MCS requires large number of users to enable access …

With the advancement of network and computer technologies, virtual cyberspace keeps
evolving, and Metaverse is the main representative. As an irreplaceable technology that …

Malicious network nodes often incur problems to network and data privacy by distributing
forged public keys. To address this issue, this paper proposes a dynamic trust relationships …

An incentive mechanism is important for mobile crowdsensing to recruit sufficient
participants to complete large-scale sensing tasks with high quality. Previous incentive …

With the popularity of sensor-rich mobile devices (eg, smart phones and wearable devices),
mobile crowdsourcing (MCS) has emerged as an effective method for data collection and …

Data quality, or sometimes referred to as data credibility, is a critical issue in mobile crowd
sensing (MCS) and more generally Internet of Things (IoT). While candidate solutions, such …

Federated learning (FL) is a promising decentralized deep learning technique that allows
users to collaboratively update models without sharing their own data. However, due to its …

Existing studies on task recommendation in crowdsourcing systems provide additional
insights into the field from their perspectives, methodologies, frameworks, and disciplines …

In recent years, gamification mechanics have been extensively adopted by crowdsourcing
platforms to improve solvers' participation and user experience. However, although gamified …