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 …

Federated learning enables distributed learning in a privacy-protected manner, but two
challenging reasons can affect learning performance significantly. First, mobile users are not …

Abstract In recent times, Mobile Crowdsensing (MCS) has garnered considerable attention
and emerged as a promising sensing paradigm. The MCS approach leverages the …

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

In this paper, we investigate the incentive problem in Spatial Crowdsourcing (SC), where
mobile social-aware workers have unknown qualities and can share their answers to tasks …

Mobile crowdsensing (MCS) represents a new sensing paradigm that utilizes the smart
mobile devices to collect and share data. Traditional MCS systems mainly leverages the …

Mobile crowdsensing (MCS) is a crucial component in the Industrial Internet of Things (IIoT),
mainly due to its role in collecting data and enhancing applications. Nonetheless, it faces …

Mobile crowd sensing (MCS) with human participants has been proposed as an efficient
way of collecting data for smart cities applications. However, there often exist situations …

By leveraging the power of crowd, the prevalence of mobile devices in mobile crowd-
sensing (MCS) networks helps and provides a wide range of sensing services through …

Mobile crowdsourcing has become an efficient paradigm for performing large scale tasks.
The incentive mechanism is important for the mobile crowdsourcing system to stimulate …