With the development of mobile networks and intelligent equipment, as a new intelligent
data sensing paradigm in large-scale sensor applications such as the industrial Internet of …

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 …

Task matching is widely used for participant selection in mobile crowdsensing (MCS).
However, accurate task matching relies on collecting a large amount of user information …

Recently, the growing popularity of Spatial Crowd-sourcing (SC), allowing untrusted
platforms to obtain a great quantity of information about workers and tasks' locations, has …

Effective incentive mechanisms are invaluable in crowdsensing to stimulate the enthusiasm
of strategic users. However, existing work focusing on multi-task allocation with the objective …

The Mobile crowdsensing (MCS) platforms perform optimal task assignments by collecting a
large amount of user information to improve system performance and efficiency. However …

In opportunistic mobile crowdsensing, participants (workers) accept to carry out the
requested sensing tasks only if they are already close to or within the regions of interest …

Federated learning (FL) represents a promising distributed machine learning paradigm that
allows smart devices to collaboratively train a shared model via providing local data sets …

With the wide adoption of unmanned aerial vehicles (UAVs) in our daily life, using UAVs to
collect sensing data from the surrounding environment has become a practice. Notably …

Vehicular crowdsensing (VCS) is a promising paradigm that utilizes the mobility of vehicles
to collect city-scale environmental data for monitoring and management purpose. However …