Collaborative mobile crowdsourcing (CMCS) allows entities, eg, local authorities or
individuals, to hire a team of workers from the crowd of connected people, to execute …

Federated learning (FL) is a promising technique for collaboratively training machine-
learning models on massively distributed clients data under privacy constraints. However …

The emergence of team-assembly technologies has brought with it new challenges in
designing and implementing socio-technical systems. Our understanding of how systems …

Mobile Crowd Sensing (MCS) typically assigns sensing tasks in the same target area to
many participants considering data quality and the diversity of sensing devices. However …

The wide spread of mobile devices has enabled a new paradigm of innovation called Mobile
Crowdsourcing (MCS) where the concept is to allow entities, eg, individuals or local …

Spatial mobile crowdsourcing (SMCS) enables a task requester to commission workers to
physically travel to specific locations to perform a set of spatial assignments (ie, tasks are …

In mobile crowd sensing (MCS), complex tasks often require collaboration among multiple
workers with diverse expertise and sensors. However, few studies consider the sensing time …

The safe evacuation of miners during an emergency within the shortest possible time is very
important for the success of a mine evacuation program. Despite developments in the field of …

Mobile crowd sourcing (MCS) enables a distributed problem-solving model in which a crowd
of smart devices' users is engaged in the task of solving a data sensing problem through an …

Autonomously monitoring and analyzing the behavior of the crowd is an open research topic
in the transportation field because of its criticality to the safety of people. Real-time …