Mobile crowdsensing (MCS) is an emerging data-driven paradigm that leverages the
collective intelligence of the crowd, their mobility, and the crowd-companioned smart mobile …

Mobile crowdsensing has empowered the Industrial Internet of Things (IIoT) in many ways,
such as vehicle-aided traffic flow scheduling, drone-aided visual inspections, etc. However …

Mobile crowd sensing (MCS) is a cloud–edge–terminal collaboration model that relies on
edge terminal devices, or “workers,” to sense data and build applications for cloud-hosted …

Amidst the robust impetus from artificial intelligence (AI) and big data, edge intelligence (EI)
has emerged as a nascent computing paradigm, synthesizing AI with edge computing (EC) …

As medical data become increasingly important in healthcare, it is crucial to have proper
access control mechanisms, ensuring that sensitive data are only accessible to authorized …

Mobile crowdsourcing federated learning (FL-MCS) allows a requester to outsource its
model-training tasks to other workers who have the desired data as well as strong …

Presently, resource-constrained devices in manufacturing Internet of Things (MIoT), such as
sensors and radio frequency identification (RFID) devices, collect a large amount of privacy …

The advent of the Internet of Medical Things (IoMT) has greatly fastened the digitization of
current medical institutions. Mobile crowdsourcing is an effective strategy for health data …

Due to the immutability of blockchain, the integration with big-data systems creates
limitations on redundancy, scalability, cost, and latency. Additionally, large amounts of …

In order to obtain evidence of a crime timely, most authorities encourage whistleblowers to
provide valuable reports by rewarding them with prizes. However, criminals will try their best …