Mobile crowdsensing (MCS) has gained significant attention in recent years and has
become an appealing paradigm for urban sensing. For data collection, MCS systems rely on …

Autonomous vehicles (AV) are game-changing innovations that promise a safer, more
convenient, and environmentally friendly mode of transportation than traditional vehicles …

Driven by privacy concerns and the visions of deep learning, the last four years have
witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An …

Currently, researchers have motivated a vision of 6G for empowering the new generation of
the Internet of Everything (IoE) services that are not supported by 5G. In the context of 6G …

The increasing number of Internet of Things (IoT) devices necessitates the need for a more
substantial fog computing infrastructure to support the users' demand for services. In this …

Recent advancements in vehicular traffic sensing have significantly enhanced traffic
information collection for the platform. However, this approach encounters two primary …

Instability within fog federations is considered as a serious problem that degrades the
performance of the provided services. The latter may affect the service availability due to fog …

Conventional systems are usually constrained to store data in a centralized location. This
restriction has either precluded sensitive data from being shared or put its privacy on the …

Task offloading combined with reinforcement learning (RL) is a promising research direction
in edge computing. However, the intractability in the training of RL and the heterogeneity of …

Intelligent Transportation System (ITS) serves as the on-the wheel communication and
service platform for the real-world driving users. Navigation service and traffic information …