Edge intelligence and, by extension, any distributed computing continuum system will bring
to our future society a plethora of new and useful applications, which will certainly …

Computing paradigms have evolved significantly in recent decades, moving from large room-
sized resources (processors and memory) to incredibly small computing nodes. Recently …

Machine learning typically relies on the assumption that training and testing distributions are
identical and that data is centrally stored for training and testing. However, in real-world …

Digital twins and the Internet of Things (IoT) have gained significant research attention in
recent years due to their potential advantages in various domains, and vehicular ad hoc …

Recent advancements in distributed systems have enabled deploying low-latency and
highly resilient edge applications close to the IoT domain at the edge of the network. The …

Computing Continuum (CC) systems are challenged to ensure the intricate requirements of
each computational tier. Given the system's scale, the Service Level Objectives (SLOs) …

Managing multi-tenant edge devices with heterogeneous capabilities scattered across an
urban area requires significant communication and computing power, which is challenging …

Recent advancements in distributed systems have enabled deploying low-latency edge
applications (ie, IoT applications) in proximity to the end-users, respectively, in edge …

The growing number of Internet of Things (IoT) devices generates massive amounts of
diverse data, including personal or confidential information (ie, sensory, images, etc.) that is …

The convergence between AI planning techniques and the Internet of Things (IoT) can solve
various operational and business challenges. However, IoT systems' stringent requirements …