In this paper, we present EdgeDrive, a networked edge cloud services framework which can
support low-latency applications during mobility taking into account needs of the driver …

The falling cost of IoT cameras, the advancement of AI-based computer vision algorithms,
and powerful hardware accelerators for deep learning have enabled the widespread …

With the upgrade of hardware architecture and device capacities, many accelerator-based
hardware platforms have been widely deployed in Mobile Edge Computing (MEC) …

The advent of Multi-Access Edge Computing (MEC) has enabled service providers to
mitigate high network latencies often encountered in accessing cloud services. The key idea …

Multiaccess edge computing (MEC) systems provide low-latency computing services for
Internet of Things (IoT) applications by processing IoT data on edge servers. In the era of …

Object detection is crucial for surveillance in edge-enabled Industrial Internet-of-Things.
Massive high-dimensional video streams without considering priority differences connect to …

For the efficient analysis of geo-distributed datasets, cloud providers implement data-parallel
jobs across geo-distributed sites (eg, datacenters and edge clusters), which are generally …

D2D edge computing is a promising solution to address the conflict between limited network
capacity and increasing application demands, where mobile devices can offload their tasks …

Edge computing seeks to enable applications with strict latency requirements by utilizing
compute resources deployed closer to the users. The diverse, dynamic, and constrained …

With the uptake of intelligent data-driven applications, edge computing infrastructures
necessitate a new generation of admission control algorithms to maximize system …