As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it
also supports artificial intelligence evolving from a centralized manner to a distributed one …

This study concentrates on deep learning-based lightweight object detection models on
edge devices. Designing such lightweight object recognition models is more difficult than …

In order to describe the roadmap of current edge computing research activities, we first
address a brief overview of the most advanced edge computing surveys published in the last …

Nowadays a wide range of applications is constrained by low-latency requirements that
cloud infrastructures cannot meet. Multiaccess Edge Computing (MEC) has been proposed …

Due to the development of small-size, energy-efficient, and powerful CPUs and GPUs for
single board computers, various edge devices are widely adopted for hosting real-world …

Applications that are constrained by low-latency requirements can hardly be executed on
cloud infrastructures, given the high network delay required to reach remote servers. Multi …

Intelligent applications based on AI have put much challenge on the edge of wireless
networks, considering the heterogeneous characteristics and insufficient resources of the …

The wide adoption of smart devices and Internet-of-Things (IoT) sensors has led to massive
growth in data generation at the edge of the Internet over the past decade. Intelligent real …

Existing machine learning inference-serving systems largely rely on hardware scaling by
adding more devices or using more powerful accelerators to handle increasing query …

Intelligent mobile image sensing powered by deep learning analyzes images captured by
cameras from mobile devices, such as smartphones or smartwatches. It supports numerous …