Abstract Machine learning has become an indispensable part of the existing technological
domain. Edge computing and Internet of Things (IoT) together presents a new opportunity to …

With improvements in the area of Internet of Things (IoT), surveillance systems have recently
become more accessible. At the same time, optimizing the energy requirements of smart …

The development of the Internet of Things (IoT) paradigm and its significant spread as an
affordable data source has brought many challenges when pursuing efficient data collection …

Mobile edge computing (MEC), as a key ingredient of the 5G ecosystem, is envisioned to
support demanding applications with stringent latency requirements. The basic idea is to …

Training deep neural networks (DNNs) is time-consuming. While most existing solutions try
to overlap/schedule computation and communication for efficient training, this paper goes …

The integration of the Internet of Things (IoT) and modern Artificial Intelligence (AI) has given
rise to a new paradigm known as the Artificial Intelligence of Things (AIoT). In this survey, we …

Due to limited resources on edge and different characteristics of deep neural network (DNN)
models, it is a big challenge to optimize DNN inference performance in terms of energy …

The advent of IoT and advances in edge computing inspired federated learning, a
distributed algorithm to enable on device learning. Transmission costs, unreliable networks …

Computation offloading is the solution for IoT devices of limited resources and high-cost
processing requirements. However, the network related issues such as latency and …

Deep learning has shown impressive performance in semantic segmentation, but it is still
unaffordable for resource-constrained mobile devices. While offloading computation tasks is …