A large number of emerging IoT applications rely on machine learning routines for analyzing
data. Executing such tasks at the user devices improves response time and economizes …

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

I would like to thank the members of my dissertation committee for taking their valuable time
to be on my committee and providing feedback that has significantly improved my …

Abstract Fog and Edge Computing are two models that reached maturity in the last decade.
Today, they are two solid concepts and plenty of literature tried to develop them. Also …

Apache Storm is a popular open-source distributed computing platform for real-time big-data
processing. However, the existing task scheduling algorithms for Apache Storm do not …

In this paper, we present a load variation aware adaptive stochastic method for user service
request allocation and service placement in Multi-Access Edge Computing (MEC) …

The recent emergence of a broad class of deep learning based augmented and virtual
reality applications motivates the need for real-time mobile cloud services. These real-time …

Industry 4.0 can be described as the next generation-factories that is characterised by
putting a high demand for automation and flexible production lines. The proposed way to …

While advancements in computing algorithms and hardware have enabled real-time stream
analytics in videos, the information-rich audio bonded with video is still usually dropped and …

With the rapid development of both hardware and software, mobile devices with their
advantages in mobility, interactivity, and privacy have enabled various applications …