Video analytics with deep learning techniques has generated immense interest in academia
and industry, captivating minds with its transformative potential. Deep learning techniques …

Driven by the Quality of Experience (QoE) demands for video analysis applications within
contexts such as smart cities, Industrial Internet of Things (IoT), and Internet of Vehicles …

Edge computing has been widely applied in video streaming systems, aiming to provide low-
latency video streaming services. However, limited resources and a lack of incentivization …

Serverless computing is envisioned as the de-facto standard for next-generation cloud
computing. However, the cold start dilemma has impeded its adoption by delay-sensitive …

In Mobile Edge Computing (MEC), the collaboration between end devices and servers
guarantees the low-latency and high-accuracy video stream analysis. However, such …

Cloud computing and edge computing models are popularly applied in emerging
applications, such as smart homes, smart parks, and connected autonomous vehicles for …

The evolution of the Internet of Things (IoT) has been driving the explosive growth of deep
neural network (DNN)-based applications and processing demands. Hence, edge …

Deep neural network (DNN) inference poses unique challenges in serving computational
requests due to high request intensity, concurrent multi-user scenarios, and diverse …

The Internet of Vehicles (IoV) has become a fundamental platform for advancing Intelligent
Transportation Systems (ITSs) and Intelligent Connected Vehicles (ICVs). However, the …

Recent work suggests that computing resources, such as GPUs in real-time edge-based
perception systems, need not have sufficient capacity to keep up with the input frame rates of …