Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous
in recent years. This has led to the generation of large quantities of data in real-time, which …

With fast increase in volume of mobile multimedia data, how to apply powerful deep learning
methods to process data with real-time response becomes a major issue. Meanwhile, edge …

Computation offloading at mobile edge computing (MEC) servers can mitigate the resource
limitation and reduce the communication latency for mobile devices. Thereby, in this study …

With 6G flagship program launched by the University of Oulu, Finland, for full future
adaptation of 6G by 2030, many institutes worldwide have started to explore various issues …

Object detection is a fundamental building block of video analytics applications. While
Neural Networks (NNs)-based object detection models have shown excellent accuracy on …

The emerging Federated Learning (FL) enables IoT devices to collaboratively learn a
shared model based on their local datasets. However, due to end devices' heterogeneity, it …

Real-time on-device object detection for video analytics fails to meet the accuracy
requirement due to limited resources of mobile devices while offloading object detection …

Video analytics pipelines have steadily shifted to edge deployments to reduce bandwidth
overheads and privacy violations, but in doing so, face an ever-growing resource tension …

Many scholastic researches have begun around the globe about the competitive
technological interventions like 5G communication networks and its challenges. The …

Edge computing has emerged as a popular paradigm for supporting mobile and IoT
applications with low latency or high bandwidth needs. The attractiveness of edge …