The popularity and proliferation of the Internet of Things (IoT) paradigm is resulting in a
growing number of devices connected to the Internet. These devices are generating and …

As deep learning, virtual reality, and other technologies become mature, real-time data
processing applications running on intelligent terminals are emerging endlessly; meanwhile …

Nowadays, a large amount of services are deployed on the edge of the network from the
cloud since processing data at the edge can reduce response time and lower bandwidth …

Applying deep learning models to large-scale IoT data is a compute-intensive task and
needs significant computational resources. Existing approaches transfer this big data from …

Machine leaning apps require heavy computations, especially with the use of the deep
neural network (DNN), so an embedded device with limited hardware cannot run the apps …

The emerging trend of edge computing has led several cloud providers to release their own
platforms for performing computation at the'edge'of the network. We compare two such …

The proliferation of camera-enabled devices and large video repositories has led to a
diverse set of video analytics applications. These applications rely on video pipelines …

Deep learning shows great promise in providing more intelligence to augmented reality (AR)
devices, but few AR apps use deep learning due to lack of infrastructure support. Deep …

Cameras are deployed at scale with the purpose of searching and tracking objects of
interest (eg, a suspected person) through the camera network on live videos. Such cross …

With more computational intensive applications deployed involving mobile edge computing
(MEC), the collaboration among mobile devices, edge and cloud servers becomes an …