Existing deep learning systems in the Internet of Things (IoT) environments lack the ability of
assigning compute tasks reasonably which leads to resources wasting. In this letter, we …

Deep learning is a promising approach for extracting accurate information from raw sensor
data from IoT devices deployed in complex environments. Because of its multilayer structure …

Billions of devices will compose the IoT system in the next few years, generating a huge
amount of data. We can use fog computing to process these data, considering that there is …

With the development of the Internet of Things (IoT) and the birth of various new IoT devices,
the capacity of massive IoT devices is facing challenges. Fortunately, edge computing can …

In this paper, we consider resource allocation for edge computing in internet of things (IoT)
networks. Specifically, each end device is considered as an agent, which makes its …

Device-edge collaboration on deep neural network (DNN) inference is a promising
approach to efficiently utilizing network resources for supporting Artificial Intelligence of …

Next-generation wireless networks have to be robust and self-sustained. Internet of things
(IoT) is reshaping the technological adaptation in the daily life of human beings. IoT …

In this article, we investigate resource allocation with edge computing in Internet-of-Things
(IoT) networks via machine learning approaches. Edge computing is playing a promising …

The Internet of Things (IoT) allows physical devices to be connected over the wireless
networks. Although device-to-device (D2D) communication has emerged as a promising …

By leveraging mobile edge computing (MEC), a huge amount of data generated by Internet
of Things (IoT) devices can be processed and analyzed at the network edge. However, the …