… ) devices can be processed and analyzed at the network edge. However, the MEC … by IoT
edge applications. Thus, we propose a resource allocation policy for the IoT edge computing …

… edge computing system, as illustrated in Figure 1. The computationally-intensive tasks generated
by IoT … the server, which is deployed at the network edge close to the end devices. The …

… mobile edge computing (CoMEC) network. The core of iRAF is a multitask deep reinforcement
… , such as the computing capability of edge servers and devices, communication channel …

… we investigate mobile edge computing (MEC) networks for intelligent internet of things (IoT), …
networks for intelligent IoT, where multiple users have some computational tasks assisted …

… In this article, we use the deep reinforcement learning algorithm to do with the ECN selection.
3) Assume that ECN w is finally assigned. After that ECN w sends a notice of reception of …

… Contributions In this paper, we propose a deep reinforcement learning method with the value
… an IoT edge computing system, where a BS with an MEC server serves N IoT devices in a …

… due to limited computing capability of … the edge computing assisted PIoT where the
computing tasks of the devices can be either processed locally by the devices, or offloaded to edge …

… performance of computing in edge servers. To solve these problems, we propose a highly
efficient IoT network dynamic clustering solution in edge computing using deep reinforcement …

… and computing … a deep reinforcement learningbased dynamic resource management (DDRM)
algorithm to solve the formulated MDP problem. Our DDRM algorithm exploits the deep …

… use deep reinforcement learning (DRL) to solve the scheduling problem in edge computing
… First, we propose a hierarchical scheduling model considering the central-edge computing …