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

… 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 …

… -stage deep reinforcement learning (DRL) scheme that effectively allocates edge resources
to serve the IoT … Unlike the typical DRL, our scheme exploits deep neural networks (DNN) to …

… of computing resources at the edge server poses great … machines (VMs) configured at the
edge server by maximizing the long-… We leverage deep reinforcement learning (DRL) to solve …

… edge devices to the cloud [13]. To the best of our knowledge, the unknown-state BH selection
problem at the edge of IoT … article to explore IoT traffic management at the edge devices …

… IoT network dynamic clustering solution with deep reinforcement learning (DRL) for an IoT
edge … balancing and computation balancing in IoT networks and edge servers respectively. In …

… , we investigate mobile edge computing (MEC) networks for intelligent internet of things (IoT), …
strategy intelligently through the deep reinforcement learning algorithm. In this algorithm, …

… such as deep reinforcement learning (DRL) in edge-assisted Internet of Things (Edge-IoT) …
with a collaborative DRL called C-DRL in Edge-IoT with the aim to obtain two major objectives…

… range of IoT devices, such as proximity sensing, an IoT device … a deep reinforcement learning
(DRL) based solution for IoT … cluster member IoT devices is collected to the edge server …

… edge-enabled IoT system, dedicated caching functions (CFs) are required to cache necessary
sensing data. This article considers an edge-enabled IoT … -driven IoT applications. Then, …