Recently, smart cities, smart homes, and smart medical systems have challenged the functionality and connectivity of the large-scale Internet of Things (IoT) devices. Thus, with the idea of offloading intensive computing tasks from them to edge nodes (ENs), edge computing emerged to supplement these limited devices. Benefit from this advantage, IoT devices can save more energy and still maintain the quality of the services they should provide. However, computational offload decisions involve federation and complex resource management and should be determined in the real-time face to dynamic workloads and radio environments. Therefore, in this work, we use multiple deep reinforcement learning (DRL) agents deployed on multiple edge nodes to indicate the decisions of the IoT devices. On the other hand, with the aim of making DRL-based decisions feasible and further reducing the transmission costs between the IoT devices and edge nodes, federated learning (FL) is used to train DRL agents in a distributed fashion. The experimental results demonstrate the effectiveness of the decision scheme and federated learning in the dynamic IoT system.INDEX TERMS Federated learning, computation offloading, IoT, edge computing.
Summary
To address the vast multimedia traffic volume and requirements of user quality of experience in the next‐generation mobile communication system (5G), it is imperative to develop efficient content caching strategy at mobile network edges, which is deemed as a key technique for 5G. Recent advances in edge/cloud computing and machine learning facilitate efficient content caching for 5G, where mobile edge computing can be exploited to reduce service latency by equipping computation and storage capacity at the edge network. In this paper, we propose a proactive caching mechanism named learning‐based cooperative caching (LECC) strategy based on mobile edge computing architecture to reduce transmission cost while improving user quality of experience for future mobile networks. In LECC, we exploit a transfer learning‐based approach for estimating content popularity and then formulate the proactive caching optimization model. As the optimization problem is NP‐hard, we resort to a greedy algorithm for solving the cache content placement problem. Performance evaluation reveals that LECC can apparently improve content cache hit rate and decrease content delivery latency and transmission cost in comparison with known existing caching strategies.
Large amounts of data will be generated due to the rapid development of the Internet of Things (IoT) technologies and 5th generation mobile networks (5G), the processing and analysis requirements of big data will challenge existing networks and processing platforms. As the most promising technology in 5G networks, edge computing will greatly ease the pressure on network and data processing analysis on the edge. In this paper, we considered the coordination between compute and cache resources between multi-level edge computing nodes (ENs), users under this system can offload computing tasks to ENs to improve quality of service (QoS). We aimed to maximize the long-term profit on the edge, while satisfying the low-latency computing of the users, and jointly optimize the edge-side node offloading strategy and resource allocation. However, it is challenging to obtain an optimal strategy in such a dynamic and complex system. To solve the complex resource allocation problem on the edge and make edge have certain adaptation and cooperation, we used double deep Q-learning (DDQN) to make decisions, ability to maximize long-term gains while making quick decisions. The simulation results prove the effectiveness of DDQN in maximizing revenue when allocation resources on the edge.
Automatic classification is one of the hot topics in the field of information retrieval and natural language processing, but it still faces many problems to be solved. The classic automated classification approach has a sluggish classification speed and poor processing accuracy for resources with a large quantity of data. Based on this, an automated classification approach based on the integration of various neural networks for fundamental nursing teaching materials was presented. The automatic classification method of teaching resources was designed by extracting the characteristics of teaching resources, establishing the model of multiple neural network integration, and designing the classification index of basic nursing teaching resources. The experimental findings suggest that this technique has higher chi-square test parameters and better outcomes for the automated classification of large instructional materials than the classic rough set automatic classification method.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.