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Edge computing

About: Edge computing is a research topic. Over the lifetime, 11657 publications have been published within this topic receiving 148533 citations.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors advocate a personalized federated learning framework in a cloud-edge architecture for intelligent IoT applications, which is able to mitigate the negative effects caused by heterogeneities in different aspects.
Abstract: Internet of Things (IoT) have widely penetrated in different aspects of modern life and many intelligent IoT services and applications are emerging. Recently, federated learning is proposed to train a globally shared model by exploiting a massive amount of user-generated data samples on IoT devices while preventing data leakage. However, the device, statistical and model heterogeneities inherent in the complex IoT environments pose great challenges to traditional federated learning, making it unsuitable to be directly deployed. In this paper, we advocate a personalized federated learning framework in a cloud-edge architecture for intelligent IoT applications. To cope with the heterogeneity issues in IoT environments, we investigate emerging personalized federated learning methods which are able to mitigate the negative effects caused by heterogeneities in different aspects. With the power of edge computing, the requirements for fast-processing capacity and low latency in intelligent IoT applications can also be achieved. We finally provide a case study of IoT based human activity recognition to demonstrate the effectiveness of personalized federated learning for intelligent IoT applications.

158 citations

Journal ArticleDOI
TL;DR: This paper develops an intent-based traffic control system by investigating Deep Reinforcement Learning for 5G-envisioned IoCVs, which can dynamically orchestrate edge computing and content caching to improve the profits of Mobile Network Operator (MNO).
Abstract: Recent developments of edge computing and content caching in wireless networks enable the Intelligent Transportation System (ITS) to provide high-quality services for vehicles. However, a variety of vehicular applications and time-varying network status make it challenging for ITS to allocate resources efficiently. Artificial intelligence algorithms, owning the cognitive capability for diverse and time-varying features of Internet of Connected Vehicles (IoCVs), enable an intent-based networking for ITS to tackle the above-mentioned challenges. In this paper, we develop an intent-based traffic control system by investigating Deep Reinforcement Learning (DRL) for 5G-envisioned IoCVs, which can dynamically orchestrate edge computing and content caching to improve the profits of Mobile Network Operator (MNO). By jointly analyzing MNO’s revenue and users’ quality of experience, we define a profit function to calculate the MNO’s profits. After that, we formulate a joint optimization problem to maximize MNO’s profits, and develop an intelligent traffic control scheme by investigating DRL, which can improve system profits of the MNO and allocate network resources effectively. Experimental results based on real traffic data demonstrate our designed system is efficient and well-performed.

158 citations

Journal ArticleDOI
TL;DR: In this paper, the authors demonstrate the transformational role of coding in fog computing for leveraging such redundancy to substantially reduce the bandwidth consumption and latency of computing, and discuss two recently proposed coding concepts, minimum bandwidth codes and minimum latency codes.
Abstract: Redundancy is abundant in fog networks (i.e., many computing and storage points) and grows linearly with network size. We demonstrate the transformational role of coding in fog computing for leveraging such redundancy to substantially reduce the bandwidth consumption and latency of computing. In particular, we discuss two recently proposed coding concepts, minimum bandwidth codes and minimum latency codes, and illustrate their impacts on fog computing. We also review a unified coding framework that includes the above two coding techniques as special cases, and enables a trade-off between computation latency and communication load to optimize system performance. At the end, we will discuss several open problems and future research directions.

158 citations

Journal ArticleDOI
TL;DR: This paper extends the classical DQN to address the decisions of multiple edge devices, and shows that the proposed method performs better than the other methods using only one dispatching rule.
Abstract: Manufacturing is involved with complex job shop scheduling problems (JSP). In smart factories, edge computing supports computing resources at the edge of production in a distributed way to reduce response time of making production decisions. However, most works on JSP did not consider edge computing. Therefore, this paper proposes a smart manufacturing factory framework based on edge computing, and further investigates the JSP under such a framework. With recent success of some AI applications, the deep Q network (DQN), which combines deep learning and reinforcement learning, has showed its great computing power to solve complex problems. Therefore, we adjust the DQN with an edge computing framework to solve the JSP. Different from the classical DQN with only one decision, this paper extends the DQN to address the decisions of multiple edge devices. Simulation results show that the proposed method performs better than the other methods using only one dispatching rule.

158 citations

Journal ArticleDOI
TL;DR: The role of ML in IoT from the cloud down to embedded devices is reviewed and the state-of-the-art usages are categorized according to their application domain, input data type, exploited ML techniques, and where they belong in the cloud-to-things continuum.
Abstract: With the numerous Internet of Things (IoT) devices, the cloud-centric data processing fails to meet the requirement of all IoT applications. The limited computation and communication capacity of the cloud necessitate the edge computing, i.e., starting the IoT data processing at the edge and transforming the connected devices to intelligent devices . Machine learning (ML) the key means for information inference, should extend to the cloud-to-things continuum too. This paper reviews the role of ML in IoT from the cloud down to embedded devices. Different usages of ML for application data processing and management tasks are studied. The state-of-the-art usages of ML in IoT are categorized according to their application domain, input data type, exploited ML techniques, and where they belong in the cloud-to-things continuum. The challenges and research trends toward efficient ML on the IoT edge are discussed. Moreover, the publications on the “ML in IoT” are retrieved and analyzed systematically using ML classification techniques. Then, the growing topics and application domains are identified.

157 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20231,471
20223,274
20212,978
20203,397
20192,698
20181,649