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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: This survey provides a review of the literature regarding the use of IoT and DL to develop smart cities and outlines the current challenges and issues faced during the development of smart city services.

144 citations

Journal ArticleDOI
TL;DR: An AI enhanced offloading framework is proposed for service accuracy maximization, which considers service accuracy as a new metric besides delay, and intelligently disseminates the traffic to edge servers or through an appropriate path to remote cloud.
Abstract: The Industrial Internet of Things (IIoT) enables intelligent industrial operations by incorporating artificial intelligence (AI) and big data technologies. An AI-enabled framework typically requires prompt and private cloud-based service to process and aggregate manufacturing data. Thus, integrating intelligence into edge computing is without doubt a promising development trend. Nevertheless, edge intelligence brings heterogeneity to the edge servers, in terms of not only computing capability, but also service accuracy. Most works on offloading in edge computing focus on finding the power-delay trade-off, ignoring service accuracy provided by edge servers as well as the accuracy required by IIoT devices. In this vein, in this article we introduce an intelligent computing architecture with cooperative edge and cloud computing for IIoT. Based on the computing architecture, an AI enhanced offloading framework is proposed for service accuracy maximization, which considers service accuracy as a new metric besides delay, and intelligently disseminates the traffic to edge servers or through an appropriate path to remote cloud. A case study is performed on transfer learning to show the performance gain of the proposed framework.

144 citations

Journal ArticleDOI
TL;DR: This paper develops a consortium blockchain-based secure energy trading mechanism for V2G, and proposes an efficient incentive mechanism based on contract theory and edge computing to improve the successful probability of block creation.
Abstract: Smart grid has emerged as a successful application of cyber-physical systems in the energy sector. Among numerous key technologies of the smart grid, vehicle-to-grid (V2G) provides a promising solution to reduce the level of demand–supply mismatch by leveraging the bidirectional energy-trading capabilities of electric vehicles. In this paper, we propose a secure and efficient V2G energy trading framework by exploring blockchain, contract theory, and edge computing. First, we develop a consortium blockchain-based secure energy trading mechanism for V2G. Then, we consider the information asymmetry scenario, and propose an efficient incentive mechanism based on contract theory. The social welfare optimization problem falls into the category of difference of convex programming and is solved by using the iterative convex–concave procedure algorithm. Next, edge computing has been incorporated to improve the successful probability of block creation. The computational resource allocation problem is modeled as a two-stage: 1) Stackelberg leader–follower game and 2) the optimal strategies are obtained by using the backward induction approach. Finally, the performance of the proposed framework is validated via numerical results and theoretical analysis.

144 citations

Journal ArticleDOI
TL;DR: The architecture of the ECC is introduced and an ECC-based dynamic service migration mechanism is proposed to provide insight into how cognitive computing is combined with edge computing to achieve a higher energy efficiency and a higher Quality of Experience (QoE) compared to edge computing.
Abstract: Driven by the vision of edge computing and the success of rich cognitive services based on artificial intelligence, a new computing paradigm, edge cognitive computing (ECC), is a promising approach that applies cognitive computing at the edge of the network. ECC has the potential to provide the cognition of users and network environmental information, and further to provide elastic cognitive computing services to achieve a higher energy efficiency and a higher Quality of Experience (QoE) compared to edge computing. This article first introduces our architecture of the ECC and then describes its design issues in detail. Moreover, we propose an ECC-based dynamic service migration mechanism to provide insight into how cognitive computing is combined with edge computing. In order to evaluate the proposed mechanism, a practical platform for dynamic service migration is built up, where the services are migrated based on the behavioral cognition of a mobile user. The experimental results show that the proposed ECC architecture has ultra-low latency and a high user experience, while providing better service to the user, saving computing resources, and achieving a high energy efficiency.

144 citations

Journal ArticleDOI
TL;DR: This article introduces a distributed Vehicular edge computing solution named the autonomous vehicular edge (AVE), which makes it possible to share neighboring vehicles' available resources via vehicle-tovehicle (V2V) communications, and extends this concept to a more general online solution called the hybrid vehicle edge cloud (HVC), which enables the efficient sharing of all accessible computing resources.
Abstract: As an enabling technology for the Internet of Vehicles (IoV), mobile edge computing (MEC) provides potential solutions for sharing the computation capabilities among vehicles, in addition to other accessible resources. In this article, we first introduce a distributed vehicular edge computing solution named the autonomous vehicular edge (AVE), which makes it possible to share neighboring vehicles' available resources via vehicle-tovehicle (V2V) communications. We then extend this concept to a more general online solution called the hybrid vehicular edge cloud (HVC), which enables the efficient sharing of all accessible computing resources, including roadside units (RSUs) and the cloud, by using multiaccess networks. We also demonstrate the impact of these two decentralized edge computing solutions on the task execution performance. Finally, we discuss several open problems and future research directions.

143 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