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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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Proceedings ArticleDOI
29 May 2017
TL;DR: This paper presents ongoing research regarding the use of blockchain technology as a platform hierarchical and distributed control systems based on IEC 61499 standard.
Abstract: Edge computing proposes a novel model for providing computational resources close to end devices that are connected to the network. It has numerous applications in Internet of Things, as well as smart grids, healthcare, smart home, etc. This paper presents ongoing research regarding the use of blockchain technology as a platform hierarchical and distributed control systems based on IEC 61499 standard. Hyperledger Fabric was selected as the blockchain solution, where function blocks are to be implemented as smart contracts on a supervisor level. The integration with the edge nodes that perform on the executive level responsible for actual process control is based on a micro-services architecture where Docker containers implement function blocks, and Kubernetes platform is used for orchestrating the execution of containers across the edge resources.

210 citations

Journal ArticleDOI
TL;DR: This paper proposes an emotion-aware connected healthcare system using a powerful emotion detection module, and good accuracies, up to 99.87%, were achieved for emotion detection.
Abstract: The recent development of big data-oriented wireless technologies in terms of emerging 5G, edge computing, interconnected devices of the Internet of Things (IoT), and data analytics, as well as techniques, have enabled connected healthcare services for a happier and healthier life. Although, the quality of the healthcare services can be enhanced through big data-oriented wireless technologies, however, the challenges remain for not considering emotional care, especially for children, elderly, and mentally ill people. In this paper, we propose an emotion-aware connected healthcare system using a powerful emotion detection module. Different IoT devices are used to capture speech and image signals of a patient in a smart home scenario. These signals are used as the input to the emotion detection module. Speech and image signals are processed separately, and classification scores using these signals are fused to produce a final score to take a decision about the emotion. If the emotion is detected as pain, caregivers can visit the patient. Several experiments were performed to validate the proposed system, and good accuracies, up to 99.87%, were achieved for emotion detection. The proposed framework would greatly contribute personalized and seamless emotion-aware healthcare services toward 5G.

210 citations

Journal ArticleDOI
TL;DR: This work shows the evolution of modern computing paradigms and related research interest, and extensively addresses Fog computing, remarking its outstanding role as the glue between IoT, Cloud, Edge, and Edge computing.
Abstract: In the last few years, Internet of Things, Cloud computing, Edge computing, and Fog computing have gained a lot of attention in both industry and academia. However, a clear and neat definition of these computing paradigms and their correlation is hard to find in the literature. This makes it difficult for researchers new to this area to get a concrete picture of these paradigms. This work tackles this deficiency, representing a helpful resource for those who will start next. First, we show the evolution of modern computing paradigms and related research interest. Then, we address each paradigm, neatly delineating its key points and its relation with the others. Thereafter, we extensively address Fog computing, remarking its outstanding role as the glue between IoT, Cloud, and Edge computing. In the end, we briefly present open challenges and future research directions for IoT, Cloud, Edge, and Fog computing.

210 citations

Journal ArticleDOI
TL;DR: By exploiting non-orthogonal multiple access (NOMA) for improving the efficiency of multi-access radio transmission, this paper studies the NOMA-enabled multi- access MEC and proposes efficient algorithms to find the optimal offloading solution.
Abstract: Multi-access mobile edge computing (MEC), which enables mobile users (MUs) to offload their computation-workloads to the computation-servers located at the edge of cellular networks via multi-access radio access, has been considered as a promising technique to address the explosively growing computation-intensive applications in mobile Internet services. In this paper, by exploiting non-orthogonal multiple access (NOMA) for improving the efficiency of multi-access radio transmission, we study the NOMA-enabled multi-access MEC. We aim at minimizing the overall delay of the MUs for finishing their computation requirements, by jointly optimizing the MUs’ offloaded workloads and the NOMA transmission-time. Despite the non-convexity of the formulated joint optimization problem, we propose efficient algorithms to find the optimal offloading solution. For the single-MU case, we exploit the layered structure of the problem and propose an efficient layered algorithm to find the MU's optimal offloading solution that minimizes its overall delay. For the multi-MU case, we propose a distributed algorithm (in which the MUs individually optimize their respective offloaded workloads) to determine the optimal offloading solution for minimizing the sum of all MUs’ overall delay. Extensive numerical results have been provided to validate the effectiveness of our proposed algorithms and the performance advantage of our NOMA-enabled multi-access MEC in comparison with conventional orthogonal multiple access enabled multi-access MEC.

209 citations

Journal ArticleDOI
TL;DR: In this paper, the problem of joint computing, caching, communication, and control (4C) in big data MEC is formulated as an optimization problem whose goal is to jointly optimize a linear combination of the bandwidth consumption and network latency.
Abstract: The concept of Multi-access Edge Computing (MEC) has been recently introduced to supplement cloud computing by deploying MEC servers to the network edge so as to reduce the network delay and alleviate the load on cloud data centers. However, compared to the resourceful cloud, MEC server has limited resources. When each MEC server operates independently, it cannot handle all computational and big data demands stemming from users devices. Consequently, the MEC server cannot provide significant gains in overhead reduction of data exchange between users devices and remote cloud. Therefore, joint Computing, Caching, Communication, and Control (4C) at the edge with MEC server collaboration is needed. To address these challenges, in this paper, the problem of joint 4C in big data MEC is formulated as an optimization problem whose goal is to jointly optimize a linear combination of the bandwidth consumption and network latency. However, the formulated problem is shown to be non-convex. As a result, a proximal upper bound problem of the original formulated problem is proposed. To solve the proximal upper bound problem, the block successive upper bound minimization method is applied. Simulation results show that the proposed approach satisfies computation deadlines and minimizes bandwidth consumption and network latency.

208 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