Data-Driven Computing and Caching in 5G Networks: Architecture and Delay Analysis

Chen, Min; Qian, Yongxian; Hao, Yixue; Li, Yong; Song, Jeungeun

doi:10.1109/mwc.2018.1700216

Cited by 183 publications

(66 citation statements)

References 14 publications

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“…It cannot be guaranteed that the unlabeled data set plays a positive role in the model training and global cognition of the cloud platform. Meanwhile, the transmission of huge amount of unlabeled data set will consume the network communication resources and thus reduce the service experience of users [39].…”

Section: Self-learning Processmentioning

confidence: 99%

AI-Skin: Skin disease recognition based on self-learning and wide data collection through a closed-loop framework

Chen

Zhou

et al. 2020

Information Fusion

Self Cite

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There are a lot of hidden dangers in the change of human skin conditions, such as the sunburn caused by long-time exposure to ultraviolet radiation, which not only has aesthetic impact causing psychological depression and lack of selfconfidence, but also may even be life-threatening due to skin canceration. Current skin disease researches adopt the auto-classification system for improving the accuracy rate of skin disease classification. However, the excessive dependence on the image sample database is unable to provide individualized diagnosis service for different population groups. To overcome this problem, a medical AI framework based on data width evolution and self-learning is put forward in this paper to provide skin disease medical service meeting the requirement of real time, extendibility and individualization. First, the wide collection of data in the close-loop information flow of user and remote medical data center is discussed. Next, a data set filter algorithm based on information entropy is given, to lighten the load of edge node and meanwhile improve the learning ability of remote cloud analysis model. In addition, the framework provides an external algorithm load module, which can be compatible with the application requirements according to the model selected. Three kinds of deep learning model, i.e. LeNet-5, AlexNet and VGG16, are loaded and compared, which have verified the universality of the algorithm load module. The experiment platform for the proposed real-time, individualized and extensible skin disease recognition system is built. And the system's computation and communication delay under the interaction scenario between tester and remote data center are analyzed. It is demonstrated that the system we put forward is reliable and effective.

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Section: Self-learning Processmentioning

confidence: 99%

AI-Skin: Skin disease recognition based on self-learning and wide data collection through a closed-loop framework

Chen

Zhou

et al. 2020

Information Fusion

Self Cite

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“…In this context, coding [95] and machine learning [96] techniques can be applied. Examples of typical scenarios are vehicular communications [97] and low-latency applications [98].…”

Section: Cachingmentioning

confidence: 99%

Orchestration and Control in Software‐Defined 5G Networks: Research Challenges

Nencioni

Garroppo

Gonzalez

et al. 2018

Wireless Communications and Mobile Computing

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The fifth generation (5G) of cellular networks promises to be a major step in the evolution of wireless technology. 5G is planned to be used in a very broad set of application scenarios. These scenarios have strict heterogeneous requirements that will be accomplished by enhancements on the radio access network and a collection of innovative wireless technologies. Softwarization technologies, such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV), will play a key role in integrating these different technologies. Network slicing emerges as a cost-efficient solution for the implementation of the diverse 5G requirements and verticals. The 5G radio access and core networks will be based on a SDN/NFV infrastructure, which will be able to orchestrate the resources and control the network in order to efficiently and flexibly and with scalability provide network services. In this paper, we present the up-to-date status of the software-defined 5G radio access and core networks and a broad range of future research challenges on the orchestration and control aspects.

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“…Data offloading is an efficient method to improve the network experience of mobile users by using the complementary technologies in 5G systems, such as Wi-Fi, to offload the mobile data originally targeted toward cellular networks [2]. By offering storage resource to the edge of network, the edge caching is recently proposed to deal with the increasing data demand of mobile users and balance the overload in cellular networks, in order to satisfy the ultra-low latency requirement of next generation mobile networks [6].…”

Section: Introductionmentioning

confidence: 99%

A Mobility-Aware Vehicular Caching Scheme in Content Centric Networks: Model and Optimization

Zhang

Luan

et al. 2019

IEEE Trans. Veh. Technol.

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Edge caching is being explored as a promising technology to alleviate the network burden of cellular networks by separating the computing functionalities away from cellular base stations. However, the service capability of existing caching scheme is limited by fixed edge infrastructure when facing the uncertainties of users' requests and locations. The vehicular caching, which uses the moving vehicles as cache carriers, is regard as an efficient method to solve the problem above. This paper studies the effectiveness of vehicular caching scheme in content centric networks by developing optimization model towards the minimization of network energy consumption. Particularly, we model the interactions between caching vehicles and mobile users as a 2-D Markov process, in order to characterize the network availability of mobile users. Based on the developed model, we propose an online vehicular caching design by optimizing network energy efficiency. Specifically, the problem of caching decision making is firstly formulated as a fractional optimization model, towards the optimal energy efficiency. Using nonlinear fractional programming technology and Lyapunov optimization theory, we derive the theoretical solution for the optimization model. An online caching algorithm to enable the optimal vehicular caching is developed based on the solution. Finally, extensive simulations are conducted to examine the performance of our proposal. By comparison, our online caching scheme outperforms the existing scheme in terms of energy efficiency, hit ratio, cache utilization, and system gain.

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Data-Driven Computing and Caching in 5G Networks: Architecture and Delay Analysis

Cited by 183 publications

References 14 publications

AI-Skin: Skin disease recognition based on self-learning and wide data collection through a closed-loop framework

AI-Skin: Skin disease recognition based on self-learning and wide data collection through a closed-loop framework

Orchestration and Control in Software‐Defined 5G Networks: Research Challenges

A Mobility-Aware Vehicular Caching Scheme in Content Centric Networks: Model and Optimization

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