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    15 March 2019, Volume 5 Issue 2
    TOPIC::EDGE COMPUTING
    Architecture,challenges and applications of edge computing
    Linzhe LI, Peilei ZHOU, Peng CHENG, Zhiguo SHI
    2019, 5(2):  3-16.  doi:10.11959/j.issn.2096-0271.2019010
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    Edge computing is a new type of computing models that performs computing tasks at the edge of the network.Compared with cloud computing,it can respond to user’s needs more quickly and reliably.Starting from the shortcomings of cloud computing,the concept and general architecture of edge computing were illustrated,and then two reference frameworks proposed by industry alliances were elaborated.Four challenges of edge computing and their latest research progress were summarized.With the development of theory and technology related to edge computing,it will become a key technology to promote the upgrade of Internet of things (IoT) services.For this reason,two applications of edge computing in manufacturing and security monitoring were introduced.

    State-of-the-art survey on resource optimization in edge computing
    Zhihao QU, Baoliu YE, Guihai CHEN, Bin TANG, Chenghao GUO
    2019, 5(2):  17-33.  doi:10.11959/j.issn.2096-0271.2019011
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    The traditional centralized architecture,known as cloud computing,cannot accommodate such user demands in an efficient and timely manner.To cope with this problem,edge computing architectures have been proposed with the core concept of that“data processing should be close to the data source”.Firstly,paradigms of edge computing was introduced,including micro data center,cloudlet,fog computing,and mobile edge computing,and the advantages of edge computing from the perspective of resource integration was discussed.Then,related works of resource optimization in edge computing was reviewed and summarized,and these works was discussed via three directions,i.e.,computation offloading,distributed caching and high performance transmission,corresponding to core resources as computing,storage and communication.Finally,trends of development and future directions were presented as well.

    A survey on the security technology of edge computing
    Jie LING, Jiahui CHEN, Yu LUO, Siliang ZHANG
    2019, 5(2):  34-52.  doi:10.11959/j.issn.2096-0271.2019012
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    With the continuous development of Internet of things applications,a large number of mobile terminal devices participate in service computing.Traditional cloud computing models cannot adapt to the rapid growth of data generated by network edge devices,and edge computing models emerge and become a research hotspot in recent years.The concept of edge computing and the reference model of edge computing in the Internet of things were introduced,the vulnerabilities of edge devices and the main research results of cryptographic security technology in edge computing were summarized,and that symmetric cryptography technology is not suitable for communication between edge devices,and identity-based cryptography technology is more suitable for communication between edge devices and edge devices was pointed out.Paired-based cryptography is more suitable for the communication between edge devices and base stations.The application of two post-quantum cryptography technologies in edge devices was discussed.Finally,some suggestions on the research of edge computing security technology were put forward.

    Edge intelligence:a new nexus of edge computing and artificial intelligence
    Zhi ZHOU, Shuai YU, Xu CHEN
    2019, 5(2):  53-63.  doi:10.11959/j.issn.2096-0271.2019013
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    Artificial intelligence (AI) and edge computing (EC) represent two of today’s most popular technologies.There is a great potential to coordinate these two emerging techniques to facilitate the further advent of both sides.Through three research cases,the profound benefits were demonstrated when AI and EC synergize.Specifically,from the perspective of EC for AI,to efficiently run deep learning at the network edge,a collaborative and on-demand deep neural network (DNN) co-inference framework with device-edge synergy was proposed.By applying DNN partitioning and right-sizing,it minimizes the inference latency under target accuracy.On the other hand,from the perspective of AI for EC,for the dynamical placement of edge computing services,two methods were proposed:an online-learning based adaptive service migration strategy and a factor graph model driven predictive service migration technique.

    Edge computing enabled smart grid
    Cong ZHANG, Xiaoyi FAN, Xiaoten LIU, Haitian PANG, Lifeng SUN, Jiangchuan LIU
    2019, 5(2):  64-78.  doi:10.11959/j.issn.2096-0271.2019014
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    How to mitigate such pressure has become a critical challenge for global grid enterprises.Empowered by the development of IoT techniques,high-speed communications,and AI-chips,traditional grid enterprises are trying to adopt these new techniques to solve the challenge,especially in heterogeneous data collection,energy scheduling,and AI-enabled monitoring.Firstly,the background of smart grid and the history of edge computing were introduced.Then,edge computing enabled solutions were illustrated in three typical grid scenarios.Finally,two edge computing platforms at home and abroad were introduced.The main technologies and application scenarios of them were also analyzed.

    Forest fire monitoring system based on edge computing
    Ke ZHANG, Ying YE, Hong ZHANG
    2019, 5(2):  79-88.  doi:10.11959/j.issn.2096-0271.2019015
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    The application of cloud computing and image processing technology has greatly changed the way of forest fire monitoring.The forest fire monitoring system based on cloud architecture has shortcomings in real-time detection and image algorithm configuration.In order to solve these problems,a forest fire monitoring system based on edge computing was designed.This system adopts the edge computing method and uses edge computers or servers in forest monitoring stations to perform image processing tasks of fire detection,which significantly improves the real-time performance of fire detection and early warning.On the other hand,the system introduces a function module for algorithm reconfiguration,which facilitates the iteration and update of the algorithms,reduces the cost of system redevelopment,and further improves the practicability of the system.

    STUDY
    A scalable CPU-MIC coordinated drug-finding tool by frequent subgraph mining
    Shaoliang PENG, Qi NIU, Kenli LI, Quan ZOU
    2019, 5(2):  89-103.  doi:10.11959/j.issn.2096-0271.2019016
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    Frequent subgraph mining is an important issue to be solved in many practical fields.Due to the computational intensiveness,the mining of the atlas and the large capacity of the results,the existing solutions can not meet the time requirements,and its efficiency is currently the main challenge.The frequent subgraph mining tool cmFSM for parallel acceleration was originally proposed.cmFSM performs parallel optimization on three levels:fine-grained OpenMP parallelization on a single node,multi-node multi-process parallelization and CPU-MIC collaborative parallelization.cmFSM is twice as fast as the best CPU-based algorithm on a single node and provides scalability in a multi-node approach.In the future,we will continue to improve the scalability of multiple solutions.The results show that even with only a few parallel computing resources,cmFSM is significantly better than the most advanced algorithms available.This fully demonstrates the effectiveness of the proposed tool in the field of bioinformatics.

    APPLICATION
    Risk management of smart grid data assets
    Aihua LI, Siguang CHEN, Yuejin ZHANG
    2019, 5(2):  104-115.  doi:10.11959/j.issn.2096-0271.2019017
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    Smart grid is a strategic level construction project being promoted.However,due to the complexity of the project itself,the level of smart grid data management in various departments in China is uneven,and there are many risks in data assetization.Therefore,risk management has become an essential part of the advancement of data assetization work.The characteristics of smart grid data assets were analyzed.Combining the characteristics of power industry,big data research methods and risk management elements,risk identification and management of smart grid data assets were advised,including data governance,data security,supply chain data sharing,customer credit risk control and smart grid robustness construction.And they were supported by an actual project and implementation effect of China’s power grid.Finally,the future development trend was analyzed.

    FORUM
    Prospects of blockchain application in smart agriculture
    Zhongfu SUN, Yongli LI, Feixiang ZHENG, Keming DU, Juncheng MA, Delong ZHANG
    2019, 5(2):  116-124.  doi:10.11959/j.issn.2096-0271.2019018
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    At present time,China is entering an important period of upgradation and transitionin agriculture,therefore,promoting smart agriculture development will be becoming the prior way with integration of modern information technology.Firstly,the current status of agriculture in China and the necessity of smart agriculture were analyzed,including possible blockchain application.Secondly,the connotation and its main trend for blockchain were briefly introduced.Thirdly,the main fields of blockchain application were summed up for smart agriculture.Finally,the prospects were set forth and some important suggestions were proposed for agricultural blockchain in future.

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