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    Research on incentive mechanism for mobile intelligent edge computing by Shuyun LUO, Yuzhou WEN, Weiqiang XU, Shenghong ZHU

    Published 2019-06-01
    “…As a new architecture,mobile edge computing gives edge users stronger capabilities of computing,storage and communication,but it needs reasonable incentives mechanism to motivate edge users to provide resources.In terms of the three typical scenarios of mobile intelligent edge computing:computation offloading,edge caching and data collection,the incentive mechanism in the above scenarios was studied at first,then the core scientific problems were proposed that need to be solved in the incentive mechanism design of mobile intelligent edge computing from three perspectives of service quality,network quality and data quality.Finally,the technical challenges in the process of solving the above problems were analysed deeply and the corresponding feasible solutions were given.…”
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  4. 1184

    Platoon intelligence: edge learning in vehicle platooning networks by Junhui Zhao, Xiaoting Ma, Wenqi Yang, Huaicheng Li, Dongming Wang

    Published 2025-01-01
    “…It is necessary to make full use of these spare resources. In this paper, we investigate a platoon intelligence-based edge learning (PIEL) framework for the deep integration of terminal and network edge. …”
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  5. 1185

    The Edge Odd Graceful Labeling of Water Wheel Graphs by Mohammed Aljohani, Salama Nagy Daoud

    Published 2024-12-01
    “…This labeling is defined as a bijection <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>g</mi><mo>:</mo><mi>E</mi><mo>(</mo><mi>G</mi><mo>)</mo><mo>→</mo><mo>{</mo><mn>1</mn><mo>,</mo><mn>3</mn><mo>,</mo><mo>…</mo><mo>,</mo><mn>2</mn><mi>m</mi><mo>−</mo><mn>1</mn><mo>}</mo></mrow></semantics></math></inline-formula>, from which an injective transformation is derived, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>g</mi><mo>*</mo></msup><mo>:</mo><mi>V</mi><mrow><mo>(</mo><mi>G</mi><mo>)</mo></mrow><mo>→</mo><mrow><mo>{</mo><mn>1</mn><mo>,</mo><mn>2</mn><mo>,</mo><mn>3</mn><mo>,</mo><mo>…</mo><mo>,</mo><mn>2</mn><mi>m</mi><mo>−</mo><mn>1</mn><mo>}</mo></mrow></mrow></semantics></math></inline-formula>, from the rule that the image of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>u</mi><mo>∈</mo><mi>V</mi><mo>(</mo><mi>G</mi><mo>)</mo></mrow></semantics></math></inline-formula> under <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>g</mi><mo>*</mo></msup></semantics></math></inline-formula> is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mo>∑</mo><mrow><mi>u</mi><mi>v</mi><mo>∈</mo><mi>E</mi><mo>(</mo><mi>G</mi><mo>)</mo></mrow></msub><mi>g</mi><mrow><mo>(</mo><mi>u</mi><mi>v</mi><mo>)</mo></mrow><mspace width="0.277778em"></mspace><mi>mod</mi><mspace width="0.277778em"></mspace><mrow><mo>(</mo><mn>2</mn><mi>m</mi><mo>)</mo></mrow></mrow></semantics></math></inline-formula>. …”
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  6. 1186

    V2X collaborative caching and resource allocation in MEC-based IoV by Fangwei LI, Haibo ZHANG, Zixin WANG

    Published 2021-02-01
    “…Aiming at the problem that with the rapidly increasing of multimedia services in IoV, a large amount of data change has bought a heavy burden on the mobile networks, a V2X collaborative caching and resource allocation framework in MEC-based IoV was constructed.A V2X cooperative caching and resource allocation mechanism was proposed to achieve the effective allocation of computing resources, caching resources, and communication resources in the network.The graph coloring model was used to allocate channels to the offloading users.Lagrange multiplier method was used to allocate power and computing resources.The simulation results show that the proposed mechanism can effectively reduce system overhead and reduce task completion delay under different system parameters.…”
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  7. 1187

    Comparative Evaluation of Intron Prediction Methods and Detection of Plant Genome Annotation Using Intron Length Distributions by Long Yang, Hwan-Gue Cho

    Published 2012-03-01
    “…Intron prediction is an important problem of the constantly updated genome annotation. Using two model plant (rice and Arabidopsis) genomes, we compared two well-known intron prediction tools: the Blast-Like Alignment Tool (BLAT) and Sim4cc. …”
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    Hybrid Onboard Smartphone Sensors Measurements to Improve Heading Estimation for Indoors Positioning Solutions by Haval Darwesh Abdalkarim, Halgurd Sarhang Maghdid

    Published 2019-10-01
    Subjects: “…localization; sensors; heading estimation; fusing multi-sensor.…”
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