The Experts below are selected from a list of 104250 Experts worldwide ranked by ideXlab platform

Lin Gao - One of the best experts on this subject based on the ideXlab platform.

  • exploiting massive d2d collaboration for Energy efficient mobile edge computing
    IEEE Wireless Communications, 2017
    Co-Authors: Xu Chen, Lin Gao
    Abstract:

    In this article we propose a novel D2D Crowd framework for 5G mobile edge computing, where a massive crowd of devices at the network edge leverage network-assisted D2D collaboration for computation and communication resource sharing. A key objective of this framework is to achieve Energy-efficient collaborative task executions at the network edge for mobile users. Specifically, we first introduce the D2D Crowd system model in detail, and then formulate the Energy-efficient D2D Crowd task assignment problem by taking into account the necessary constraints. We next propose a graph-matching-based optimal task assignment policy, and further evaluate its performance through extensive numerical study, which shows superior performance of more than 50 percent Energy Consumption Reduction over the case of local task executions. Finally, we also discuss the directions of extending the D2D Crowd framework by taking into account a variety of application factors.

  • Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing
    IEEE Wireless Communications, 2017
    Co-Authors: Xu Chen, Lingjun Pu, Weigang Wu, Lin Gao, Di Wu
    Abstract:

    In this article we propose a novel Device-to-Device (D2D) Crowd framework for 5G mobile edge computing, where a massive crowd of devices at the network edge leverage the network-assisted D2D collaboration for computation and communication resource sharing among each other. A key objective of this framework is to achieve Energy-efficient collaborative task executions at network-edge for mobile users. Specifically, we first introduce the D2D Crowd system model in details, and then formulate the Energy-efficient D2D Crowd task assignment problem by taking into account the necessary constraints. We next propose a graph matching based optimal task assignment policy, and further evaluate its performance through extensive numerical study, which shows a superior performance of more than 50% Energy Consumption Reduction over the case of local task executions. Finally, we also discuss the directions of extending the D2D Crowd framework by taking into variety of application factors.

Xu Chen - One of the best experts on this subject based on the ideXlab platform.

  • exploiting massive d2d collaboration for Energy efficient mobile edge computing
    IEEE Wireless Communications, 2017
    Co-Authors: Xu Chen, Lin Gao
    Abstract:

    In this article we propose a novel D2D Crowd framework for 5G mobile edge computing, where a massive crowd of devices at the network edge leverage network-assisted D2D collaboration for computation and communication resource sharing. A key objective of this framework is to achieve Energy-efficient collaborative task executions at the network edge for mobile users. Specifically, we first introduce the D2D Crowd system model in detail, and then formulate the Energy-efficient D2D Crowd task assignment problem by taking into account the necessary constraints. We next propose a graph-matching-based optimal task assignment policy, and further evaluate its performance through extensive numerical study, which shows superior performance of more than 50 percent Energy Consumption Reduction over the case of local task executions. Finally, we also discuss the directions of extending the D2D Crowd framework by taking into account a variety of application factors.

  • Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing
    IEEE Wireless Communications, 2017
    Co-Authors: Xu Chen, Lingjun Pu, Weigang Wu, Lin Gao, Di Wu
    Abstract:

    In this article we propose a novel Device-to-Device (D2D) Crowd framework for 5G mobile edge computing, where a massive crowd of devices at the network edge leverage the network-assisted D2D collaboration for computation and communication resource sharing among each other. A key objective of this framework is to achieve Energy-efficient collaborative task executions at network-edge for mobile users. Specifically, we first introduce the D2D Crowd system model in details, and then formulate the Energy-efficient D2D Crowd task assignment problem by taking into account the necessary constraints. We next propose a graph matching based optimal task assignment policy, and further evaluate its performance through extensive numerical study, which shows a superior performance of more than 50% Energy Consumption Reduction over the case of local task executions. Finally, we also discuss the directions of extending the D2D Crowd framework by taking into variety of application factors.

Di Wu - One of the best experts on this subject based on the ideXlab platform.

  • Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing
    IEEE Wireless Communications, 2017
    Co-Authors: Xu Chen, Lingjun Pu, Weigang Wu, Lin Gao, Di Wu
    Abstract:

    In this article we propose a novel Device-to-Device (D2D) Crowd framework for 5G mobile edge computing, where a massive crowd of devices at the network edge leverage the network-assisted D2D collaboration for computation and communication resource sharing among each other. A key objective of this framework is to achieve Energy-efficient collaborative task executions at network-edge for mobile users. Specifically, we first introduce the D2D Crowd system model in details, and then formulate the Energy-efficient D2D Crowd task assignment problem by taking into account the necessary constraints. We next propose a graph matching based optimal task assignment policy, and further evaluate its performance through extensive numerical study, which shows a superior performance of more than 50% Energy Consumption Reduction over the case of local task executions. Finally, we also discuss the directions of extending the D2D Crowd framework by taking into variety of application factors.

Bourouina Tarik - One of the best experts on this subject based on the ideXlab platform.

  • Thermal aspects of a micro thermal conductivity detector for micro gas chromatography
    IEEE, 2019
    Co-Authors: Ali Hany, Pirro Michele, Poulichet Patrick, Cesar William, Marty Frédéric, Azzouz Imadeddine, Gnambodoe-capochichi Martine, Nefzaoui E., Bourouina Tarik
    Abstract:

    International audienceWe report on a micro Thermal Conductivity Detector (µTCD) design, simulation, fabrication and experimental characterization. The considered detector has been fabricated and integrated in a full micro gas chromatography device. It has been experimentally tested for different carrier gases, air, nitrogen and ethanol with different concentrations of three pollutants, Toluene, Ethylbenzene, and Xylene for instance. The device reveals to be sensitive for concentrations as low as 500 ppm. We consider different operation modes under constant temperature and constant current. We also consider steady state and transient operation modes. We show that for the same device, sensitivity strongly depends on operation mode. In addition to sensitivity enhancement, the transient operation mode enables Energy Consumption Reduction and fast responses with measurements as fast as 0.9 ms. Experimental results are in very good agreement with 3D numerical multi-physical simulations based on Finite Element Method

Guibing Hong - One of the best experts on this subject based on the ideXlab platform.

  • current situation of Energy conservation in high Energy consuming industries in taiwan
    Energy Policy, 2007
    Co-Authors: David Yihliang Chan, Kuanghan Yang, Minhsien Chien, Guibing Hong
    Abstract:

    Abstract Growing concern in Taiwan has arisen about Energy Consumption and its adverse environmental impact. The current situation of Energy conservation in high Energy-consuming industries in Taiwan, including the iron and steel, chemical, cement, pulp and paper, textiles and electric/electrical industries has been presented. Since the Energy Consumption of the top 100 Energy users (T100) comprised over 50% of total industry Energy Consumption, focusing Energy Consumption Reduction efforts on T100 Energy users can achieve significant results. This study conducted on-site Energy audits of 314 firms in Taiwan during 2000–2004, and identified potential electricity savings of 1,022,656 MWH, fuel oil savings of 174,643 kiloliters (KL), steam coal savings of 98,620 ton, and natural gas (NG) savings of 10,430 kilo cubic meters. The total potential Energy saving thus was 489,505 KL of crude oil equivalent (KLOE), representing a Reduction of 1,447,841 ton in the carbon dioxide emissions, equivalent to the annual carbon dioxide absorption capacity of a 39,131-ha plantation forest.

  • current situation of Energy conservation in high Energy consuming industries in taiwan
    Energy Policy, 2007
    Co-Authors: David Yihliang Chan, Kuanghan Yang, Minhsien Chien, Chunghsuan Hsu, Guibing Hong
    Abstract:

    Abstract Growing concern in Taiwan has arisen about Energy Consumption and its adverse environmental impact. The current situation of Energy conservation in high Energy-consuming industries in Taiwan, including the iron and steel, chemical, cement, pulp and paper, textiles and electric/electrical industries has been presented. Since the Energy Consumption of the top 100 Energy users (T100) comprised over 50% of total industry Energy Consumption, focusing Energy Consumption Reduction efforts on T100 Energy users can achieve significant results. This study conducted on-site Energy audits of 314 firms in Taiwan during 2000–2004, and identified potential electricity savings of 1,022,656 MWH, fuel oil savings of 174,643 kiloliters (KL), steam coal savings of 98,620 ton, and natural gas (NG) savings of 10,430 kilo cubic meters. The total potential Energy saving thus was 489,505 KL of crude oil equivalent (KLOE), representing a Reduction of 1,447,841 ton in the carbon dioxide emissions, equivalent to the annual carbon dioxide absorption capacity of a 39,131-ha plantation forest.