IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v14y2018i6p1550147718780933.html
   My bibliography  Save this article

Green city: An efficient task joint execution strategy for mobile micro-learning

Author

Listed:
  • Li Yang
  • Ruijuan Zheng
  • Junlong Zhu
  • Mingchuan Zhang
  • Ruoshui Liu
  • Qingtao Wu

Abstract

Mobile micro-learning has received extensive attention in the research of smart cities because it is a novel fusion service mode of the mobile Internet, cloud computing, and micro-learning. However, due to the explosively increased applications of the mobile micro-learning and the limited resources of mobile terminals, an effective energy saving approach for mobile micro-learning is urgently required. For this end, this article proposes an efficient task joint execution strategy to reduce energy consumption. First, a new matching method of time series is proposed to obtain the latest requested record, which can provide guidance for the selection of a future service mode. Second, a mapping-level service mode and a cloud-level service mode are proposed to achieve seamless switching. Finally, the genetic algorithm is used to find the optimal executive strategy. In addition, the experimental results show that the proposed method can effectively realize the target of energy saving by using real data set.

Suggested Citation

  • Li Yang & Ruijuan Zheng & Junlong Zhu & Mingchuan Zhang & Ruoshui Liu & Qingtao Wu, 2018. "Green city: An efficient task joint execution strategy for mobile micro-learning," International Journal of Distributed Sensor Networks, , vol. 14(6), pages 15501477187, June.
    • Handle: RePEc:sae:intdis:v:14:y:2018:i:6:p:1550147718780933
    DOI: 10.1177/1550147718780933
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1550147718780933
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1550147718780933?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Fred Glover, 1990. "Tabu Search: A Tutorial," Interfaces, INFORMS, vol. 20(4), pages 74-94, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. H. A. J. Crauwels & C. N. Potts & L. N. Van Wassenhove, 1998. "Local Search Heuristics for the Single Machine Total Weighted Tardiness Scheduling Problem," INFORMS Journal on Computing, INFORMS, vol. 10(3), pages 341-350, August.
    2. E A Silver, 2004. "An overview of heuristic solution methods," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(9), pages 936-956, September.
    3. Cazzaro, Davide & Fischetti, Martina & Fischetti, Matteo, 2020. "Heuristic algorithms for the Wind Farm Cable Routing problem," Applied Energy, Elsevier, vol. 278(C).
    4. Xi Chen & Enlu Zhou, 2015. "Population model-based optimization," Journal of Global Optimization, Springer, vol. 63(1), pages 125-148, September.
    5. David R. Morrison & Jason J. Sauppe & Wenda Zhang & Sheldon H. Jacobson & Edward C. Sewell, 2017. "Cyclic best first search: Using contours to guide branch‐and‐bound algorithms," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(1), pages 64-82, February.
    6. Vaithyanathan, Shivakumar & Burke, Laura I. & Magent, Michael A., 1996. "Massively parallel analog tabu search using neural networks applied to simple plant location problems," European Journal of Operational Research, Elsevier, vol. 93(2), pages 317-330, September.
    7. Kolahan, F. & Liang, M., 1998. "An adaptive TS approach to JIT sequencing with variable processing times and sequence-dependent setups," European Journal of Operational Research, Elsevier, vol. 109(1), pages 142-159, August.
    8. Ko, Young Dae, 2019. "The airfare pricing and seat allocation problem in full-service carriers and subsidiary low-cost carriers," Journal of Air Transport Management, Elsevier, vol. 75(C), pages 92-102.
    9. Piñeyro, Pedro & Viera, Omar, 2010. "The economic lot-sizing problem with remanufacturing and one-way substitution," International Journal of Production Economics, Elsevier, vol. 124(2), pages 482-488, April.
    10. Tarasewich, Peter & McMullen, Patrick R., 2001. "A pruning heuristic for use with multisource product design," European Journal of Operational Research, Elsevier, vol. 128(1), pages 58-73, January.
    11. Tsai, Ying-Wei & D. Gemmill, Douglas, 1998. "Using tabu search to schedule activities of stochastic resource-constrained projects," European Journal of Operational Research, Elsevier, vol. 111(1), pages 129-141, November.
    12. Daniels, Richard L. & Rummel, Jeffrey L. & Schantz, Robert, 1998. "A model for warehouse order picking," European Journal of Operational Research, Elsevier, vol. 105(1), pages 1-17, February.
    13. Coslovich, Luca & Pesenti, Raffaele & Ukovich, Walter, 2006. "A two-phase insertion technique of unexpected customers for a dynamic dial-a-ride problem," European Journal of Operational Research, Elsevier, vol. 175(3), pages 1605-1615, December.
    14. Rolland, Erik & Schilling, David A. & Current, John R., 1997. "An efficient tabu search procedure for the p-Median Problem," European Journal of Operational Research, Elsevier, vol. 96(2), pages 329-342, January.
    15. Fernando Rubio & Ismael Rodríguez, 2019. "Water-Based Metaheuristics: How Water Dynamics Can Help Us to Solve NP-Hard Problems," Complexity, Hindawi, vol. 2019, pages 1-13, April.
    16. Martina Fischetti & Michele Monaci, 2016. "Proximity search heuristics for wind farm optimal layout," Journal of Heuristics, Springer, vol. 22(4), pages 459-474, August.
    17. Rex K. Kincaid & Keith E. Laba & Sharon L. Padula, 1997. "Quelling Cabin Noise in Turboprop Aircraft via Active Control," Journal of Combinatorial Optimization, Springer, vol. 1(3), pages 229-250, October.
    18. Graeme J. Doole & David J. Pannell, 2008. "Optimisation of a Large, Constrained Simulation Model using Compressed Annealing," Journal of Agricultural Economics, Wiley Blackwell, vol. 59(1), pages 188-206, February.
    19. Mayer, D. G. & Belward, J. A. & Widell, H. & Burrage, K., 1999. "Survival of the fittest--genetic algorithms versus evolution strategies in the optimization of systems models," Agricultural Systems, Elsevier, vol. 60(2), pages 113-122, May.
    20. M. Calhoun, Kevin & Deckro, Richard F. & Moore, James T. & Chrissis, James W. & Van Hove, John C., 2002. "Planning and re-planning in project and production scheduling," Omega, Elsevier, vol. 30(3), pages 155-170, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:intdis:v:14:y:2018:i:6:p:1550147718780933. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.