IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i23p3117-d694138.html
   My bibliography  Save this article

Automatic Path Planning Offloading Mechanism in Edge-Enabled Environments

Author

Listed:
  • Dušan Herich

    (Department of Cybernetics and Artificial Intelligence, Technical University of Košice, Vysokoškolská 4, 042 00 Košice, Slovakia)

  • Ján Vaščák

    (Department of Cybernetics and Artificial Intelligence, Technical University of Košice, Vysokoškolská 4, 042 00 Košice, Slovakia)

  • Iveta Zolotová

    (Department of Cybernetics and Artificial Intelligence, Technical University of Košice, Vysokoškolská 4, 042 00 Košice, Slovakia)

  • Alexander Brecko

    (Department of Cybernetics and Artificial Intelligence, Technical University of Košice, Vysokoškolská 4, 042 00 Košice, Slovakia)

Abstract

The utilization of edge-enabled cloud computing in unmanned aerial vehicles has facilitated advances in autonomous control by employing computationally intensive algorithms frequently related to traversal among different locations in an environment. A significant problem remains in designing an effective strategy to offload tasks from the edge to the cloud. This work focuses on creating such a strategy by employing a network evaluation method built on the mean opinion score metrics in concoction with machine learning algorithms for path length prediction to assess computational complexity and classification models to perform an offloading decision on the data provided by both network metrics and solution depth prediction. The proposed system is applied to the A* path planning algorithm, and the presented results demonstrate up to 94% accuracy in offloading decisions.

Suggested Citation

  • Dušan Herich & Ján Vaščák & Iveta Zolotová & Alexander Brecko, 2021. "Automatic Path Planning Offloading Mechanism in Edge-Enabled Environments," Mathematics, MDPI, vol. 9(23), pages 1-25, December.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:23:p:3117-:d:694138
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/23/3117/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/23/3117/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Azadeh, K. & de Koster, M.B.M. & Roy, D., 2017. "Robotized Warehouse Systems: Developments and Research Opportunities," ERIM Report Series Research in Management ERS-2017-009-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    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. Bhoopalam, Anirudh Kishore & Agatz, Niels & Zuidwijk, Rob, 2018. "Planning of truck platoons: A literature review and directions for future research," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 212-228.
    2. Wenquan Dong & Mingzhou Jin & Yanyan Wang & Peter Kelle, 2021. "Retrieval scheduling in crane-based 3D automated retrieval and storage systems with shuttles," Annals of Operations Research, Springer, vol. 302(1), pages 111-135, July.
    3. Calzavara, Martina & Sgarbossa, Fabio & Persona, Alessandro, 2019. "Vertical Lift Modules for small items order picking: an economic evaluation," International Journal of Production Economics, Elsevier, vol. 210(C), pages 199-210.
    4. Xie, Lin & Thieme, Nils & Krenzler, Ruslan & Li, Hanyi, 2021. "Introducing split orders and optimizing operational policies in robotic mobile fulfillment systems," European Journal of Operational Research, Elsevier, vol. 288(1), pages 80-97.
    5. Rieder, Mathias & Verbeet, Richard, 2020. "Realization and validation of a collaborative automated picking system," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain. Proceedings of the Hamburg International Conference of Lo, volume 29, pages 521-558, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    6. AERTS, Babiche & CORNELISSENS, Trijntje & SÖRENSEN, Kenneth, 2018. "The influence of e-commerce on the design of warehouses - a literature review," Working Papers 2018013, University of Antwerp, Faculty of Business and Economics.

    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:gam:jmathe:v:9:y:2021:i:23:p:3117-:d:694138. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.