IDEAS home Printed from https://ideas.repec.org/a/spr/joheur/v26y2020i5d10.1007_s10732-020-09445-x.html
   My bibliography  Save this article

IoT networks 3D deployment using hybrid many-objective optimization algorithms

Author

Listed:
  • Sami Mnasri

    (University of Toulouse)

  • Nejah Nasri

    (University of Tabuk
    University of Sfax)

  • Malek Alrashidi

    (University of Tabuk)

  • Adrien Bossche

    (University of Toulouse)

  • Thierry Val

    (University of Toulouse)

Abstract

When resolving many-objective problems, multi-objective optimization algorithms encounter several difficulties degrading their performances. These difficulties may concern the exponential execution time, the effectiveness of the mutation and recombination operators or finding the tradeoff between diversity and convergence. In this paper, the issue of 3D redeploying in indoor the connected objects (or nodes) in the Internet of Things collection networks (formerly known as wireless sensor nodes) is investigated. The aim is to determine the ideal locations of the objects to be added to enhance an initial deployment while satisfying antagonist objectives and constraints. In this regard, a first proposed contribution aim to introduce an hybrid model that includes many-objective optimization algorithms relying on decomposition (MOEA/D, MOEA/DD) and reference points (Two_Arch2, NSGA-III) while using two strategies for introducing the preferences (PI-EMO-PC) and the dimensionality reduction (MVU-PCA). This hybridization aims to combine the algorithms advantages for resolving the many-objective issues. The second contribution concerns prototyping and deploying real connected objects which allows assessing the performance of the proposed hybrid scheme on a real world environment. The obtained experimental and numerical results show the efficiency of the suggested hybridization scheme against the original algorithms.

Suggested Citation

  • Sami Mnasri & Nejah Nasri & Malek Alrashidi & Adrien Bossche & Thierry Val, 2020. "IoT networks 3D deployment using hybrid many-objective optimization algorithms," Journal of Heuristics, Springer, vol. 26(5), pages 663-709, October.
    • Handle: RePEc:spr:joheur:v:26:y:2020:i:5:d:10.1007_s10732-020-09445-x
    DOI: 10.1007/s10732-020-09445-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10732-020-09445-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10732-020-09445-x?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sinha, Ankur & Korhonen, Pekka & Wallenius, Jyrki & Deb, Kalyanmoy, 2014. "An interactive evolutionary multi-objective optimization algorithm with a limited number of decision maker calls," European Journal of Operational Research, Elsevier, vol. 233(3), pages 674-688.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Eyhab Al-Mahadeen & Mansoor Alghamdi & Ahmad S. Tarawneh & Majed Abdullah Alrowaily & Malek Alrashidi & Ibrahim S. Alkhazi & Almoutaz Mbaidin & Anas Ali Alkasasbeh & Mohammad Ali Abbadi & Ahmad B. Has, 2023. "Smartphone User Identification/Authentication Using Accelerometer and Gyroscope Data," Sustainability, MDPI, vol. 15(13), pages 1-25, July.
    2. Ghada A. Altarawneh & Ahmad B. Hassanat & Ahmad S. Tarawneh & Ahmad Abadleh & Malek Alrashidi & Mansoor Alghamdi, 2022. "Stock Price Forecasting for Jordan Insurance Companies Amid the COVID-19 Pandemic Utilizing Off-the-Shelf Technical Analysis Methods," Economies, MDPI, vol. 10(2), pages 1-18, February.
    3. Carine M. Rebello & Márcio A. F. Martins & Daniel D. Santana & Alírio E. Rodrigues & José M. Loureiro & Ana M. Ribeiro & Idelfonso B. R. Nogueira, 2021. "From a Pareto Front to Pareto Regions: A Novel Standpoint for Multiobjective Optimization," Mathematics, MDPI, vol. 9(24), pages 1-21, December.

    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. Yadav, Deepanshu & Nagar, Deepak & Ramu, Palaniappan & Deb, Kalyanmoy, 2023. "Visualization-aided multi-criteria decision-making using interpretable self-organizing maps," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1183-1200.
    2. Kadziński, Miłosz & Tervonen, Tommi & Tomczyk, Michał K. & Dekker, Rommert, 2017. "Evaluation of multi-objective optimization approaches for solving green supply chain design problems," Omega, Elsevier, vol. 68(C), pages 168-184.

    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:spr:joheur:v:26:y:2020:i:5:d:10.1007_s10732-020-09445-x. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.