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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
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    References listed on IDEAS

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    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.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.

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