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Improved Dragonfly Optimization Algorithm for Detecting IoT Outlier Sensors

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  • Maytham N. Meqdad

    (Computer Techniques Engineering Department, Al-Mustaqbal University College, Hillah 51001, Iraq)

  • Seifedine Kadry

    (Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway
    Department of Electrical and Computer Engineering, Lebanese American University, Byblos 1102, Lebanon
    Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman 20550, United Arab Emirates)

  • Hafiz Tayyab Rauf

    (Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent ST4 2DE, UK)

Abstract

Things receive digital intelligence by being connected to the Internet and by adding sensors. With the use of real-time data and this intelligence, things may communicate with one another autonomously. The environment surrounding us will become more intelligent and reactive, merging the digital and physical worlds thanks to the Internet of things (IoT). In this paper, an optimal methodology has been proposed for distinguishing outlier sensors of the Internet of things based on a developed design of a dragonfly optimization technique. Here, a modified structure of the dragonfly optimization algorithm is utilized for optimal area coverage and energy consumption reduction. This paper uses four parameters to evaluate its efficiency: the minimum number of nodes in the coverage area, the lifetime of the network, including the time interval from the start of the first node to the shutdown time of the first node, and the network power. The results of the suggested method are compared with those of some other published methods. The results show that by increasing the number of steps, the energy of the live nodes will eventually run out and turn off. In the LEACH method, after 350 steps, the RED-LEACH method, after 750 steps, and the GSA-based method, after 915 steps, the nodes start shutting down, which occurs after 1227 steps for the proposed method. This means that the nodes are turned off later. Simulations indicate that the suggested method achieves better results than the other examined techniques according to the provided performance parameters.

Suggested Citation

  • Maytham N. Meqdad & Seifedine Kadry & Hafiz Tayyab Rauf, 2022. "Improved Dragonfly Optimization Algorithm for Detecting IoT Outlier Sensors," Future Internet, MDPI, vol. 14(10), pages 1-16, October.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:10:p:297-:d:944192
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    References listed on IDEAS

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    1. Ehtisham Lodhi & Fei-Yue Wang & Gang Xiong & Ghulam Ali Mallah & Muhammad Yaqoob Javed & Tariku Sinshaw Tamir & David Wenzhong Gao, 2021. "A Dragonfly Optimization Algorithm for Extracting Maximum Power of Grid-Interfaced PV Systems," Sustainability, MDPI, vol. 13(19), pages 1-27, September.
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    4. Ali Alferaidi & Kusum Yadav & Yasser Alharbi & Navid Razmjooy & Wattana Viriyasitavat & Kamal Gulati & Sandeep Kautish & Gaurav Dhiman & Ramin Ranjbarzadeh, 2022. "Distributed Deep CNN-LSTM Model for Intrusion Detection Method in IoT-Based Vehicles," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-8, March.
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