IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v14y2022i10p297-d944192.html
   My bibliography  Save this article

Improved Dragonfly Optimization Algorithm for Detecting IoT Outlier Sensors

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/14/10/297/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/14/10/297/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yang, Dixiong & Li, Gang & Cheng, Gengdong, 2007. "On the efficiency of chaos optimization algorithms for global optimization," Chaos, Solitons & Fractals, Elsevier, vol. 34(4), pages 1366-1375.
    2. 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.
    3. Shabana Urooj & Fadwa Alrowais & Ramya Kuppusamy & Yuvaraja Teekaraman & Hariprasath Manoharan, 2021. "New Gen Controlling Variable Using Dragonfly Algorithm in PV Panel," Energies, MDPI, vol. 14(4), pages 1-14, February.
    4. 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.
    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. Hamza Mohammed Ridha Al-Khafaji, 2022. "Improving Quality Indicators of the Cloud-Based IoT Networks Using an Improved Form of Seagull Optimization Algorithm," Future Internet, MDPI, vol. 14(10), pages 1-13, September.
    2. Sun, Yeong-Jeu, 2009. "An exponential observer for the generalized Rossler chaotic system," Chaos, Solitons & Fractals, Elsevier, vol. 40(5), pages 2457-2461.
    3. Lina Wang & Ehtisham Lodhi & Pu Yang & Hongcheng Qiu & Waheed Ur Rehman & Zeeshan Lodhi & Tariku Sinshaw Tamir & M. Adil Khan, 2022. "Adaptive Local Mean Decomposition and Multiscale-Fuzzy Entropy-Based Algorithms for the Detection of DC Series Arc Faults in PV Systems," Energies, MDPI, vol. 15(10), pages 1-16, May.
    4. Cui, Yunfei & Geng, Zhiqiang & Zhu, Qunxiong & Han, Yongming, 2017. "Review: Multi-objective optimization methods and application in energy saving," Energy, Elsevier, vol. 125(C), pages 681-704.
    5. Wei-Chiang Hong & Yucheng Dong & Chien-Yuan Lai & Li-Yueh Chen & Shih-Yung Wei, 2011. "SVR with Hybrid Chaotic Immune Algorithm for Seasonal Load Demand Forecasting," Energies, MDPI, vol. 4(6), pages 1-18, June.
    6. Imene Khenissi & Tawfik Guesmi & Ismail Marouani & Badr M. Alshammari & Khalid Alqunun & Saleh Albadran & Salem Rahmani & Rafik Neji, 2023. "Energy Management Strategy for Optimal Sizing and Siting of PVDG-BES Systems under Fixed and Intermittent Load Consumption Profile," Sustainability, MDPI, vol. 15(2), pages 1-28, January.
    7. Coelho, Leandro dos Santos, 2009. "Reliability–redundancy optimization by means of a chaotic differential evolution approach," Chaos, Solitons & Fractals, Elsevier, vol. 41(2), pages 594-602.
    8. Salil Bharany & Sandeep Sharma & Surbhi Bhatia & Mohammad Khalid Imam Rahmani & Mohammed Shuaib & Saima Anwar Lashari, 2022. "Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization," Sustainability, MDPI, vol. 14(10), pages 1-22, May.
    9. Sultan Almotairi & Elsayed Badr & Mustafa Abdul Salam & Alshimaa Dawood, 2023. "Three Chaotic Strategies for Enhancing the Self-Adaptive Harris Hawk Optimization Algorithm for Global Optimization," Mathematics, MDPI, vol. 11(19), pages 1-27, October.
    10. Cheng, Shen & Zhao, Gaiju & Gao, Ming & Shi, Yuetao & Huang, Mingming & Yousefi, Nasser, 2021. "Optimal hybrid energy system for locomotive utilizing improved Locust Swarm optimizer," Energy, Elsevier, vol. 218(C).
    11. Wu, Cong & Li, Jiaxuan & Liu, Wenjin & He, Yuzhe & Nourmohammadi, Samad, 2023. "Short-term electricity demand forecasting using a hybrid ANFIS–ELM network optimised by an improved parasitism–predation algorithm," Applied Energy, Elsevier, vol. 345(C).
    12. Martin Ćalasan & Dražen Jovanović & Vesna Rubežić & Saša Mujović & Slobodan Đukanović, 2019. "Estimation of Single-Diode and Two-Diode Solar Cell Parameters by Using a Chaotic Optimization Approach," Energies, MDPI, vol. 12(21), pages 1-14, November.
    13. El-Shorbagy, M.A. & Mousa, A.A. & Nasr, S.M., 2016. "A chaos-based evolutionary algorithm for general nonlinear programming problems," Chaos, Solitons & Fractals, Elsevier, vol. 85(C), pages 8-21.
    14. Hossein Lotfi, 2022. "A Multiobjective Evolutionary Approach for Solving the Multi-Area Dynamic Economic Emission Dispatch Problem Considering Reliability Concerns," Sustainability, MDPI, vol. 15(1), pages 1-23, December.
    15. Rani, Mamta & Agarwal, Rashi, 2009. "A new experimental approach to study the stability of logistic map," Chaos, Solitons & Fractals, Elsevier, vol. 41(4), pages 2062-2066.
    16. Naanaa, Anis, 2015. "Fast chaotic optimization algorithm based on spatiotemporal maps for global optimization," Applied Mathematics and Computation, Elsevier, vol. 269(C), pages 402-411.
    17. Li, Huan & Li, Kun & Zafetti, Nicholas & Gu, Jianfeng, 2020. "Improvement of energy supply configuration for telecommunication system in remote area s based on improved chaotic world cup optimization algorithm," Energy, Elsevier, vol. 192(C).
    18. Rani, Mamta & Agarwal, Rashi, 2009. "Generation of fractals from complex logistic map," Chaos, Solitons & Fractals, Elsevier, vol. 42(1), pages 447-452.
    19. Deepak Kumar & Mamta Rani, 2022. "Alternated Superior Chaotic Biogeography-Based Algorithm for Optimization Problems," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 13(1), pages 1-39, January.
    20. He, Yao-Yao & Zhou, Jian-Zhong & Xiang, Xiu-Qiao & Chen, Heng & Qin, Hui, 2009. "Comparison of different chaotic maps in particle swarm optimization algorithm for long-term cascaded hydroelectric system scheduling," Chaos, Solitons & Fractals, Elsevier, vol. 42(5), pages 3169-3176.

    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:jftint:v:14:y:2022:i:10:p:297-:d:944192. 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.