IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i10p6159-d818850.html
   My bibliography  Save this article

Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization

Author

Listed:
  • Salil Bharany

    (Department of Computer Engineering & Technology, Guru Nanak Dev University, Amritsar 143005, India)

  • Sandeep Sharma

    (Department of Computer Engineering & Technology, Guru Nanak Dev University, Amritsar 143005, India)

  • Surbhi Bhatia

    (Department of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Al Hofuf 36362, Saudi Arabia)

  • Mohammad Khalid Imam Rahmani

    (College of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi Arabia)

  • Mohammed Shuaib

    (College of Computer Science & IT, Jazan University, Jazan 45142, Saudi Arabia)

  • Saima Anwar Lashari

    (College of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi Arabia)

Abstract

FANET (flying ad-hoc networks) is currently a trending research topic. Unmanned aerial vehicles (UAVs) have two significant challenges: short flight times and inefficient routing due to low battery power and high mobility. Due to these topological restrictions, FANETS routing is considered more complicated than MANETs or VANETs. Clustering approaches based on artificial intelligence (AI) approaches can be used to solve complex routing issues when static and dynamic routings fail. Evolutionary algorithm-based clustering techniques, such as moth flame optimization, and ant colony optimization, can be used to solve these kinds of problems with routes. Moth flame optimization gives excellent coverage while consuming little energy and requiring a minimum number of cluster heads (CHs) for routing. This paper employs a moth flame optimization algorithm for network building and node deployment. Then, we employ a variation of the K-Means Density clustering approach to choosing the cluster head. Choosing the right cluster heads increases the cluster’s lifespan and reduces routing traffic. Moreover, it lowers the number of routing overheads. This step is followed by MRCQ image-based compression techniques to reduce the amount of data that must be transmitted. Finally, the reference point group mobility model is used to send data by the most optimal path. Particle swarm optimization (PSO), ant colony optimization (ACO), and grey wolf optimization (GWO) were put to the test against our proposed EECP-MFO. Several metrics are used to gauge the efficiency of our proposed method, including the number of clusters, cluster construction time, cluster lifespan, consistency of cluster heads, and energy consumption. This paper demonstrates that our proposed algorithm performance is superior to the current state-of-the-art approaches using experimental results.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:6159-:d:818850
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/10/6159/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/10/6159/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Farhan Aadil & Khalid Bashir Bajwa & Salabat Khan & Nadeem Majeed Chaudary & Adeel Akram, 2016. "CACONET: Ant Colony Optimization (ACO) Based Clustering Algorithm for VANET," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-21, May.
    2. Salil Bharany & Sandeep Sharma & Sumit Badotra & Osamah Ibrahim Khalaf & Youseef Alotaibi & Saleh Alghamdi & Fawaz Alassery, 2021. "Energy-Efficient Clustering Scheme for Flying Ad-Hoc Networks Using an Optimized LEACH Protocol," Energies, MDPI, vol. 14(19), pages 1-20, September.
    3. 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.
    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. Mohammed Shuaib & Sumit Badotra & Muhammad Irfan Khalid & Abeer D. Algarni & Syed Sajid Ullah & Sami Bourouis & Jawaid Iqbal & Salil Bharany & Lokesh Gundaboina, 2022. "A Novel Optimization for GPU Mining Using Overclocking and Undervolting," Sustainability, MDPI, vol. 14(14), pages 1-15, July.
    2. Mohammed I. Alghamdi, 2022. "Optimization of Load Balancing and Task Scheduling in Cloud Computing Environments Using Artificial Neural Networks-Based Binary Particle Swarm Optimization (BPSO)," Sustainability, MDPI, vol. 14(19), pages 1-20, September.
    3. Sanjay Kumar & Rafeeq Ahmed & Salil Bharany & Mohammed Shuaib & Tauseef Ahmad & Elsayed Tag Eldin & Ateeq Ur Rehman & Muhammad Shafiq, 2022. "Exploitation of Machine Learning Algorithms for Detecting Financial Crimes Based on Customers’ Behavior," Sustainability, MDPI, vol. 14(21), pages 1-24, October.
    4. Edeh Michael Onyema & M. Anand Kumar & Sundaravadivazhagn Balasubaramanian & Salil Bharany & Ateeq Ur Rehman & Elsayed Tag Eldin & Muhammad Shafiq, 2022. "A Security Policy Protocol for Detection and Prevention of Internet Control Message Protocol Attacks in Software Defined Networks," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    5. Akashdeep Bhardwaj & Keshav Kaushik & Salil Bharany & Ateeq Ur Rehman & Yu-Chen Hu & Elsayed Tag Eldin & Nivin A. Ghamry, 2022. "IIoT: Traffic Data Flow Analysis and Modeling Experiment for Smart IoT Devices," Sustainability, MDPI, vol. 14(21), pages 1-18, November.
    6. Keshav Kaushik & Akashdeep Bhardwaj & Salil Bharany & Naif Alsharabi & Ateeq Ur Rehman & Elsayed Tag Eldin & Nivin A. Ghamry, 2022. "A Machine Learning-Based Framework for the Prediction of Cervical Cancer Risk in Women," Sustainability, MDPI, vol. 14(19), pages 1-15, September.
    7. Shadab Alam & Mohammed Shuaib & Sadaf Ahmad & Dushantha Nalin K. Jayakody & Ammar Muthanna & Salil Bharany & Ibrahim A. Elgendy, 2022. "Blockchain-Based Solutions Supporting Reliable Healthcare for Fog Computing and Internet of Medical Things (IoMT) Integration," Sustainability, MDPI, vol. 14(22), pages 1-17, November.
    8. Manreet Sohal & Salil Bharany & Sandeep Sharma & Mashael S. Maashi & Mohammed Aljebreen, 2022. "A Hybrid Multi-Cloud Framework Using the IBBE Key Management System for Securing Data Storage," Sustainability, MDPI, vol. 14(20), pages 1-24, October.
    9. Akashdeep Bhardwaj & Keshav Kaushik & Mashael S. Maashi & Mohammed Aljebreen & Salil Bharany, 2022. "Alternate Data Stream Attack Framework to Perform Stealth Attacks on Active Directory Hosts," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    10. Salil Bharany & Sandeep Sharma & Osamah Ibrahim Khalaf & Ghaida Muttashar Abdulsahib & Abeer S. Al Humaimeedy & Theyazn H. H. Aldhyani & Mashael Maashi & Hasan Alkahtani, 2022. "A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing," Sustainability, MDPI, vol. 14(10), pages 1-89, May.
    11. Supreet Kaur & Sandeep Sharma & Ateeq Ur Rehman & Elsayed Tag Eldin & Nivin A. Ghamry & Muhammad Shafiq & Salil Bharany, 2022. "Predicting Infection Positivity, Risk Estimation, and Disease Prognosis in Dengue Infected Patients by ML Expert System," Sustainability, MDPI, vol. 14(20), pages 1-20, October.

    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. Ghassan Husnain & Shahzad Anwar & Gulbadan Sikander & Armughan Ali & Sangsoon Lim, 2023. "A Bio-Inspired Cluster Optimization Schema for Efficient Routing in Vehicular Ad Hoc Networks (VANETs)," Energies, MDPI, vol. 16(3), pages 1-20, February.
    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. Akashdeep Bhardwaj & Keshav Kaushik & Mashael S. Maashi & Mohammed Aljebreen & Salil Bharany, 2022. "Alternate Data Stream Attack Framework to Perform Stealth Attacks on Active Directory Hosts," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    4. Mohammed I. Alghamdi, 2022. "Optimization of Load Balancing and Task Scheduling in Cloud Computing Environments Using Artificial Neural Networks-Based Binary Particle Swarm Optimization (BPSO)," Sustainability, MDPI, vol. 14(19), pages 1-20, September.
    5. Keshav Kaushik & Akashdeep Bhardwaj & Salil Bharany & Naif Alsharabi & Ateeq Ur Rehman & Elsayed Tag Eldin & Nivin A. Ghamry, 2022. "A Machine Learning-Based Framework for the Prediction of Cervical Cancer Risk in Women," Sustainability, MDPI, vol. 14(19), pages 1-15, September.
    6. Salil Bharany & Sandeep Sharma & Osamah Ibrahim Khalaf & Ghaida Muttashar Abdulsahib & Abeer S. Al Humaimeedy & Theyazn H. H. Aldhyani & Mashael Maashi & Hasan Alkahtani, 2022. "A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing," Sustainability, MDPI, vol. 14(10), pages 1-89, May.
    7. Satheeshkumar Palanisamy & Balakumaran Thangaraju & Osamah Ibrahim Khalaf & Youseef Alotaibi & Saleh Alghamdi & Fawaz Alassery, 2021. "A Novel Approach of Design and Analysis of a Hexagonal Fractal Antenna Array (HFAA) for Next-Generation Wireless Communication," Energies, MDPI, vol. 14(19), pages 1-18, September.
    8. Manreet Sohal & Salil Bharany & Sandeep Sharma & Mashael S. Maashi & Mohammed Aljebreen, 2022. "A Hybrid Multi-Cloud Framework Using the IBBE Key Management System for Securing Data Storage," Sustainability, MDPI, vol. 14(20), pages 1-24, October.
    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. Mudassir Khan & A. Ilavendhan & C. Nelson Kennedy Babu & Vishal Jain & S. B. Goyal & Chaman Verma & Calin Ovidiu Safirescu & Traian Candin Mihaltan, 2022. "Clustering Based Optimal Cluster Head Selection Using Bio-Inspired Neural Network in Energy Optimization of 6LowPAN," Energies, MDPI, vol. 15(13), pages 1-14, June.
    11. 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).
    12. Sahar Ebadinezhad & Ziya Dereboylu & Enver Ever, 2019. "Clustering-Based Modified Ant Colony Optimizer for Internet of Vehicles (CACOIOV)," Sustainability, MDPI, vol. 11(9), pages 1-22, May.
    13. 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.
    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. Rejab Hajlaoui & Eesa Alsolami & Tarek Moulahi & Hervé Guyennet, 2019. "Construction of a stable vehicular ad hoc network based on hybrid genetic algorithm," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 71(3), pages 433-445, July.
    18. Edeh Michael Onyema & M. Anand Kumar & Sundaravadivazhagn Balasubaramanian & Salil Bharany & Ateeq Ur Rehman & Elsayed Tag Eldin & Muhammad Shafiq, 2022. "A Security Policy Protocol for Detection and Prevention of Internet Control Message Protocol Attacks in Software Defined Networks," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    19. Abida Sharif & Jian Ping Li & Muhammad Asim Saleem & Gunasekaran Manogran & Seifedine Kadry & Abdul Basit & Muhammad Attique Khan, 2021. "A dynamic clustering technique based on deep reinforcement learning for Internet of vehicles," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 757-768, March.
    20. Mohammed Shuaib & Sumit Badotra & Muhammad Irfan Khalid & Abeer D. Algarni & Syed Sajid Ullah & Sami Bourouis & Jawaid Iqbal & Salil Bharany & Lokesh Gundaboina, 2022. "A Novel Optimization for GPU Mining Using Overclocking and Undervolting," Sustainability, MDPI, vol. 14(14), pages 1-15, July.

    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:jsusta:v:14:y:2022:i:10:p:6159-:d:818850. 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.