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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
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    References listed on IDEAS

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    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. 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.
    3. 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.
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    4. 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.
    5. 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.
    6. 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.
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    10. 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.
    11. 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.

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