Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization

Salil Bharany, Sandeep Sharma, Surbhi Bhatia, Mohammad Khalid Imam Rahmani, Mohammed Shuaib and Saima Anwar Lashari
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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

Sustainability, 2022, vol. 14, issue 10, 1-22

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.

Keywords: FANETS; energy efficiency; clustering; routing; WSN; Cloud; transmission range; bio-inspired (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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