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A Drones Optimal Path Planning Based on Swarm Intelligence Algorithms

Mahmoud Ragab1,2,3,*, Ali Altalbe1, Abdullah Saad Al-Malaise ALGhamdi4, S. Abdel-khalek5,6, Rashid A. Saeed7

1 Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
2 Center of Artificial Intelligence for Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia
3 Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
4 Department of Mathematics, Faculty of Science, Al-Azhar University, Naser City 11884, Cairo, Egypt
5 Department of Mathematics, Faculty of Science, Taif University, Taif, Saudi Arabia
6 Department of Mathematics, Faculty of Science, Sohag University, Sohag, Egypt
7 Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia

* Corresponding Author: Mahmoud Ragab. Email: email

Computers, Materials & Continua 2022, 72(1), 365-380. https://doi.org/10.32604/cmc.2022.024932

Abstract

The smart city comprises various interlinked elements which communicate data and offers urban life to citizen. Unmanned Aerial Vehicles (UAV) or drones were commonly employed in different application areas like agriculture, logistics, and surveillance. For improving the drone flying safety and quality of services, a significant solution is for designing the Internet of Drones (IoD) where the drones are utilized to gather data and people communicate to the drones of a specific flying region using the mobile devices is for constructing the Internet-of-Drones, where the drones were utilized for collecting the data, and communicate with others. In addition, the SIRSS-CIoD technique derives a tuna swarm algorithm-based clustering (TSA-C) technique to choose cluster heads (CHs) and organize clusters in IoV networks. Besides, the SIRSS-CIoD technique involves the design of a biogeography-based optimization (BBO) technique to an optimum route selection (RS) process. The design of clustering and routing techniques for IoD networks in smart cities shows the novelty of the study. A wide range of experimental analyses is carried out and the comparative study highlighted the improved performance of the SIRSS-CIoD technique over the other approaches.

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Cite This Article

APA Style
Ragab, M., Altalbe, A., ALGhamdi, A.S.A., Abdel-khalek, S., Saeed, R.A. (2022). A drones optimal path planning based on swarm intelligence algorithms. Computers, Materials & Continua, 72(1), 365-380. https://doi.org/10.32604/cmc.2022.024932
Vancouver Style
Ragab M, Altalbe A, ALGhamdi ASA, Abdel-khalek S, Saeed RA. A drones optimal path planning based on swarm intelligence algorithms. Comput Mater Contin. 2022;72(1):365-380 https://doi.org/10.32604/cmc.2022.024932
IEEE Style
M. Ragab, A. Altalbe, A.S.A. ALGhamdi, S. Abdel-khalek, and R.A. Saeed "A Drones Optimal Path Planning Based on Swarm Intelligence Algorithms," Comput. Mater. Contin., vol. 72, no. 1, pp. 365-380. 2022. https://doi.org/10.32604/cmc.2022.024932



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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