Open Access

ARTICLE

Optimal Deep Learning Enabled Communication System for Unmanned Aerial Vehicles

Anwer Mustafa Hilal^1,*, Jaber S. Alzahrani², Dalia H. Elkamchouchi³, Majdy M. Eltahir⁴, Ahmed S. Almasoud⁵, Abdelwahed Motwakel¹, Abu Sarwar Zamani¹, Ishfaq Yaseen¹

1 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, 16278, Saudi Arabia
2 Department of Industrial Engineering, College of Engineering at Alqunfudah, Umm Al-Qura University, Saudi Arabia
3Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
4 Department of Information Systems, College of Science & Art at Mahayil, King Khalid University, Muhayel Aseer, 62529, Saudi Arabia
5 Department of Information Systems, College of Computer and Information Sciences, Prince Sultan University, Saudi Arabia

* Corresponding Author: Anwer Mustafa Hilal. Email:

Computer Systems Science and Engineering 2023, 45(1), 955-969. https://doi.org/10.32604/csse.2023.030132

Received 18 March 2022; Accepted 26 April 2022; Issue published 16 August 2022

Abstract

Recently, unmanned aerial vehicles (UAV) or drones are widely employed for several application areas such as surveillance, disaster management, etc. Since UAVs are limited to energy, efficient coordination between them becomes essential to optimally utilize the resources and effective communication among them and base station (BS). Therefore, clustering can be employed as an effective way of accomplishing smart communication systems among multiple UAVs. In this aspect, this paper presents a group teaching optimization algorithm with deep learning enabled smart communication system (GTOADL-SCS) technique for UAV networks. The proposed GTOADL-SCS model encompasses a two stage process namely clustering and classification. At the initial stage, the GTOADL-SCS model includes a GTOA based clustering scheme to elect cluster heads (CHs) and organize clusters. Besides, the GTOADL-SCS model develops a fitness function containing three input parameters as residual energy of UAVs, average neighoring distance, and UAV degree. For classification process, the GTOADL-SCS model applies pre-trained densely connected network (DenseNet201) feature extractor with gated recurrent unit (GRU) classifier. For ensuring the enhanced performance of the GTOADL-SCS model, a widespread simulation analysis is performed and the comparative study reported the significant outcomes over the existing approaches with maximum packet delivery ratio (PDR) of 92.60%.

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Keywords

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