Optimization of Drone Base Station Location for the Next-Generation Internet-of-Things Using a Pre-Trained Deep Learning Algorithm and NOMA

• Published in: Mathematics (Basel) Vol. 11; no. 8; p. 1947
• Main Authors: Alsolai, Hadeel, Mtouaa, Wafa, Maashi, Mashael S., Othman, Mahmoud, Yaseen, Ishfaq, Alneil, Amani A., Osman, Azza Elneil, Alsaid, Mohamed Ibrahim
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-04-2023
• Subjects: Algorithms; Communication; convolutional neural networks; Deep learning; Drone aircraft; drone base station; Energy consumption; Food science; Internet of Things; Location based services; Machine learning; Mathematical optimization; Mobile communication systems; mobile communications; Network latency; Network management systems; Neural networks; Nonorthogonal multiple access; Optimization techniques; Ordinary differential equations; Particle swarm optimization; Power; pre-trained CNN; Signal quality; Statistical tests; swarm intelligence; Unmanned aerial vehicles; Wireless communication systems; Writers; Saudi Arabia
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math11081947

Next-generation Internet-of-Things applications pose challenges for sixth-generation (6G) mobile networks, involving large bandwidth, increased network capabilities, and remarkably low latency. The possibility of using ultra-dense connectivity to address the existing problem was previously well-acknowledged. Therefore, placing base stations (BSs) is economically challenging. Drone-based stations can efficiently address Next-generation Internet-of-Things requirements while accelerating growth and expansion. Due to their versatility, they can also manage brief network development or offer on-demand connectivity in emergency scenarios. On the other hand, identifying a drone stations are a complex procedure due to the limited energy supply and rapid signal quality degradation in air-to-ground links. The proposed method uses a two-layer optimizer based on a pre-trained VGG-19 model to overcome these issues. The non-orthogonal multiple access protocol improves network performance. Initially, it uses a powerful two-layer optimizer that employs a population of micro-swarms. Next, it automatically develops a lightweight deep model with a few VGG-19 convolutional filters. Finally, non-orthogonal multiple access is used to schedule radio and power resources to devices, which improves network performance. We specifically examine how three scenarios execute when various Cuckoo Search, Grey Wolf Optimization, and Particle Swarm Optimization techniques are used. To measure the various methodologies, we also run non-parametric statistical tests, such as the Friedman and Wilcoxon tests. The proposed method also evaluates the accuracy level for network performance of DBSs using number of Devices. The proposed method achieves better performance of 98.44% compared with other methods.