Journal of Intelligent Systems and Internet of Things

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https://doi.org/10.54216/JISIoT

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2690-6791ISSN (Online) 2769-786XISSN (Print)

Volume 11 , Issue 2 , PP: 85-96, 2024 | Cite this article as | XML | Html | PDF | Full Length Article

Enabling Metaheuristics with Deep Learning based Resource Allocation in Unmanned Aerial Vehicles Wireless Networks

Ravindra Raman Cholla 1 , J. Anitha Josephine 2 * , Priya N. 3 , C. Anuradha 4 , R. Kavitha 5

  • 1 Department of CSE, Jain (Deemed-to-be-University), Global Campus, Bangalore, Karnataka -562112, India - (raman1179@gmail.com)
  • 2 Department of CSE (Cyber Security), Jain (Deemed-to-be-University), Global Campus, Bangalore, Karnataka -562112, India - (anitha.josephine88@gmail.com)
  • 3 Department of CSE (Cyber Security), Jain (Deemed-to-be-University), Global Campus, Bangalore, Karnataka -562112, India - (priya.n@jainuniversity.ac.in)
  • 4 Department of CSE, Sri Sai ram Institute of Technology, TamilNadu, India - (anuradha.cse@sairamit.edu.in)
  • 5 Department of IT, SRM Institute of Science and Technology, Ramapuram, TamilNadu, India - (kavithar8@srmist.edu.in)
  • Doi: https://doi.org/10.54216/JISIoT.110208

    Received: August 17, 2023, Revised: December 11, 2023 Accepted: January 19, 2024
    Abstract

    Unmanned aerial vehicle (UAV) network offers a variety of applications in public safety, disaster management, advertising and broadcasting, overload situation, etc. Due to the dynamic characteristics of MU, it is challenging to provide robust transmission services to mobile users (MU). Resource allocation (RA), including sub-channel, serving user, and transmit power, is a crucial problem; also, it is critical to enhance the coverage and energy efficiency of UAV-enabled communication protocol. Furthermore, system resources are limited (for example, spectrum, and transmission power) and UAV transmission coverage and on-board energy are limited. In order to meet the QoE of any user with limited UAV energy and limited resource system, we jointly enhance UAV trajectory, user communication scheduling, and bandwidth allocation and transmit power to satisfy user QoE requirements and increase energy efficiency. Thus, the study proposes a new mud ring optimization with deep belief network-based resource allocation scheme (MRODBN-RAS) technique for UAV-enabled wireless networks. The proposed MRODBN-RAS approach focuses on the effectual accomplishment of the computational and energy-effective decision. Besides, the MRODBN-RAS technique assumed the UAV as a learning agent by forming RA decisions as actions. In addition, the MRODBN-RAS technique designed a reward function to reduce the weighted resource utilization. The MRODBN-RAS technique uses DBN model with hyperparameter tuning using MRO algorithm to allocate the resources. The design of the MRO algorithm helps in the optimal selection of the hyperparameter related to the DBN model. The simulation results of the MRODBN-RAS method are examined under various measures. The extensive comparison study highlighted the better performance of the MRODBN-RAS approach over existing techniques.

    Keywords :

    Unmanned aerial vehicle , Deep Belief Network , Base Station , Resource allocation , Line-Of-Sight

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    Cite This Article As :
    Raman, Ravindra. , Anitha, J.. , N., Priya. , Anuradha, C.. , Kavitha, R.. Enabling Metaheuristics with Deep Learning based Resource Allocation in Unmanned Aerial Vehicles Wireless Networks. Journal of Intelligent Systems and Internet of Things, vol. , no. , 2024, pp. 85-96. DOI: https://doi.org/10.54216/JISIoT.110208
    Raman, R. Anitha, J. N., P. Anuradha, C. Kavitha, R. (2024). Enabling Metaheuristics with Deep Learning based Resource Allocation in Unmanned Aerial Vehicles Wireless Networks. Journal of Intelligent Systems and Internet of Things, (), 85-96. DOI: https://doi.org/10.54216/JISIoT.110208
    Raman, Ravindra. Anitha, J.. N., Priya. Anuradha, C.. Kavitha, R.. Enabling Metaheuristics with Deep Learning based Resource Allocation in Unmanned Aerial Vehicles Wireless Networks. Journal of Intelligent Systems and Internet of Things , no. (2024): 85-96. DOI: https://doi.org/10.54216/JISIoT.110208
    Raman, R. , Anitha, J. , N., P. , Anuradha, C. , Kavitha, R. (2024) . Enabling Metaheuristics with Deep Learning based Resource Allocation in Unmanned Aerial Vehicles Wireless Networks. Journal of Intelligent Systems and Internet of Things , () , 85-96 . DOI: https://doi.org/10.54216/JISIoT.110208
    Raman R. , Anitha J. , N. P. , Anuradha C. , Kavitha R. [2024]. Enabling Metaheuristics with Deep Learning based Resource Allocation in Unmanned Aerial Vehicles Wireless Networks. Journal of Intelligent Systems and Internet of Things. (): 85-96. DOI: https://doi.org/10.54216/JISIoT.110208
    Raman, R. Anitha, J. N., P. Anuradha, C. Kavitha, R. "Enabling Metaheuristics with Deep Learning based Resource Allocation in Unmanned Aerial Vehicles Wireless Networks," Journal of Intelligent Systems and Internet of Things, vol. , no. , pp. 85-96, 2024. DOI: https://doi.org/10.54216/JISIoT.110208