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Efficient Power Control for UAV Based on Trajectory and Game Theory

Computers, Materials & Continua 2023年第3期74卷 5589-5606页

作者： Fadhil Mukhlif Ashraf Osman Ibrahim norafida ithnin Roobaea Alroobaea Majed Alsafyani Information Assurance and Security Research Group(IASRG) School of ComputingFaculty of EngineeringUniversiti Teknologi MalaysiaJohorMalaysia Faculty of Computing and Informatics University Malaysia SabahKota KinabaluMalaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

Due to the fact that network space is becoming more limited,the implementation of ultra-dense networks(UDNs)has the potential to enhance not only network coverage but also network throughput.Unmanned Aerial Vehicle(UAV)communications have recently garnered a lot of attention due to the fact that they are extremely versatile and may be applied to a wide variety of contexts and purposes.A cognitive UAV is proposed as a solution for the Internet of Things ground terminal’s wireless nodes in this article.In the IoT system,the UAV is utilised not only to determine how the resources should be distributed but also to provide power to the wireless nodes.The quality of service(QoS)offered by the cognitive node was interpreted as a price-based utility function,which was demonstrated in the form of a non-cooperative game theory in order to maximise customers’net utility functions.An energyefficient non-cooperative game theory power allocation with pricing strategy abbreviated as(EE-NGPAP)is implemented in this study with two trajectories Spiral and Sigmoidal in order to facilitate effective power management in Internet of Things(IoT)wireless nodes.It has also been demonstrated,theoretically and by the use of simulations,that the Nash equilibrium does exist and that it is one of a kind.The proposed energy harvesting approach was shown,through simulations,to significantly reduce the typical amount of power thatwas sent.This is taken into consideration to agree with the objective of 5G networks.In order to converge to Nash Equilibrium(NE),the method that is advised only needs roughly 4 iterations,which makes it easier to utilise in the real world,where things aren’t always the same.

关键词： UAV spiral&sigmoid trajectory drones IoT game theory energy efficiency 6G

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Game Theory-Based IoT Efficient Power Control in Cognitive UAV

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Computers, Materials & Continua 2022年第7期72卷 1561-1578页

作者： Fadhil Mukhlif norafida ithnin Omar B.Abdulghafoor Faiz Alotaibi Nourah Saad Alotaibi Information Assurance and Security Research Group(IASRG) School of ComputingFaculty of EngineeringUniversiti Teknologi MalaysiaJohorMalaysia Electronic and Telecommunication Department College of EngineeringThe American University of KurdistanIraq Faculty of Computer Science and Information Technology Universiti Putra MalaysiaSelangorMalaysia Computer Department Faculty of Applied CollegeImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia

With the help of network densification,network coverage as well as the throughput can be improved via ultra-dense networks(UDNs).In tandem,Unmanned Aerial Vehicle(UAV)communications have recently garnered much attention because of their high agility as well as widespread applications.In this paper,a cognitive UAV is proposed for wireless nodes power pertaining to the IoT ground terminal.Further,the UAV is included in the IoT system as the source of power for the wireless nodes as well as for resource allocation.The quality of service(QoS)related to the cognitive node was considered as a utility function based on pricing scheme that was modelled as a non-cooperative game theory in order to maximise users’net utility function.Moreover,an energy efficiency non-cooperative game theory power allocation with pricing scheme(EE-NGPAP)is proposed to obtain an efficient power control within IoT wireless nodes.Further,uniqueness and existence of the Nash equilibrium have been demonstrated mathematically and through simulation.Simulation results show that the proposed energy harvest algorithm demonstrated considerable decrease in transmitted power consumption in terms of average power reduction,which is regarded to be apt with the 5Gnetworks’vision.Finally,the proposed algorithm requires around 4 iterations only to converge to NE which makes the algorithm more suitable in practical heterogeneous scenarios.

关键词： UAV drones WSN IoT game theory energy efficiency 5G&B5G networks

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Intelligence COVID-19 Monitoring Framework Based on Deep Learning and Smart Wearable IoT Sensors

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Computers, Materials & Continua 2023年第10期77卷 583-599页

作者： Fadhil Mukhlif norafida ithnin Roobaea Alroobaea Sultan Algarni Wael Y.Alghamdi Ibrahim Hashem Information Assurance and Security Research Group(IASRG) Faculty of ComputingUniversiti Teknologi Malaysia(UTM)Johor BahruJohor81310Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia College of Computing and Informatics Department of Computer ScienceUniversity of SharjahSharjah27272United Arab Emirates

The World Health Organization(WHO)refers to the 2019 new coronavirus epidemic as COVID-19,and it has caused an unprecedented global crisis for several nations.Nearly every country around the globe is now very concerned about the effects of the COVID-19 outbreaks,which were previously only experienced by Chinese residents.Most of these nations are now under a partial or complete state of lockdown due to the lack of resources needed to combat the COVID-19 epidemic and the concern about overstretched healthcare systems.Every time the pandemic surprises them by providing new values for various parameters,all the connected research groups strive to understand the behavior of the pandemic to determine when it will stop.The prediction models in this research were created using deep neural networks and Decision Trees(DT).DT employs the support vector machine method,which predicts the transition from an initial dataset to actual figures using a function trained on a model.Extended short-term memory networks(LSTMs)are a special sort of recurrent neural network(RNN)that can pick up on long-term dependencies.As an added bonus,it is helpful when the neural network can both recall current events and recall past events,resulting in an accurate prediction for COVID-19.We provided a solid foundation for intelligent healthcare by devising an intelligence COVID-19 monitoring framework.We developed a data analysis methodology,including data preparation and dataset splitting.We examine two popular algorithms,LSTM and Decision tree on the official datasets.Moreover,we have analysed the effectiveness of deep learning and machine learning methods to predict the scale of the pandemic.Key issues and challenges are discussed for future improvement.It is expected that the results these methods provide for the Health Scenario would be reliable and credible.

关键词： Healthcare framework AI COVID-19 machine&deep learning LSTM RNN decision tree

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Efficient Resource Allocation Algorithm in Uplink OFDM-Based Cognitive Radio Networks

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Computers, Materials & Continua 2023年第5期75卷 3045-3064页

作者： Omar Abdulghafoor Musbah Shaat Ibraheem Shayea Ahmad Hamood Abdelzahir Abdelmaboud Ashraf Osman Ibrahim Fadhil Mukhlif Herish Badal norafida ithnin Ali Khadim Lwas Electronic&Telecommunications Engineering Department The American University of KurdistanDuhokKurdistan RegionIraq CTTC BarcelonaSpain Faculty of Electrical and Electronics Engineering Istanbul Technical UniversityIstanbulTurkey Department of Information Systems King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computing and Informatics University Malaysia SabahKota KinabaluMalaysia Information Assurance and Security Research Group(IASRG) School of ComputingFaculty of EngineeringUniversiti Teknologi MalaysiaJohorMalaysia R&D Ministry of Industry and MineralsBaghdadIraq

The computational complexity of resource allocation processes,in cognitive radio networks(CRNs),is a major issue to be managed.Furthermore,the complicated solution of the optimal algorithm for handling resource allocation in CRNs makes it unsuitable to adopt in real-world applications where both cognitive users,CRs,and primary users,PUs,exist in the identical geographical area.Hence,this work offers a primarily price-based power algorithm to reduce computational complexity in uplink scenarioswhile limiting interference to PUs to allowable threshold.Hence,this paper,compared to other frameworks proposed in the literature,proposes a two-step approach to reduce the complexity of the proposed mathematical model.In the first step,the subcarriers are assigned to the users of the CRN,while the cost function includes a pricing scheme to provide better power control algorithm with improved reliability proposed in the second stage.The main contribution of this paper is to lessen the complexity of the proposed algorithm and to offer flexibility in controlling the interference produced to the users of the primary networks,which has been achieved by including a pricing function in the proposed cost function.Finally,the performance of the proposed power and subcarrier algorithm is confirmed for orthogonal frequency-division multiplexing(OFDM).Simulation results prove that the performance of the proposed algorithm is better than other algorithms,albeit with a lesser complexity of O(NM)+O(Nlog(N)).

关键词： Cognitive radio resource allocation OFDM pricing

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