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Federated Learning with Blockchain Assisted Image Classification for Clustered uav networks

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Computers, Materials & Continua 2022年第7期72卷 1195-1212页

作者： Ibrahim Abunadi Maha M.Althobaiti Fahd N.Al-Wesabi Anwer Mustafa Hilal Mohammad Medani Manar Ahmed Hamza Mohammed Rizwanullah Abu Serwar Zamani Department of Information Systems Prince Sultan UniversityRafha StreetRiyadh11586Saudi Arabia Department of Computer Science College of Computing and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Computer and Self Development Prince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia

The evolving“Industry 4.0”domain encompasses a collection of future industrial developments with cyber-physical systems(CPS),Internet of things(IoT),big data,cloud computing,***,the industrial Internet of things(IIoT)directs data from systems for monitoring and controlling the physical world to the data processing system.A major novelty of the IIoT is the unmanned aerial vehicles(uavs),which are treated as an efficient remote sensing technique to gather data from large *** are commonly employed in the industrial sector to solve several issues and help decision *** the strict regulations leading to data privacy possibly hinder data sharing across autonomous *** learning(FL)becomes a recent advancement of machine learning(ML)which aims to protect user *** this aspect,this study designs federated learning with blockchain assisted image classification model for clustered uav networks(FLBIC-Cuav)on IIoT *** proposed FLBIC-Cuav technique involves three major processes namely clustering,blockchain enabled secure communication and FL based image *** uav cluster construction process,beetle swarm optimization(BSO)algorithm with three input parameters is designed to cluster the uavs for effective *** addition,blockchain enabled secure data transmission process take place to transmit the data from uavs to cloud ***,the cloud server uses an FL with Residual Network model to carry out the image classification process.A wide range of simulation analyses takes place for ensuring the betterment of the FLBIC-Cuav *** experimental outcomes portrayed the betterment of the FLBIC-Cuav approach over the recent state of art methods.

关键词： uav networks clustering secure communication blockchain federated learning image classification

Energy-Efficient Multi-uav Coverage Deployment in uav networks:A Game-Theoretic Framework

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China Communications 2018年第10期15卷 194-209页

作者： Lang Ruan Jinlong Wang Jin Chen Yitao Xu Yang Yang Han Jiang Yuli Zhang Yuhua Xu College of Communications Engineering Army Engineering University of PLA Key Embedded Technology and Intelligent System Laboratory College of Information Science and Engineering Guilin University of Technology

uav cooperative control has been applied in many complex uav communication networks. It remains challenging to develop uav cooperative coverage and uav energy-efficient communication technology. In this paper, we investigate current works about uav coverage problem and propose a multi-uav coverage model based on energy-efficient communication. The proposed model is decomposed into two steps: coverage maximization and power control, both are proved to be exact potential games(EPG) and have Nash equilibrium(NE) points. Then the multi-uav energy-efficient coverage deployment algorithm based on spatial adaptive play(MUECD-SAP) is adopted to perform coverage maximization and power control, which guarantees optimal energy-efficient coverage deployment. Finally, simulation results show the effectiveness of our proposed approach, and confirm the reliability of proposed model.

关键词： uav networks multi-uav coverage energy-efficient potential games Nash equilibrium

Optimal Deep Reinforcement Learning for Intrusion Detection in uavs

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Computers, Materials & Continua 2022年第2期70卷 2639-2653页

作者： V.Praveena A.Vijayaraj P.Chinnasamy Ihsan Ali Roobaea Alroobaea Saleh Yahya Alyahyan Muhammad Ahsan Raza Department of Computer Science and Engineering Dr.N.G.P Institute of TechnologyCoimbatore641048India Department of Information Technology Vignan’s Foundation for ScienceTechnology&ResearchGuntur522213India Department of Information Technology Sri Shakthi Institute of Engineering and TechnologyCoimbatore641062India Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur50603Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science Community College in DwadmiShaqra University11961Saudi Arabia Department of Information Technology Bahauddin Zakariya UniversityMultan60000Pakistan

In recent years,progressive developments have been observed in recent technologies and the production cost has been continuously *** such scenario,Internet of Things(IoT)network which is comprised of a set of Unmanned Aerial Vehicles(uav),has received more attention from civilian tomilitary *** network security poses a serious challenge to uav networks whereas the intrusion detection system(IDS)is found to be an effective process to secure the uav *** IDSs are not adequate to handle the latest computer networks that possess maximumbandwidth and data *** order to improve the detection performance and reduce the false alarms generated by IDS,several researchers have employed Machine Learning(ML)and Deep Learning(DL)algorithms to address the intrusion detection *** this view,the current research article presents a deep reinforcement learning technique,optimized by BlackWidow Optimization(DRL-BWO)algorithm,for uav *** addition,DRL involves an improved reinforcement learning-based Deep Belief Network(DBN)for intrusion *** parameter optimization of DRL technique,BWO algorithm is *** helps in improving the intrusion detection performance of uav *** extensive set of experimental analysis was performed to highlight the supremacy of the proposed *** the simulation values,it is evident that the proposed method is appropriate as it attained high precision,recall,F-measure,and accuracy values such as 0.985,0.993,0.988,and 0.989 respectively.

关键词： Intrusion detection uav networks reinforcement learning deep learning parameter optimization

Power Optimized Multiple-uav Error-Free Network in Cognitive Environment

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Computers, Materials & Continua 2023年第5期75卷 3189-3201页

作者： Shakti Raj Chopra Parulpreet Singh Ahmed Alhussen Nitin Mittal MohdAnul Haq School of Electronics and Electrical Engineering Lovely Professional UniversityPhagwaraPunjab144411India Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia University Centre for Research and Development Chandigarh UniversityMohaliPunjab140413India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

Many extensive uav communication networks have used uav cooperative *** networking services can be offered using unmanned aerial vehicles(uavs)as aerial base *** only is coverage maximization,but also better connectivity,a fundamental design challenge that must be *** number of applications for unmanned aerial vehicles(uavs)operating in unlicensed bands is fast expanding as the Internet of Things(IoT)*** bands,however,have become overcrowded as the number of systems that use them *** Radio(CR)and spectrum allocation approaches have emerged as a potential approach for resolving spectrum scarcity in wireless networks,and hence as technological solutions for future generations,from this *** a result,combining CR with uavs has the potential to give significant benefits for large-scale uav *** paper examines existing research on the subject of uav covering and proposes a multi-uav cognitive-based error-free model for energy-efficient *** maximization,power control,and enhanced connection quality are the three steps of the proposed *** satisfy the desired signal-to-noise ratio,the covering zone efficacy is investigated as a function of the distance among uavs stationed in a specific geographic region depending on multiple deployment configurations like as rural,suburban,and urban macro deployment scenarios of the ITU-R M.2135 standard(SNR).

关键词： uav networks multi-uav coverage energy-efficient FER SNR

Metaheuristic Based Data Gathering Scheme for Clustered uavs in 6G Communication Network

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Computers, Materials & Continua 2022年第6期71卷 5311-5325页

作者： Ahmed S.Almasoud Siwar Ben Haj Hassine Nadhem NEMRI Fahd N.Al-Wesabi Manar Ahmed Hamza Anwer Mustafa Hilal Abdelwahed Motwakel Mesfer Al Duhayyim Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer Science College of Science&Arts at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

The sixth-generation(6G)wireless communication networks are anticipated in integrating aerial,terrestrial,and maritime communication into a robust system to accomplish trustworthy,quick,and low latency *** enables to achieve maximum throughput and delay for several ***,the evolution of 6G leads to the design of unmanned aerial vehicles(uavs)in providing inexpensive and effective solutions in various application areas such as healthcare,environment monitoring,and so *** the uav network,effective data collection with restricted energy capacity poses a major issue to achieving high quality network *** can be addressed by the use of clustering techniques foruavs in 6G *** this aspect,this study develops a novel metaheuristic based energy efficient data gathering scheme for clustered unmanned aerial vehicles(MEEDG-Cuav).The proposed MEEDG-Cuav technique intends in partitioning the uav networks into various clusters and assign a cluster head(CH)to reduce the overall energy ***,the quantum chaotic butterfly optimization algorithm(QCBOA)with a fitness function is derived to choose CHs and construct *** experimental validation of the MEEDG-Cuav technique occurs utilizing benchmark dataset and the experimental results highlighted the better performance over the other state of art techniques interms of different measures.

关键词： 6G network mobile communication uav networks energy efficiency clustering metaheuristics