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Beamforming Design Towards Sum-Rate Maximization for Holographic Active RIS-Aided Uplink Near-Field communications

IEEE Internet of Things Journal 2025年第12期12卷 18582-18595页

作者： Singh, Sandeep Singh, Keshav Singh, Sandeep Kumar Shin, Hyundong Duong, Trung Q. Kaohsiung80424 Taiwan National Sun Yat-sen University Institute of Communications Engineering Kaohsiung80424 Taiwan Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Motilal Nehru National Institute of Technology Allahabad Department of Electronics and Communication Engineering Prayagraj India Kyung Hee University Department of Electronics and Information Convergence Engineering Deogyeong-daero Gyeonggi-do Yongin-si1732 Korea Republic of Memorial University Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom

Holographically-driven active reconfigurable intelligent surface (HARIS), leveraging densely packed sub-wavelength elements, overcomes the limitations of conventional RIS in signal processing, unlocking advanced capabilities for next-generation networks. Thus, to exploit its full potential, this work proposes the integration of HARIS into an Internet of Things (IoT) multi-user uplink near-field-driven wireless communication system. A sum-rate maximization problem is formulated to provide efficient resource utilization by jointly optimizing the equalizer design, power allocation at each IoT user, and the HARIS phase shift, while satisfying strict constraints of quality of service (QoS) requirement and limited power budget at each IoT user and HARIS. Due to the non-convex nature of the problem, we propose an alternating optimization (AO)-based algorithm, incorporating techniques such as minimum mean square error (MMSE), convex upper bound approximation, and semidefinite relaxation (SDR). Then, extensive simulations validate the algorithm's efficacy and convergence, demonstrating up to 63% higher performance with HARIS than passive RIS. Additionally, we highlight that near-field communication yields up to 90% higher sum-rate than hybrid 76% and far-field model 73%. Moreover, we demonstrate the impact of imperfect channel state information (iCSI) on the system performance. © 2014 IEEE.

关键词： Mean square error

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Secure Key Management Based Mobile Authentication in Cloud

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computer Systems Science & engineering 2022年第12期43卷 887-896页

作者： S.Shanmuga Priya D.Yuvaraj T.Satyanarayana Murthy Varghese S.Chooralil S.Navaneetha Krishnan P.Banumathy P.SundaraVadivel Department of Computer Science and Engineering M.I.E.T Engineering CollegeTrichyTamilnaduIndia Department of Computer Science and Engineering Cihan UniversityDuhokKurdistan RegionIraq Department of Computer Science and Engineering Bapatla Engineering CollegeBapatlaAndhra PradeshIndia Department of Computer Science and Engineering Rajagiri School of Engineering and TechnologyKakkanadKeralaIndia Department Electronics and Communication Engineering SACS MAVMM Engineering CollegeMaduraiTamilnaduIndia Department of Information Technology Karaikal Polytechnic CollegeVarichikudy-KaraikalIndia Department of Computer Science and Engineering SACS MAVMM Engineering CollegeMaduraiIndia

Authentication is important to the security of user data in a mobilecloud environment. Because of the server’s user credentials, it is subject toattacks. To maintain data authentication, a novel authentication mechanism is proposed. It consists of three independent phases: Registration, login, and authentication and key agreement. The user registers with the Registration Center (RC)by producing a secret number that isn’t stored in the phone, which protects againstprivileged insider attacks. The user and server generate a nonce for dynamic useridentity and agree on a session secret key for safe communication. The passwordsare not stored on the computer or provided in plain text, they are resistant toreplay, guessing, and stolen verification attacks. The suggested protocol uses aone-way hash function and XOR operations, with the client having remote accessto a large number of servers over a secure communication channel. Concentrateson HMAC and SHA3 for Collision Free Hashing and to overcome length extension attacks. HMACs are substantially less affected by collisions than their underlying hashing algorithms alone. So adding an HMAC to an MD5 or SHA hashwould make it substantially more difficult to break via a rainbow table.

关键词： Registration center SIM IMEI

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Phishing Website Detection using Natural Language Processing 7

Phishing Website Detection using Natural Language Processing

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Mariappan, E. Jean Celia Grace, C. Joe Patrick Gnanaraj, S. Elizabeth Paulsyah, D. Muthukumaran, N. St. Mother Theresa Engineering College Department of Computer Science And Engineering Tamilnadu Tuticorin India Francis Xavier Engineering College Department of Information Technology Tamilnadu Tirunelveli India St. Mother Theresa Engineering College Department of Mechanical Engineering Tamilnadu Tuticorin India Francis Xavier Engineering College Department of Computer Science And Engineering Tamilnadu Tirunelveli India Sri Eswar Engineering College Department of Electronics And Communication Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798350359299

Given the rapid proliferation of online services, there has been a corresponding uptick in nefarious activities aimed at misleading users into unintended actions. The internet, as a platform, has become a fertile ground for innovative methods employed by attackers, particularly the widespread menace of phishing. By leveraging counterfeit websites, malicious entities seek to unlawfully gather sensitive data including user information, login credentials, social security numbers, and financial particulars. Accurately distinguishing between a legitimate website and a phishing attempt presents a significant challenge. This research work introduces a model for analyzing phishing websites employing machine learning techniques. The model strives to forecast the authenticity of a website by utilizing a variety of classification Algorithms and features rooted in natural language processing (NLP). © 2024 IEEE.

关键词： Websites

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AI-Based Methods to Detect and Counter Cyber Threats in Cloud Environments to Strengthen Cloud Security

AI-Based Methods to Detect and Counter Cyber Threats in Clou...

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2024 International Conference on Electrical, electronics and Computing Technologies, ICEECT 2024

作者： Chaudhary, Diwakar Verma, S.K. Mohan Shrimal, Vijay Madala, Ravikiran Baliyan, Rashi Sathish, M. Noida International University School of Business Management Greater Noida India Chandigarh University Department of Computer Science Engineering Punjab Mohali India University of Cumberlands Department of Information North Carolina Durham United States Rajalakshmi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350378092

Traditional cloud security solutions often employ outdated techniques and find it difficult to stay up to date with the ever-evolving cyber threat scenario. Complex attacks may be made against cloud systems since static signatures and rule-based methods have their limits. To address these problems, the study proposes a novel AI-based approach to enhance cyber threat detection and mitigation in cloud settings. By use of sophisticated anomaly detection and machine learning (ML) methods, the proposed system continuously analyzes large amounts of data to dynamically identify and eliminate new threats. Some of the key components include robust preprocessing, a variety of ML model selections, repetitive model training, real-time threat detection, and automated response mechanisms. Compared to the current system, the results demonstrate notable increases in detection accuracy, false positive and false negative rates, and area under the curve (AUC) values across many test datasets. Specifically, the proposed system reduces the false negative and false positive rates to 2.0% and 1.2%, respectively, and achieves 98.5% detection accuracy as compared to 89.7% for the existing system. Moreover, on several test datasets, the proposed system routinely produces higher AUC values than the existing system, proving its efficiency in improving the accuracy of cyber threat identification in cloud systems. © 2024 IEEE.

关键词： Cloud security

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Cloud resource management using adaptive firefly algorithm and artificial neural network

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International Journal of Cloud Computing 2022年第5-6期11卷 480-491页

作者： Manigandan, S.K. Manjula, S. Nagaraju, V. Ramya, D. TapasBapu, B.R. Department of Computer Science and Engineering VelTech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department of Information Technology Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Electronics and Communication Engineering S.A. Engineering College Chennai India

Cloud computing can be characterised in basic terms as one of the stages for giving conveyed figuring assets. Registering assets may incorporate capacity, data transfer capacity, memory space, handling components, etc. These assets are leased to the clients utilising the compensation per-utilised model. The interest for the assets may not be static, yet they can be mentioned on-request because of the developing web office. The clients of the cloud want to have a limited reaction time and cost yet then again, the cloud supplier centres on accomplishing proficient cloud assets distribution and limiting the support costs. Resource management is a practice of provisioning and managing the cloud resources efficiently. It also provides the techniques to provision the resources, schedule the jobs, and balance the loads. This proposal provides a resource management technique for efficient provisioning of resources and the scheduling of jobs on the static and dynamic cloudlet requests. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Neural networks

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Exploring the Quality of Service Impacts of Cloud Computing over Wireless Networks

Exploring the Quality of Service Impacts of Cloud Computing ...

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2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Gopal, Santhosh Kumar Mohammed, Abdul Sajid Saddi, Venkata Ramana Jiwani, Nasmin Logeshwaran, J. Sciences CharlotteNC United States School of Computer and Information Sciences University of The Cumberlands KY United States Ace American Insurance Chubb Group RaleighNC United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350394399

Cloud computing is a paradigm that enables users to access statistics and programs through the use of a virtualized infrastructure. As such, it could be leveraged to lessen the prices of strolling an allotted system, even at the same time growing its competencies and flexibility. Unfortunately, cloud computing additionally has a big effect on the great of service of the underlying Wi-Fi community. In this paper, we discover the best carrier influences of cloud computing over wi-wireless networks in phrases of latency, throughput, and packet loss. Wi-Fi, we talk about the demanding situations and solutions associated with integrating Wi-Fi networks with cloud computing architectures. We then evaluate the overall performance of different eventualities, consisting of using 4G and 5G, as well as baseline overall performance without cloud integration. Our outcomes show that cloud computing can improve the general quality of service of Wi-Fi wireless networks despite the fact that there may be full-size variations between exceptional community generations. © 2024 IEEE.

关键词： Cloud computing

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Deep Fake Detection Using computer Vision-Based Deep Neural Network with Pairwise Learning

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Intelligent Automation & Soft Computing 2023年第2期35卷 2449-2462页

作者： R.Saravana Ram M.Vinoth Kumar Tareq M.Al-shami Mehedi Masud Hanan Aljuaid Mohamed Abouhawwash Department of Electronics and Communication Engineering Anna UniversityUniversity College of EngineeringDindigul624622India Department of Computer Science and Engineering Anna UniversityUniversity College of EngineeringDindigul624622India Department of Mathematics Faculty of ScienceSana’a UniversitySana’a 13509Yemen Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)Riyadh11671Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Deep learning-based approaches are applied successfully in manyﬁelds such as deepFake identiﬁcation,big data analysis,voice recognition,and image *** is the combination of deep learning in fake creation,which states creating a fake image or video with the help of artiﬁcial intelligence for political abuse,spreading false information,and *** artiﬁcial intel-ligence technique has a wide demand,increasing the problems related to privacy,security,and *** paper has analyzed the features related to the computer vision of digital content to determine its *** method has checked the computer vision features of the image frames using the fuzzy clustering feature extraction *** the proposed deep belief network with loss handling,the manipulation of video/image is found by means of a pairwise learning *** proposed approach has improved the accuracy of the detection rate by 98%on various datasets.

关键词： Deep fake deep belief network fuzzy clustering feature extraction pairwise learning

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A Comprehensive Study on Drones and Big Data for Supply Chain Optimization Using a Novel Approach 1

A Comprehensive Study on Drones and Big Data for Supply Chai...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Dankan Gowda, V. Palanikkumar, D. Suganthi, N. Malleswari, A.S. Thapa, Sanjog Tanguturi, Rama Chaithanya Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Dr Ngp Institute of Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India Dayananda Sagar College of Engineering Department of Electrical and Electronics Engineering Karnataka Bangalore India Aditya College of Engineering and Technology Department of Computer Science & Engineering Andhra Pradesh Surampalem India Lamar University Department of Information System and Analysis BeaumontTX United States Woxsen University School of Business Hyderabad India

ISBN: (纸本)9798350367720

This research paper aims to provide qualitative and quantitative insights regarding the application of drones and big data techniques in the SCM field through a new scientific method. The proposed method exploits the opportunities of drones for real-time data capturing and big data technologies to enrich decision making in supply chain. The study is pertinent to modern tendencies in supply chain systems and proves the efficiency of the new method through the precise calculations and contrastive analysis. The following are some of the findings that suggest efficiency, cost decrease, and outstanding performance of the supply chain. The studies being offered in this research will prove beneficial for practitioners and researchers who endeavor to advance the concepts and practice of logistics and supply chain management. © 2024 IEEE.

关键词： Big Data Cost Reduction Data Analytics Decision-making Drones Efficiency Improvement Logistics Novel Approach Real-time Data Collection Supply Chain Optimization

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Optimized Power Allocation in Downlink NOMA with Imperfect Successive Interference Cancellation

Optimized Power Allocation in Downlink NOMA with Imperfect S...

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IEEE Region 10 International Conference TENCON

作者： Soumik Biswas Debasish Bera Department of Electronics and Communication Engineering Indian Institute of Information Technology Kalyani India Department of Computer Science and Engineering Indian Institute of Information Technology Kalyani India

ISBN: (数字)9798350350821

ISBN: (纸本)9798350350838

It is expected that physical-layer security will finally thrive to provide an extra layer of defence for a variety of devices in next-generation 6G wireless communication. In this work, physical-layer security using non-orthogonal multiple access (NOMA) with single-input-single-output (SISO) assumptions are considered where the users and eavesdroppers are spatially uniformly deployed. The channels between the transmitter (base station (BS)) and the NOMA users and eavesdroppers are considered to be independent Nakagami-m. We analyse the secrecy sum rate (SSR) of the NOMA system in the case of imperfect successive interfere cancellation (SIC) and propose an optimised power allocation scheme with minimised transmit power requirement to satisfy the Quality of Service (QoS) for all legitimate users. Closed-form solutions for the SSR are also derived. The performance of the proposed schemes has been evaluated both by analysis and by simulations. Results are evaluated for a wide range of the signal-to-noise ratio (SNR).

关键词： Wireless communication NOMA Base stations Interference cancellation Transmitters Quality of service Downlink Resource management Security Signal to noise ratio

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Map Building of Indoor Environment with Sensors using Neural Network

Map Building of Indoor Environment with Sensors using Neural...

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2023 Winter Summit on Smart Computing and Networks, WiSSCoN 2023

作者： Latha Mary, S Angel Ulagapriya, K. Poonguzhali, A. Menaha, R. David, Beaulah Priyadharshini, T.R. Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Sri Sairam College of Engineering Department of Electronics and Communication Engineering Bangalore India Sri Eshwar College of Engineering Department of Information Technology Coimbatore India Hindusthan College of Engineering and Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798350334050

The necessity of a blueprint of a building structure is a mandatory requirement for any reconnaissance or rescue operations. In our project, we build a modular system combining sensors related to sonar, laser, micro-wave to read sensory values and generate a 2D path of any building. The data is fetched and stored to feed to anOptimal Neural Network (ONN)-based computing system to create a 2D route with minimal discrepancies of error. Here the NN architecture is fine-tuned using Modified Dolphin Partner Optimization (MDPO) Exploration of unknown environments and space using autonomous vehicles has recently gained good attention in the field of Robotic Mapping. The recent advancements in the Internet of Things have enabled us to establish an ideal frame of reference for sonar and lidar-based systems. New effects are displayed by the sensors according to the physical characteristics of a room. The range data from sensors in various surroundings are interpreted by NNs. The distorted errors due to the material medium, particles, and moving objects present in the environment pose a threat to building a high-quality path map. The sensor fusion technique is applied to the rotatable modular array sensor to minimalize discrepancies caused by cloth materials during sonar readings, particle noise in an environment for Lidar reading, and moving human bodies present in the environment for path building and obstacle detection. © 2023 IEEE.

关键词： Optical radar

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