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Distributed field mapping for mobile sensor teams using a derivative‐free optimisation algorithm

IET Cyber-Systems and Robotics 2024年第2期6卷 20-34页

作者： Tony X.Lin Jia Guo Said Al-Abri Fumin Zhang School of Electrical&Computer Engineering Georgia Institute of TechnologyAtlantaGeorgiaUSA College of Engineering Sultan Qaboos UniversityMuscatOman Department of Electronic&Computer Engineering Hong Kong University of Science and TechnologyHong KongChina

The authors propose a distributed field mapping algorithm that drives a team of robots to explore and learn an unknown scalar field using a Gaussian Process(GP).The authors’strategy arises by balancing exploration objectives between areas of high error and high *** computing high error regions is impossible since the scalar field is unknown,a bio-inspired approach known as Speeding-Up and Slowing-Down is leveraged to track the gradient of the GP *** approach achieves global field-learning convergence and is shown to be resistant to poor hyperparameter tuning of the *** approach is validated in simulations and experiments using 2D wheeled robots and 2D flying mini-ature autonomous blimps.

关键词： environment sensing multi-robot systems

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Federated learning-based hybrid convolutional recurrent neural network for multi-class intrusion detection in IoT networks

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Discover Internet of Things 2025年第1期5卷 1-22页

作者： Selvam, Prabu Karthikeyan, P. Manochitra, S. Sujith, A.V.L.N. Ganesan, T. Ayyasamy, Rajaram Shuaib, Mohammed Alam, Shadab Rajendran, A. School of Computing SRM Institute of Science and Technology Tiruchirappalli621105 India Department of Electronics and Communication Engineering Velammal College of Engineering and Technology Tamil Nadu Madurai625009 India Department of Computer Science Engineering St Joseph’s College of Engineering Tamil Nadu Chennai600119 India School of Engineering Malla Reddy University Telangana Hyderabad500043 India Department of Computer Science Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of Electronics and Communication Engineering E.G.S Pillay Engineering College Tamil Nadu Nagapattinam611002 India Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of ECE Karpagam College of Engineering Tamil Nadu Othakkal Mandapam India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

As Internet of Things (IoT) devices leads an significant challenges in securing the systems from cyber-attacks in large-scale IoT networks. Traditional methods faces struggle to precisely detecting the complex intrusion patterns due to the heterogeneity and distributed IoT devices. There is a pressing need for scalable, real-time, and privacy-preserving solutions to secure IoT and IIoT systems. For multi-class intrusion detection in IoT networks, this study proposed an novel hybrid convolutional recurrent neural network (CRNN) model based on federated learning. By integrating federated learning, the proposed approach ensures privacy by enabling decentralized model training among IoT devices without exchanging sensitive data. The hybrid CRNN model improves detection accuracy for a variety of attack types in IoT networks by combining Recurrent Neural Networks for sequential pattern recognition and Convolutional Neural Networks for feature extraction from raw data. The proposed model classifies incoming network traffic into different attack classes. The experimental results conducted on the Edge-IIoT dataset reveal that the proposed model has attained the detection accuracy of 98.93% with balanced precision and recall for all the attack types and low-support classes such as SQL Injection and Man-in-the-Middle. This balance improves model’s ability to reduce both false negatives and false positives compared with conventional approaches. Together with real-time detection, our approach excels in a privacy-preserving manner, providing a practical, scalable solution for securing complex IoT networks, and addressing critical gaps in current IDS frameworks. © The Author(s) 2025.

关键词： Federated learning

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Detection of Distributed Denial of Service in The Application Layer of IoT Using Machine Learning 15

Detection of Distributed Denial of Service in The Applicatio...

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Nanthiya, D. Gopal, S.B. Manjula Devi, R. Praveenkumar, R. Vijayakumar, S.D. Raja, T. Dharanish Sriram, L. Kongu Engineering college Department of Computer Technology -UG Perundurai India Kongu Engineering College Department of Electronics and Communication Engineering Perundurai India Kpr institute of Engineering and Technology Department of Computer science and Engineering Coimbatore India Builders Engineering College Department of Electronics and Communication Engineering Kangeyam India Vivekanandha college of Technology for women Department of Electronics and Communication Engineering Tiruchengode India

ISBN: (纸本)9798350370249

Cybersecurity prevents harmful assaults on computers, networks, and mobile devices. Included in the category of cyber-attacks are threats like as malware, distributed denial of service assaults, phishing, spoofing, code injection, and supply chain attacks. Massive denial-of-service assaults are destructive. As part of a Distributed Denial-of-Service (DDoS) assault, hackers bypass botnets and go straight for the server. In order to temporarily block or suspend a website, these botnets send a flood of requests to the server. The server typically takes requests and returns responses. There will be a brief outage if the server gets more requests than it can handle. While this attack makes the service inaccessible, it prevents data theft. A machine-learning approach is used to detect server-side DDoS assaults. The term 'machine learning' refers to software that can learn new tasks and solve old ones without any input from a human. Learning Vector Quantization, Support Vector Machine, Naive Bayes, K-Nearest Neighbour, Random Forest, and Decision Tree are the six ML classification models used since these ML procedures enhance accuracy. The main goal of this research is to use the CIDAD dataset to find ways to protect the application layer of the Internet of Things (IoT) against Distributed Denial of Service (DDoS) attacks. The lower layers of the IoT network architecture are disregarded by this dataset, which comprises information at the COAP level. © 2024 IEEE.

关键词： CIDAD Cyber security Cyber-attacks DDoS attacks

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A Privacy-preserving Energy Management System Based on Homomorphic Cryptosystem for IoT-enabled Active Distribution Network

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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 167-178页

作者： Qian Hu Siqi Bu Wencong Su Vladimir Terzija the Department of Physics Hong Kong Baptist UniversityHong KongChina the Department of Electrical and Electronic Engineering Shenzhen Research InstituteThe Hong Kong Polytechnic UniversityHong KongChina the Department of Electrical and Computer Engineering University of Michigan-DearbornDearbornMI 48128USA the School of Engineering Newcastle UniversityNewcastleUK

Active distribution network(ADN),as a typically cyber-physical system,develops with the evolution of Internet of Things(IoTs),which makes the network vulnerable to cybersecurity *** this paper,the eavesdropping attacks that lead to privacy breaches are addressed for the IoT-enabled ADN.A privacy-preserving energy management system(EMS)is proposed and empowered by secure data exchange protocols based on the homomorphic *** the information transmission among distributed generators and load customers in the EMS,private information including power usage and electricity bidding price can be effectively protected against eavesdropping *** correctness of the final solutions,e.g.,optimal market clearing price and unified power utilization ratio,can be deterministically *** simulation results demonstrate the effectiveness and the computational efficiency of the proposed homomorphically encrypted EMS.

关键词： Eavesdropping attack energy management system homomorphic cryptosystem Internet of Things(IOTs) active distribution network(ADN) privacy-preserving

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Ultra-low-defect homoepitaxial micro-LEDs with enhanced efficiency and monochromaticity for high-PPI AR/MR displays

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PhotoniX 2024年第1期5卷 335-356页

作者： Yibo Liu Guobin Wang Feng Feng Mengyuan Zhanghu Zhengnan Yuan Zichun Li Ke Xu Hoi Sing Kwok Zhaojun Liu Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong Kong SARChina Department of Electrical and Electronic Engineering Southern University of Science and TechnologyShenzhenChina Suzhou Institute of Nano-Tech and Nano-Bionics Chinese Academy of SciencesJiangsu Institute of Advanced SemiconductorsSuzhouChina

The issue of brightness in strong ambient light conditions is one of the critical obstacles restricting the application of augmented reality(AR)and mixed reality(MR).Gallium nitride(GaN)-based micro-LEDs,renowned for their exceptional brightness and stability,are considered the foremost contenders for AR ***,conventional heteroepitaxial growth micro-LED devices confront formidable challenges,including substantial wavelength shifts and efficiency *** this paper,we firstly demonstrated the high-quality homoepitaxial GaN-on-GaN micro-LEDs microdisplay,and thoroughly analyzed the possible benefits for free-standing GaN substrate from the material-level characterization to device optoelectronic properties and microdisplay application compared with sapphire *** GaN-on-GaN structure exhibits a superior crystal quality with ultra-low threading dislocation densities(TDDs)of~105 cm^(-2),which is three orders of magnitude lower than that of *** an in-depth size-dependent optoelectronic analysis of blue/green emission GaN-on-GaN/Sapphire micro-LEDs from 100×100 shrink to 3×3μm^2),real that a lower forward voltage and series resistance,a consistent emission wavelength(1.21 nm for blue and 4.79 nm for green@500 A/cm2),coupled with a notable reduction in efficiency droop ratios(15.6%for blue and 28.5%for green@500 A/cm^(2))and expanded color gamut(103.57%over Rec.2020)within GaN-on-GaN 10μm *** but not least,the GaN-on-GaN micro-display with 3000 pixels per inch(PPI)showcased enhanced display uniformity and higher luminance in comparison to its GaN-on-Sapphire counterpart,demonstrating significant potentials for high-brightness AR/MR applications under strong ambient light.

关键词： optoelectronic ambient epitaxial

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DNA Computing with Water Strider Based Vector Quantization for Data Storage Systems

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computers, Materials & Continua 2023年第3期74卷 6429-6444页

作者： A.Arokiaraj Jovith S.Rama Sree Gudikandhula Narasimha Rao K.Vijaya Kumar Woong Cho Gyanendra Prasad Joshi Sung Won Kim Department of Networking and Communications College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science Engineering Aditya Engineering CollegeSurampalemAndhra Pradesh533437India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering Vignan’s Institute of Engineering for WomenVisakhapatnam530049India Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of *** accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can be employed,which encodes and decodes binary data to and from synthesized strands of *** quantization(VQ)is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression *** article introduces a newDNAComputingwithWater StriderAlgorithm based Vector Quantization(DNAC-WSAVQ)technique for Data Storage *** proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data ***,the DNAC-WSAVQ model initially performsDNA encoding on the input images to generate a binary encoded *** addition,aWater Strider algorithm with Linde-Buzo-Gray(WSA-LBG)model is applied for the compression process and thereby storage area can be considerably *** order to generate optimal codebook for LBG,the WSA is applied to *** performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several *** comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.

关键词： DNA computing data storage image compression vector quantization ws algorithm space saving

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Adaptive Marine Predator Optimization Algorithm(AOMA)-Deep Supervised Learning Classification(DSLC)based IDS framework for MANET security

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Intelligent and Converged Networks 2024年第1期5卷 1-18页

作者： M.Sahaya Sheela A.Gnana Soundari Aditya Mudigonda C.Kalpana K.Suresh K.Somasundaram Yousef Farhaoui Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600069India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai 602105India JNIAS School of Planning and Architecture Hyderabad 500034India Department of Computer Science and Engineering NPR College of Engineering and TechnologyNatham Dindigul 624401India Department of Computer Science and Engineering PSNA College of Engineering and TechnologyPoolangulathupatti 620009India Department of Computer Science and Engineering Sri Muthukumaran Institute of TechnologyChennai 600069India Department of Computer Science Moulay Ismail UniversityMeknes 5003Morocco

Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks *** MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular *** machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of ***,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of ***,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning ***,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of ***,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of ***,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training *** evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

关键词： Intrusion Detection System(IDS) Security Mobile Ad-hoc Network(MANET) min-max normalization Adaptive Marine Predator Optimization Algorithm(AOMA) Deep Supervise Learning Classification(DSLC)

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A Target Recognition Method for High-Resolution Range Profiles in Imbalanced Datasets 2

A Target Recognition Method for High-Resolution Range Profil...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Su, Yitao Yin, Junjun Yang, Jian University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China Tsinghua University Department of Electronic Engineering Beijing China

ISBN: (纸本)9798331515669

Traditional high-resolution range profile (HRRP) recognition methods face challenges in automatically extracting targets' temporal and spatial information and demonstrate low recognition accuracy on multi-class imbalanced datasets. We propose an HRRP target recognition approach based on a lightweight Transformer and fully convolutional network (FCN) model to address these challenges. The Transformer's encoder and the FCN are utilized to extract deep temporal correlation features and deep spatial structural features of HRRP, respectively. An adaptive weight matrix is introduced at the decision level to enhance the expression capabilities of temporal features and spatial features. To address the class imbalance issue, we design an adaptive focal loss function combined with a label smoothing strategy, which enhances the model's sensitivity to minority classes and improves its generalization ability. Extensive experiments conducted on the MSTAR dataset validate the effectiveness of the proposed method. The model demonstrates superior recognition performance on both balanced and imbalanced datasets, and the proposed loss function outperforms other methods. © 2024 IEEE.

关键词： Automatic target recognition

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An Analysis of the Effect of Varying Finger Tissue Thickness on the Non-Invasive Determination of Blood Glucose Level 6

An Analysis of the Effect of Varying Finger Tissue Thickness...

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6th International Conference on Sustainable Technologies for Industry 5.0, STI 2024

作者： Aminuzzaman, Mir Md. Hossam-E-Haider, Md Miah, Md Salim Himel, Raian Habib Ahad, Sakib Abdul Al Oafi Himon, Md. Abdullah Military Institute of Science and Technology Department of Electrical Electronic and Communication Engineering Dhaka Bangladesh Islamic University of Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798331531973

The microwave-based non-invasive technique has become increasingly popular for determining human Blood Glucose Level (BGL) due to its numerous advantages. A microwave sensor is designed and analyzed in this work that can determine glucose level from the human fingertip. However, variations in finger thickness among individuals may lead to discrepancies in the sensor's readings. The effect of varying thickness of finger tissues on the result of the sensor is examined. The sensor consists of a microstrip patch antenna loaded with a metamaterial element known as Complementary Split Ring Resonator (CSRR). Various sensor configurations with varied numbers of CSRR pairs were tested, and sensor with single pair of CSRR was chosen as the optimal configuration based on the plot of the sensor's reflection coefficient (S11). Different shapes of CSRRs are also utilized, from which sensor with circular shaped CSRR is found as the most effective. The potentiality of the sensor is ensured by its impressive performance in terms of amplitude of reflection coefficient (S11), gain pattern and radiation efficiency. Variations in the thickness of the skin, blood, fat, and nail layers cause fluctuations in the return loss of the sensor, thereby impacting the accuracy of glucose detection. The potential resolution of this matter is being discussed. Furthermore, the maximum 1-g Specific Absorption Rate (SAR) of the sensor is obtained as 0.519 W/kg which ensures the radiation safety of the device. © 2024 IEEE.

关键词： Microstrip antennas

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Cutting-edge Facial Authentication System in Matlab (CFAS-M)

Cutting-edge Facial Authentication System in Matlab (CFAS-M)

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2024 International Conference on communication, Computing and Internet of Things, IC3IoT 2024

作者： Sudha, L. Vadhani, R. Aruna, K.B. Sureka, V. Sasikala, V. S.A. Engineering College Computer Science And Engineering Department Chennai India Information Technology Prathysha Engineering College India Sri Sairam Engineering College Department Of Electronic And Communication Engineering India

ISBN: (纸本)9798350352689

Facial Authentication Systems have gained significant traction in recent years due to their convenience, security, and versatility in various applications. Facial recognition algorithms, including traditional methods like Eigen faces, Fisher faces, and Principal Component Analysis (PCA). Firstly, these methods heavily rely on handcrafted features, which may not adequately capture the complexities and variations present in facial images, leading to reduced accuracy. Additionally, traditional algorithms are often sensitive to changes in pose, lighting conditions, and facial expressions, making them less robust in real-world scenarios where such variations are common. Moreover, their limited discriminative power can result in higher rates of false acceptance or rejection, undermining the reliability of the system. Furthermore, traditional facial recognition algorithms struggle with noise and occlusions, as they may obscure critical facial features, further diminishing recognition accuracy. Lastly, these algorithms may lack scalability and adaptability, making them less suitable for deployment in diverse environments and populations where facial characteristics vary significantly. Using advanced MATLAB features, aims to serve as a foundation for secure authentication, seamless access control, and enhanced user experiences. The system consists of interconnected modules to ensure accuracy, robustness, and real-time capabilities. It begins with an advanced face detection module employing algorithms like Viola-Jones and deep learning networks to precisely locate faces within images and video streams. To enhance user interaction, an intuitive graphical user interface (GUI) created with MATLAB's App Designer allows users to enroll new faces, perform recognition tasks, and interact with the system easily. The proposed work also prioritizes accountability and monitoring, featuring a logging and reporting module that records system activities, recognition outcomes, and user inte

关键词： Face recognition

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