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Survey on recent advances in IoT application layer protocols and machine learning scope for research directions

Digital Communications and Networks 2022年第5期8卷 727-744页

作者： Praveen Kumar Donta Satish Narayana Srirama Tarachand Amgoth Chandra Sekhara Rao Annavarapu Department of Computer Science and Engineering Indian Institute of Technology(Indian School of Mines)Dhanbad826004India School of Computer and Information Sciences University of HyderabadHyderabad500046India Mobile&Cloud Lab Institute of Computer ScienceFaculty of Science and TechnologyUniversity of Tartu50090Estonia

The Internet of Things(IoT)has been growing over the past few years due to its flexibility and ease of use in real-time *** IoT's foremost task is ensuring that there is proper communication among different types of applications and devices,and that the application layer protocols fulfill this ***,as the number of applications grows,it is necessary to modify or enhance the application layer protocols according to specific IoT applications,allowing specific issues to be addressed,such as dynamic adaption to network conditions and ***,several IoT application layer protocols have been enhanced and modified according to application ***,no existing survey articles focus on these *** this article,we survey traditional and recent advances in IoT application layer protocols,as well as relevant real-time applications and their adapted application layer protocols for improving *** changing the nature of protocols for each application is unrealistic,machine learning offers means of making protocols intelligent and is able to adapt *** this context,we focus on providing open challenges to drive IoT application layer protocols in such a direction.

关键词： Internet of things Machine learning Application layer Request-response protocols Publish-subscribe protocols

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Enhancing Hypertrophic Cardiomyopathy Heart Risk Assessment with Deep Learning and Multi-Omics Data Integration

Enhancing Hypertrophic Cardiomyopathy Heart Risk Assessment ...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Mohammed, Afreen Fatima Sahu, Dinesh Prasad Tyagi, Rinki Pundhir, Prachi Hussien, Rami Riad Kamboj, Surbhi Cvr College of Engineering Department of CSE-DS Hyderabad India School of Computer Science Engineering & Technology Bennett University Greater Noida India Raj Kumar Goel Institute of Technology Department of Computer Science and Engineering Ghaziabad India Abes Engineering College Department of Information Technology Ghaziabad India The Islamic University Najaf Iraq Kiet School of Pharmacy Kiet Group of Institutions Ghaziabad India

ISBN: (纸本)9798350379716

This research explores the possibility of improving risk assessment for hypertrophic cardiomyopathy (HCM) by combining multi-omics data and deep learning methodologies. In this analysis, the research included 513 patients and established prediction models based on genomics, transcriptomics, and proteomics data. The research also used several deep learning architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTM), and graph neural network solvers. The notable results were that the CNN model's accuracy was 93%, with corresponding precision and recall rates of 91% and 92%, respectively. In addition, the performance of the models was increased when combining different omics data hierarchically. CNNs showed improvements in accuracy from 85% using single-omics to 92% with integrated multi-omics. Revealing the intricate biological relationships associated with Hypertrophic cardiomyopathy (HCM) necessitates high-quality, comprehensive data integration. These insights not only contribute to a more comprehensive understanding of HCM's pathophysiology but also serve as seeds for the discovery of novel biomarkers and new therapeutic targets. In summary, this work demonstrates the power of multi-omics and deep learning approaches for developing precision medicine in HCM with applications to improved risk stratification and patient management strategies. © 2024 IEEE.

关键词： Convolutional neural networks

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Nodal Transmission in 5G Wireless Networking

Nodal Transmission in 5G Wireless Networking

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Agrawal, Trapty Kakati, Pallabi Das, Sunanda Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350357769

The increase in data transmission network speed and reliability is inextricably linked to the fast growth of information technology. This calls for the ongoing improvement and upgrading of current standards. Next-generation communication networks are being developed and deployed with diligence by several organizations. This article provides a thorough examination of the software options available that are designed for analyzing and evaluating data network behaviors. It specifically emphasizes tools with substantial built-in functionality and flexibility that are ideally suited for mobile communication systems. It becomes clear that the NS3 network simulator is the best option. This article provides an overview as well as a unique NS3 solution that makes it possible to estimate the dependability of data transmission from a network node's first movement to its removal from a base station. © 2023 IEEE.

关键词： 5G Wireless Networking Communication Networks Data Transmission Interference Analysis Network Security Nodal Transmission NS3 Network Simulator Signal Quality And Distribution Algorithms Simulation Modeling

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Beyond The Internet of Things: Imagining IoT's Structure and Potential Routes

Beyond The Internet of Things: Imagining IoT's Structure and...

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2023 International Conference on Advances in Computation, Communication and information technology, ICAICCIT 2023

作者： Varalakshmi, K.R. Mahesh, T.R. Singh, Akhilendra Pratap Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India

ISBN: (纸本)9798350344387

wireless sensor system technology has made it possible for ubiquitous sensing, which affects many aspects of contemporary life. Indicators of environmental health, from sensitive ecologies as well as biodiversity to urban settings, may now be measured, inferred, and understood. The Internet of Things is enabled by the extensive utilization of these devices in a network that facilitates communication and action. In this network, the actuators and sensors smoothly interact with our environment as data is exchanged across platforms to provide a unified operational view. Near field communication (NFC) is the upcoming technology that will enable the transformation of the worldwide web into a fully integrated Future Internet. This technology is being advanced through the adoption of variousenabling device methods, such as tags, audience members, encased sense and actuators nodes, and nodes for near field interaction. The demand for data-on-demand, using complex and straightforward queries, increases significantly as studies transition from web to web2 to web3. This study presents a cloud-centric vision for the worldwideimplementation of the Internet of Things. The future of IoT research is expected to be influenced by many significant technology enablers and their corresponding application domains, which are emphasized. A cloud architecture based on the interplay of public as well as private clouds and employing Aneka is shown. Study conducted elaborate on the need of unity, the global web, and distributed processing in our last section of our IoT vision for the community of technical researchers. © 2023 IEEE.

关键词： Cloud-centric Internet of Things Data storage and analytics Internet of Things (IoT) New protocols Participatory Sensing Quality of Service (QoS) Radio Frequency Identification (RFID) User-cantered cloud-based paradigm Visualization Wireless Sensor Networks

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Low-Rate DDoS Attack on SDN Controller and Its Impact 16

Low-Rate DDoS Attack on SDN Controller and Its Impact

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16th International Conference on Security of information and Networks, SIN 2023

作者： Virender Sharma, Lokesh Roy, Satyabrata Rawat, Umashankar Manipal University Jaipur Department of Information Technology Jaipur India Manipal University Jaipur Department of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350343212

Software Defined Network (SDN) has changed the perception we used to have towards computer communication. As number of devices grow in the network, management of the inter device communications become a daunting task. SDN offers an easier approach to manage the large scaling networks. Control plane has the global vision of the routing devices. Any kind of network misbehavior can be easily traced and responded in real time. SDN does not come without its challenges. Its architecture exposes many surfaces vulnerable to attacks. Controller being the central device to control the network is the most lucrative point for the attackers. This paper shows how controller as well as data plane are vulnerable to Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) attacks. Although various solutions have been proposed by researchers to detect and mitigate Distributed Denial of Service (DDoS) attacks, but these solutions are limited in applications and scope. We will perform Low-Rate DDoS (LR-DDoS) attacks on controller to eat up the controller resources. Such low-density attacks are effective and most difficult to detect because of their low rate and distributed in nature. We will also focus on how to detect these attacks and possible solutions to mitigate the attacks. © 2023 IEEE.

关键词： Controllers

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Magnificent Frigatebird Optimization: A New Bio-Inspired Metaheuristic Approach for Solving Optimization Problems

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computers, Materials & Continua 2024年第8期80卷 2721-2741页

作者： Tareq Hamadneh Khalid Kaabneh Ibraheem AbuFalahah Gulnara Bektemyssova Galymzhan Shaikemelev Dauren Umutkulov Sayan Omarov Zeinab Monrazeri Frank Werner Mohammad Dehghani Department of Mathematics Al Zaytoonah University of JordanAmman11733Jordan Faculty of Information Technology Al-Ahliyya Amman UniversityAmman19328Jordan Department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan Department of Computer Engineering International Information Technology UniversityAlmaty050000Kazakhstan Department of Big Data Analytics and Data Science Software Development Company《QazCode》Almaty050063Kazakhstan Department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran Faculty of Mathematics Otto-von-Guericke UniversityP.O.Box 4120Magdeburg39016Germany

This paper introduces a groundbreaking metaheuristic algorithm named Magnificent Frigatebird Optimization(MFO),inspired by the unique behaviors observed in magnificent frigatebirds in their natural *** foundation of MFO is based on the kleptoparasitic behavior of these birds,where they steal prey from other *** this process,a magnificent frigatebird targets a food-carrying seabird,aggressively pecking at it until the seabird drops its *** frigatebird then swiftly dives to capture the abandoned prey before it falls into the *** theoretical framework of MFO is thoroughly detailed and mathematically represented,mimicking the frigatebird’s kleptoparasitic behavior in two distinct phases:exploration and *** the exploration phase,the algorithm searches for new potential solutions across a broad area,akin to the frigatebird scouting for vulnerable *** the exploitation phase,the algorithm fine-tunes the solutions,similar to the frigatebird focusing on a single target to secure its *** evaluate MFO’s performance,the algorithm is tested on twenty-three standard benchmark functions,including unimodal,high-dimensional multimodal,and fixed-dimensional multimodal *** results from these evaluations highlight MFO’s proficiency in balancing exploration and exploitation throughout the optimization *** studies with twelve well-known metaheuristic algo-rithms demonstrate that MFO consistently achieves superior optimization results,outperforming its competitors across various *** addition,the implementation of MFO on four engineering design problems shows the effectiveness of the proposed approach in handling real-world applications,thereby validating its practical utility and robustness.

关键词： Optimization metaheuristic magnificent frigatebirds exploration exploitation

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Performance Comparison of Different Versions of YOLO for Indian Sign Language Captioning in Real Time of Multiple Signers

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Procedia computer Science 2025年 259卷 991-1000页

作者： Rohit Raj R Sreemathy Mousami Turuk Jayashree Jagdale Mohammad Anish Department of Electronics and Telecommunication Engineering Pune Institute of Computer Technology Pune 411043 India Department of Information Technology Pune Institute of Computer Technology Pune 411043 India

Communication is an essential aspect of life, enabling people to exchange messages. While most individuals can communicate verbally, people who are deaf use different techniques, such as sign language, for communication. To facilitate interaction for the deaf community, Indian Sign Language (ISL) recognition using You Only Look Once (YOLO) architecture has become a prevalent and effective approach for detecting signs in real time using deep learning techniques. For this research, experimentations were performed using various YOLO versions, including YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOv9, YOLO NAS, YOLOv10, and YOLO 11. The self-created dataset consists of 115 ISL signs (classes). We used 10 different signers and created 20 to 40 images for each class. The models are tested on 115 ISL signs to recognize the signs accurately in real time. All YOLO versions are trained on our self-created annotated dataset. The results showed that YOLO NAS outperformed the other versions. After fine-tuning some key parameters, YOLOv5, YOLOv8, YOLOv9, YOLOv10, YOLO 11, and YOLO NAS, a mean average precision (mAP) of 0.969, 0.975, 0.974, 0.960, 0.978 and 0.982 respectively with value of 0.50 as intersection over union (IoU) threshold were achieved. These results demonstrate the potential of these models for real-time ISL recognition. Our model also recognises signs of multiple persons in a single frame, which can be further used for ISL conversation in real time.

关键词： YOLO YOLO Versions YOLO NAS Indian Sign Language Deep Learning Mean Average Precision

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Multidimensional Voronoi Constellations vs. Short Blocklength Probabilistic Shaping: A Comparison for Multilevel Coding Approach

Multidimensional Voronoi Constellations vs. Short Blocklengt...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on information Photonics and Optical Communications, IPOC 2024

作者： Sheng, Yajie Chen, Bin Lei, Yi Deng, Jingxin Xu, Jiwei Fu, Mengfan Zhuge, Qunbi Li, Shen Department of Computer Science and Information Engineering Hefei University of Technology Hefei China State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Department of Electrical and Computer Engineering Université Laval Québec Canada

Performance of concatenated multilevel coding with probabilistic shaping (PS) and Voronoi constellations (VCs) is analysed over AWGN channel. Numerical results show that VCs provide up to 1.3 dB SNR gains over PS-QAM ... 详细信息

ISBN: (纸本)9798350379266

关键词： Probability distributions

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Investigating the Power of Advanced Neural Network Architectures for Higher Quality information Extraction 2

Investigating the Power of Advanced Neural Network Architect...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Ramya, G. Wanjale, Kirti H. Srivastav, Vaibhav Muthukumar, B. Mishra, Awakash Mukuntharaj, C. Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Animation and Virtual Reality Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore641032 India

ISBN: (纸本)9798350308846

This paper explores the ability of superior neural community architectures to enhance the high quality of information extraction. It evaluates current architectures in the context of textual content report processing and examines how architectures can be changed to fit such responsibilities. The authors first speak about the capabilities of deep mastering frameworks for extracting data from text documents in terms of extracting semantic capabilities, categorizing records, and removing unique linguistic abilities. Then, they provide an in-depth overview of the diverse neural community architectures, including convolution and extended quick-term memory networks (LSTMs) and their packages for information extraction. Outcomes show that LSTMs are the most straightforward and efficient compared to other architectures, demonstrating accuracy, performance, and scalability benefits. The authors then discuss how current architectures, which include Caps Net and BERT, may be similarly optimized for information extraction responsibilities and recommend a technique for utilizing these strategies. In the end, the authors display how advanced neural community architectures may be used to extract better exceptional information from textual content documents, which has terrific ability implications for report processing, expertise extraction, and other duties. © 2023 IEEE.

关键词： Architectures Community Convolution Duties Semantic

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Real-Time Multi-Feature Approximation Model-Based Efficient Brain Tumor Classification Using Deep Learning Convolution Neural Network Model

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computer Systems Science & engineering 2023年第9期46卷 3883-3899页

作者： Amarendra Reddy Panyala M.Baskar Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyKattankulathurChengalpattuChennai603203TamilnaduIndia Department of Information Technology MLR Institute of TechnologyHyderabad500043TelanganaIndia Department of Computing Technologies School of ComputingSRM Institute of Science and TechnologyKattankulathurChengalpattuChennai603203TamilnaduIndia

The deep learning models are identified as having a significant impact on various *** same can be adapted to the problem of brain tumor ***,several deep learning models are presented earlier,but they need better classification *** efficient Multi-Feature Approximation Based Convolution Neural Network(CNN)model(MFACNN)is proposed to handle this *** method reads the input 3D Magnetic Resonance Imaging(MRI)images and applies Gabor filters at multiple *** noise-removed image has been equalized for its quality by using histogram ***,the features like white mass,grey mass,texture,and shape are extracted from the *** features are trained with deep learning Convolution Neural Network(CNN).The network has been designed with a single convolution layer towards dimensionality *** texture features obtained from the brain image have been transformed into a multi-dimensional feature matrix,which has been transformed into a single-dimensional feature vector at the convolution *** neurons of the intermediate layer are designed to measure White Mass Texture Support(WMTS),GrayMass Texture Support(GMTS),WhiteMass Covariance Support(WMCS),GrayMass Covariance Support(GMCS),and Class Texture Adhesive Support(CTAS).In the test phase,the neurons at the intermediate layer compute the support as mentioned above values towards various classes of *** on that,the method adds a Multi-Variate Feature Similarity Measure(MVFSM).Based on the importance ofMVFSM,the process finds the class of brain image given and produces an efficient result.

关键词： CNN deep learning brain tumor classification MFA-CNN MVFSM 3D MRI texture Gabor

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