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An Energy Efficient and Reliable Strategies for Intra-Cluster and Inter-Cluster communications in Wireless Sensor Networks

An Energy Efficient and Reliable Strategies for Intra-Cluste...

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2023 IEEE International Conference on Integrated Circuits and communication Systems, ICICACS 2023

作者： Balmuri, Kavitha Rani Sujatha, J. Arvind, Sudha Lal, Bechoo Kumari C, Sapna Cmr Technical Campus Department of Information Technology Hyderabad India H. K. E. Society's S. L. N. College of Engineering Department of Computer Science & Engineering Raichur India Cmr Technical Campus Department of Electronics & Communication Engineering Kandlakoya India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

The development and widespread adoption of numerous wireless technologies are opening the door to novel forms of remote monitoring. Because of their adaptability, performance, low cost, and mobility, sensing devices are gaining in popularity. Due to their portability and flexibility, Wireless Sensor Networks (WSN) can be a viable alternative to hardwired infrastructure in geographically dispersed settings. There has been a rise in their popularity due to their capacity to detect and report changes in a wide range of environmental and physical variables, including temperature, sound, vibration, pressure, and many more. The primary goal of this study is to identify intra- and inter-cluster communication protocols that are suitable in terms of energy efficiency, scalability, and dependability. At this stage, the specified region is partitioned into clusters, and the leader nodes of those clusters are chosen according to predetermined criteria. Once a cluster has formed, periodic and aperiodic data can be sensed and communicated among members using appropriate MAC protocols. In order to take advantage of the best features of CSMA/CA and variable TDMA, these protocols need a third, hybrid way of data sensing. As a result, the proposed protocols require less power to run while still providing excellent reliability and scalability for a static network's periodic and event-based data transmission. © 2023 IEEE.

关键词： Wireless sensor networks

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Secrecy Capacity Optimization in RF/FSO Systems: Impact of Mixed Rayleigh and Log-Normal Fading with Atmospheric Turbulence

Secrecy Capacity Optimization in RF/FSO Systems: Impact of M...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective communication Technologies, INSPECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India ABV Indian Institute of Information Technology and Management Data Science and Quantum Computing Gwalior India

ISBN: (纸本)9798350379525

This paper investigates the secrecy capacity optimization in RF/FSO systems with mixed Rayleigh and log-normal fading, while masking eavesdroppers channel state information () as well as suffering from atmospheric turbulence. We explore the impact of several parameters including beamforming angles, modulation schemes and hybrid RF/FSO link configurations through a combination of simulations as well as analytical models. Secrecy capacity is enhanced for an improved SNR and vice versa under higher turbulence conditions and cooler influents eavesdropped scenarios. Performance of the hybrid RF/FSO systems dominates considerably over single-link solutions, especially at longer distances. All these mechanisms appreciably increase the secrecy capacity, while beamforming and advanced modulation techniques offer most of the improvement. The study also demonstrates the interdependencies of system parameters, providing guidelines for designing secure communication systems under different environmental and operational conditions. © 2024 IEEE.

关键词： Beamforming

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Enhancing Security in Physical Layer communications Based on Continuous Wavelet Transform with Convolutional Vision Transformer 4

Enhancing Security in Physical Layer Communications Based on...

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4th IEEE International Conference on Mobile Networks and Wireless communications, ICMNWC 2024

作者： Obaid, Saif Siri, Dharmapuri Sathish Kumar, G. Kumar, S. Senthil Rajasekaran, Arun Sekar College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Gojan School of Business and Technology Department of Information Technology Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India Sr University Department of Electronics and Communication Engineering Warangal India

ISBN: (纸本)9798350352931

Nowadays, the Enhancing Security in Physical Layer communications underway on implementing the 6G radio because the attacks are increasing more on wireless edges such as jamming and false based stations and it placed a low-cost software radio. However, in Cyber-Physical System (CPS) the unauthorized access affects system process, such information lacks on the system and there is no adverse effect about such data. The quick environment changes and unpredicted events are such various factor that affect CPS, and it gives false alarm on the detection of cyber-physical attack. To overcome these drawbacks an ensemble method combining Continuous Wavelet Transform (CWT) with Convolutional Vision Transformer (CVT) to enhance physical layer communication security. The process involves collecting Real-World Photoplethysmography (RW-PPG) dataset and then the preprocessing is done with the Principal Component Analysis (PCA) and applying CWT with CVT signal transformations. Classification is then performed using Automatic Modulation Classification (AMC) and Specific Emitter Identification (SEI). The proposed CWT with CVT model obtained high results in the context of precision (PPV), achieving a score of 0.692, Sensitivity (TPR) of 0.841, Specificity (TNR) of 0.985, and Area Under the Curve (AUC) of 0.827 when compared to existing methods like Recurrent Neural Network (RNN) respectively. © 2024 IEEE.

关键词： Convolution

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Classification of Skin Diseases in the Internet of Medical Things using Hybrid Deep Learning 2

Classification of Skin Diseases in the Internet of Medical T...

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2nd IEEE International Conference on Networks, Multimedia and information Technology, NMITCON 2024

作者： Latha, Dandu Madhavi Anusha, Kondepati Samrin, Rafath Reddy, Pundru Chandra Shaker Sudhakar, Bolleddu Sathish, Goski Geethanjali College of Engineering and Technology Electronics and Communication Engineering Telangana Hyderabad India Marri Laxman Reddy Institute of Technology and Management Computer Science and Engineering Telangana Hyderabad India King Khalid University Department of Computer Science College of Computer Science Asir Abha Saudi Arabia Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India Degree Lecturer of Computer Science Mjptbcwreis Telangana India Department of Information Technology Hyderabad India

ISBN: (纸本)9798350372892

When it comes to the IoMT, skin lesion examination is absolutely essential for making an accurate diagnosis. Skin lesion analysis relies heavily on computer-aided design(CAD) technologies to enhance accuracy and efficiency. Using hybrid deep learning approaches, this work focuses on segmenting and classifying skin lesions from dermoscopy images. Mask Region-based Convolutional Neural Network (MRCNN) for semantic separation and ResNet50 for lesion-detection are two state-of-the-art methodologies that are combined in this research's hybrid deep learning model. A skin lesion's exact location can be determined with the use of the MRCNN's border delineation capabilities. For comprehensive model training, we gather a large collection of dermoscopy photos that have been annotated. This dataset is used to train a hybrid deep-learning(DL) model that can capture images' nuanced representations. An accuracy rate of 95.49% in segmenting lesions into different groups demonstrates the model's capability to do so. Another significant improvement over older method is the high reliability and accuracy of skin lesion classification. After being tested on the ISIC2020 Challenge dataset, the model achieved an impressive 96.75% accuracy rate. Classification and segmentation models outperform the state-of-the-art in Internet-of-Medical-Things(IoMT). When it comes to segmenting and classifying skin lesions, this paper's hybrid deep learning approach works wonders. Outperforming the existing gold standards, the results demonstrate that the model can enhance diagnostic accuracy in an IoMT environment. © 2024 IEEE.

关键词： Convolutional neural networks

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Predicting Hand Injury Severity Using Bayesian Networks in Cloud-Based Emergency Medicine 10

Predicting Hand Injury Severity Using Bayesian Networks in C...

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10th International Conference on communication and Signal Processing, ICCSP 2024

作者： Yuvaraj, S. Thinakaran, K. Srinivas, S. Porandla Sivakumar, S. Ishwarya, M.V. Thirumaraiselvan, P. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Chennai Tamil Nadu Kattankulathur India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Malla Reddy Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Department of Computer Science Tamil Nadu Perambalur India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Thazhambur India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

Predicting the severity of hand injuries in emergency care is the focus of this study, which suggests using cloud-based Bayesian networks. It is crucial to have accurate severity prediction in order to treat emergency hand injuries promptly since they are prevalent. By using the scalability and flexibility of cloud computing, this method constructs a Bayesian severity assessment network with patient data, injury characteristics, and historical medical records. The model's training data comes from a massive database of hand injury cases with different results. Emergency medical professionals may use real-time parameter modifications to strengthen and adapt the Bayesian network. Through the use of the cloud, healthcare providers may simply exchange data and reach consensus. It demonstrates that, compared to other well-established methods of evaluating hand injury severity, the model performs better in making predictions. The suggested method improves severity forecasts and advances cloud-based emergency care. The method for emergency hand injury care uses Bayesian networks in the cloud to improve scalability and accessibility. The findings show that the data-driven and efficient hand injury severity prediction method might transform emergency care by improving patient outcomes. © 2024 IEEE.

关键词： Bayesian networks

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A Beam-Scanning Reflectarray with the Weighted Average Phase Compensation for the150-160 GHz communications 6

A Beam-Scanning Reflectarray with the Weighted Average Phase...

引用

6th IEEE International Conference on Electronic information and communication Technology, ICEICT 2023

作者： Zhou, Zhengdong Feng, Botao Ding, Xiao Deng, Li Gu, Wenzhe Chung, Kwok L. College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Macau University of Science and Technology Faculty of Humanities and Arts 999078 China College of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China School of Computer Science and Engineering Huizhou University Huizhou516007 China

ISBN: (纸本)9798350399059

In this paper, a 15×15 beam scanning reflectarray operating in 150-160 GHz is proposed. The element consists of 3-layer patches conducted with metal vias. The proposed weighted average phase compensation method is used to obtain stable beam and low gain variations during the beam scanning process. Furthermore, the comparisons of radiation between normal phase compensation and weighted average phase are given to verify the effectiveness of the method. Results show that a large scanning angle of ±50° and a maximum gain of 18.7 dBi with gain variations less than 1.8 dB can be achieved, and hence the proposed antenna array is suitable for future 6G communication system. © 2023 IEEE.

关键词： Antenna arrays

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Dual-Model Approach for Lung Disease Classification using Convolutional Neural Networks and Support Vector Machines 2

Dual-Model Approach for Lung Disease Classification using Co...

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2nd IEEE International Conference on Integrated Intelligence and communication Systems, ICIICS 2024

作者： Shaik, Mohammed Ali Fatima, Afshan Parveen, Masrath Rani, A. Soumya Mohammad, Akheel Rahim, Abdul School of Computer Science & Artificial Intelligence SR University Warangal India Department of Computer Science & Engineering CMR Institute of Technology Hyderabad India Department of Information Technology Vidya Jyothi Institute of Technology Hyderabad India Hyderabad India Department of Computer Science & Engineering Dr. Vrk women's College of Engineering and Technology Hyderabad India Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women Secunderabad India

ISBN: (纸本)9798331504960

This paper proposes a solution of integrating the use of Support Vector Machine (SVM) and Convolutional Neural Networks (CNNs) for categorizing lung conditions of people with and without lung disease. The proposed model has achieved a high F1-Score of 97.4%, a high Precision of 93.17% and high Recall of 93. 63 for the Healthy class. The F1 Scores of this study for the Infected Lung class were as follows: the accuracy of F1 Score was 97.4%;Precision was 94.12%;and Recall was 92.06%. The combining of SVM and CNN model was quite accurate with the accuracy of 97% and the model was accurate in multiple domains of assessment. These results suggest that the application of CNNs and SVMs to the task of medical image analysis constitutes a promising and accurate approach for diagnosing lung diseases. © 2024 IEEE.

关键词： Lung cancer

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Deep Learning for Channel Prediction in Non-Stationary Wireless Fading Environments

Deep Learning for Channel Prediction in Non-Stationary Wirel...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective communication Technologies, INSPECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Data Science and Quantum Computing ABV Indian Institute of Information Technology and Management Gwalior India

ISBN: (纸本)9798350379525

In non-stationary wireless fading channels deep learning models are effective in predicting channel states as indicated by this study. This work compared the LSTM and GRU models with autoregressive methods or Kalman filter, implying that deep learning is indeed able to capture temporal dependence between variables and was likely more adapted than linear method when dealing with many changes in dynamics. From the perspective of pre-processing data, keys like attention mechanism and transfer learning were decisive elements in helping them to improve prediction scores and make models more robust. These results showed that these enhanced procedures allow the models to generalise over a wide range of non-stationary situations, generating consistent performance in various diverse environments. This study indicates the promise of deep learning for enhancing wireless network operation, which could lead to greater self-awareness and utilization features in emerging future-generation wireless systems. © 2024 IEEE.

关键词： Prediction models

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Optimizing Advanced Neural Network Architectures to Enhance the Accuracy of Extracting Online information 2

Optimizing Advanced Neural Network Architectures to Enhance ...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Mukuntharaj, C. Vijayaraj, T. Mehta, Pradnya S. Kulhar, Kuldeep Singh Agrawal, Trapty Kavitha, R. Karpagam College of Engineering Department of Electronics and Communication Engineering Coimbatore641032 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Civil Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Information Technology Karnataka Bangalore India

ISBN: (纸本)9798350308846

The extraction of records from online assets has become increasingly important for organizations to remain competitive. This requires using state-of-the-art records analytics gear, including deep getting based totally models, to better extract meaningful statistics from web resources. One of the demanding situations is to broaden extra superior neural community architectures, which can increase the records extraction accuracy. This paper explores the capacity of such architectures for enhancing the accuracy of statistics extraction from online resources. It critiques advances in deep studying fashions, encompassing CNNs, RNN and generative hostile networks. It describes how those methods may be implemented to optimize the accuracy of fact extraction from online sources. The paper additionally evaluates case studies to illustrate how these fashions can be used to beautify the accuracy of extracting online records. Sooner or later, it provides suggestions on how companies can optimize their online form extraction strategies to enhance accuracy. © 2023 IEEE.

关键词： Accuracy Enhance Evaluates Extraction Strategies

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Computational Overhead Analysis for Mitigation of Content Poisoning Attack in NDN-based IoT Body Area Networks 3

Computational Overhead Analysis for Mitigation of Content Po...

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3rd International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2023

作者： Priya, M. Castro, S. Kumar, Sathesh K. Kalaivani, N. Dept. of Computer Science and Engineering E. G. S. Pillay Engineering College Nagapattinam611002 India Dept. of Information Technology Karpagam College of Engineering Coimbatore641032 India Dept. of Electronics and Communication Engineering Vivekanandha College of Engineering for Women Tiruchengode637205 India Dept. of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Coimbatore641041 India

The Body Sensors Network has developed into a significant element of the Medical IoT. Massive data gathering from a resource-constrained network represents a laborious procedure in IoT equipped BSN/WBAN, creating a number of difficult problems. Conventional communication protocols may be used on IoT platforms without any alterations and provide impractical techniques for large-scale networks. The new architecture known as ICN has the potential to overcome these difficulties in huge data collection. According to this perspective, the proposed work indicates a CPA mitigation strategy for information centric WBANs. © 2023 Elsevier B.V.. All rights reserved.

关键词： Internet of things

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