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2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Alheeti, Khattab M. Ali Alzahrani, Abdulkareem Al Dosary, Duaa College of Computer and Information Technology University of Anbar Computer Networking Systems Dept. Ramadi Iraq Albaha University Faculty of Computer Science and Information Technology Computer Engineering and Science Department Albaha Saudi Arabia College of Computer Sciences and Information Technology University of Anbar Ramadi Iraq

ISBN: (纸本)9781665441544

Detecting 3D objects is a core task for autonomous vehicles (AVs), as it allows them to drive safely and responsibly. To detect objects quickly and accurately, AVs have LiDAR sensors, which can capture 3D data from 360° in various conditions, including harsh weather. However, LiDAR sensors may face a spoofing attack via laser satirizing. This type of attack does not require physical contact with the sensors nor does it need to change them. It can mislead AVs by providing them with false information, which can then put passengers, pedestrians, and other vehicles in danger. Therefore, this paper proposes a model utilizing machine learning (i.e., decision trees) to protect AVs from such attacks. The performance of the proposed model indicates that it can detect LiDAR spoofing attacks. © 2022 IEEE.

关键词： Autonomous vehicles

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IoT Based Environmental Monitoring System using LSTM-WAO-EEMD 2

IoT Based Environmental Monitoring System using LSTM-WAO-EEM...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Babu, Uppara Raghu Sharma, Neelam Kumar, K Senthil Madhukar, Deshmukh Deepak Sonawane, Pavankumar Ravindra Nishant, Neerav Srinivasa Ramanujan Institute of Technology Civil Engineering Department Andhra Pradesh Ananthapuramu India Lovely Professional University Department of Physical Education Punjab India Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Networking and Communications Tamil Nadu Chennai India Pravara Rural Engineering Collage Loni Mechanical Engineering Maharashtra India JSPM's Rajarshi Shahu College of Engineering Tathawade Mechanical Engineering Department Pune India Babu Banarasi das University School of Engineering Department of Computer Science and Engineering Uttar Pradesh Lucknow India

ISBN: (纸本)9798350325799

In this proposed approach, the system takes a look at the environmental monitoring systems that are based on the Internet of Things. The suggested system's primary function is to supply remote locations with environmental parameters via the internet. The proposed method is a practical and easy way to monitor the surrounding environment and atmosphere. The system is a representation of ambient and environmental parameter monitoring by use of low-power wireless sensors linked to the Internet that report their readings to a centralized server. Finally, data collected from all over the world may be seen on the base station remotely from any Internet-connected device. From the physical level, with its sensors and communication protocol, to the cyber level, with its data management and storage, this work comprises the full solution, a cyber-physical system. It solves the issue of incompatible systems by providing a clear structure for exchanging information between sensors of varying measuring capabilities. This improves the effectiveness of monitoring systems. Using real-time sensors, the suggested system monitors conditions like moisture, temperature, humidity, rainfall, gas concentration, and seismic notification. An open-source platform known as Thing speak checks in on these variables every 2 minutes. The sensor data is then preprocessed, the features are selected, and the model is trained. EEMD-WOA-LSTM is used in the proposed method. When compared to two other models, including LSTM and GRU, the suggested method fares exceptionally well. © 2023 IEEE.

关键词： Empirical mode decomposition

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Multimodal Sensor Data Fusion Based Cyberattack Detection in Industrial Internet of Things Environment 7

Multimodal Sensor Data Fusion Based Cyberattack Detection in...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Nithya, R. Sundari, J.Jerlin Adaikala Rajesh Kanna, B. Balamurugan, M.S. Sindhuja, R. Srivastava, Aviral School of Computing Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Psg College of Arts and Science Department of Computer Technology Coimbatore641014 India Alagappa Chettiar College of Engineering and Technology Dept. of Electronics and Communication Engineering Karaikudi India Panimalar Engineering College Department of ECE Chennai India Saveetha Engineering College Department of Medical Electronics Chennai India Amity University Rajasthan Department of CSE Jaipur300202 India

ISBN: (纸本)9798350340600

The Industrial Internet of Things (IIoT) cites the usage of classical Internet of Things (IoT) in different applications and industrial fields. Smart grids, smart homes, supply chain management, connected cars, and smart cities are the different applications of IIoT. But this system is widely targeted by cyber-criminals. Big data analytics and Deep learning (DL) have considerable potential in developing and designing strong security systems for IIoT networks. This manuscript introduces Multimodal Sensor Data Fusion based Cyberattack Detection with glow worm swarm optimization (MSDF-CDGSO) technique in IIoT environment. The MSDF-CDGSO technique examines the IIoT data for identifying the existence of cyberattacks in the IIoT environment. In the presented MSDF-CDGSO technique, the IIoT devices collect data from different devices and fused them together. At the preliminary stage, the MSDF-CDGSO technique preprocesses the input data to scale them properly. For cyberattack detection, the MSDF-CDGSO technique uses class-specific cost regulation extreme learning machine (CCR-ELM) model. Finally, the GSO method is exploited to adjust the parameter values of the CCR-ELM algorithm. The simulation result analysis of the MSDF-CDGSO method is investigated on benchmark database. A widespread comparison results highlighted that the MSDF-CDGSO technique reaches improved cybersecurity in the IIoT environment. © 2023 IEEE.

关键词： Sensor data fusion

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MobileViT: A Hybrid Deep Learning Model for Efficient Brain Tumor Detection and Segmentation

MobileViT: A Hybrid Deep Learning Model for Efficient Brain ...

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Progressive Innovations in Intelligent Systems and Data Science (ICPIDS), International Conference on

作者： PSRB Shashank L Anand R Pitchai Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India Department of Networking and Communications SRM Institute of Science and Technology Chennai India Department of Computer science and Engineering B V Raju Institute of Technology Medak Telangana India

ISBN: (数字)9798331534691

ISBN: (纸本)9798331534707

Early and accurate diagnosis of brain tumors is crucial to the right management. Standard Convolutional Neural Networks (CNNs) has proved useful in medical image analysis; however, due to their inherent high computational requirements, they cannot run efficiently on resource constraint systems including the mobile. In this article, we explore the deployment of MobileViT as a condense model that integrates convolutional layers and ViT for forecasting as well as delineation of brain tumors from MRI scans. MobileViT is developed with efficiency as well as performance in mind and they are best fit for use in mobile end devices especially in telemedicine services. MobileViT is evaluated in association with the BraTS 2021 dataset for the classification and segmentation of brain tumors in terms of accuracy, computational complexity, and the ability to learn specific characteristics as well as overall features within an image. The study shows that MobileViT is best suited for achieving the right balance of the model's complexity, performance, and model inference time; it achieves a Die Coefficient of 0.87 in segmentation tasks and 40% less inference time than CNN models. Further, recommendations are proposed to enhance the practicalities of application of telemedicine systems so that accurate diagnosis for patients in other low resource settings can be made in real-time, a measure with big implications for quicker identification or patients problems, and improved patient prognosis.

关键词： Image segmentation Accuracy Computational modeling Telemedicine Brain tumors Brain modeling Transformers Real-time systems Convolutional neural networks Standards

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Intelligent Reflecting Surface-Assisted Millimeter Wave Communications: Cross Attention-aided Variational Autoencoder-based Precoding Design

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IEEE Transactions on Cognitive Communications and networking 2025年

作者： Wu, Meng-Hsun Chen, Hong-Yunn Yang, Ta-Wei Hsu, Chih-Chuan Huang, Chih-Wei Chou, Cheng-Fu National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan National Pingtung University Department of Computer Science and Information Engineering Pingtung Taiwan National Taiwan University Graduate Institute of Networking and Multimedia Taipei Taiwan National Taiwan University Department of Forestry and Resource Conservation Taipei Taiwan National Central University Department of Communication Engineering Taoyuan Taiwan

Sixth-generation technology, which surpasses 5G with faster speeds, lower latency, and increased capacity, highlights the pivotal intelligent reflecting surface (IRS) and optimizes wireless communication by precisely handling incoming radio waves, resulting in enhanced network coverage, capacity, and energy efficiency. In real-world scenarios, however, hybrid precoding in the IRS is affected by noise and interference. To address this, we propose DVAE-CATT-Precoding (variational denoising autoencoder model with cross attention precoding), in which cross attention enhances channel precoding by reevaluating the significance of the time domain. A variational autoencoder (VAE) is trained with reconstruction and regularization losses for parameter optimization, and its learned latent space facilitates feature capture for intra-channel precoding in both the frequency and spatial domains. We use an autoencoder to reconstruct the latent space, improving our analog precoding module's ability to capture global domain features. Additionally, a CNN models frequency and spatial domain intricacies for efficient channel precoding, reducing interference in the proposed digital precoding module. Numerical results show remarkable improvements in MSE, achievable rate, generalizability, and robustness compared to previous works. © 2015 IEEE.

关键词： 5G mobile communication systems

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A Survey on Mobile Payment Request Verification over Cloud using Key Distribution 4

A Survey on Mobile Payment Request Verification over Cloud u...

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4th International Conference on Recent Trends in computer Science and Technology, ICRTCST 2021

作者： Saranya, A. Naresh, R. Srm Institute Of Science And Technology Department Of Computer Science And Engineering Tamilnadu Chennai603 203 India Srm Institute Of Science And Technology Department Of Networking And Communications Tamilnadu Chennai603 203 India

ISBN: (纸本)9781665466332

Payment request examination of some of the cryptography's most widely used encryption algorithms, with the goal of first identifying as many flaws and vulnerabilities as possible, and then identifying the elements of these flaws that could have been avoided with proper implementation. The number of attacks on the Internet is flourishing. An encrypted communications algorithm was necessary to overcome these flaws. In this survey paper, we looked at the importance of existing mobile user payment encryption solutions and literature analysis of these algorithms. In cryptography, a mobile payment encryption technique is used. The algorithm converts data from a readable format into an unreadable, secure format. © 2022 IEEE.

关键词： Cryptography

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Heterogeneous Statistical QoS Provisioning for Scalable Software-Defined 6G Mobile Networks

Heterogeneous Statistical QoS Provisioning for Scalable Soft...

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Annual Conference on Information Sciences and Systems (CISS)

作者： Xi Zhang Qixuan Zhu H. Vincent Poor Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University TX USA Department of Electrical and Computer Engineering Princeton University Princeton NJ USA

Due to the explosively increasing number of mobile users and the new types of data demands in the fifth generation (5G) mobile wireless network, research in wireless networks has shifted toward the development of the sixth-generation (6G) wireless network. Although the research for software-defined network (SDN) architectures in 5G mainly focuses on the dynamic programming for the internet backbone, these software programming techniques can be also applied at the network edge to support the exponentially increasing demands from mobile users under constrained wireless resources. In order to study the interference problem resulted by massive mobile users, the scaling law is a powerful tool to show how fast the levels of network imperfections can be tolerated as the number of mobile users increases. In this paper, we investigate the scaling behavior of software-defined architectures over 6G wireless networks. We consider the wireless channel in three scenarios: single-input-single-output (SISO), multiple-input-single-output (MISO), and multiple-input-multiple-output (MIMO), where we derive the corresponding scaling law for each scenario, respectively. Our derived scaling law shows how the network performance scales with the number of mobile users in a wireless network. Then, we propose a software-defined network slicing scheme to select the optimal mobile users and derive their optimal resource allocations, according to our derived scaling law, under SISO, MISO, and MIMO wireless channel, respectively. Finally, we validate and evaluate the derived scaling behavior of the software-defined architecture over 6G wireless networks through numerical analyses.

关键词： 6G mobile communication Wireless networks Microprocessors computer architecture Quality of service MISO communication Behavioral sciences

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Quality of Service Aware Order Allocation for Inter-Regional Online Food Delivery Systems

Quality of Service Aware Order Allocation for Inter-Regional...

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International Conference on Advanced Communication Technology (ICACT)

作者： Farhana Huq Nahar Sultana Md. Abdur Razzaque Green Networking Research Group University of Dhaka Bangladesh Department of Computer Science and Engineering University of Dhaka Bangladesh

The allocation of orders in an online food delivery system that maximizes Quality of Service (QoS) to customers by assuring long distance delivery is a challenging problem because of the different order completion properties and service expectations. Existing works in the literature are constrained by focusing either on reducing order completion time or minimizing costs associated with order delivery within a predetermined region. In this paper, we have developed a framework for optimal order allocation of inter-regional food delivery orders as an optimization function which minimizes service time to escalate QoS of the system with required constraints. The results of performance studies depict that the proposed system achieves competitive service time as well as enhances the number of orders of the system.

关键词： Costs Focusing Quality of service Communications technology Resource management Optimization

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A Effective Encryption and Different Integrity Schemes to Improve the Performance of Cloud Services

A Effective Encryption and Different Integrity Schemes to Im...

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2022 International Conference for Advancement in Technology, ICONAT 2022

作者： Narayanan, K. Lakshmi Naresh, R. SRM Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai603 203 India SRM Institute of Science and Technology Department of Networking and Communications Tamilnadu Chennai603 203 India

ISBN: (纸本)9781665425773

Recent modern era becomes a multi-user environment. It's hard to store and retrieve data in secure manner at the end user side is a hectic challenge. Difference of Cloud computing compare to Network Computing can be accessed from multiple company servers. Cloud computing makes the users and organization to opt their services. Due to effective growth of the Cloud Technology. Data security, Data Privacy key validation and tracing of user are severe concern. It is hard to trace malicious users who misuse the secrecy. To reduce the rate of misuse in secrecy user revocation is used. Audit Log helps in Maintaining the history of malicious user also helps in maintaining the data integrity in cloud. Cloud Monitoring Metrics helps in the evaluation survey study of different Metrics. In this paper we give an in Depth survey about Back-end of cloud services their concerns and the importance of privacy in cloud, Privacy Mechanism in cloud, Ways to Improve the Privacy in cloud, Hazards, Cloud Computing Issues and Challenges we discuss the need of cryptography and a survey of existing cryptographic algorithms. We discuss about the auditing and its classifications with respect to comparative study. In this paper analyzed various encryption schemes and auditing schemes with several existing algorithms which help in the improvement of cloud services. © 2022 IEEE.

关键词： Web services

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Extraction of Fetal ECG Using ANFIS and the Undecimated-Wavelet Transform 3

Extraction of Fetal ECG Using ANFIS and the Undecimated-Wave...

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3rd IEEE Global Conference for Advancement in Technology, GCAT 2022

作者： Sharma, Avinash Dankan Gowda, V. Deenadhayalan, R. Madasamy Raja, G. Anandaram, Harishchander Department of Computer Science Engineering Mullana Haryan Ambala India Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Paavai Engineering College Department of It Tamilnadu Namakkal India Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Tamilnadu Coimbatore India

ISBN: (数字)9781665468558

ISBN: (纸本)9781665468534

A trans-thoracic interpretation of the electrical activity of the heart throughout time collected and externally recorded by skin electrodes is electrocardiograph. An electrocardiographic device creates this recording, and it does not involve any invasive procedures. This research focuses on the extraction of fetal ECG from the maternal ECG using adaptive neuro fuzzy inference system (ANFIS) and undecimated wavelet transform (UWT), which combines the desirable qualities of ANFIS and UWT. The objective of this paper is to improve the accuracy of fetal ECG analysis. In a lot of applications, the network needs to imitate non-linear and time-varying functions, which means that the functions could change based on the incoming signal and other parameter alterations. This is necessary since non-linear functions are more complex than linear functions. In situations like these, a greater number of signals may be reproduced at random, and the approaches that are used were evaluated for their ability to accurately determine the fetal ECG as well as for their potential use in other diagnostic applications. This hybrid network was able to quickly tackle the difficult problem, making it superior to conventional adaptive approaches. © 2022 IEEE.

关键词： Electrocardiograms

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