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Towards an IoT Architecture Based on Machine Learning for Missing Data Prediction on the Edge 6

Towards an IoT Architecture Based on Machine Learning for Mi...

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6th IEEE international conference on Cloud computing and Artificial Intelligence: technologies and Applications, CloudTech 2023

作者： Lachguer, Abderrahim Sadiq, Abderrahmane Es-Saady, Youssef El Hajji, Mohamed Ibnou Zohr University IRF-SIC Laboratory Agadir Morocco

ISBN: (纸本)9798350303063

internet of things (IoT) technologies have revolutionized data collection and utilization. However, the persisting issue of missing data from IoT sensors, often caused by device malfunctions or poor internet connectivity, poses a significant challenge. In this study, we proposed a distributed architecture that uses two techniques to generate missing meteorological data. the first technique uses historical feature values recorded in a specific station, while the second technique uses data of the same feature obtained from another station located in a different area. Our approach not only generates or predicts new data but also addresses the problem of latency by deploying models at the network edge, thereby enhancing data privacy and system reliability. To validate the proposed approach, we utilized data from two air quality stations situated in Marrakesh. the experimental results demonstrate that both techniques achieved high accuracy in imputing temperature and relative humidity data. Furthermore, we evaluated the performance of several well-known machine learning algorithms for regression problems, including MLPRegressor, SVR, GBR, and Linear Regression, in terms of accuracy and inference time. the findings indicate that MLPRegressor exhibited a slight advantage in terms of data imputation accuracy. However, when prioritizing inference time or when there is limited training data available, Linear Regression remains the optimal choice. © 2023 IEEE.

关键词： internet of things

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Unraveling the Efficiency of Multi-user Massive MIMO Precoding Techniques in Millimeter Wave Frequency Bands 5th

Unraveling the Efficiency of Multi-user Massive MIMO Precodi...

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5th international conference on Advances in distributed computing and Machine Learning, ICADCML 2024

作者： Gelmecha, Demissie Jobir Singh, Ram Sewak Sharma, Divya Negeri, Wesene Derbe Department of Electronics and Communication Engineering School of Electrical Engineering and Computing Adama Science and Technology University Po.Box:1888 Adama Ethiopia Department of Electronics and Communication Engineering Ajay Kumar Garg Engineering College Affiliated to Dr. APJ Abdul Kalam Technical University Lucknow India

ISBN: (纸本)9789819718405

Millimeter wave (mm-wave) and massive multiple input multiple outputs (MIMO) systems are the two key enabling technologies of 5G and beyond. these technologies use large antennas system within short-range wavelength and high-frequency bands. the mm-wave frequency bands are easily susceptible to path loss and inter-user interference. In this manuscript, the performance of large downlink multiple input multiple output systems using precoding techniques such as maximum ratio transmission (MRT), zero-forcing (ZF), regularized zero-forcing (RZF), dirty paper coding (DPC), lower order truncated polynomial expansion (LTPE) and lower order truncated polynomial expansion-dirty paper coding (LTPE-DPC) are evaluated and compared. the main objectives of linear precoding are to cancel interference between user terminals and mitigate noise amplification, improve spectral efficiency and energy efficiency, and also optimize the transmission of signals from the base station to the user terminals. the simulation results have shown that proposed LTPE-DPC reduces the massive MIMO system's computational complexity and improved the performance like spectral efficiency, energy efficiency and sum rates when compared with the existing precoding techniques at mm-wave frequency bands. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Singular value decomposition

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An Optimized DDoS Attack Detection Using Deep Convolutional Generative Adversarial Networks 5

An Optimized DDoS Attack Detection Using Deep Convolutional ...

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5th international conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Akana, Chandra Mouli Venkata Srinivas Kumar, Arvind Tiwari, Mohit Yunus, Aslanov Zabit Vijayakumar, E. Singh, Mukesh Bonam Venkata Chalamayya Engineering College Odalarevu India School of Computer Applications Manav Rachna International Institute of Research and Studies India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India Azerbaijan State University of Economics - Unec Baku Azerbaijan KIT-Kalaignar Karunanidhi Institute of Technology Department of Computer Applications Tamilnadu Coimbatore India Graphic Era University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350321425

Attacks known as distributed Denial-of-Service (DDoS) are rising as a result of recent, dramatic increase in demand for internet access. When the amount and characteristics of network traffic, which may include harmful DDoS contents, expand dramatically, traditional basic algorithms using machine learning for classifying DDoS attacks frequently fail because it cannot automatically extract high-value characteristics. In order to effectively identify and classify DDoS attacks, a hybrid technique called DFNN-SAE-DCGAN that combines three deep learning-based models is suggested. the Deep Feed-Forward Neural Network (DFNN) and Stacked Auto encoder offers an efficient method for extracting features that identifies the most pertinent feature sets without the assistance of a person. To avoid the operational overhead and presumption associated with processing massive set of features with distortion and redundant characteristic values, the Deep Convolutional Generative Adversarial Networks (DCGAN) component of the proposed model classifies the attacks into various DDoS attack types using the restricted and minimized characteristic sets generated by the DFNN-SAE as inputs. the experimental results show a very high and resilient accuracy rate and an F1-score of 98.5%, which is higher than the performance of many similar approaches. these results were acquired by thorough and extensive trials on various performance aspects on the CICDDoS2019 dataset. this demonstrates that the suggested methodology may be utilized to defend against the increasing amount of DDoS attacks. © 2023 IEEE.

关键词： Denial-of-service attack

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A Comparative Study on Cloud, Fog and Edge computing 5

A Comparative Study on Cloud, Fog and Edge Computing

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5th international conference on Electrical, Electronics, Communication, Computer technologies and Optimization Techniques (ICEECCOT)

作者： DeepShah Mukesh Patel Sch Technol Management & Engn Comp Engn Mumbai Maharashtra India

ISBN: (纸本)9781665432726

Cloud computing is taking care of and getting to data and applications on far away servers housed on the web rather than a PC's hard drive or a local server. Cloud computing is another term for internet-based computing. Fog computing is a technique for loosening up cloud computing to the association's edge. It suggests a decentralized computing structure that exists between the cloud and data making contraptions. Fog computing works with cloud computing exercises like planning, taking care of, and sorting out between end contraptions and server ranches. Edge computing is a coursed computing perspective that moves getting ready and data amassing closer to the data sources. this is relied upon to overhaul reaction times while also saving exchange speed. this paper sees Cloud computing, Fog computing, and Edge computing.

关键词： Cloud computing Fog computing Edge computing Decentralization internet of things

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internet of Medical things: A CT Image Denoising in Tetrolet Domain 5

Internet of Medical Things: A CT Image Denoising in Tetrolet...

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5th international conference on Information Systems and Computer Networks, ISCON 2021

作者： Diwakar, Manoj Pandey, Richa Sharma, Ritik Saun, Sapna Singh, Prabhishek Pandey, Neeraj Kumar Graphic Era Deemed to Be University Department of Computer Science Engineering Uttarakhand Dehradun India Graphic Era Hill University Campus-Haldwani Department of Computer Science Engineering Uttarakhand India Amity University Uttar Pradesh Amity School of Engineering Technology Department of Computer Science Engineering Noida India DIT University School of Computing Dehradun India

ISBN: (纸本)9781665403412

the internet of Medical things (IoMT) is a huge community of linked medical equipment and technologies that communicate with a large number of Servers over the internet to provide a variety of services such as medical image denoising. Projection-based reconstructed images in computed tomography (CT) are noisy due to thermal noise and electrical noise, both of which are approximately AWGN in magnitude. Because of the variety of clinical features and textures included in CT reconstructed images, denoising is a difficult process. It is possible to estimate the true noise of a single image, and then to minimize both the real noise and the additional noise by reconstructing CT images. A unique modified approach is proposed in this work, in which the noise will be calculated actual as well as added, followed by a proposed method for denoising, which is based on thresholding in tetrolet domain. Different denoising schemes are used in the experiments, and the results are compared to one another. Experimentation has revealed that both in terms of PSNR and visual quality, the suggested technique yields promising results that are favorable to both. © 2021 IEEE.

关键词： Image denoising

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Benchmarking 5G MEC and Cloud infrastructures for planning IoT messaging of CCAM data 26

Benchmarking 5G MEC and Cloud infrastructures for planning I...

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IEEE 26th international conference on Intelligent Transportation Systems (ITSC)

作者： Mogollon, Felipe Fernandez, Zaloa Perez, Josu Martin, Angel Vicomtech Fdn Basque Res & Technol Alliance BRTA Paseo Mikeletegi 57 San Sebastian 20009 Spain

ISBN: (纸本)9798350399462

Vehicles embed lots of sensors supporting driving and safety. Combined with connectivity, they bring new possibilities for Connected, Cooperative and Automated Mobility (CCAM) services that exploit local and global data for a wide understanding beyond the myopic view of local sensors. internet of things (IoT) messaging solutions are ideal for vehicular data as they ship core features like the separation of geographic areas, the fusion of different producers on data/sensor types, and concurrent subscription support. Multi-access Edge computing (MEC) and Cloud infrastructures are key to hosting a virtualized and distributed IoT platform. Currently, the are no benchmarks for assessing the appropriate size of an IoT platform for multiple vehicular data types such as text, image, binary point clouds and video-formatted samples. this paper formulates and executes the tests to get a benchmarking of the performance of a MEC and Cloud platform according to actors' concurrency, data volumes and business levels parameters.

关键词： internet of things

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internet of Medical things (IoMT) Application for Detection of Replication Attacks Using Deep Graph Neural Network 5th

Internet of Medical Things (IoMT) Application for Detection ...

引用

5th international conference on Recent Innovations in computing, ICRIC 2022

作者： Sharma, Amit Singh, Pradeep Kumar Tselykh, Alexey Bozhenyuk, Alexander Institute of Computer Technologies and Informational Security Southern Federal University Rostov-on-Don344006 Russia Chandigarh India Department of Information and Analytical Security Systems Southern Federal University Rostov-on-Don344006 Russia Southern Federal University 44 Nekrasovsky Street Taganrog347922 Russia Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9789811998751

the fast developments in micro-computing, mini-hardware manufacturing, and machine-to-machine communications have paved the way for innovative internet of things (IoT) solutions that are reshaping a great deal of networking software. the internet of things (IoT) has introduced a new branch of IoT that is known as the internet of Medical things (IoMT) systems. One of the applications that have been transformed by IoT is the healthcare system. Remote monitoring of patients suffering from chronic conditions is made possible by IoMT devices. As a result, it can deliver rapid diagnostics for patients, which in the event of an emergency may save their life. However, ensuring the safety of these vital systems is one of the primary obstacles standing in the way of their widespread use. the objective of this paper is to detect the replication attack, i.e., DoS (Denial-of-service) attack by using multilayer perceptron (MLP) classification algorithm in graph neural network. the proposed approach achieves better detection accuracy of 98.4% when compared with existing state-of-the-art classification models. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： internet of things

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Towards Efficient Federated Learning via Vehicle Selection and Resource Optimization in IoV 5th

Towards Efficient Federated Learning via Vehicle Selection a...

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5th international conference on Neural computing for Advanced Applications (NCAA)

作者： Gong, Nan Yan, Guozhi Zhang, Hao Xiao, Ke Yang, Zuoxiu Li, Chuzhao Liu, Kai Chongqing Univ Coll Comp Sci Chongqing Peoples R China Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing Peoples R China Chongqing Normal Univ Sch Comp & Informat Sci Chongqing Peoples R China Chongqing Univ Natl Elite Inst Engn Chongqing Peoples R China China Automot Engn Res Inst Co Ltd Chongqing Peoples R China

ISBN: (纸本)9789819770038;9789819770045

Federated Learning (FL) is a novel distributed machine learning paradigm that holds great potential for collaborative model training in internet of Vehicles (IoV). However, employing FL in IoV presents several challenges. First of all, the heterogeneous vehicular resources may lead to the bias of vehicle selection for model training and result in poor performance with respect to data diversity. Moreover, executing FL tasks with limited resources and energy is also a non-trivial problem. In view of this, we investigate an efficient FL framework in IoV, and then formulate the Vehicle Selection and Resource Optimization (VSRO) problem, which aims at minimizing the energy consumption and training latency without jeopardizing model accuracy. On this basis, we first propose a vehicle selection algorithm based on Lyapunov optimization method that dynamically selects vehicles to participate in each round of FL, taking into account the computational and transmission capabilities of vehicles, as well as their participation frequency in FL tasks. Furthermore, a resource scheduling algorithm is proposed for the selected vehicles, which utilizes the Lagrange dual problem and sub-gradient projection to iteratively determine the optimal training process. the simulation results demonstrate that the proposed algorithms can achieve best performance on enhancing model accuracy and simultaneously reducing system energy consumption and latency.

关键词： internet of Vehicles Federated learning Efficient training Vehicle selection Resource optimization Lyapunov optimization

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Artificial Intelligence Application for Security Issues and Challenges in IoT 5

Artificial Intelligence Application for Security Issues and ...

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5th international conference on Contemporary computing and Informatics, IC3I 2022

作者： Kwatra, Chetna Vaid Jain, Anupam Royappa, Angelina Bagchi, Susama Babu, S B G Tilak Chowdhary, Harish Lovely Professional University Punjab Pakhwara India Commerce and Management Poddar International College India Velammal Engineering College India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Aditya Engineering College India National Forensic Sciences University School of Doctoral Studies and Research India

ISBN: (纸本)9798350398267

More and more gadgets are getting internet access as a result of advances in technology, collectively known as the 'internet of things' (IoT). In common parlance, the network of physical and digital components that make up commonplace objects is referred to as the 'internet of things' (IoT). the internet of things is a new and exciting area of research thanks to the worldwide proliferation of advanced technologies. One example is the internet of things (IoT), which lets almost any object, user, service, network, location, or set of circumstances connect with each other. Since IoT's popularity has skyrocketed in the last several years, so have security concerns. AI is at the top of the list because it makes it possible to create complex algorithms, which are needed to protect critical infrastructure like networks, systems, and the internet of things (IoT). Even so, hackers have learned to use AI to their advantage, and some of them even use AI to do bad things. this article aims to do just that by presenting and summarising the relevant literature in these areas. In order to do this, it gathers information about internet of things devices, AI systems, AI attacks, and AI defenses. © 2022 IEEE.

关键词： internet of things

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Research and design of distributed testing framework for 5G network 23

Research and design of distributed testing framework for 5G ...

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4th international conference on computing, Networks and internet of things, CNIOT 2023

作者： He, Yugang Wang, Fengli Sun, Qian Tian, Lin Wang, Yuanyuan Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400700705

As the 5G network continues to evolve with micro-services, the protocol implementation in the network is decentralised to individual network elements, with multiple network elements collaborating to provide services to the terminals. the functional testing of the network therefore requires the simultaneous testing of multiple network *** current test system cannot test multiple network elements simultaneously and has the problem that test anomalies are difficult to *** paper proposes a distributed testing framework for 5G networks, which is based on the TTCN-3 testing framework as a prototype and designed using keyword-driven technology and remote procedure call (RPC) technology based on C/S *** framework has proven to have the following advantages: good scalability of the test nodes, the ability to test multiple network elements simultaneously, and the ability to locate test anomalies at the element signalling level. © 2023 ACM.

关键词： 5G mobile communication systems

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