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Detection Collision Flows in SDN Based 5G Using Machine Learning Algorithms

computers, Materials & Continua 2023年第1期74卷 1413-1435页

作者： Aqsa Aqdus Rashid Amin Sadia Ramzan Sultan S.Alshamrani Abdullah Alshehri El-Sayed M.El-kenawy Department of Computer Science University of Engineering and Technology Taxila47050Pakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Technology Al Baha UniversityAl BahaSaudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt

The rapid advancement of wireless communication is forming a hyper-connected 5G network in which billions of linked devices generate massive amounts of *** traffic control and data forwarding functions are decoupled in software-defined networking(SDN)and allow the network to be *** switch in SDN keeps track of forwarding information in a flow *** SDN switches must search the flow table for the flow rules that match the packets to handle the incoming *** to the obvious vast quantity of data in data centres,the capacity of the flow table restricts the data plane’s forwarding ***,the SDN must handle traffic from across the whole *** flow table depends on Ternary Content Addressable Memorable Memory(TCAM)for storing and a quick search of regulations;it is restricted in capacity owing to its elevated cost and energy *** the flow table is abused and overflowing,the usual regulations cannot be executed *** this case,we consider lowrate flow table overflowing that causes collision flow rules to be installed and consumes excessive existing flow table capacity by delivering packets that don’t fit the flow table at a low *** study introduces machine learning techniques for detecting and categorizing low-rate collision flows table in SDN,using Feed ForwardNeuralNetwork(FFNN),K-Means,and Decision Tree(DT).We generate two network topologies,Fat Tree and Simple Tree Topologies,with the Mininet simulator and coupled to the OpenDayLight(ODL)*** efficiency and efficacy of the suggested algorithms are assessed using several assessment indicators such as success rate query,propagation delay,overall dropped packets,energy consumption,bandwidth usage,latency rate,and *** findings showed that the suggested technique to tackle the flow table congestion problem minimizes the number of flows while retaining the statistical consistency of the 5G *** putting the proposed flow method

关键词： 5G networks software-defined networking(SDN) OpenFlow load balancing machine learning(ML) feed forward neural network(FFNN) k-means and decision tree(DT)

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DEFECTED GROUND SQUARE PATCH EDGE TRUNCATED POLARIZATION RECONFIGURABLE ANTENNA

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2024年第5期83卷 11-24页

作者： Nikam, Pritam B. Kumar, Jayendra Baidya, Achinta Bhosale, Akshay Hake, Shahadev School of Electrical & Electronics Engineering SGU Maharashtra 416118 India School of Electronics Engineering VIT-AP University Andhra Pradesh 522237 India Department of Electronics and Communication Mizoram University Aizawl796004 India Department of E&Tc SCTR's Pune Institute of Computer Technology Pune411043 India

In this paper, a polarization reconfigurable antenna is reported. The proposed antenna operates in linear and circular polarization. Linear polarization is obtained using a square patch antenna and circular polarization is realized by truncating the diagonal corners of the square patch. Wideband circular polarization is obtained using two triangular defects to the ground plane beneath the truncated edge. To implement polarization reconfigurability, two pin diodes are placed on the truncated edges. These pin diodes electronically transform the proposed structure into square or truncated square patches resulting in linear or circular polarizations respectively. The proposed antenna operates in the 4.75 to 3 GHz band with circular polarization and the 4.75 to 1 GHz band with linear polarization. The antenna exhibits a higher peak gain of 7.7 dBi due to a low-loss foam substrate with the main lobe at 0°. A prototype of the antenna is developed and tested. © 2024 by Begell House, Inc.

关键词： Circular polarization

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Optimization for IoT Jobs in Multi-Cloud Computing Environments

Optimization for IoT Jobs in Multi-Cloud Computing Environme...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Atre, Anshul Singh, Kamlesh Kumar Chaurasia, Brijesh Kumar Amity University Amity School of Engineering & Technology Department of Computer Science & Engineering UP Lucknow India Amity University Amity School of Engineering & Technology Department of Electronics Communications and Engineering UP Lucknow India Pranveer Singh Institute of Technology Department of Computer Science & Engineering Kanpur India

ISBN: (纸本)9798350385199

Effective network communication is essential in the current digital era, and cloud computing (CC) and the Internet of Things (IoT) are significant aspects of daily life. Accelerating and lowering the latency of data transfers to the cloud for processing, analysis, and storage is the aim of cloud computing. IoT integration and job scheduling are the two most challenging issues in the multi-cloud computing environment because of the diverse and numerous IoT devices. The significant objectives of cloud computing-based IoT optimization are to reduce the total time needed to complete all input operations, determine which resources are required for the Message Queuing Telemetry Transport (MQTT), and other crucial protocols used in this work to display delay and evaluate the performance of different jobs at different IoT nodes and bit counts. Results and analysis show the viability of cloudSim is successfully achieved with the use of delayed quality of communication of jobs for different nodes. © 2024 IEEE.

关键词： Cloud platforms

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engineering Framework for Smart Poultry Farming Using Internet of Things

Engineering Framework for Smart Poultry Farming Using Intern...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Manju, K. Dinesh, P.M. Deepa, K. Kathan, Karthik Lakshmi, G. Prasanna Alagarsamy, Manjunathan Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Tiruchirapalli India Kongunadu College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Trichy621215 India Sandip University School of Computer Science and Engineering Maharashtra Nashik India K.Ramakrishnan College of Technology Department of Electronics and Communication Engineering Tamil Nadu Trichy621112 India

ISBN: (纸本)9798350390049

Automation is more important now. This article focuses on automating chicken farms using mobile communication devices and wireless sensor networks. Chicken is the world's most popular food because it contains more protein, less fat, and less cholesterol than other poultry. Chicken's nutrition is another benefit. This project utilizes the Internet of Things to monitor and adjust chicken farm environmental conditions. Temperature, moisture, water level, ammonia gas, and waste level were considered. This research aimed to improve chicken farm chick growth. The individual also monitors chicken behavior and feeding. Another usage for the sensor module is water level management and monitoring. If all sensors are connected to the microcontroller, all sensor data may be gathered. After that, a Wi-Fi module uploads the data to a website. A computer or mobile device connected to the internet allows the poultry farm owner to examine its interior surroundings. This informs the owner on agricultural conditions. This system will maintain temperature, humidity, and water level without human intervention using a DC motor, ventilation window, and co-exhaust fan. This will be done without human intervention. Devices will activate as needed when threshold values are attained. Our method allows semi-automated chicken product production and bird behavior monitoring. This allows us to deliver nutritious chicken meat while reducing bird killing. © 2024 IEEE.

关键词： Humidity sensors

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Logistic Regression with Elliptical Curve Cryptography to Establish Secure IoT

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computer Systems Science & engineering 2023年第6期45卷 2635-2645页

作者： J.R.Arunkumar S.Velmurugan Balarengadurai Chinnaiah G.Charulatha M.Ramkumar Prabhu A.Prabhu Chakkaravarthy Department of Information Technology Sree Vidyanikethan Engineering College(Autonomous)TirupatiAndhra PradeshIndia Department of Electronics and Communication Engineering TJS Engineering CollegeKavaraipettaiChennaiTamil NaduIndia Department of Computer Science and Engineering Vidyavardhaka College of EngineeringMysuruKarnatakaIndia Department of Electronics and Communication Engineering Peri Institute of TechnologyChennaiTamil NaduIndia Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennaiTamil NaduIndia

Nowadays,Wireless Sensor Network(WSN)is a modern technology with a wide range of applications and greatly attractive benefits,for example,self-governing,low expenditure on execution and data communication,long-term function,and unsupervised access to the *** Internet of Things(IoT)is an attractive,exciting *** applying communication technologies in sensors and supervising features,WSNs have initiated communication between the IoT *** IoT offers access to the highest amount of information collected through WSNs,it leads to privacy management ***,this paper provides a Logistic Regression machine learning with the Elliptical Curve Cryptography technique(LRECC)to establish a secure IoT structure for preventing,detecting,and mitigating *** approach uses the Elliptical Curve Cryptography(ECC)algorithm to generate and distribute security *** algorithm is a light weight key;thus,it minimizes the routing ***,the Logistic Regression machine learning technique selects the transmitter based on intelligent *** main application of this approach is smart *** approach provides continuing reliable routing paths with small *** addition,route nodes cooperate with IoT,and it handles the resources proficiently and minimizes the 29.95%delay.

关键词： Wireless sensor network internet of things security elliptical curve cryptography machine learning regression analysis

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Advancing Underwater Communication with NBO-MFO Hybrid Routing in UWSNs 10

Advancing Underwater Communication with NBO-MFO Hybrid Routi...

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

作者： Kalaiselvan, S.A. Kumar, M.Lenin Amanullah, M. Mani Chigurupati, T.R. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vignan Institute of Technology and Science Department of Electronics and Communications Engineering Telangana India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Tkr College of Engineering and Technology Department of Computer Science and Engineering Telangana India

ISBN: (纸本)9798350353068

In this research provides a solution for optimizing routing path selection in Underwater Wireless Sensor Networks (UWSNs). The algorithm combines Namib Beetle Optimization (NBO) and Moth-Flame Optimization (MFO), integrating them into the ILEACH methodology for efficient routing path selection. The algorithm operates in an iterative manner, commencing with NBO's feature selection process, evaluating fitness through average classification error and feature selection ratio. Simultaneously, MFO mimics moth behavior, updating positions via a logarithmic spiral function based on flame locations. The mutual learning phase facilitates information exchange between NBO and MFO, enhancing their collective intelligence and refining the routing path selection process. ILEACH's contribution lies in its fitness aggregation and selection methods. It amalgamates various criteria, including energy levels, distances, and node conditions, into a comprehensive fitness metric. Subsequently, ILEACH selectively identifies optimal routing paths aligned with energy-efficient criteria and node statuses, ensuring efficient data transmission and energy conservation within UWSNs. This integrated approach fosters a comprehensive strategy for routing path selection in UWSNs, ensuring sustainability and efficiency in data transmission within challenging underwater environments. © 2024 IEEE.

关键词： Iterative methods

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Development of Area-Based Explosive Trace Detection Using Deep Transfer Learning 1

Development of Area-Based Explosive Trace Detection Using De...

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1st International Conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Ohemu, Monday F. Azeta, Ambrose A. Adeyanju, Ibrahim A. Obasi, Chukwuemeka C. Airforce Institute of Technology Department of Electrical/Electronics Engineering Kaduna Nigeria Namibia University of Science and Technology Department of Software Engineering Namibia Federal University Department of Computer Engineering Oye-Ekiti Nigeria Edo State University Department of Computer Engineering Uzairue Nigeria

ISBN: (纸本)9798350348637

Terrorism poses a significant global threat, necessitating comprehensive security measures to protect people and critical infrastructure. With the increasing frequency of explosive-based attacks, there is a pressing need for advanced detection systems to mitigate potential devastation. This paper presents the development of an area-based explosive trace detection system using deep transfer learning. Leveraging deep learning technology trained on a substantial dataset collected from sensor networks, the proposed model, named Deep Transfer Learning for Explosive Trace Detection (DTLETD), demonstrates remarkable capabilities in classifying explosive gases based on concentrations of Carbon (C), Hydrogen (H), Oxygen (O), and Nitrogen (N). The methodology involves converting the dataset into 2D data using a serial data to image generator, followed by the adaptation of a pre-trained model for transfer learning. The DTLETD model outperformed conventional Convolutional Neural Network (CNN) models, achieving a training time reduction of about 92 seconds compared to 1287 seconds for the CNN model. Additionally, the transfer learning model exhibits faster convergence with nearly zero losses during training and validation, yielding an impressive accuracy of 99.7% and an average AUC value of approximately 0.89. This research significantly advances the field of explosive trace detection by integrating machine learning-based approaches with deep transfer learning techniques, thereby enhancing security protocols and mitigating potential threats. © 2024 IEEE.

关键词： Convolutional neural networks

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Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 66-75页

作者： Lingkang Zeng Wei Yao Ze Hu Hang Shuai Zhouping Li Jinyu Wen Shijie Cheng State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronics EngineeringHuazhong University of Science and TechnologyWuhan 430074China Dispatching and Control Center Central China Branch of State Grid Corporation of ChinaWuhan 430077China. Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN 37996USA

Generator tripping scheme(GTS)is the most commonly used scheme to prevent power systems from losing safety and ***,GTS is composed of offline predetermination and real-time scenario ***,it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power *** improve efficiency of predetermination,this paper proposes a framework of knowledge fusion-based deep reinforcement learning(KF-DRL)for intelligent predetermination of ***,the Markov Decision Process(MDP)for GTS problem is formulated based on transient instability ***,linear action space is developed to reduce dimensionality of action space for multiple controllable ***,KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making *** can enhance the efficiency and learning ***,the graph convolutional network(GCN)is introduced to the policy network for enhanced learning *** simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.

关键词： Deep reinforcement learning generator tripping scheme graph convolutional network invalid action masking knowledgefusion

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AI-Powered IoT Framework for Enhancing Building Safety through Stability Detection 8

AI-Powered IoT Framework for Enhancing Building Safety throu...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Raja, S.R. Devi, T. Rathi Raj, J. Relin Francis Sankar, V. Krishna Krishnan, R. Santhana Venkatalakshmi, R. Hindustan Institute of Technology and Science Computer Applications Chennai India Christ University Department of Computer Science and Engineering Bangalore India Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Saveetha School of Engineering Chennai India Scad College of Engineering and Technology Department of Civil Engineering Cheranmahadevi India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Psna College of Engineering and Technology Department of Information Technology Dindigul India

ISBN: (纸本)9798350376425

The rapid urbanization and increasing structural complexities of modern buildings have heightened the need for advanced monitoring systems to ensure building safety. The research presents an AI-powered IoT framework that enhances building safety through advanced stability detection mechanisms. The proposed framework employs a novel algorithm, Ensemble Learning with IoT Sensor Data Aggregation (EnIoT-SDA), which integrates ensemble learning techniques with aggregated sensor data to provide accurate and real-time stability assessments of building structures. The effectiveness of EnIoT-SDA was evaluated through a comprehensive simulation analysis, comparing its performance against existing algorithms, including Support Vector Machine (SVM), Gradient Boosting Machines (GBM), and Fuzzy Logic Systems (FLS). Simulation metrics, such as accuracy, false positive rate, computational time, and detection latency, were used to assess and compare the algorithms' performance. The results demonstrated that EnIoT-SDA outperformed the existing methods in several key areas, offering improved accuracy and reduced detection latency, thus establishing its potential as a robust solution for building safety monitoring. The study underscores the significant advancements brought by integrating ensemble learning with IoT sensor data and highlights areas for future research and development in this domain. © 2024 IEEE.

关键词： Support vector machines

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Predicting Lung Cancer Disease Using Optimized Weighting-Based Enhanced Neural Network Classification 3

Predicting Lung Cancer Disease Using Optimized Weighting-Bas...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Chitra, T. Hemanth, S.V. Karthikeyan, S. Reddy, Vallem Ranadheer Banupriya, K.M. Amirthayogam, G. Christian College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Oddanchatram624619 India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana Hyderabad501401 India PSNA College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624622 India Vaagdevi Engineering College Department of Computer Science and Engineering Telangana Bollikunta506005 India Arunai Engineering College Department of Computer Science and Engineering Tamil Nadu Tiruvannamalai606603 India Department of Information Technology Tamilnadu Chennai603103 India

ISBN: (纸本)9798350372120

Lung cancer is a highly lethal disease, claiming the lives of approximately 1 million individuals annually. Cancer is characterized by abnormal and rapid cell growth, making it difficult to control, though early diagnosis through Computed Tomography (CT) scan images can significantly improve chances of survival. However, detecting lung cancer early is challenging and can increase the risk of complications such as infection, inflammation, and tumor growth in the lungs. Moreover, the current methods for analyzing lung cancer prognosis have low accuracy. To solve this problem, we proposed an Optimized Weighting-Based Enhanced Neural Network (OWENN) method to classify patients based on attributes accurately. Furthermore, they utilize the Adaptive Median Filter (AMF) technique during image pre-processing to calculate the mean value of each pixel and remove noise. Moreover, Improved Particle Swarm Optimization (IPSO) can be implemented to extract tumors from lung images efficiently. Finally, the OWENN method improves the accuracy of cancer or non-cancer detection by classifying patients based on their selected attributes. Afterward, the experimental results indicate that the suggested OWENN approach can attain lung cancer prediction by evaluating sensitivity, precision, accuracy, and time delay. © 2024 IEEE.

关键词： Median filters

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