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Enhancing Network Function Virtualization and Service Function Chaining: Innovative Optimization Strategies and Their Impact

Enhancing Network Function Virtualization and Service Functi...

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Pioneering Developments in computer science & Digital Technologies (IC2SDT), International Conference on

作者： Deepak Upadhyay Mridul Gupta Kunj Bihari Sharma Abhay Upadhyay Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350365016

ISBN: (纸本)9798350365023

Throughout this paper, the reader was able to better understand how innovative optimization techniques through NFV and SFC frameworks could and should be applied to address the developing and shifting focus of contemporary network infrastructures. Focusing on performance metrics performance via latency, throughput, resource utilization, and operational cost, the presented algorithms and machine learning models developed and executed due to this research showed innovative and dynamic optimization of network functions. Through further extensive simulations equalling 125 seconds, our findings reported creative results of improving performance in several significant areas: latency was reduced, throughput was enhanced, resource utilization was more efficient, and operational cost was reduced. These results indicated that our optimization strategies can pay off and bring a number of benefits to NFV and SFC that address a clear need for change in the current type of network management models. Therefore, this paper is valuable and serves as a testament of our strategies' power along with opening various doors for following network optimization research. Therefore, one can see how important it is to implement innovation in the face of change.

关键词： Measurement Technological innovation Costs Machine learning algorithms Heuristic algorithms Machine learning Throughput Network function virtualization Resource management Optimization

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Developing xApps for Rogue Base Station Detection in SDR-Enabled O-RAN

Developing xApps for Rogue Base Station Detection in SDR-Ena...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Huang, Jun-Hong Cheng, Shin-Ming Kaliski, Rafael Hung, Cheng-Feng National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan National Sun Yat-sen University Dept. of Electrical Engineering Kaohsiung Taiwan

ISBN: (纸本)9781665494274

In order to support the diverse requirements of 5G communications, a multitude of RAN components are required. To enable multiple vendor support for 5G, each of whom can independently choose components, Open-RAN (O-RAN) defined a set of standards to which the components must adhere. In addition, O-RAN defines the management elements used to manage each component to secure the 5G networks. While the proposed architecture can manage both 4G and 5G environments, including 5G NSA (Non-Standalone), it inherently suffers from the same vulnerabilities found in 4G LTE. Consequently, an attacker can use unprotected signaling and a low-cost Software Defined Radio (SDR) to launch rogue base station (RBS) attacks on the user equipment (UE), even in O-RAN architectures. In this paper, we consider the stability of signals collected from high-quality operational BSs versus cheap RBSs. Using signal stability features, we develop a machine learning (ML) based RBS detector located on the UE. With the aid of an O-RAN xAPP, ML models can be retrained using the data collected from multiple UEs, and the updated model can be delivered to UEs to enable higher detection accuracy. We conduct extensive experiments by implementing an RBS using a USRP B210, enabling O-RAN using E- Release, and data collected from operational BSs. Moreover, the detector is implemented as an Android APP, which realizes the connection to the O-RAN xAPP. The experimental results show that our detector can achieve more than 99% accuracy, precision 'recall' and F1 score. © 2023 IEEE.

关键词： Base stations

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Integration of Q-Learning with Maximal Ratio Combining to Enhance IoT Communications in Dynamic Fading Environments

Integration of Q-Learning with Maximal Ratio Combining to En...

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Annual Information Technology, Electromechanical engineering and Microelectronics Conference (IEMECON)

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Rashmi Sharma Abhay Upadhyay Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India Dept. of Computer Science and Engineering Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798350387315

ISBN: (纸本)9798350387322

This paper serves to show how Q-learning can be used in complement with Maximal Ratio Combining to better IoT communication systems under dynamic fading, high-density networks, and severe interference sources. The ML-enhanced MRC demonstrated promising improvements in signal strength and quality, allowing for performance that was either above or at least equal to the regular MRC. The ML-enhanced presented the added benefit of dynamic adaptation, modifying its performance to meet real-life needs by adapting to issues such as increased density and interference. These results support that integrating Q-learning into MRC-based frameworks significantly increases the reliability and efficiency of IoT communication protocols. Future investigations will aim to improve ML algorithms more appropriate for devices with few resources and evaluate their potential with state-of-the-art technology to foster future applications across intelligent IoT networks. These approaches have the potential to support more efficient operations and increased connectivity in IoT systems.

关键词： Fading channels Performance evaluation Q-learning Protocols Diversity reception Interference Metrology Robustness Real-time systems Internet of Things

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Optimization of Imprecise Multiplier Circuits by using Binary Decision Diagram 9

Optimization of Imprecise Multiplier Circuits by using Binar...

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9th IEEE International Symposium on Smart Electronic systems, iSES 2023

作者： Choudhary, Pooja Bhargava, Lava Vinod, G.U. Suhag, Ashok Kumar Fujita, Mashahiro Singh, Virendra Swami Keshvanand Institute of Technology Management and Gramothan Dept. of Electronics and Communication Engineering Jaipur India Malaviya National Institute of Technology Jaipur India Malaviya National Institute of Technology Dept. of Electronics and Communication Engineering Jaipur India Qualcomm Bangalore India Bml Munjal University Dept. of Electronics and Communication Engineering Haryana India Vlsi Design and Education Center University of Tokyo Japan Indian Institute of Technology Dept. of Electrical Engineering Dept. of Computer Science Engineering Bombay India

ISBN: (纸本)9798350383249

Imprecise multipliers are employed to enhance the efficiency and reduce the power consumption of arithmetic circuits while tolerating acceptable levels of inaccuracy in the outcomes. The multiplier circuit has a broader range of applications within embedded systems, particularly in the fields of digital signal processing, IoT and multimedia, etc. We proposed a systematic formal approach utilizing the Look-Up Table (LUT) employing the Burch method to evaluate the imprecise multiplier based on the Binary Decision Diagram (BDD) considering Worst Case Error constraint. Implemented 13 bit multiplier using normal and proposed imprecise multiplier shows 98% reduction in number of nodes. The proposed 25 bit imprecise multiplier based on Burch HL shows an 89% decrement in a number of nodes w.r.t. Burch LH method. BDD generation for imprecise multiplier is 75% faster than normal. © 2023 IEEE.

关键词： Timing circuits

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An Optimized Grid Computing based Feature Selection approaches from Brain Image dataset using Pretrained models

An Optimized Grid Computing based Feature Selection approach...

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Advances in Signal Processing, Power, Communication, and Computing (ASPCC), IEEE International Conference on

作者： Sradhanjali Nayak Pradyut Kumar Biswal Pravakar Mishra Sateesh Kumar Pradhan Dept. of Computer Science NIIS Institute of Business Administration Bhubaneswar India Dept. of ETC IIIT Bhubaneswar Bhubaneswar India Dept. of Computer Science and Engineering NIIS Institute Of Business Administration Bhubaneswar India Dept. of Computer Science and Application Utkal University Bhubaneswar India

ISBN: (数字)9798350355093

ISBN: (纸本)9798350355109

The outcome of simulated experiments expressed the efficiency of grid-based algorithm in the domain of image processing in the context of Brain MRI Images for Tumor Detection. It provides a faster approach in detecting object and achieving higher rate of detection in comparison with existing algorithms for the similar purpose. The foremost goal of this work is improving the rate of detection by using grid based feature fusion approach with Genetic Algorithm(GA), Particle Swarm Optimization(PSO) and Scalp Swarm Optimization Algorithm. This technique produces efficient result due to hybrid fusion of gridded Images and optimization for exhaustive and precise image analysis. In the initial phase Each image is segmented into 4 grids. Pretrained models like Inception-V2, Resnet 50, Mobnetv2, VGG-16 are used to extract features before and after the images are gridded. The extracted features are analysed with classifiers such as Decision tree, Support vector Machine, Multiplayer and Self Attention Transformer that not only chooses the best set of features as well as best feature extractor. Among all these models the VGG-16 gives best result in terms of Accuracy, sensitivity, specificity and F-score. Hence VGG-16 is utilized as evaluation functions in the optimization Algorithms. The performance of the approach is measured by the values produced against the terms as precision, Recall, F-score, and accuracy rate. In the first phase of iteration, features extracted from each grid are assigned with initial weight. Optimization algorithms are used for optimizing the weight values in the combined features to bring best accuracy ratio. Grid based segmentation of Image stabilize the computation time thereby reduces the time complexity. Weighted sum Scalp Swarm Optimisation assigns best weighted values to segmentation parameters to get maximum performance. The experimental outcomes proves the efficiency of the proposed strategy.

关键词： Image segmentation Accuracy Scalp Signal processing algorithms Feature extraction Grid computing Transformers Particle swarm optimization Optimization Tumors

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An Ensemble Deep Learning Framework for Binary Classification of COVID-19 Cases Using the VGG16 (Visual Geometry Group 16) Architecture: A Comprehensive Analysis and Performance Evaluation

An Ensemble Deep Learning Framework for Binary Classificatio...

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Disruptive Technologies (ICDT), International Conference on

作者： Deepak Upadhyay Mridul Gupta Somesh Mishra Saksham Mittal Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

This paper presents a novel approach to deep learning by putting forth a cooperative system that uses the VGG16 architecture to categorise COVID-19 examples into two groups. Our model is distinguished by its remarkable recall metrics and precision, which achieve a careful balance that is essential for accurate categorization. What's more impressive is how well the model performs in the non-COVID category, effectively differentiating between COVID and non-COVID cases. With a remarkable overall accuracy of 96%, the model successfully classifies cases from both groups, demonstrating the potential of our suggested framework as a useful diagnostic tool useful in various clinical contexts. This work clarifies the effectiveness of deep learning techniques, concentrating on the VGG16 architecture in the crucial job of binary classification for COVID-19 identification. Our results open up new avenues for investigation in the field of accurate medical diagnosis in addition to providing insights into the real-world applications of sophisticated machine learning. The study highlights the ensemble approach's encouraging benefits, showing how it may strengthen diagnostic precision and advance clinical decision-making.

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An Approach of Fog Computing and Edge Computing for Computing Resources Optimization Strategies

An Approach of Fog Computing and Edge Computing for Computin...

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Pioneering Developments in computer science & Digital Technologies (IC2SDT), International Conference on

作者： Deepak Upadhyay Abhay Upadhyay Mridul Gupta Kunj Bihari Sharma Devansh Yadav Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India

ISBN: (数字)9798350365016

ISBN: (纸本)9798350365023

This paper investigates optimization strategies for fog and edge computing systems, focusing on the key challenges of resource allocation, load balancing, latency minimization, and power efficiency. We analyze the results of the simulation study to determine the impact and potential of our strategies and find that the improvements achieved are small to moderate, varying between 1.75% and 2.95%. This knowledge demonstrates that system optimization requires small, gradual increments and that fog and edge computing are dynamic systems. Our results suggest that optimization based on integration and adaptability can produce a significant difference in the performance and reliability of distributed systems.

关键词： system performance Focusing Minimization Load management Reliability Resource management Dynamical systems Distributed computing Optimization Edge computing

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Detecting Helmets Of The Bike Riders Using Deep Learning Algorithms

Detecting Helmets Of The Bike Riders Using Deep Learning Alg...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Yousuf Mia Md. Solaimanur Rahman Animesh Basak Sheikh Mufrad Hossain Md. Sabab Zulfiker Merina Akter Sumaia Dept. of Computer Science & Engineering Daffodil International University Dhaka Bangladesh Dept. of Computer Science & Engineering Model Institute of Science & Technology Gazipur Bangladesh

We propose real-time bike helmet detection in our study. A lot of people ride bikes in our nation. Motorbikes are more popular than vehicles because they are cheaper to maintain, take up less parking space, and provide more mobility and adaptability in urban situations. Bike riding is entertaining yet risky. Bicyclist safety is the planned system's main purpose. Many drivers don't wear helmets even though they're mandated by law. In emerging countries, mortality has been growing steadily. A helmet detection technology that identifies drivers without helmets is needed to safeguard the public. We employ a real-time 3202 dataset for this approach. We gather wearing helmet 1911 and no helmet 1291 data and utilize algorithms like VGG16, Resnet50, MobileNet v.02, Inception V3, EfficientNet, and CNN. The EfficientNet achieved 98% accuracy. Each person's comparison statement techniques are in the implementation section. To construct the optimum model for the conditions, this inquiry uses model validation approaches.

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Channel Capacity Enhancement in Dual Branch MRC under Extreme Fading Using Quantum Computing

Channel Capacity Enhancement in Dual Branch MRC under Extrem...

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IEEE Region 10 Symposium

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India

ISBN: (数字)9798350364866

ISBN: (纸本)9798350364873

We have investigated the potential of channel capacity and signal integrity enhancement in dual branch MRC systems even under extreme fading through classical and quantum computing. We have used the classical algorithm to gauge the performance of the quantum algorithm, including QAOA and QEC. Our simulation results show that quantum-assisted MRC work optimally by providing superior SNR and BER. Quantum algorithms not only maximizes the effective channel capacity but also minimizes end-to-end system latency and number of error rate more efficiently. The addition of Quantum Machine Learning for the dynamic parameter setting in real-time has demonstrated that it may be possible to combat severe fading effects. To the best of our knowledge, this paper laid the cornerstone to explore more opportunities in implementing quantum-enabled techniques for advanced telecommunication system development to realize more robust and efficient technologies.

关键词： Fading channels Technological innovation Quantum algorithm Channel capacity Simulation Machine learning Real-time systems Telecommunications Signal integrity Signal to noise ratio

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Online Transit Entanglement Routing in Quantum Networks: Architecture Design and Optimization

Online Transit Entanglement Routing in Quantum Networks: Arc...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Wan-Ting Ho Shing-Yan Fang Wei-Chia Hsieh Li-Feng Cheni Jian-Jhih Kuo Ming-Jer Tsai Dept. of Computer Science National Tsing Hua University Hsinchu Taiwan Dept. of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Quantum networks (QNs) gradually gain significant attention due to their higher security compared with classical networks. Conventional approaches in QN routing often aggregate multiple requests into a batch before determining their routing paths. However, this approach may overlook the limited lifetime of qubits, resulting in critical decoherence. In this paper, we present a new online entanglement routing architecture with an online optimization problem and propose a novel [1, O(log |V|)]-competitive algorithm supporting online requests with admission control, aiming to maximize the number of admitted requests. Finally, extensive simulation results show that our algorithm can outperform the existing approaches by up to 98%.

关键词： Simulation Data security Aggregates Qubit Decision making Focusing Routing Quantum networks Global communication Optimization

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