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Performance Analysis of Three Spectrum Sensing Detection Techniques with Ambient Backscatter Communication in Cognitive Radio Networks

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computer Modeling in engineering & Sciences 2023年第10期137卷 813-825页

作者： Shayla Islam Anil Kumar Budati Mohammad Kamrul Hasan Saoucene Mahfoudh Syed Bilal Hussian Shah Institute of Computer Science&Digital Innovation ICSDIUCSI UniversityKuala Lumpur56000Malaysia Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)Selangor43600Malaysia School of Engineering Computing and InformaticsDar Al-Hekma UniversityJeddah22246Saudi Arabia

In wireless communications, the Ambient Backscatter Communication (AmBC) technique is a promisingapproach, detecting user presence accurately at low power levels. At low power or a low Signal-to-Noise Ratio(SNR), there is no dedicated power for the users. Instead, they can transmit information by reflecting the ambientRadio Frequency (RF) signals in the spectrum. Therefore, it is essential to detect user presence in the spectrum forthe transmission of data without loss or without collision at a specific time. In this paper, the authors proposed anovel Spectrum Sensing (SS) detection technique in the Cognitive Radio (CR) spectrum, by developing the *** Matched Filter Detection with Inverse covariance (MFDI), Cyclostationary Feature Detection with Inversecovariance (CFDI) and Hybrid Filter Detection with Inverse covariance (HFDI) approaches are used with AmBCto detect the presence of users at low power levels. The performance of the three detection techniques is measuredusing the parameters of Probability of Detection (PD), Probability of False Alarms (Pfa), Probability of MissedDetection (Pmd), sensing time and throughput at low power or low SNR. The results show that there is a significantimprovement via the HFDI technique for all the parameters.

关键词： Ambient backscatter communication cognitive radio MFDI CFDI HFDI

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Comparative Analysis of Blockchain based Digital Currency Transactions and UPI

Comparative Analysis of Blockchain based Digital Currency Tr...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Ch Rahul A N Sharma Prudhvi Sri Harsha Krovvidi Siva Charan Kadagala Shinu M. Rajagopal Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

In the rapidly evolving landscape of digital transactions, Blockchain, a decentralized ledger technology, is at the forefront. This research seeks to offer a comprehensive comparison with the widely adopted UPI, aiming to dissect the strengths and challenges of blockchain in real-world scenarios. In the context of the Unified Payments Interface (UPI), leveraging the power of blockchain through tools like Ganache and Truffle involves delving into critical parameters such as Transaction Volume, Overhead, Busy Server, Failed Transactions, and Concurrent User Interaction. The primary objective is to evaluate the performance of blockchain, equipped with Ganache and Truffle, in terms of transaction efficiency and user interactions, providing valuable insights into its practical applications in the realm of digital transactions. Moreover, this project aligns with the United Nations Sustainable Development Goals (SDGs), contributing to financial inclusion, innovation, and sustainable economic growth by exploring how blockchain can address challenges in existing payment systems, fostering transparency, security, and accessibility. This research aligns with the global agenda of leveraging technology to create a more inclusive and sustainable financial ecosystem.

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Bayesian Federated Estimation of Causal Effects from Observational Data 38

Bayesian Federated Estimation of Causal Effects from Observa...

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38th Conference on Uncertainty in Artificial Intelligence, UAI 2022

作者： Vo, Thanh Vinh Lee, Young Hoang, Trong Nghia Leong, Tze-Yun School of Computing National University of Singapore Singapore Harvard University United States School of Electrical Engineering and Computer Science Washington State University United States

ISBN: (纸本)9781713863298

We propose a Bayesian framework for estimating causal effects from federated observational data sources. Bayesian causal inference is an important approach to learning the distribution of the causal estimands and understanding the uncertainty of causal effects. Our framework estimates the posterior distributions of the causal effects to compute the higher-order statistics that capture the uncertainty. We integrate local causal effects from different data sources without centralizing them. We then estimate the treatment effects from observational data using a non-parametric reformulation of the classical potential outcomes framework. We model the potential outcomes as a random function distributed by Gaussian processes, with defining parameters that can be efficiently learned from multiple data sources. Our method avoids exchanging raw data among the sources, thus contributing towards privacy-preserving causal learning. The promise of our approach is demonstrated through a set of simulated and real-world examples. © 2022 Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022. All right reserved.

关键词： Higher order statistics

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A verifiable privacy-preserving data collection scheme supporting multi-party computation in fog-based smart grid

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Frontiers of computer Science 2022年第1期16卷 143-153页

作者： Zhusen LIU Zhenfu CAO Xiaolei DONG Xiaopeng ZHAO Haiyong BAO Jiachen SHEN Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai200062China Cyberspace Security Research Center Peng Cheng LaboratoryShenzhen518055China School of Computer Science and Information Engineering Zhejiang Gongshang UniversityHangzhou310018China

Incorporation of fog computing with low latency,preprocession(e.g.,data aggregation)and location awareness,can facilitate fine-grained collection of smart metering data in smart grid and promotes the sustainability and efficiency of the ***,much attention has been paid to the research on smart grid,especially in protecting privacy and data ***,most previous works do not focus on privacy-preserving data aggregation and function computation query on enormous data simultaneously in smart grid based on fog *** this paper,we construct a novel verifiable privacy-preserving data collection scheme supporting multi-party computation(MPC),named VPDC-MPC,to achieve both functions simultaneously in smart grid based on fog ***-MPC realizes verifiable secret sharing of users’data and data aggregation without revealing individual reports via practical cryptosystem and verifiable secret sharing ***,we propose an efficient algorithm for batch verification of share consistency and detection of error reports if the external adversaries modify the SMs’***,VPDC-MPC allows both the control center and users with limited resources to obtain arbitrary arithmetic analysis(not only data aggregation)via secure multi-party computation between cloud servers in smart ***,VPDC-MPC tolerates fault of cloud servers and resists *** also present security analysis and performance evaluation of our scheme,which indicates that even with tradeoff on computation and communication overhead,VPDC-MPC is practical with above features.

关键词： smart grid fog computing data aggregation verifiable secret sharing error detection secure multi-party computation secure function query privacy-preserving

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MFS: A Serverless FaaS Simulator

MFS: A Serverless FaaS Simulator

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International Conference on Web Research (ICWR)

作者： Mahrad Hanaforoosh Mehrdad Ashtiani Mohammad Abdollahi Azgomi Trustworthy Computing Laboratory School of Computer Engineering Iran University of Science and Technology Tehran Iran Social Computing Laboratory School of Computer Engineering Iran University of Science and Technology Tehran Iran

Serverless computing is one of the concepts that has become a trend these days. Serverless computing facilitates the deployment of software on the cloud, fog, edge, and IoT infrastructures. Serverless computing helps developers implement their codebase without worrying about server management hassles. These are the reasons why the concept of serverless computing is presented in the cloud, the edge, the fog, and the Internet of things. Research on serverless computing, on the other hand, requires extensive hardware equipments. It can also be time consuming. Simulation is needed to overcome these problems. MFS is a simulator based on the Apache Open Whisk architecture that seeks to facilitate the evaluation process of serverless computing researches. Compared to similar researches, this simulator supports more features including containers, heterogeneous processing resources (e.g., CPU, GPU, and TPU), different runtimes, and Apache OpenWhisk architecture. It also calculates and reports metrics such as average response time, utilization of different computing resources, rejection ratio, missed ratio, number of containers used, and costs. The mentioned metrics can be calculated in overall and individually for each physical machine. The status of all functions is also reported at the end of the simulation (e.g., completed, missed, and rejected).

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Enabling an On-demand Access to Community Sentiments using LSTM RNNs Web Service Architecture

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Procedia computer Science 2023年 230卷 584-597页

作者： N Prabakaran A. Anbarasi N. Deepa P Pandiaraja School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamilnadu India 632014 Department of computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai 603203 Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chennai 603203 Department of Computer Science and Engineering M. Kumarasamy college of Engineering Karur India 639113

Analyzing community response has always played an important role in marketing and development of various products ranging from services to manufacturing. Analyzing the responses and interpreting them helps improve the quality of the product. This task is traditionally done by community managers using surveys and focus groups. One other hands-off solution would be using the technology to analyze sentiments from users’ digital interactions in various online communities. Sentiment Analysis is the use of Natural Language Processing to process text to identify and retrieve sentiments behind the textual data. Sentiment Analysis can be approached either by supervised classification where a machine learning model is trained using labeled data or lexicon-based unsupervised analysis where a sentiment dictionary is used to find the overall sentiment. The idea is to build a system which presents community sentiment results on discussion threads from reddit on demand with the end user not to concern about data processing. We propose a streamlined pipeline architecture for a web application with several closely connected components in the backend that allows users to simply send the reddit discussion thread's URL, which the backend receives and scrapes the comments using the Reddit API, creates a dataset, cleans it and performs sentiment analysis using a RNNs. We used a Long Short-Term Memory (LSTM) a variant of RNN like Bidirectional RNNs and Multi-layered RNNs are also used to get better results.

关键词： Sentiment Analysis Community Response Analysis Social Media Mining Monitor sentiments Sentiment analysis architecture LSTM RNN On demand architecture

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Understanding the dynamics of Cyberbully content diffusion using Textual Emotion Mining 7

Understanding the dynamics of Cyberbully content diffusion u...

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Chawla, Shivangi Mehrotra, Monica Rani, Seema Jamia Millia Islamia Department of Computer Science New Delhi India Galgotias University School of Computing Science & Engineering UP India

ISBN: (纸本)9781665421683

With the world going digital and people from all walks of life-different race, colors, ethnicity, geography, literacy, and socio-economic status have come closer. The time to respond to a comment or an expression on the internet, the identity masks leveraged by the social media sites, and lack of moderation, have given way to cyberbullying. Cyberbullying figures are alarming and the concern is grave with the world moving forward with an overlap of the digital and real world. Swearing, use of foul language, expressions of strong disagreement, access, and use of inappropriate content has become more widespread in digital spaces and are considered uncivil and inappropriate in different intensities by different people. The present article investigates the diffusion of cyberbully in one of the popular social media sites, Twitter. It explores how the emotions present in such content drive its diffusion. The outcomes reveal that tweets containing anger, disgust, fear, and guilt as emotions tend to achieve faster and broader diffusion. with anger, hope, and bittersweet being less shared. The findings will aid to build approaches that intend to minimize the diffusion of cyberbully information and thereby protect the emotional and mental health of social media users. © 2022 IEEE.

关键词： Social networking (online)

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Protecting Data Mobility in Cloud Networks Using Metadata Security

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computer Systems Science & engineering 2022年第7期42卷 105-120页

作者： R.Punithavathi M.Kowsigan R.Shanthakumari Miodrag Zivkovic Nebojsa Bacanin Marko Sarac Department of Information Technology M.Kumarasamy College of EngineeringKarur639113India Department of Computer Science and Engineering School of ComputingSRM Institute of TechnologyKattankulathur CampusChennai603203India Department of Information Technology Kongu Engineering CollegeErode638060India Singidunum University Belgrade11000Serbia

At present,health care applications,government services,and banking applications use big data with cloud storage to process and implement *** mobility in cloud environments uses protection protocols and algorithms to secure sensitive user ***,data may have highly sensitive information,lead-ing users to consider using big data and cloud processing regardless of whether they are secured are *** to sensitive data in cloud systems produce high risks,and existing security methods do not provide enough security to sensitive user data in cloud and big data *** present,several security solu-tions support cloud *** of them include Hadoop Distributed File Sys-tem(HDFS)baseline Kerberos security,socket layer-based HDFS security,and hybrid security systems,which have time complexity in providing security ***,mobile data security algorithms are necessary in cloud environ-ments to avoid time risks in providing *** our study,we propose a data mobility and security(DMoS)algorithm to provide security of data mobility in cloud *** analyzing metadata,data are classiﬁed as secured and open data based on their *** data are sensitive user data,whereas open data are open to the *** the basis of data classiﬁcation,secured data are applied to the DMoS algorithm to achieve high security in *** pro-posed approach is compared with the time complexity of three existing algo-rithms,and results are evaluated.

关键词： Data mobility data security cloud computing big data DMoS algorithm

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Transforming High-Frequency Trading Predictions with CNN-LSTM Networks

Transforming High-Frequency Trading Predictions with CNN-LST...

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Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： S. Baghavathi Priya Priyanga S Sri Surya Shobith Kamisetty Venkata Sai Vinay Neelapala Nithila G Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Electronics & Communication Engineering School of Electronics Engineering (SENSE) Vellore Institute of Technology Vellore India Department of Computer Science & Engineering School of Computer Science and Engineering (SCOPE) Vellore Institute of technology Vellore India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

High-Frequency Trading (HFT) has transformed financial markets by enabling rapid and automated trading. However, the prediction of critical market metrics, such as trading volumes, remains a challenge due to the noisy and complex nature of financial data. This paper presents a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model designed specifically for predicting trading volumes in the NIFTY50 index using high-resolution, minutelevel Yahoo Finance data. By combining CNN's spatial feature extraction capabilities with LSTM's temporal modeling strengths, the proposed approach effectively captures the intricate patterns and sequential dependencies inherent in high-frequency trading datasets. Comprehensive data preprocessing, including noise reduction and temporal segmentation, coupled with advanced cross-validation techniques, ensures robust performance across market conditions. Comprehensive evaluations using metrics such as Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2) demonstrate the model's efficacy in capturing intricate financial dynamics. This work underscores the potential of hybrid deep learning models in enhancing predictive analytics for high-frequency trading applications in emerging markets.

关键词： Fluctuations Noise reduction Finance Predictive models Feature extraction Convolutional neural networks Indexes Noise measurement Predictive analytics Long short term memory

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Semi-FLChain: Toward Secure and Efficient Semi-Supervised Federated Learning for IoT Using Blockchain 5

Semi-FLChain: Toward Secure and Efficient Semi-Supervised Fe...

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5th IEEE International Conference on Power, Intelligent computing and Systems, ICPICS 2023

作者： Qi, Yuanhang Liu, Guisong School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China School of Computer Science University of Electronic Science and Technology of China Zhongshan Institute Guangdong Zhongshan528402 China School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Sichuan Chengdu611130 China

ISBN: (纸本)9798350333442

This paper proposes a novel semi-supervised federated learning (SSFL) algorithm for Internet of Things (IoT) systems using blockchain technology, called Semi-FLChain. The algorithm is designed to improve the efficiency and security of SSFL by combining hybrid data augmentation and Bayesian estimation to enhance the consistency regularization and pseudo-labeling methods in SSFL. The proposed algorithm aims to overcome the limitations of existing SSFL methods, such as the low accuracy of pseudo-labeling and the high computational cost of consistency regularization. The use of blockchain technology in Semi-FLChain further ensures the security and privacy of IoT devices and their data during the training process. The experimental results demonstrate that Semi-FLChain outperforms state-of-the-art SSFL algorithms in terms of both accuracy and efficiency, making it a promising solution for secure and efficient federated learning in IoT systems. © 2023 IEEE.

关键词： Blockchain

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