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International Conference on science Technology Engineering and Management (ICONSTEM)

作者： R. Ratheesh S. Vijayalakshmi B. Arunsundar M. Swarna G. Saranya Department of ECE Agni College of Technology Chennai Department of ECE R.M.K. Engineering College Chennai Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Department of ECE Dr. M.G.R. Educational and Research Institute University Chennai Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Kuniyamuthur Coimbatore

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

In the realm of modern transportation systems, Vehicular Ad Hoc Networks (VANETs) play a pivotal role in enabling efficient communication among vehicles and infrastructure elements for enhanced road safety and traffic management. However, the inherent vulnerabilities in VANETs, such as privacy breaches and security threats, necessitate robust frameworks to mitigate risks and ensure secure data transmission. This paper presents a comprehensive architecture for Privacy-Enriched data Communication over VANETs using Software-Defined Network (SDN) assistance. The proposed framework integrates key components including vehicles, Roadside Units (RSUs), and SDN controllers to facilitate secure and privacy-aware communication. Vehicles act as data sources, employing pseudonyms and encryption mechanisms to protect user privacy. RSUs serve as access points, collecting and relaying data while enforcing access control policies. SDN controllers provide centralized management, dynamically allocating resources and enforcing privacy policies through encryption, pseudonymization, and access control mechanisms. Communication protocols such as IEEE 802.11p and LTE enable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, while the OpenFlow protocol facilitates communication between SDN controllers and network devices. Additionally, trust management mechanisms evaluate the trustworthiness of network entities based on behavioral analysis, reliability metrics, and security considerations, ensuring the integrity and resilience of the network. By amalgamating SDN-based management with privacy enhancement mechanisms and trust management protocols, the proposed framework offers a robust solution for addressing privacy and security challenges in VANETs, thereby fostering safer and more secure transportation ecosystems.

关键词： Access control data privacy Protocols Vehicle-to-infrastructure Vehicular ad hoc networks Transportation Encryption

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Stochastic cluster head selection model for energy balancing in IoT enabled heterogeneous WSN

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Measurement: Sensors 2024年 35卷

作者： Anto Pravin, R. Murugan, K. Thiripurasundari, C. Ranjith Christodoss, Prasanna Puviarasi, R. Abdul Lathif, Syed Ismail Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Tamil Nadu India Department of Electronics and Communication Engineering KSK College of Engineering and Technology Tamil Nadu India Department of Computing Mathematics and Physics Messiah University Mechanicsburg PA United States Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu India

Energy dissipation is the most important design limitation for Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs). In order to prolong the life of WSNs, the energy of nodes must be used in an effective way. Clustering is a strategy that may effectively use the energy of the sensors, extending the life and scalability by managing the network load balance. The energy usage for network operation is reduced by using an evolutionary algorithm called Genetic Algorithm (GA). The Stochastic Cluster Head Selection Model (SCHSM) is described in the proposed protocol by taking the factors such as distance, node energy, density and capacity of nodes for developing the fitness function. The proposed protocol is designed for multiple movable sink nodes and this greatly improves the energy balancing factor in the network. For minimizing the communication gap among sensors and sinks, movable sinks can be placed carefully. Simulation results are analyzed for the system effectiveness. © 2024 The Authors

关键词： Internet of things

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Smart Signaling: A Smart Internet of Things Assisted Traffic Light Controlling and Monitoring System using Intelligent Sensors

Smart Signaling: A Smart Internet of Things Assisted Traffic...

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International Conference on science Technology Engineering and Management (ICONSTEM)

作者： G. Saranya R. Ratheesh S. Vijayalakshmi B. Arunsundar M. Swarna Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Kuniyamuthur Coimbatore Department of ECE Agni College of Technology Chennai Department of ECE R.M.K. Engineering College Chennai Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Department of ECE Dr. M.G.R. Educational and Research Institute University Chennai

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

In contemporary urban environments, efficient traffic management stands as a paramount challenge, necessitating innovative solutions to mitigate congestion, enhance safety, and reduce environmental impact. This paper proposes a Smart Internet of Things (IoT)-assisted Traffic Light Controlling and Monitoring System designed to revolutionize traffic management through real-time data acquisition, analysis, and dynamic control mechanisms. The system integrates edge devices equipped with a variety of sensors, cloud-based infrastructure, and intelligent algorithms to optimize traffic flow at intersections. Leveraging technologies such as AWS IoT, Lambda, and QuickSight, alongside communication protocols like MQTT and HTTP, the system enables seamless device management, secure data transmission, and scalable real-time analytics. Hardware components including microcontrollers, sensors, and actuators facilitate precise data collection, allowing for accurate traffic monitoring and adaptive signal control. Through comprehensive traffic flow analysis, congestion reduction strategies, environmental impact assessments, safety enhancements, and system performance optimizations, the proposed system aims to revolutionize urban traffic management, leading to smoother traffic flow, reduced congestion, improved safety, and enhanced environmental sustainability.

关键词： System performance Urban areas Control systems Real-time systems Sensor systems Safety Internet of Things

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Systematic Resource Allocation in Cloud RAN with Caching as a Service under Two Timescales

Systematic Resource Allocation in Cloud RAN with Caching as ...

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作者： Tang, Jianhua Quek, Tony Q. S. Chang, Tsung-Hui Shim, Byonghyo Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha410081 China Institute of New Media and Communications Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China Department of Electrical and Computer Engineering Institute of New Media and Communications Seoul National University Seoul08826 Korea Republic of

Recently, cloud radio access network (C-RAN) with caching as a service (CaaS) was proposed to merge the functionalities of communication, computing, and caching (CCC) together. In this paper, we dissect the interactions of CCC in C-RAN with CaaS from two dimensions: physical resource dimension and time dimension. In the physical resource dimension, we identify how to segment the baseband unit (BBU) pool resources (i.e., computation and storage) into different types of virtual machines (VMs). In the time dimension, we address how the long-term resource segmentation in the BBU pool impacts on the short-term transmit beamforming at the remote radio heads. We formulate the problem as a stochastic mixed-integer nonlinear programming (SMINLP) to minimize the system cost, including the server cost, VM cost and wireless transmission cost. After a series of approximation, including sample average approximation, successive convex approximation, and semidefinite relaxation, the SMINLP is approximated as a global consensus problem. The alternating direction method of multipliers (ADMM) is utilized to obtain the solution in a parallel fashion. Simulation results verify the convergence of our proposed algorithm, and also confirm that the proposed scheme is more cost-saving than that without considering the integration of CCC. © 1972-2012 IEEE.

关键词： Integer programming

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A Multi-DOF Robot System Based on LightGBM-Driven EEG Decoding Model for BCI Human-Machine Interaction

A Multi-DOF Robot System Based on LightGBM-Driven EEG Decodi...

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Chinese Control Conference (CCC)

作者： Weidong Yan Zhaoliang Xu Yang Li The Department of Automation Science and Electrical Engineering Beihang University Beijing China The Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Advanced Institute of Information Technology Peking University Hangzhou China

In this study, a multi-degree-of-freedom (Multi-DOF) robot (MDR) system based on a LightGBM-driven electroencephalogram (EEG) decoding model is designed and developed to assist subjects with hand motor dysfunction in their daily activities and neurorehabilitation. The system mainly consists of a motor imagery electroencephalogram (MI-EEG) evoking layer, an intention decoding layer, and an interaction executive layer. The MI-EEG evoking layer initially displays a virtual reality (VR) motion imagination scenario, instructing the subjects to imagine a real hand gripping movement, simultaneously collecting the electrical EEG signals and preprocessing the EEG signals. Secondly, in the intention decoding layer, a network combining temporal-spectral feature fusion and LightGBM (TSFF-LightGBM) for MI-BCI classification is used to more effectively boost brain decoding accuracy and decrease decoding time. Finally, in the interaction executive layer, the Multi-DOF wearable robot is developed to offer hand grasp motion kinesthetic feedback and visual feedback synced with MI. The following are the key benefits of the proposed MDR system: (1) We propose a new lightweight network structure more suitable for brain computer interface (BCI) interaction systems, achieving more accurate decoding and shorter identification time in different data sets, which helps to improve the practicability of the system and promote the practical clinical application of BCI rehabilitation technology. (2) We integrate BCI, VR, a wearable Multi-DOF robot, motion kinesthetic feedback, and visual feedback to improve human-machine interaction. Compared to the most recent investigations, the average accuracy of the MDR system on the publicly accessible datasets BCI IV 2a and HGD reached 75.89% and 93.53%, respectively.

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Employing texture features of chest x-ray images and machine learning in covid-19 detection and classification

Mendel

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Mendel 2021年第1期27卷 9-17页

作者： Alquran, Hiam Alsleti, Mohammad Alsharif, Roaa Qasmieh, Isam Abu Alqudah, Ali Mohammad Harun, Nor Hazlyna Binti Department of Biomedical Systems and Informatics Engineering Yarmouk University Irbid21163 Jordan The Institute of Biomedical Technology King Hussein Medical Center Royal Jordanian Medical Service Amman11855 Jordan College of applied medical Sciences Radiological Science Program King Saud University Jeddah21435 Saudi Arabia Data Science Research Lab School of Computing Universiti Utara Malaysia Sintok Kedah06010 Malaysia

The novel coronavirus (nCoV-19) was first detected in December 2019. It had spread worldwide and was declared coronavirus disease (COVID-19) pandemic by March 2020. Patients presented with a wide range of symptoms affecting multiple organ systems predominantly the lungs. Severe cases required intensive care unit (ICU) admissions while there were asymptomatic cases as well. Although early detection of the COVID-19 virus by Real-time reverse transcription-polymerase chain reaction (RT-PCR) is effective, it is not efficient;as there can be false negatives, it is time consuming and expensive. To increase the accuracy of in-vivo detection, radiological image-based methods like a simple chest X-ray (CXR) can be utilized. This reduces the false negatives as compared to solely using the RT-PCR technique. This paper employs various image processing techniques besides extracted texture features from the radiological images and feeds them to different artificial intelligence (AI) scenarios to distinguish between normal, pneumonia, and COVID-19 cases. The best scenario is then adopted to build an automated system that can segment the chest region from the acquired image, enhance the segmented region then extract the texture features, and finally, classify it into one of the three classes. The best overall accuracy achieved is 93.1% by exploiting Ensemble classifier. Utilizing radiological data to conform to a machine learning format reduces the detection time and increase the chances of survival. © 2021, Brno University of Technology. All rights reserved.

关键词： COVID-19

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An adaptive transformer model for anomaly detection in wireless sensor networks in real-time

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Measurement: Sensors 2023年 25卷

作者： Siva Kumar, A. Raja, S. Pritha, N. Raviraj, Havaldar Babitha Lincy, R. Jency Rubia, J. Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamilnadu Kattankulathur India School of Electrical & Communication Department of Electronics & Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu India Department of ECE Panimalar Engineering College Tamilnadu India Department of BME KLE Dr. M S Sheshgiri College of Engineering and Technology Belgaum India Computer and Communication Engineering Department Sri Eshwar College of Engineering Tamilnadu India Department of ECE K. Ramakrishnan College of Engineering Tamilnadu India

Defense, environmental monitoring, healthcare, home automation, and other fields are just a few of the many that make use of wireless sensor networks. There are sensor nodes in these networks that are sent out into the field to collect data and relay it back to the network's hub. The data streams may be infused with anomalous data due to interferences, malfunctioning nodes, etc. The security of the network depends on the prompt identification of any irregularities. Due to noise, missing data, and the dynamic nature of the network, identifying and detecting abnormal data is *** study introduces a dynamic context-capturing deep learning model for sensor data anomaly detection, constructed as a Transformer with a spatio-temporal attention mechanism. The proposed model is trained and tested with a public dataset built from real-time sensor data captured in a water treatment plant. With an accuracy of 0.09142, F1 of 0.9109, and precision of 0.9191, the STA-Tran model outperforms the best existing methods. © 2022

关键词： Anomaly detection

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Federated Learning in Healthcare:A Benchmark Comparison of Engineering and Statistical Approaches for Structured data Analysis

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Health data science 2024年第1期4卷 45-57页

作者： Siqi Li Di Miao Qiming Wu Chuan Hong Danny D’Agostino Xin Li Yilin Ning Yuqing Shang Ziwen Wang Molei Liu Huazhu Fu Marcus Eng Hock Ong Hamed Haddadi Nan Liu Centre for Quantitative Medicine Duke-NUS Medical SchoolSingaporeSingapore Department of Biostatistics and Bioinformatics Duke UniversityDurhamNCUSA Department of Biostatistics Columbia University Mailman School of Public HealthNew YorkNYUSA Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingaporeSingapore Programme in Health Services and Systems Research Duke-NUS Medical SchoolSingaporeSingapore Health Services Research Centre Singapore Health ServicesSingaporeSingapore Department of Emergency Medicine Singapore General HospitalSingaporeSingapore Department of Computing Imperial College LondonLondonEnglandUK Institute of Data Science National University of SingaporeSingaporeSingapore

Background:Federated learning(FL)holds promise for safeguarding data privacy in healthcare *** the term“FL”was originally coined by the engineering community,the statistical field has also developed privacy-preserving algorithms,though these are less *** goal was to bridge this gap with the ffrst comprehensive comparison of FL frameworks from both ***:We assessed 7 FL frameworks,encompassing both engineering-based and statistical FL algorithms,and compared them against local and centralized modeling of logistic regression and least absolute shrinkage and selection operator(Lasso).Our evaluation utilized both simulated data and real-world emergency department data,focusing on comparing both estimated model coefffcients and the performance of model ***:The ffndings reveal that statistical FL algorithms produce much less biased estimates of model ***,engineering-based methods can yield models with slightly better prediction performance,occasionally outperforming both centralized and statistical FL ***:This study underscores the relative strengths and weaknesses of both types of methods,providing recommendations for their selection based on distinct study ***,we emphasize the critical need to raise awareness of and integrate these methods into future applications of FL within the healthcare domain.

关键词： holds utilized slightly

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The incel lexicon: Deciphering the emergent cryptolect of a global misogynistic community

arXiv

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arXiv 2021年

作者： Gothard, Kelly Dewhurst, David Rushing Minot, Joshua R. Adams, Jane Lydia Danforth, Christopher M. Dodds, Peter Sheridan Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core The University of Vermont BurlingtonVT05401 United States Charles River Analytics 625 Mount Auburn Street CambridgeMA02138 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05401 United States Department of Computer Science University of Vermont BurlingtonVT05401 United States

Evolving out of a gender-neutral framing of an involuntary celibate identity, the concept of 'incels' has come to refer to an online community of men who bear antipathy towards themselves, women, and society-at-large for their perceived inability to find and maintain sexual relationships. By exploring incel language use on Reddit, a global online message board, we contextualize the incel community's online expressions of misogyny and real-world acts of violence perpetrated against women. After assembling around three million comments from incel-themed Reddit channels, we analyze the temporal dynamics of a data driven rank ordering of the glossary of phrases belonging to an emergent incel lexicon. Our study reveals the generation and normalization of an extensive coded misogynist vocabulary in service of the group's identity. Copyright © 2021, The Authors. All rights reserved.

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A First Approach to Risk-Limiting Audits for Single Transferable Vote Elections

arXiv

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arXiv 2021年

作者： Blom, Michelle Stuckey, Peter J. Teague, Vanessa Vukcevic, Damjan School of Computing and Information Systems University of Melbourne Parkville Australia Department of Data Science and AI Monash University Clayton Australia Thinking Cybersecurity Pty. Ltd. Melbourne Australia School of Mathematics and Statistics University of Melbourne Parkville Australia Melbourne Integrative Genomics University of Melbourne Parkville Australia

Risk-limiting audits (RLAs) are an increasingly important method for checking that the reported outcome of an election is, in fact, correct. Indeed, their use is increasingly being legislated. While effective methods for RLAs have been developed for many forms of election-for example: first-past-the-post, instant-runoff voting, and D'Hondt elections-auditing methods for single transferable vote (STV) elections have yet to be developed. STV elections are notoriously hard to reason about since there is a complex interaction of votes that change their value throughout the process. In this paper we present the first approach to risk-limiting audits for STV elections, restricted to the case of 2-seat STV elections. Copyright © 2021, The Authors. All rights reserved.

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