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Evaluating the Performance of 5G NR in Indoor Environments: An Experimental Study

Procedia Computer Science 2024年 241卷 16-23页

作者： Bikash Chandra Singh Rafael Diaz Sachin Shetty School of Cybersecurity Old Dominion University Norffolk VA 23529 USA Virginia Modeling Analysis and Simulation Center (VMASC) Old Dominion University Sufolk VA 23435 USA

The 5G wireless standard has emerged as a trans-formative technology with the potential to revolutionize various industries by providing enhanced connectivity and communication capabilities. This advanced standard offers a diverse range of applications, including Ultra-Reliable Low-Latency Communication (URLLC), Enhanced Mobile Broadband (eMBB), and Massive Machine Type Communication (mMTC). In this Scientific research paper, we present a comprehensive analysis of the performance and capabilities of a deployed indoor 5G network in a controlled laboratory environment. The experimental setup comprises an Amarisoft Callbox, serving as the 5G core, along with a Remote Radio Head (RRH) and user equipment (UEs). Our primary objective is to assess the network's performance by evaluating the downlink, uplink, and joint downlink/uplink transmissions for TCP and UDP protocols between the gNodeB and user equipment (UE). To achieve this, we carefully examine key performance metrics such as latency, power consumption, CPU utilization, and signal quality. Through various configurations that involve MIMO 2 technology and a 30 kHz sub-carrier spacing, we investigate the impact of different NR Bandwidth settings. By establishing TCP and UDP connections for both uplink and downlink scenarios, we meticulously measure and scrutinize the system's performance, thereby providing valuable insights into its efficiency and suitability for diverse application requirements.

关键词： Indoor 5G Latency CPU utilization Power consumption Signal quality

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From Narratives to Conceptual Models via Natural Language Processing 22

From Narratives to Conceptual Models via Natural Language Pr...

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Proceedings of the Winter simulation Conference

作者： David Shuttleworth Jose Padilla Department of Computational Modeling and Simulation Engineering Old Dominion University Norfolk VA USA Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk VA USA

This paper explores the use of natural language processing (NLP) towards the semi-automatic generation of conceptual models, and eventual simulation specifications, from descriptions of a phenomenon. Narratives describing the problem are transformed into a list of concepts and relationships and visualized using a network graph. The process relies on pattern-based grammatical rules and an NLP dependency parser identifying important concept types, namely actors, factors, and mechanisms. We use three conceptualizations, created by potential users, to understand how the NLP-generated model should and could be adjusted. The objective of the research is to develop potential standard approaches users can use to generate conceptual models; develop a conceptual modeling assistant that subject matter experts can use to make them participant in the simulation creation process; and to identify how narratives should be written so an NLP-based conceptual modeling assistant may provide a thorough description of a phenomenon.

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MIA-BAD: An Approach for Enhancing Membership Inference Attack and its Mitigation with Federated Learning

arXiv

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arXiv 2023年

作者： Banerjee, Soumya Roy, Sandip Ahamed, Sayyed Farid Quinn, Devin Vucovich, Marc Nandakumar, Dhruv Choi, Kevin Rahman, Abdul Bowen, Edward Shetty, Sachin Virginia Modeling Analysis and Simulation Center Old Dominion University VA United States Deloitte & Touche LLP

The membership inference attack (MIA) is a popular paradigm for compromising the privacy of a machine learning (ML) model. MIA exploits the natural inclination of ML models to overfit upon the training data. MIAs are trained to distinguish between training and testing prediction confidence to infer membership information. Federated Learning (FL) is a privacy-preserving ML paradigm that enables multiple clients to train a unified model without disclosing their private data. In this paper, we propose an enhanced Membership Inference Attack with the Batch-wise generated Attack Dataset (MIA-BAD), a modification to the MIA approach. We investigate that the MIA is more accurate when the attack dataset is generated batch-wise. This quantitatively decreases the attack dataset while qualitatively improving it. We show how training an ML model through FL, has some distinct advantages and investigate how the threat introduced with the proposed MIA-BAD approach can be mitigated with FL approaches. Finally, we demonstrate the qualitative effects of the proposed MIA-BAD methodology by conducting extensive experiments with various target datasets, variable numbers of federated clients, and training batch sizes. © 2023, CC BY-NC-ND.

关键词： Privacy-preserving techniques

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Accelerating Hybrid Agent-Based Models and Fuzzy Cognitive Maps: How to Combine Agents Who Think Alike? 24

Accelerating Hybrid Agent-Based Models and Fuzzy Cognitive M...

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Proceedings of the Winter simulation Conference

作者： Philippe J. Giabbanelli Jack T. Beerman Virginia Modeling Analysis and Simulation Center Old Dominion University Norfolk VA USA School of Data Science University of Virginia Charlottesville VA USA

ISBN: (纸本)9798331534202

While Agent-Based Models can create detailed artificial societies based on individual differences and local context, they can be computationally intensive. Modelers may offset these costs through a parsimonious use of the model, for example by using smaller population sizes (which limits analyses in sub-populations), running fewer what-if scenarios, or accepting more uncertainty by performing fewer simulations. Alternatively, researchers may accelerate simulations via hardware solutions (e.g., GPU parallelism) or approximation approaches that operate a tradeoff between accuracy and compute time. In this paper, we present an approximation that combines agents who `think alike', thus reducing the population size and the compute time. Our innovation relies on representing agent behaviors as networks of rules (Fuzzy Cognitive Maps) and empirically evaluating different measures of distance between these networks. Then, we form groups of think-alike agents via community detection and simplify them to a representative agent. Case studies show that our simplifications remain accuracy.

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Phased Framework for Automated Educational Content Curation 13

Phased Framework for Automated Educational Content Curation

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13th International Conference on Society and Information Technologies, ICSIT 2022

作者： Smith, Katherine Johnson, Jessica Ayaz, Gul Horner, Charis Virginia Modeling Analysis and Simulation Center Old Dominion University SuffolkVA23435 United States Design Interactive Inc. OrlandoFL32817 United States

ISBN: (纸本)9781950492633

This paper applies modern, data-driven techniques to the traditionally manual, time consuming process of educational content curation. Specifically, educational content produced through a peer-to-peer knowledge sharing process is considered. After an overall introduction, an overview of previous work in the application of machine learning to education is provided. Then, a three-phase framework is developed to support implementation of an educational content curation process designed to inspire and enable learning. This process involves evaluation of individual content items, situation of content items in a repository of existing content, and correlation of content items to a learner and their needs. An initial example implementation of this framework is conceptualized. Finally, directions for future work are described. © 2022 by the International Institute of Informatics and Systemics. All rights reserved.

关键词： Knowledge management

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Trustworthy Artificial Intelligence Framework for Proactive Detection and Risk Explanation of Cyber Attacks in Smart Grid

Trustworthy Artificial Intelligence Framework for Proactive ...

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2023 Winter simulation Conference, WSC 2023

作者： Munir, Md. Shirajum Shetty, Sachin Rawat, Danda B. Old Dominion University Virginia Modeling Analysis and Simulation Center 1030 University Blvd SuffolkVA23435 United States Howard University Department of Electrical and Computer Science 2400 Sixth Street NW WashingtonDC20059 United States

ISBN: (纸本)9798350369663

The rapid growth of distributed energy resources (DERs), such as renewable energy sources, generators, consumers, and prosumers in the smart grid infrastructure, poses significant cybersecurity and trust challenges to the grid controller. Consequently, it is crucial to identify adversarial tactics and measure the strength of the attacker's DER. To enable a trustworthy smart grid controller, this work investigates a trustworthy artificial intelligence (AI) mechanism for proactive identification and explanation of the cyber risk caused by the control/status message of DERs. Thus, proposing and developing a trustworthy AI framework to facilitate the deployment of any AI algorithms for detecting potential cyber threats and analyzing root causes based on Shapley value interpretation while dynamically quantifying the risk of an attack based on Ward's minimum variance formula. The experiment with a state-of-the-art dataset establishes the proposed framework as a trustworthy AI by fulfilling the capabilities of reliability, fairness, explainability, transparency, reproducibility, and accountability. © 2023 IEEE.

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Broadening Access to simulations for End-Users via Large Language Models: Challenges and Opportunities 24

Broadening Access to Simulations for End-Users via Large Lan...

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Proceedings of the Winter simulation Conference

作者： Philippe J. Giabbanelli Jose J. Padilla Ameeta Agrawal Virginia Modeling Analysis and Simulation Center Old Dominion University Norfolk VA USA Dept. of Computer Science Portland State University Portland OR USA

ISBN: (纸本)9798331534202

Large Language Models (LLMs) are becoming ubiquitous to create intelligent virtual assistants that assist users in interacting with a system, as exemplified in marketing. Although LLMs have been discussed in modeling & simulation (M&S), the community has focused on generating code or explaining results. We examine the possibility of using LLMs to broaden access to simulations, by enabling non-simulation end-users to ask what-if questions in everyday language. Specifically, we discuss the opportunities and challenges in designing such an end-to-end system, divided into three broad phases. First, assuming the general case in which several simulation models are available, textual queries are mapped to the most relevant model. Second, if a mapping cannot be found, the query can be automatically reformulated and clarifying questions can be generated. Finally, simulation results are produced and contextualized for decision-making. Our vision for such system articulates long-term research opportunities spanning M&S, LLMs, information retrieval, and ethics.

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Impacts of meteorology and emission variations on the heavy air pollution episode in North China around the 2020 Spring Festival

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Science China Earth Sciences 2021年第2期64卷 329-339页

作者： Wenbo XUE Xurong SHI Gang YAN Jinnan WANG Yanling XU Qian TANG Yanli WANG Yixuan ZHENG Yu LEI Center of Air Modeling and Systems Analysis Chinese Academy for Environmental PlanningBeijing 100012China State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation Chinese Academy for Environmental PlanningBeijing 100012China

Based on the Weather Research and Forecasting model and the Models-3 community multi-scale air quality model(WRF-CMAQ),this study analyzes the impacts of meteorological conditions and changes in air pollutant emissions on the heavy air pollution episode occurred over North China around the 2020 Spring Festival(January to Februray 2020).Regional reductions in air pollutant emissions required to eliminate the PM2.5 heavy pollution episode are also *** results found that meteorological conditions for the Beijing-Tianjin-Hebei and surrounding"2+26"cities are the worst during the heavy pollution episode around the 2020 Spring Festival as compared with two other typical heavy pollution episodes that occurred after ***,because of the substantial reductions in air pollutant emissions in the"2+26"cities in recent years,and the32%extra reduction in emissions during January to February 2020 compared with the baseline emission levels of the autumn and winter of 2019 to 2020,the maximum PM2.5 level during this heavy pollution episode around the 2020 Spring Festival was much lower than that in the other two typical ***,these emission reductions are still not enough to eliminate regional heavy pollution *** with the actual emission levels during January to February 2020,a 20%extra reduction in air pollutant emissions in the"2+26"cities(or a 45%extra reduction compared with baseline emission levels of the autumn and winter of 2019 to 2020)could help to generally eliminate regionwide severe pollution episodes,and avoid heavy pollution episodes that last three or more consecutive days in Beijing;a 40%extra reduction in emissions(or a 60%extra reduction compared with baseline emission levels of the autumn and winter of 2019 to 2020)could help to generally eliminate regionwide and continuous heavy pollution *** analysis finds that during the clean period after the heavy pollution episode around the 2020 Spring Festival,the regionwide

关键词： "2+26"cities 2020 Spring Festival Heavy air pollution episode WRF-CMAQ model

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Accelerating Hybrid Agent-Based Models and Fuzzy Cognitive Maps: How to Combine Agents who Think Alike?

Accelerating Hybrid Agent-Based Models and Fuzzy Cognitive M...

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simulation Winter Conference

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

While Agent-Based Models can create detailed artificial societies based on individual differences and local context, they can be computationally intensive. Modelers may offset these costs through a parsimonious use of the model, for example by using smaller population sizes (which limits analyses in sub-populations), running fewer what-if scenarios, or accepting more uncertainty by performing fewer simulations. Alternatively, researchers may accelerate simulations via hardware solutions (e.g., GPU parallelism) or approximation approaches that operate a tradeoff between accuracy and compute time. In this paper, we present an approximation that combines agents who ‘think alike’, thus reducing the population size and the compute time. Our innovation relies on representing agent behaviors as networks of rules (Fuzzy Cognitive Maps) and empirically evaluating different measures of distance between these networks. Then, we form groups of think-alike agents via community detection and simplify them to a representative agent. Case studies show that our simplifications remain accuracy.

关键词： Analytical models Technological innovation Accuracy Uncertainty Costs Computational modeling Fuzzy cognitive maps Graphics processing units Parallel processing Hardware

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Categorizing Social Media Screenshots for Identifying Author Misattribution

arXiv

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arXiv 2024年

作者： Farris, Ashlyn M. Nelson, Michael L. Mathematics Department Harding University United States Department of Computer Science Virginia Modeling Analysis and Simulation Center Old Dominion University United States

Mis/disinformation is a common and dangerous occurrence on social media. Misattribution is a form of mis/disinformation that deals with a false claim of authorship, which means a user is claiming someone said (posted) something they never did. We discuss the difference between misinformation and disinformation and how screenshots are used to spread author misattribution on social media platforms. It is important to be able to find the original post of a screenshot to determine if the screenshot is being correctly attributed. To do this we have built several tools to aid in automating this search process. The first is a Python script that aims to categorize Twitter posts based on their structure, extract the metadata from a screenshot, and use this data to group all the posts within a screenshot together. We tested this process on 75 Twitter posts containing screenshots collected by hand to determine how well the script extracted metadata and grouped the individual posts, F1 = 0.80. The second is a series of scrapers being used to collect a dataset that can train and test a model to differentiate between various social media platforms. We collected 16,620 screenshots have been collected from Facebook, Instagram, Truth Social, and Twitter. Screenshots were taken by the scrapers of the web version and mobile version of each platform in both light and dark mode. Copyright © 2024, The Authors. All rights reserved.

关键词： Tweets

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