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A Standard-compliant Assessment of Beyond-eMBB QoS/QoE in 5G Networks

A Standard-compliant Assessment of Beyond-eMBB QoS/QoE in 5G...

引用

2024 IEEE Conference on Standards for Communications and Networking, CSCN 2024

作者： Caso, Giuseppe Rajiullah, Mohammad Brunstrom, Anna De Nardis, Luca Alay, Özgü Neri, Marco Karlstad University Department of Mathematics and Computer Science Karlstad Sweden Sapienza University of Rome Department of Information Engineering Electronics and Telecommunications Rome Italy University of Oslo Department of Informatics Oslo Norway Mobile Network Testing Rohde & Schwarz Rome Italy

ISBN: (纸本)9798331507428

5th Generation (5G) mobile systems are being deployed to address the Quality of Service and Experience (QoS/QoE) requirements of several use cases, including enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communication (URLLC). While eMBB performance testing inherits well-established methodologies and Key Performance Indicators (KPIs), beyond-eMBB services (i.e., URLLC and eMBBURLLC real-time applications) are often tested by adopting simplistic or in-house methodologies, which do not help towards accurate assessment and comparison. In this paper, we fill this gap by providing a detailed analysis of a methodology, recently standardized by the International Telecommunication Union Telecommunication Standardization Sector (ITU-T), that targets systematic performance evaluations of beyond-eMBB services. The methodology relies on the definition of a QoE KPI, i.e., the interactivity score (i-score), on top of three QoS KPIs measuring service latency, stability, and continuity. To this aim, we perform a multi-service measurement campaign on two 5G networks across two cities in Sweden, during which we run a large number of tests compliant with the ITU-T methodology, and analyze the collected data. Our results empirically validate the methodology, showcasing its ability of capturing heterogeneous service characteristics and requirements, as well as the interdependencies between i-score and QoS KPIs, and the impact of different factors on QoS/QoE performance, including user mobility, connection capability, and server location. © 2024 IEEE.

关键词： 5G mobile communication systems

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Blockchain-based Energy Trading among UAVs and Base Stations for Net-Zero 10

Blockchain-based Energy Trading among UAVs and Base Stations...

引用

10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Ayaz, Ferheen Nekovee, Maziar Saeed, Nagham University of London City St. George's Department of Computer Science United Kingdom University of Sussex 6 G Lab School of Engineering and Informatics United Kingdom University of West London School of Computing and Engineering United Kingdom

ISBN: (纸本)9798350373011

The recent development in Internet-of-Things (IoT) networks utilizing Unmanned Aerial Vehicles (UAVs) have greatly enhanced system analysis and management. For example, an energy management system can exploit UAVs to intelligently predict output from renewable resources and estimate grid supply. However, UAVs are battery-operated and need to be charged regularly. It has become crucial to maintain supply and achieve net-zero with negligible emissions for high energy demanding IoT networks, such as those incorporating UAVs. A promising solution is to utilize backup batteries of cellular base stations (BSs) which often remain idle when grid supply is consistent. A P2P energy exchange between a BS seller and UAV buyer is possible in exchange of some incentives. However, BS operators may become selfish and not share their energy. Also, they can behave maliciously by selling energy at unreasonably high prices or sharing false information about the original source of energy. This paper proposes an energy trading system between 5G BSs and UAVs. A Reinforcement Learning (RL) algorithm is used by UAVs to select BS which maximizes incentives for both BSs and UAVs. Blockchain security increase energy exchange by 0.26 kWh and 4.9 kWh in presence of malicious and selfish BS seller respectively. © 2024 IEEE.

关键词： Sales

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Characteristics of Accuracy Function on Multiclass Classification Based on Best, Average, and Worst (BAW) Subset of Random Forest Model 24

Characteristics of Accuracy Function on Multiclass Classific...

引用

24th International Electronics Symposium, IES 2022

作者： Susetyoko, Ronny Yuwono, Wiratmoko Purwantini, Elly Iman, Budi Nur Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia Politeknik Elektronika Negeri Surabaya Department of Electrical Engineering Surabaya Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

This study aims to determine the effect of percentage of training data and number of features on average accuracy of classification model. Characteristics of the accuracy function of 2 datasets with different feature data types were also studied. All possible random forest models were calculated for accuracy based on some percentage level of training data and number of features. The accuracy function was constructed based on subsets of best accuracy (Best), average accuracy (Average), and worst accuracy (Worst) using polynomial regression. There were no significant difference in the average accuracy of various percentage levels of training data. However, there were significant difference in the average accuracy based on the number of features. The best polynomial model for average accuracy function of the 2 datasets had R2 99.50% and R2 96.99%, respectively. Based on individual testing (with a 95% confidence level), there was a linear effect of the percentage of training data on the average accuracy, and there were linear and quadratic effects on the number of features on the average accuracy. In the first dataset, classification model using 7 features obtain classification accuracies ranging from 96.9%-97.6%. In the second dataset using 7 features, the classification accuracies range from 74.0%-75.0%. Characteristics of the Best, Average, and Worst accuracy (BAW) functions could be an important description of the accuracy function characteristics and to determine the number of features. © 2022 IEEE.

关键词： Decision trees

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Hyperparameter Optimization of 2D CNN LSTM for Natural Disaster Classification from Social Media Messages

Hyperparameter Optimization of 2D CNN LSTM for Natural Disas...

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computer Sciences, engineering, and Technology Innovation (ICoCSETI), International Conference on

作者： Maulana Ainur Rafiq Mohammad Reza Faisal Radityo Adi Nugroho Irwan Budiman Dodon Turianto Nugrahadi Mera Kartika Delimayanti Department of Computer Science Lambung Mangkurat University Banjarbaru Indonesia Computer and Informatics Engineering Department Politeknik Negeri Jakarta Depok Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

Social media plays a crucial role in providing information related to natural disasters, which can help accelerate the response to such events. Messages shared by social media users can offer valuable insights into natural disaster occurrences. The use of a 2D CNN model combined with LSTM has been applied in previous research for classifying natural disasters. Word embedding techniques such as Word2Vec, FastText, and GloVe have been utilized, but there are still limitations in the word representations used. This research aims to address these shortcomings by incorporating wikipedia text into the word embedding process, which is expected to enhance word representations. Additionally, the research also applies hyperparameter tuning using a grid search method to optimize the performance of the 2D CNN + LSTM model. The results obtained from this research show improved accuracy compared to previous research, with accuracy values reaching 83.55% for floods, 92.11% for forest fires, and 81.83% for earthquakes. These findings demonstrate that the inclusion of wikipedia text in word embedding and the optimization of the 2D CNN + LSTM model through hyperparameter tuning with grid search significantly improve the model’s accuracy.

关键词： Accuracy Social networking (online) Disasters Encyclopedias Forestry Internet Online services Tuning Long short term memory Optimization

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Designing a Code Obfuscation Scheme for Software Protection

Designing a Code Obfuscation Scheme for Software Protection

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2024 International Conference on Electrical and computer engineering Researches, ICECER 2024

作者： Azeta, Ambrose A. Awal, Abdulganiyu Azeta, Joy I. Hamunyela, Suama L. Santos, Albano dos Namibia University of Science and Technology Department of Software Engineering Windhoek Namibia Covenant University Department of Computer Science and Information Sciences Ota Nigeria Covenant University Department of Biological Sciences Ota Nigeria Namibia University of Science and Technology Department of Informatics Windhoek Namibia

ISBN: (纸本)9798331539733

Software piracy and intellectual property theft has brought about a financial decrease in revenue for the producers of software systems. Moreso, malicious end-users act as Man-At-The-End (MATE) attackers and dangerously inspect software codes either statically or dynamically to steal them. Developers need to create client applications that contain confidential information and data. As a result, large scale software firms constantly strive to secure hidden algorithms and vital programming components contained in their applications in order to have competitive advantage. The focus of this study is to design a code obfuscation scheme for software and intellectual property protection. The methodology employed includes use of programming tool and algorithms that described the key processes of the scheme. To demonstrate the strength and capability of our approach, user evaluation was conducted to arrive at the cost, potency and resilience of the scheme. Results indicate that majority of the respondents believe that the developed obfuscator is inexpensive and that it does not adversely affect memory and execution time. The current study would provide software developers the tool to obscure their intellectual property against theft. © 2024 IEEE.

关键词： Attackers intellectual property obfuscation scheme software protection

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Quantum-Based Prediction Model for Carbon Neutrality

Quantum-Based Prediction Model for Carbon Neutrality

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International Conference of Advanced informatics: Concept, Theory and Application (ICAICTA)

作者： Ghifari Munawar Kridanto Surendro School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Department of Computer Engineering and Informatics Politeknik Negeri Bandung Bandung Indonesia

ISBN: (数字)9798331520311

ISBN: (纸本)9798331520328

Carbon neutrality is a global target pursued by cities worldwide to achieve a balance between carbon emissions and removals, reaching a net-zero carbon state. Mitigation measures are being implemented to reduce emissions and enhance carbon sequestration, aiming to meet the targets set for 2050 or 2060. However, challenges posed by urban sprawl and increasing urbanization raise concerns about the feasibility of achieving carbon neutrality. Various studies have been conducted to project the attainment of this goal by developing prediction models. Machine learning (ML) prediction models use socio-economic, energy, and technological data to forecast carbon neutrality. These models consider factors like GDP per capita, urbanization rate, total energy consumption, and forest stock volume, formulating scenarios based on policy documents and historical data. Some models have incorporated optimization methods like the sparrow search algorithm, genetic neural network, and aquila optimizer to improve prediction accuracy. However, classical optimization methods have limitations, such as susceptibility to getting trapped in local optima, which can affect model performance. Quantum-based optimization methods, particularly quantum annealing (QA), are emerging as potential solutions to address these challenges by leveraging the principles of quantum mechanics to optimize complex problem spaces. QA enhances ML processes like feature selection, hyperparameter optimization, and regression model optimization. This study provides a review of pipeline processes from state-of-the-art methods, as well as their potential quantum-based enhancements, to achieve more precise predictive models.

关键词： Annealing Computational modeling Urban areas Quantum mechanics Optimization methods Predictive models Carbon neutral Quantum annealing Hyperparameter optimization Feature extraction

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NetDriller-V3: A Powerful Social Network Analysis Tool 14

NetDriller-V3: A Powerful Social Network Analysis Tool

引用

14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022

作者： Afra, Salim Ozyer, Tansel Rokne, Jon Alhajj, Reda University of Calgary Department of Computer Science Alberta Canada Ankara Medipol University Department of Computer Engineering Ankara Turkey Istanbul Medipol University Department of Computer Engineering Istanbul Turkey University of Southern Denmark Department of Health Informatics Odense Denmark

ISBN: (纸本)9781665456616

The development in technology has led to the generation of huge amounts of data from various sources, including biological data, social networking data, etc. Accordingly, social network analysis has received considerable attention with the availability of more raw datasets which could be realized using a network structure. Most of the datasets can be represented as a social network which is a graph consisting of actors having relationships. Many tools exist for social network analysis inspired to extract knowledge from the networks. NetDriller has been developed as a social network extraction, manipulation and analysis tool to cover the lack that exists in other tools. It is capable of constructing social networks from raw data by employing a variety of data mining and machine learning techniques. In this paper, we describe an extend version of NetDriller, which has some new essential functions, including social network construction using data collection from Twitter, DBLP and IEEE. We also added (1) a new chart for viewing the network property and metrics, and (2) new graph manipulation techniques using GUI to keep the tool up to date with the huge volume of networks and the different types of raw data available on the web. © 2022 IEEE.

关键词： Data mining

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Shrinking VOD Traffic via Rényi-Entropic Optimal Transport 24

Shrinking VOD Traffic via Rényi-Entropic Optimal Transport

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2024 ACM SIGMETRICS/IFIP Performance Conference on Measurement and Modeling of computer Systems, SIGMETRICS/PERFORMANCE 2024

作者： Lo, Chi-Jen [roger] Marina, Mahesh K. Sastry, Nishanth Xu, Kai Fadaei, Saeed Li, Yong Department of Engineering University of Cambridge United Kingdom School of Informatics The University of Edinburgh United Kingdom School of Computer Science University of Surrey United Kingdom MIT-IBM Watson AI Lab CambridgeMA United States Department of Electronic Engineering Tsinghua University China

ISBN: (纸本)9798400706240

In response to the exponential surge in Video on Demand (VOD) traffic, numerous research endeavors have concentrated on optimizing and enhancing infrastructure efficiency. In contrast, this paper explores whether users' demand patterns can be shaped to reduce the pressure on infrastructure. Our main idea is to design a mechanism that alters the distribution of user requests to another distribution which is much more cache-efficient, but still remains 'close enough' (in terms of cost) to fulfil individual user's preference. © 2024 Copyright held by the owner/author(s).

关键词： Video on demand

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Non-stationary domain generalization: theory and algorithm 24

Non-stationary domain generalization: theory and algorithm

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Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence

作者： Thai-Hoang Pham Xueru Zhang Ping Zhang Department of Computer Science and Engineering The Ohio State University and Department of Biomedical Informatics The Ohio State University Department of Computer Science and Engineering The Ohio State University

Although recent advances in machine learning have shown its success to learn from independent and identically distributed (IID) data, it is vulnerable to out-of-distribution (OOD) data in an open world. Domain generalization (DG) deals with such an issue and it aims to learn a model from multiple source domains that can be generalized to unseen target domains. Existing studies on DG have largely focused on stationary settings with homogeneous source domains. However, in many applications, domains may evolve along a specific direction (e.g., time, space). Without accounting for such non-stationary patterns, models trained with existing methods may fail to generalize on OOD data. In this paper, we study domain generalization in non-stationary environment. We first examine the impact of environmental non-stationarity on model performance and establish the theoretical upper bounds for the model error at target domains. Then, we propose a novel algorithm based on adaptive invariant representation learning, which leverages the non-stationary pattern to train a model that attains good performance on target domains. Experiments on both synthetic and real data validate the proposed algorithm.

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Neuro-Fuzzy Employee Ranking System in the Public Sector 8

Neuro-Fuzzy Employee Ranking System in the Public Sector

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8th International Conference on Fuzzy Systems and Data Mining, FSDM 2022

作者： Michalopoulos, Dimitrios Karras, Aristeidis Karras, Christos Sioutas, Spyros Giotopoulos, Konstantinos C. Department of Management Science and Technology University of Patras Patras Greece Department of Computer Engineering and Informatics University of Patras Patras Greece

ISBN: (纸本)9781643683461

One of the biggest problems the public sector faces is the proper utilization of human resources. Within a particular combination of practices, dynamic resource allocation management aims to increase employee productivity. The question that arises is how to match the abilities of employees on a measurable scale through their connection with the logic of neuro-fuzzy systems. Productivity and efficiency are two sides of the same coin and are directly related to the deployment of tasks. In this context, the new era of digital transformation along with the automation of processes allows the direct measurement of the workload status at any time. After the deployment of numerous experiments we verify that skill management is linked to performance measurement. Hard skills are selected since there is no direction of how to quantify soft skills and especially when applied to employees of a public body. ANFIS neuro-fuzzy system was selected since it uses the learning algorithm derived from neural network theory along with human criteria. Therefore the proposed system is based on two distinct factors, Skill management and Neuro-Fuzzy Inference System. The model initialization is system-data driven, which enhances the accuracy compared to traditional HR systems and secures a non-subjective procedure on employee management applied to the public sector. © 2022 The authors and IOS Press.

关键词： Efficiency

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