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Proceedings of the 33rd Annual International Conference on computer science and Software Engineering

作者： Grabarnik Genady Ya. Poltronieri Filippo Tortonesi Mauro Zaccarini Mattia Shwartz Larisa Department of Mathematics and Computer Science St. John’s University Queens New York USA Distributed Systems Research Group University of Ferrara Ferrara Italy AI for IT IBM T.J. Watson Research Center Yorktown Heights New York USA

The ever-increasing complexity of applications and networks man-agement demands alternative solutions for identifying of suitable deployment configurations in line with established Service Level Agreements. Some existing approaches utilize methods such as mathematical simulation and application re-enactment under a sim-ulated load implemented as digital twin. In this paper, we describe a mixed-modal framework for varying composition of the above approaches hence improving the performance of application’s ser-vice level modeling further. This framework facilitates the best simulation model performance at any given time by anticipating the model drift and by continuously monitoring performance of the mixed-modal simulation model.

关键词： System Operation Modeling Simulation Mathematical Simulation Re-Enactment Simulation Digital Twin

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The Use of Public Cloud Services Among Students for Online Learning 7

The Use of Public Cloud Services Among Students for Online L...

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7th International Conference on Information Management and Technology, ICIMTech 2022

作者： Hassan, Rosilah Murad, Dina Fitria Wahi, Wahiza Leandros, Riyan Asmak Ismail, Nurul Halimatul Awwad, Samer A. B. Selangor Bangi Malaysia BINUS Online Learning Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Selangor Bangi Malaysia Applied College Princess Nourah Bint Abdulrahman University Department of Computer Science and Information System Riyadh Saudi Arabia Deanship of Information and Communication Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

A public cloud is a computing service offered by third-party service providers to Internet users. Some cloud service providers offer both free and paid services, where the prices are scaled to meet the needs and offered more benefits to clients than free cloud services. This paper aims to study the familiarity of public cloud services among respondents of multiple age groups and to find out the advantages and disadvantages of using the public cloud. The survey was taken randomly through google form media. Respondents are the public who use cloud services in Malaysia. A Google Form is used to gather data from the public among multiple age groups, from individuals aged 12 to 45 years and above. Based on the conducted survey, respondents show positive responses towards their knowledge and the usage of public cloud services. Findings from the survey revealed that almost 90.3% of the respondents use public cloud services, and about 75% of them think that the public cloud is reliable and safe to store data. Most respondents have indicated that Google Drive is their preferred public cloud, and the majority them use the public cloud for academic purposes. In conclusion, public cloud services are rather efficient for users with diverse usage necessities. Some public cloud services are reliable and affordable for students, as students need cloud storage for academic purposes, especially during pandemics where everything is conducted online. © 2022 IEEE.

关键词： Surveys

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Weighted Sum Rate Enhancement by Using Dual-Side IOS-Assisted Full-Duplex for Multiuser MIMO systems

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IEEE Internet of Things Journal 2025年第12期12卷 19561-19573页

作者： Fang, Sisai Chen, Gaojie Huang, Chong Gao, Yue Li, Yonghui Wong, Kai-Kit Chambers, Jonathon A. Sun Yat-sen University School of Flexible Electronics State Key Laboratory of Optoelectronic Materials and Technologies Guangdong Guangzhou510220 China Institute for Communication Systems University of Surrey 5GIC 6GIC GuildfordGU2 7XH United Kingdom Fudan University School of Computer Science Shanghai200433 China University of Sydney School of Electrical and Information Engineering SydneyNSW2006 Australia University College London Department of Electronic and Electrical Engineering LondonWC1E 7JE United Kingdom University of Leicester School of Engineering LeicesterLE1 7RH United Kingdom

This article established a novel multi-input multioutput (MIMO) communication network, in the presence of full-duplex (FD) transmitters and receivers with the assistance of dual-side intelligent omni surface (IOS). Compared with the traditional IOS, the dual-side IOS allows signals from both sides to reflect and refract simultaneously, which further exploits the potential of metasurfaces to avoid frequency dependence, and size, weight, and power (SWaP) limitations. By considering both the downlink and uplink transmissions, we aim to maximize the weighted sum rate, subject to the transmit power constraints of the transmitter, the users and the dual-side reflecting and refracting phase shifts constraints. However, the formulated sum rate maximization problem is not convex, hence we exploit the weighted minimum mean square error (WMMSE) approach, and tackle the original problem iteratively by solving two subproblems. For the beamforming matrices optimization of the downlink and uplink, we resort to the Lagrangian dual method combined with a bisection search to obtain the results. Furthermore, we resort to the quadratically constrained quadratic programming (QCQP) method to optimize the reflecting and refracting phase shifts of both sides of the IOS. Simulation results validate the efficacy of the proposed algorithm and demonstrate the superiority of the dual-side IOS. © 2014 IEEE.

关键词： Mean square error

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Network Vulnerability Analysis in Wasserstein Spaces 16th

Network Vulnerability Analysis in Wasserstein Spaces

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16th International Conference on Learning and Intelligent Optimization, LION 16 2022

作者： Ponti, Andrea Irpino, Antonio Candelieri, Antonio Bosio, Anna Giordani, Ilaria Archetti, Francesco Department of Economics Management and Statistics University of Milano-Bicocca Milano Italy Department of Computer Science Systems and Communication University of Milano-Bicocca Milano Italy Dip. Matematica e Fisica Universitá della Campania "Luigi Vanvitelli" Caserta Caserta Italy Oaks S.R.L Milano Italy Consorzio Milano Ricerche Milano Italy

ISBN: (纸本)9783031248658

The main contribution of this paper is the proposal of a new family of vulnerability measures based on a probabilistic representation framework in which the network and its components are modelled as discrete probability distributions. The resulting histograms are embedded in a space endowed with a metric given by the Wasserstein distance. This representation enables the synthesis of a set of discrete distributions through a barycenter and the clustering of distributions. We show that analyzing the networks as discrete probability distributions in the Wasserstein space enables the definition of a new family of vulnerability measures and the assessment of the criticality of each component. Computational results on real-life networks confirm the validity of our basic assumption that distributional representation can capture the topological information embedded in a network graph and yield more meaningful metrics than vulnerability measures based on average values. The computation of the Wasserstein distance is equivalent to the solution of a min-flow problem: its computational complexity has limited its diffusion outside the imaging science community. To avoid this computational bottleneck in this paper, we focus on a statistical approach that drastically reduces the computational hurdles. This approach has been implemented in a software tool HistDAWass. The linear complexity of this approach has also enabled the analysis of large-scale networks.

关键词： Probability distributions

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Certain Investigation on IoT based Cotton Dust Pollution Monitoring in Textile Plants 8

Certain Investigation on IoT based Cotton Dust Pollution Mon...

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8th International Conference on Inventive Computation Technologies, ICICT 2025

作者： Sivanesan, P. Anand, J.V. Vaddanam, Trilok sai Irfan, Shaik Mohammad Department of Information Communication Technology School of Computing SASTRA University Tamilnadu Thanjavur 613401 India Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Tamil Nadu Chennai 600116 India Dept. of Computer Science and Business Systems SASTRA University Tamilnadu Thanjavur India Dept. of ECE Siddhartha Academy of Higher Education Andhra Pradesh Vijayawada India

ISBN: (纸本)9798331512248

The objective of the work is to track the level of cotton dust in the textile sector. The IoT-enabled cotton dust monitoring system deploys three sensors: Sharp GP2Y1010AU0F;Shinyei PPD42NS;and Honeywell HPMA115S0-XXX measuring the dust levels. The Optical dust sensor GP2Y1010AU0F has a threshold of 1 mg/mS, which is placed inside the mask and when that threshold is crossed, buzzer alerts the worker with the air contaminants. It measures inhaled cotton dust and sends data to IoT, real-time concentration height of dust into the mobile devices. Experimental results show that Sharp GP2Y1010AU0F sensor has achieved award for acquiring the highest mean dust concentration of 95.167 mg/mS with a standard deviation of 3.014 among the three sensors, while Honeywell HPMA115S0-XXX and Shinyei PPD42NS recorded a mean of 87.833 mg/mS and 77.500 mg/mS, respectively. The observed response times of the sensors as follows for GP2Y1010AU0F-8s, HPMA115S0-XXX-6s, and PPD42NS- 5s, recording the fastest response by Honeywell HPMA115S0-XXX. A comparative study in accuracy, precision and sensitivity, with a response time, shows the merits and demerits for each sensor. Results emphasize the effectiveness of the system proposed for monitoring in terms of airborne cotton dust exposure in improving safety in textile workplaces. A meta-analysis is performed with Bayesian framework using JASP software. © 2025 IEEE.

关键词： Dust density concentration Meta-Analysis with Bayes factor

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Effect of phase imbalance on the mm-wave signal in the DWDM-RoF system 9

Effect of phase imbalance on the mm-wave signal in the DWDM-...

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9th IEEE International Conference on Photonics, ICP 2022

作者： Mahmood, Rawa Muayad Yaakob, Syamsuri Ahmad, Faisul Arif Ahmad Anas, Siti Barirah Zan, Zuraidah Hassan, Ahmed Kadir, Muhammad Zamzuri Abdul Beson, Mohd Rashidi Che Mahmud, Azwan Faculty of Engineering Wipnet Department of Computer and Communication Systems Engineering Serdang Selangor43400 Malaysia Kulliyyah of Science International Islamic University Malaysia Jalan Sultan Ahmad Shah Bandar Indera Mahkota 25200 Kuantan Pahang Malaysia Kangar Perlis01000 Malaysia Multimedia University Faculty of Engineering Cyberjaya Selangor Malaysia

ISBN: (数字)9781665427661

ISBN: (纸本)9781665427661

Dense wavelength division multiplexing (DWDM) and radio over fiber (RoF) are promising technologies that are able to provide unlimited transmission capacity, which meets the growing demands of bandwidth in communication systems. In this paper, an architecture for DWDM-RoF access networks uses a single Mach Zehnder modulator (MZM) to generate the mm-wave signal. This paper aims to study the effect of phase imbalance on the mm-wave signal to reduce the insertion loss in the system. It is found that the phase imbalance affects the insertion loss. The phase imbalance can be controlled by adjusting the injected phase, and the best result is collected at π 3 and 5π 12 phases. © 2022 IEEE.

关键词： Radio-over-fiber

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Balancing Subjectivity and Objectivity in Network Selection: A Decision-Making Framework Towards Digital Twins

arXiv

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arXiv 2025年

作者： Mefgouda, Brahim Idoudi, Hanen Al-Quraan, Mohammad Lotfi, Ismail Alhussein, Omar Mohjazi, Lina Muhaidat, Sami KU 6G Research Center Khalifa University Abu Dhabi United Arab Emirates Department of Communication and Information Systems Khalifa University Abu Dhabi United Arab Emirates Al Ahsa Saudi Arabia James Watt School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom Department of Computer Science Khalifa University Abu Dhabi United Arab Emirates Department of Systems and Computer Engineering Carleton University Ottawa Canada

Selecting the optimal radio access technology (RAT) during vertical handovers (VHO) in heterogeneous wireless networks (HWNs) is critical. Multi-attribute decision-making (MADM) is the most common approach used for network selection (NS) in HWNs. However, existing MADM-NS methods face two major challenges: the rank reversal problem (RRP), where the relative ranking of alternatives changes unexpectedly, and inefficient handling of user and/or service requirements. These limitations result in suboptimal RAT selection and diminished quality of service, which becomes particularly critical for time-sensitive applications. To address these issues, we introduce in this work a novel weighting assignment technique called BWM-GWO, which integrates the Best-Worst Method (BWM) with the Grey Wolf Optimization (GWO) algorithm through a convex linear combination. The proposed framework achieves a balanced decision-making process by using BWM to compute subjective weights that capture user/service preferences, while employing GWO to derive objective weights aimed at minimizing RRP. The development and validation of this framework establish a digital model for NS in HWNs, marking the initial step toward realizing a digital twin (DT). Experimental results show that integrating the proposed BWM-GWO technique with MADM-NS reduces RRP occurrence by up to 71.3% while significantly improving user and service satisfaction compared to benchmark approaches. © 2025, CC BY.

关键词： Decision making

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Control flow vs. data flow in distributed systems integration: Revival of flow-based programming for the industrial internet of things

arXiv

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arXiv 2021年

作者： Hasselbring, Wilhelm Wojcieszak, Maik Dustdar, Schahram The Department of Computer Science Kiel University Germany Kiel Germany The Distributed Systems Group TU Wien Vienna Austria

When we consider the application layer [1] of networked infrastructures, data and control flow are important concerns in distributed systems integration. Modularity is a fundamental principle in software design [2], in particular for distributed system architectures. Modularity emphasizes high cohesion of individual modules and low coupling between modules. Microservices are a recent modularization approach with the specific requirements of independent deployability and, in particular, decentralized data management [3]. Cohesiveness of microservices goes hand-in-hand with loose coupling, making the development, deployment, and evolution of microservice architectures flexible and scalable [4]. However, in our experience with microservice architectures, interactions and flows among microservices are usually more complex than in traditional, monolithic enterprise systems, since services tend to be smaller and only have one responsibility, causing collaboration needs. We suggest that for loose coupling among microservices, explicit control-flow modeling and execution with central workflow engines should be avoided on the application integration level. On the level of integrating microservices, data-flow modeling should be dominant. Control-flow should be secondary and preferably delegated to the microservices. We discuss coupling in distributed systems integration and reflect the history of business process modeling with respect to data and control flow. To illustrate our recommendations, we present some results for flow-based programming in our Industrial DevOps project Titan, where we employ flow-based programming for the Industrial Internet of Things. Copyright © 2021, The Authors. All rights reserved.

关键词： Data transfer

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Advanced Interoperable Framework for Real-Time Predictive Analysis Leveraging Machine Learning and IoT in Smart Health Monitoring systems

Advanced Interoperable Framework for Real-Time Predictive An...

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Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： D. Antony Pradeesh Y. Syamala P. Priya Madhavi Sripathi Srilakshmi Kaza Rohith Bala Jaswanth B Department of Electronics and Communication Systems KG College of Arts and Science Coimbatore India Department of IoT Seshadri Rao Gudlavalleru Engineering College Vijayawada Andhra Pradesh Department of Computer Science The Quaide Milleth College for Men Chennai Tamilnadu Department of Business and Management Studies Seshadri Rao Gudlavalleru Engineering College Vijayawada Andhra Pradesh Department of Electronics and Communication Engineering Seshadri Rao Gudlavalleru Engineering College Vijayawada Andhra Pradesh

As health care evolves rapidly, devices enabled by the Internet of Things, and algorithms driven by Machine Learning have surfaced as transformative elements. However, these technologies often fall short due to a lack of interoperability between existing systems, real-time predictive capabilities, and strong security measures. This paper presents a novel framework designed to overcome these challenges and optimize Internet of Things applications in health monitoring systems. Our framework has been specifically created to work seamlessly across the Internet of Things devices while monitoring a wide array of health parameters. It integrates advanced machine learning models, including Extreme Gradient Boosting, Light Gradient Boosting Machine, Categorical Boosting, Random Forest and Neural Network to improve prediction accuracy. The framework also allows real-time analysis and prediction services with real-time updates available through an Internet portal. Furthermore, we employ robust security and privacy measures to safeguard patient data. Experimental results illustrate our framework's superior performance over existing systems, demonstrating its promise for creating efficient, secure smart health monitoring systems.

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Machine Learning based Beamwidth Adaptation for mmWave Vehicular communications

Machine Learning based Beamwidth Adaptation for mmWave Vehic...

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IEEE International Conference on Telecommunications and Malaysia communications (ICT-MICC)

作者： Setinder Manic Chuan Heng Foh Abdulkadir Kose Haeyoung Lee Chee Yen Leow Periklis Chatzimisios Klaus Moessner Chakkaphong Suthaputchakun 5GIC & 6GIC Institute for Communication Systems (ICS) University of Surrey Guildford UK Department of Computer Engineering Abdullah Gul University Kayseri Turkey School of Physics Engineering and Computer Science University of Hertfordshire Hatfield UK Wireless Communication Centre Universiti Teknologi Malaysia Johor Malaysia Department of Information and Electronic Systems Engineering International Hellenic University Thessaloniki Greece Faculty of Electronics and Information Technology Technical University Chemnitz Germany Electrical and Computer Engineering Department Bangkok University Khlong Nueng Thailand

The incorporation of mmWave technology in vehicular networks has unlocked a realm of possibilities, propelling the advancement of autonomous vehicles, enhancing interconnectedness, and facilitating communication for intelligent transportation systems (ITS). Despite these strides in connectivity, challenges such as high path-loss have arisen, impacting existing beam management procedures. This work aims to address this issue by improving beam management techniques, specifically focusing on enhancing the service time between vehicles and base stations through adaptive mmWave beamwidth adjustments, accomplished using a Contextual Multi-Armed Bandit Algorithm. By leveraging various conditions to train the ML agent of the Contextual Multi-Armed Bandit Algorithm, it seeks to learn about vehicle mobility profiles and optimize the usage of different antenna beamwidth settings to maximize seamless connection time. The extensive simulation results showcase the effectiveness of an adaptive beamwidth for mobility profiles, extending the connection time a vehicle experiences with a base station when compared to the existing strategies.

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