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Base Station Microgrid Energy Management in 5G Networks - A Brief Review 8th

Base Station Microgrid Energy Management in 5G Networks - A ...

引用

8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Zhu, Yingqi Li, Kang Zhang, Li School of Electronic and Electrical Engineering University of Leeds LeedsLS2 9JT United Kingdom

ISBN: (纸本)9789819602247

The number of 5G base stations (BSs) has soared in recent years due to the exponential growth in demand for high data rate mobile communication traffic from various intelligent terminals. The 5G BSs powered by microgrids with energy storage and renewable generation can significantly reduce the carbon emissions and operational costs. The base station microgrid energy management system (BSMGEMS) is crucial to unleash these potentials. This paper presents a brief review of BSMGEMS. The work begins with outlining the main components and energy consumptions of 5G BSs, introducing the configuration and components of base station microgrids (BSMGs), as well as categorizing the energy management systems (EMSs) and communication network topology. Subsequently, the dispatch optimization strategy and energy trading models are reviewed. The paper concludes with a few suggestions for future research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 5G mobile communication systems

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Invisible Shield: Real-Time Industrial Threats Detection for IoT-based systems

Invisible Shield: Real-Time Industrial Threats Detection for...

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international conference on ent computing and Control systems (ICICCS)

作者： Kadaganchi Sudhakar Pasula Manish Mavillapally Rohit Shaik Kaleem Ahmed Batharaju Sneha Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Hyderabad Telangana

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

The Industrial Internet of Things' (IIoT) explosive growth has significantly changed industrial environments by linking smart devices via Supervisory Control and data acquisition (SCADA) systems. This integration also introduces significant cyber security vulnerabilities even though it brings about flexibility, resource efficiency, and operational agility. Current IDS using traditional machine learning fail to classify cyber attacks precisely because of complexity in data, limited availability, and mislabeling issues. To overcome these challenges, this study presents a scalable and effective ensemble detection framework that utilizes Pyramidal Recurrent Units (PRUs) and Decision Tree (DT) models. This framework aims to detect and counter cyber attacks across large IIoT networks, especially in SCADA-based environments. The propose of developing this project is to ensure scalable and efficient Deep Learning and Decision Tree based ensemble cyber attack detection framework to resolve trustworthiness issues in the SCADA based IIoT networks. Our proposed detection method can be applied to various IIoT domains. It is easy to implement and deploy, improving efficiency and accuracy while addressing the limitations of earlier *** framework enhances the security of SCADA-based IIoT systems, making industrial networks more reliable, trustworthy, and resilient. The results indicates that the system performs well across different network settings, showcasing its adaptability and robustness in detecting IIoT-based SCADA systems.

关键词： Deep learning Accuracy SCADA systems Feature extraction Threat assessment Complexity theory Security Decision trees Cyberattack Industrial Internet of Things

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Explaining Cyber Risks in Transportaion 5.0: A data Driven Approach

Explaining Cyber Risks in Transportaion 5.0: A Data Driven A...

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AI in Cybersecurity (ICAIC), international conference on

作者： Binod Tandan Prashant Vajpayee Gahangir Hossain Dept. of Informtation Science University of North Texas Denton Texas

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

The primary objective of the research is to explore the cybersecurity risks associated with AI-enabled intelligent transportation systems (Transportation 5.0) within the context of developing future smart cities. In Transpiration 5.0, the critical infrastructure increasingly relies on automated systems, which enhances the threat of ransomware attacks, specifically targeting vital cyber-physical systems (CPS), smart grids, and intelligent transportation systems (ITS). The paper explores transportation ransomware attack data to incorporate advanced visualization and extract relevant data attributes for cyber risk evaluation. The study uses the NIST risk management framework and ISACA risk quantification to develop a cyber risk evaluation method. With the findings of the literature review, the research highlights ransomware as a significant threat to intelligent transportation systems (ITS), given the negative impact of malware on IoT, IIoT, and network-connected devices. While substantial research exists on malware detection techniques, performance, accuracy, and cloud-based strategies, it’s crucial for organizations to comprehend the risks of ransomware attacks to implement effective security controls and enable cyber excellence. The research executes exploratory data analysis on the given dataset to find out the correlation between different attributes such as severity, data loss, affected systems, and ransom amount for evolving a risk method to evaluate cyber risk. The focus is to simplify the risk evaluation process by fitting the right data attributes into a standard risk management framework and risk formula for broader reusability. The method was applied to different transportation modes to calculate the risk scores respectively and generate insights accordingly. The overall analysis supports that effective risk predictions along with consistent monitoring can help to control ransomware attack incidents and improve cyber resiliency. This paper proposes a data-d

关键词： data analysis Correlation Transportation data visualization NIST Ransomware Risk management Synthetic data Resilience Industrial Internet of Things

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Framework for Cloud-Based data Management in Autonomous Vehicles

Framework for Cloud-Based Data Management in Autonomous Vehi...

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ieee Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Harshith Pallapothu Gopi Krishna G Digvijay Sunil Teja Yadav S Nagasundari S Dept. of Computer Science & Engineering PES University Bengaluru India Dept. of Computer Science & Engineering Research Center for Information Security Forensics and Cyber Resilience PES University Bengaluru India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The increasing adoption of autonomous vehicles has driven the need for robust data management solutions that support real-time operations and ensure vehicle safety and efficiency. This work introduces a cloud-based framework for management of sensor data from autonomous vehicles, focusing on optimizing payload transmission rates. The framework leverages AWS IoT Core and AWS IoT Analytics to ensure efficient data flow from vehicle sensors to cloud storage. This framework demonstrates its potential for scalable deployment in real-world autonomous vehicle networks, contributing to the evolution of connected vehicle technologies and intelligent transport systems. This framework ensures reliable data transmission up to 250 payloads per second, beyond which data loss occurs.

关键词： Cloud computing Connected vehicles Web services Vehicle safety Propagation losses Real-time systems Reliability Autonomous vehicles Payloads Predictive maintenance

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Research and Application of Automatic Tobacco Baking System Based on Multi-Source Fusion

Research and Application of Automatic Tobacco Baking System ...

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Electrical Automation and Artificial Intelligence (ICEAAI), international conference on

作者： Shuwei Chen Shuai Shi Shandong Youren Intelligent Technology Co. Ltd Jinan Shandong Province China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

With the rapid growth of tobacco market demand and the gradual aging of traditional roasting technology, automated tobacco roasting system has become an important means to improve production efficiency and guarantee tobacco quality. In this study, an automated tobacco roasting system based on multi-source data fusion is designed to collect environmental parameters such as temperature, humidity and airflow in the roasting process in real time through IoT devices and edge computing, and combine with deep learning and fuzzy control algorithms to achieve accurate regulation of the roasting curve. The system adopts a modular structure, including a data acquisition and monitoring module, an intelligent algorithm processing unit and a feedback control system, to ensure the efficient and stable operation of the roasting process. In the experimental process, through the comparison and verification of the automated process and traditional process, it is found that the automated system shows significant advantages in the proportion of top-grade tobacco, unit cost and error rate control: the proportion of top-grade tobacco is increased to 50.12%, the unit cost is reduced by 29%, and the error rate is reduced to 0.23%. The system not only greatly improves the appearance quality and market competitiveness of tobacco, but also realises the dynamic adaptation to different varieties of tobacco, providing theoretical basis and practical support for the intelligent development of the tobacco industry.

关键词： Deep learning Temperature distribution Costs Error analysis Process control Production Regulation Real-time systems Temperature control Monitoring

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Research on Lattice-Based Attribute-Based Encryption Schemes for Carbon data Sharing in Power systems under Carbon Neutrality

Research on Lattice-Based Attribute-Based Encryption Schemes...

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ieee international conference on Big data Security on Cloud (BigdataSecurity), High Performance and Smart computing (HPSC) and intelligent data and Security (IDS)

作者： Limei Zhang Qianting Zhu Wenjing Xu School of Economics and Management China University of Petroleum Beijing Beijing China School of Business and Administration China University of Petroleum Beijing Beijing China

ISBN: (数字)9798331595104

ISBN: (纸本)9798331595111

Driven by global carbon neutrality goals, carbon data sharing in power systems has become a critical component of low-carbon transformation. However, the high sensitivity and privacy requirements of carbon data pose significant challenges to traditional data security technologies. To address these challenges, this paper proposes an innovative lattice-based attribute encryption scheme designed to enhance the security and privacy of carbon data sharing in power systems. The scheme integrates the quantum-resistant properties of lattice-based cryptography with the fine-grained access control mechanisms of attribute-based encryption, enabling efficient encryption and secure sharing of carbon data. Through theoretical analysis, this paper demonstrates the scheme's superiority in resisting quantum computing attacks and supporting dynamic access policies. Experimental results indicate that the proposed scheme outperforms existing methods in terms of encryption efficiency (approximately 30% improvement), decryption success rate (over 99.5%), and resistance to attacks (withstanding 99.9% of simulated attacks). Furthermore, in large-scale power system environments, the scheme exhibits excellent scalability, supporting concurrent data sharing for over 10,000 nodes with a system latency of less than 200 milliseconds. This research not only provides an efficient and secure solution for carbon data sharing in power systems but also offers new insights into the application of lattice-based cryptography in real-world industrial scenarios, with significant theoretical and practical implications.

关键词： Resistance data privacy Sensitivity Scalability Heuristic algorithms Power system dynamics Quantum mechanics Carbon neutral Encryption Carbon

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Big data Mining for Efficient Real-Time Traffic Management

Big Data Mining for Efficient Real-Time Traffic Management

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), ieee conference on

作者： Priyanka Kaushik Priyanka Rawat Prakash Divakaran Vandana Mishra Chaturvedi Ranjeet Kumar Roy Manas Manglik Kaushik Dept. of CSE (AIML) CSE-APEX Chandigarh University Chandigarh India Dept. of Computer Science and Engineering Chandigarh University Chandigarh India Himalayan University Itanagar India D Y Patil Deemed to be University Navi Mumbai India Dept. of Mathematics Lingayas Vidyapeeth Haryana India Dept. of Electrical and Electronics Manipal Institute of Technology MAHE Karnataka India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The application of big data mining for real-time traffic management aims to address the growing challenges of urban congestion, traffic accidents, and environmental pollution. As cities expand and vehicle numbers rise, traditional traffic management systems struggle to cope with the increasing complexity. Big data mining involves analysing vast amounts of heterogeneous data from sources such as GPS devices, traffic cameras, social media, and sensors. By employing advanced analytics, machine learning, and predictive modelling techniques, it is possible to extract valuable insights into traffic patterns, predict congestion, and identify potential incidents. These insights enable the development of dynamic and adaptive traffic management strategies, including real-time traffic signal optimization, dynamic routing, and incident response. The integration of big data mining with intelligent transportation systems (ITS) can significantly enhance traffic flow, reduce travel time, improve safety, and minimize environmental impact, paving the way for smarter and more sustainable urban mobility solutions.

关键词： Technological innovation Urban areas Machine learning Big data Traffic control Real-time systems data mining Vehicle dynamics Streams intelligent transportation systems

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Climatic Efficiency Analysis of Ten Leading UK Offshore Wind Farms: A data-Driven Approach 8th

Climatic Efficiency Analysis of Ten Leading UK Offshore Win...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Ma, Liyang Li, Chenyi Zhang, Long Department of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom

ISBN: (纸本)9789819602315

As wind energy becomes increasingly important in global energy systems, accurately evaluating the performance of wind farms is essential for understanding wind generation efficiency and fostering sustainable development. The aim of this paper is to assess wind power generation efficiency under different climatic wind conditions. A data-driven approach is used to simulate the power generation of ten leading wind farms in the UK, using real wind data and geographic information while considering wake effects. Furthermore, to evaluate whether these wind farms perform optimally when climate-related wind patterns change, we modify the layouts of the wind farms and compare their performance with the original designs. Extensive results from 15 years (2008 to 2023) of wind data show that the originally designed layouts of these ten leading wind farms will no longer be optimal when the wind resource changes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Offshore wind farms

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Research of Simulation on Real-Time Transmission and Processing of High Orbit Remote Sensing Satellite data Based on Virtualization Technology

Research of Simulation on Real-Time Transmission and Process...

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ieee international conference on Big data Security on Cloud (BigdataSecurity), High Performance and Smart computing (HPSC) and intelligent data and Security (IDS)

作者： Yuming Zhou Feng Xiong Yuanyuan Liu Xiaobing Yin Beijing Institute of Remote Sensing Information Beijing China

ISBN: (数字)9798331595104

ISBN: (纸本)9798331595111

Efficient collaborative operation of high orbit remote sensing satellite data transmission and processing equipment is an effective guarantee for the efficient production, quality and cost of remote sensing data image products. In order to improve the operational efficiency of remote sensing data transmission and processing system equipment, optimize equipment configuration schemes, save equipment cost and accelerate data processing efficiency, this paper builds a network transmission simulation model based on virtualization technology in response to the high real-time and large data volume characteristics of high orbit satellite observation. Based on Arena software, simulation research was conducted on the operational efficiency of system processing equipment. Combined with the specific engineering example of Gaofen-4 satellite, model parameter setting and simulation are carried out, and the operational efficiency under different equipment configuration schemes are analyzed. The study provides a reference for decision-making of the actual operation of high orbit remote sensing satellite data transmission and processing.

关键词： Satellites systems operation data processing Orbits Real-time systems data models Software data communication Virtualization Remote sensing

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Research on Simulation Model of Urban Environment Design and Renewal Using Virtual Reality Technology

Research on Simulation Model of Urban Environment Design and...

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Electronics, Energy systems and Power Engineering (EESPE), ieee international conference on

作者： Wei Ma Changchun University of Architecture and Civil Engineering Changchun China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This paper proposes a research method of urban environment design and renewal simulation model based on virtual reality technology, aiming to improve the efficiency and accuracy of urban planning and renewal process. This method takes point cloud data processing as the core technology, combines advanced 3D modeling and virtual reality technology, and constructs a high-precision urban environment simulation system. The system includes key modules such as data acquisition, optimization algorithm design, model construction, simulation verification and interactive operation. Among them, through the improved point cloud data processing algorithm, the rapid reconstruction of complex urban spatial structure is realized, and efficient 3D rendering and large-scale scene simulation are supported. data analysis of multiple actual urban scenes shows that this method has excellent performance in various environments. The simulation system supports accurate visual comparison of different urban design schemes, interactive scene adjustment through real-time dynamic operation, and quantitative evaluation of design results. Experimental data show that the system can control the simulation error within 5% in multiple urban renewal scenarios; the data processing efficiency is improved by about 30% compared with traditional methods, and the 3D reconstruction time is shortened to 70% of the original.

关键词： Point cloud compression Solid modeling Accuracy Three-dimensional displays Heuristic algorithms Urban planning Virtual reality data processing Rendering (computer graphics) data models

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