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Comparison of Control Strategies for Energy Management in Photovoltaic or Battery Hybrid distributed Power Generation System

Comparison of Control Strategies for Energy Management in Ph...

引用

2021 international conference on Smart Generation computing, Communication and networking, SMART GENCON 2021

作者： Karlekar, Akanksha S. Hasabe, Ramchandra P. Walchand College of Engineering Electrical Engineering Department Sangli India Sangli India

ISBN: (纸本)9781665425032

Power generation from a renewable source is an affair of heightened interest leading to research. Natural resource-battery hybrid power generation mainly rely on renewable power source and the battery system chained. Along with the production of this power, sustaining the reliability of the system is also crucial part which can be achieved by an investigated as well as analyzed management and control strategy. This paper comprises of comparison between two such management and control strategies i.e. System 1-conventional power management and control scheme with System 2-three-port assembled power converters. In system 1 two separate DC/DC converters are used whereas in system 2 the three ports (battery system, PV array and DC load) are interfaced with the help of IGBT power switches. Both the algorithms are examined in MATLAB/Simulink software and the outputs are compared, implying that both systems achieve management and control but system 2 proves to be more reliable, less complex and feasible for higher power density working. © 2021 IEEE.

关键词： DC-DC converters

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Design and Implementation of a distributed Network Management System for a Multi-Branch University using GNS3

Design and Implementation of a Distributed Network Managemen...

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international conference on computing and networking Technology (ICCNT)

作者： Md Apu Hosen Shahadat Hoshen Moz Md. Ali Akber Dept. of Computer Science and Engineering Northern University of Business and Technology Khulna Khulna Bangladesh

The rapid digitalization of communication necessitates continuous skill updates to keep pace with evolving technology. Network management involves multiple functionalities that play a crucial role in optimizing network performance and ensuring continuous availability. It is very important to ensure a proper network management system for a university that has multiple branches to effectively manage and monitor their network infrastructure. A proper network management system provides centralized control and visibility over the entire network infrastructure, allowing administrators to efficiently manage and monitor network devices, configurations, and performance from a single interface. this research has developed a network management system that facilitates real-time network monitoring and troubleshooting, enabling proactive identification and resolution of issues to ensure smooth operations across all branches. It simplifies configuration management, ensuring consistency and compliance in network settings and updates. Resource optimization is achieved through insights into network utilization and performance metrics, allowing administrators to allocate resources efficiently and plan for future expansion. Security and compliance measures can be enforced, protecting sensitive data and ensuring adherence to regulatory requirements. This study utilized GNS3 to create a network layout for a university with multiple branches, along with essential security configurations for the network design. A diverse range of protocols was implemented to safeguard and cater to the users of the secure university network. Furthermore, Switches, Data Routers, firewalls, and BGP Routers were incorporated into the network to enhance communication efficiency and streamline operations.

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Collaborative Task Offloading Based on Deep Reinforcement Learning in Heterogeneous Edge Networks

Collaborative Task Offloading Based on Deep Reinforcement Le...

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international Wireless Communications and Mobile computing conference, IWCMC

作者： Yupeng Du Zhenglei Huang Shujie Yang Han Xiao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Research Institute of China Mobile Beijing China

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

The field of multimedia video has shown a vigorous development trend in recent years. The massive heterogeneous data computing tasks generated by new multimedia applications cause user experience to face two major challenges: latency and energy consumption. The emergence of multimedia edge networks allows users’ computing tasks to be offloaded to edge servers for execution, which will greatly reduce communication delays. But on the contrary, multimedia edge networks also face problems such as limited communication and computing resources, unknown global system information and difficulty in multi-agent collaboration. In this paper, we first describe a multimedia edge network architecture. In this architecture, multiple edge servers use communication base stations to provide multimedia computing services to a large number of users. Then a multi-agent collaborative decisionmaking model is proposed, which ensures that agents with different strategies can ultimately achieve consistent decisionmaking. Finally, we provide a distributed computing task scheduling algorithm based on deep reinforcement learning. A large number of simulation experiments have proven the superiority of this method in minimizing delay, energy consumption and convergence.

关键词： Energy consumption Scheduling algorithms Collaboration Multimedia computing Streaming media Servers Multimedia communication

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Dynamic Staleness Control for Asynchronous Federated Learning in Decentralized Topology 18th

Dynamic Staleness Control for Asynchronous Federated Learni...

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18th international conference on Wireless Artificial Intelligent computing Systems and Applications, WASA 2024

作者： Ma, Qianpiao Liu, Jianchun Jia, Qingmin Zhou, Xiaomao Hu, Yujiao Xie, Renchao Purple Mountain Laboratories Jiangsu Nanjing China School of Computer Science and Technology University of Science and Technology of China Anhui Hefei China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9783031714665

Decentralized federated learning (DFL) has emerged as a promising paradigm for distributed machine learning over edge nodes (i.e., workers) without relying on a centralized parameter server. Most existing DFL researches rely on synchronous communication among workers. However, due to edge heterogeneity and dynamic network conditions, synchronous DFL mechanisms may suffer from inefficient model training and poor scalability. Meanwhile, the existing asynchronous DFL (ADFL) mechanisms present the challenge of stale models among workers, leading to diminished training quality, especially on Non-IID data. In this paper, we propose a novel staleness-aware ADFL (SA-ADFL) mechanism, aiming to realize a trade-off between model training efficiency and quality by dynamic staleness control. Specifically, we provide rigorous theoretical proof for SA-ADFL and formulate the worker scheduling problem to minimize total model training time under flexible long-term staleness constraints. Then we decompose the original round-coupled problem into a series of single-round sub-problems by leveraging the Lyapunov optimization, enabling efficient worker selection to minimize training time in each round while ensuring staleness queue stability. Experimental results demonstrate that our SA-ADFL accelerates model training by approximately 52.9% while maintaining equivalent model training accuracy compared with the state-of-the-art mechanisms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Encryption Techniques for Hadoop distributed File System (HDFS)

Encryption Techniques for Hadoop Distributed File System (HD...

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Advances in computing, Communication Control and networking (ICAC3N), international conference on

作者： Anupam Mishra Nitika Kapoor Department of Computer Science and Technology Chandigarh University Gharuan Mohali Punjab India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

The Hadoop distributed File System (HDFS) was created with the express purpose of making the management of large data sets a breeze. The absence of something in data encryption methods in HDFS, however, presents serious security concerns. While the introduction of encryption methods has improved data security, traditional algorithms struggle when processing larger files. This study aims to provide a novel hybrid encryption technique for protecting massive datasets by fusing Cipher-Text Policy Attribute-Based Encryption (CP-ABE) and Advanced Encryption Standard (AES). Java-based open-source project that includes the MapReduce framework for data processing and the HDFS. It modifies many Portable Operating System Interface (POSIX) standards to provide streaming access to file system information. Initiated as a platform for the open-source web crawler Apache Nutch, HDFS has already expanded to include other similar programs. Authors have compared the method to the one that was present in the system, they have implemented it to protect HDFS, and performed experimental research to prove its performance. Further, the authors have tested the suggested HDFS technique by encrypting files of various sizes and compared it to other algorithms, such as AES and AES with One-Time-Pad (OTP). The HDFS method improves performance significantly when it relates to file encryption.

关键词： File systems Operating systems Clustering algorithms distributed databases Process control Computer architecture Passwords

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Performance Analyses for Applying Machine Learning on Bitcoin Miners

Performance Analyses for Applying Machine Learning on Bitcoi...

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20th international conference on Electronics, Information, and Communication (ICEIC)

作者： Fan, Wenjun Kim, Jinoh Kim, Ikkyun Zhou, Xiaobo Chang, Sang-Yoon Univ Colorado Coll Engn & Appl Sci Dept Comp Sci Colorado Springs CO 80918 USA Texas A&M Univ Dept Comp Sci Commerce TX 75428 USA ETRI Cybersecur Res Div Daejeon 34129 South Korea

ISBN: (纸本)9781728191614

Bitcoin and cryptocurrency rely on peer-to-peer (P2P) networking. Incorporating intelligence on Bitcoin miners to analyze and control networking can improve the information delivery and defend against networking threats. However, applying machine learning (ML) for building intelligence introduces a challenge because miners participate in the resource-intensive distributed consensus protocol and the ML application can consume much computing resources. In this paper, we study the feasibility and the interplay between ML algorithms and mining operations. Our prototype-based experiments measure and compare the performance of the ML algorithms to evaluate the implementation overhead and efficiencies of the ML algorithms and their impacts on mining operations, i.e., the mining reduction when the ML algorithm is running in parallel.

关键词： Machine Learning Blockchain Bitcoin mining Anomaly Detection P2P networking

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Investigating the Applicability of In-Network computing to Industrial Scenarios 4

Investigating the Applicability of In-Network Computing to I...

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4th IEEE international conference on Industrial Cyber-Physical Systems (ICPS)

作者： Kunze, Ike Glebke, Rene Scheiper, Jan Bodenbenner, Matthias Schmitt, Robert H. Wehrle, Klaus Rhein Westfal TH Aachen Commun & Distributed Syst Aachen Germany Rhein Westfal TH Aachen Lab Machine Tools & Prod Engn WZL Aachen Germany

ISBN: (纸本)9781728162072

Moving computation functionality into the network and onto networking devices has recently shown great promise for performance improvements, e.g., by reducing path lengths and processing latencies, or by increasing bandwidth efficiency. Yet, In-Network computing (INC) is challenged by the limitations of networking hardware which is generally not designed for complex calculations. Thus, while possible, developing INC approaches is a constant struggle between desired and available functionality. In this paper, we demonstrate the applicability of INC to industrial contexts by offloading a seemingly simple task from an industrial assembly scenario - coordinate transformations - to programmable switches and SmartNICs. We find that even such a task puts heavy demands on the devices, but that the right dose of approximation and diligent problem reformulation can enable the necessary operations on all platforms, thus setting the stage for improving latencies by significantly reducing communication paths. Yet, our results further indicate that these gains in latency come in hand with a trade-off regarding the achievable accuracy.

关键词： in-network computing latency approximation

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distributed Edge computing to Assist LPWAN: Fog-MEC Model 5

Distributed Edge Computing to Assist LPWAN: Fog-MEC Model

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5th international conference on Future Networks and distributed Systems: The Premier conference on Smart Next Generation networking Technologies, ICFNDS 2021

作者： Mahmoud, Mona Ashraf Ateya, Abdelhamied Muthanna, Ammar Zaghloul, Adel Kirichek, Ruslan Koucheryavy, Andrey Electronics and Communications Engineering Zagazig University Zagazig Egypt Telecommunication Networks and Data Transmission St. Petersburg State University of Telecommunication St. Petersburg193232 Russia Software Engineering and Computer Science Department St. Petersburg State University of Telecommunication St. Petersburg193232 Russia

ISBN: (纸本)9781450387347

Low power wide area network (LPWAN) is one of the main Internet of Things (IoT) networks that is widely used for the outdoor-IoT applications. A main feature with such networks is the long-range coverage that enables the dense deployment over such networks. However, the introduction of massive number of IoT devices puts many constraints and limitations on the design and development of such networks. A promising solution for a part of such challenges is the introduction of distributed computing technology to enable this massive number of deployed devices. This work considers such deployment of heterogeneous forms of distributed computing units to assist the design and development of LPWAN networks for dense deployed applications. Two main forms of the distributed computing are considered in this work;multiple access edge computing (MEC), and fog computing. The integration of fog and MEC units is introduced in a way that achieves higher latency, energy, and availability efficiency. A proof-of-concept of the developed model is introduced for dense deployment scenarios. The developed fog-MEC model is evaluated for LPWAN for heterogeneous simulation scenarios, and the simulation results validate the developed model. © 2021 ACM.

关键词： Internet of things

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A Novel Evolutionary Neural Network-based Approach for Online Bus Scheduling

A Novel Evolutionary Neural Network-based Approach for Onlin...

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IEEE international conference on networking, Sensing and Control

作者： Wenyang Tian Feiyang Wang Xingquan Zuo School of Computer Science Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

Bus scheduling is vital to ensure service quality and save operational costs. Current studies typically consider the problem as an optimization problem and solve it using exact or heuristic approaches. In this paper, we consider the problem as a sequential decision-making problem and propose an Evolutionary Neural Network-based Approach (ENNA) for it. A neural network is used to make a decision at each departure time in the bus timetable. It selects which bus should depart at that specific time. By this means, a complete bus scheduling scheme is generated by a series of decisions made by the neural network. The structure and weights of the neural network are optimized by a genetic algorithm. To meet the real-world operational needs of bus duty types and improve bus utilization, we design a bus duty type transition rule that dynamically decides the duty type of buses. Experimental results demonstrate that the proposed ENNA outperforms manual scheduling schemes and can generate bus scheduling schemes with fewer buses.

关键词： Network topology Decision making Artificial neural networks Transforms Dynamic scheduling Real-time systems Topology Sensors Optimization Genetic algorithms

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Predictive Meteorology with PySpark: Analyzing and Forecasting Weather Data

Predictive Meteorology with PySpark: Analyzing and Forecasti...

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Advances in computing, Communication Control and networking (ICAC3N), international conference on

作者： Thota Bhuvana Chandra Kona Mourya Sai Chandra Gurram Kumar Sai K Sai Niketh Irigineni Manohar K.S Nisha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

The spread of huge information across numerous realms, including social networks and financial markets, has led to the emergence of big data analytics as a means to gain useful understandings. This study explores using big data analytics in climate forecasting using instruments like Hadoop and Apache Spark. The challenges of predicting complex meteorological situations are talked about, emphasizing the likely benefits of advanced analytics. The paper stresses the importance of Hadoop’s distributed data system(HDFS)and the MapReduce algorithm for productive data handling. Two separate implementations, MapReduce and Spark, are used to analyze data, showing their strengths in managing enormous datasets. Additionally, sequence analysis is exhibited using an Autoregressive Integrated Moving Averages (ARIMA) model to predict future temperatures. The experimental results reveal valuable discoveries, like ordinary temperature variations, extreme temperature stations, and the application of sequence analysis for temperature prediction. The paper ends by stressing the relevance of big data analytics, Hadoop, and Spark in improving weather prediction. This has wide implications for sectors reliant on precise meteorological insights.

关键词： Analytical models Sequences distributed databases Weather forecasting Big Data Predictive models Temperature control

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