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Paving the Way for an Urban Intelligence OpenStack-Based Architecture

Paving the Way for an Urban Intelligence OpenStack-Based Arc...

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international conference on Smart computing (SMARTCOMP)

作者： Giuseppe Tricomi Luca D'Agati Francesco Longo Giovanni Merlino Antonio Puliafito Stefano Silvestri CINI: National Interuniversity Consortium for Informatics Rome Italy Department of Engineering University of Messina Italy Institute for High Performance Computing and Networking of ICAR National Research Council (CNR) Naples Italy

ISBN: (数字)9798350349948

ISBN: (纸本)9798350349955

This paper describes a novel architecture aiming to create an open-source template for implementing a platform enabling support for the deployment and integration of services and workflows that compose, manage, and continuously evolve a complex system: the Urban Intelligence. More specifically, the proposed Smart City IT architecture is an evolution of a concrete urban intelligence architecture meant mostly to support Data scientist activities and, obviously, the exploitation of Smart City services by citizens and users. The proposed solution, due to its open-source nature, is fully replicable and improves its ancestor by provisioning new characteristics: i) managing facilities of urban Cyber-Physical System via exploitation of a combination of Administrator/Data scientist workflows and IoT management platform, ii) the definition of workflow directly on the Edge (even through the exploitation of FaaS paradigm directly on IoTs), and iii) enhancement of Smart City infrastructure security via the reduction of external attack surface. The prototype of the proposed IT solution has been implemented leveraging open-source frameworks and technologies belonging to the OpenStack ecosystem.

关键词： Smart cities distributed ledger Scalability Data security Ecosystems Prototypes Computer architecture

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Next Generation Vehicle Electrical Distribution System (EDS) Architecture

Next Generation Vehicle Electrical Distribution System (EDS)...

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international conference on computing and networking Technology (ICCNT)

作者： Arun Yadav Sachin Kumar Garima Srivastava Lucknow campus Amity University

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Traditional EDS architectures, primarily designed for internal combustion engine vehicles, are being reimagined to accommodate the distinctive characteristics of EVs, including high-voltage power distribution, energy storage systems, and complex power management requirements. The next generation of EDS for EVs and Non EVs prioritizes modularity, scalability, and efficiency to optimize the integration of various electrical components while ensuring robust performance and safety. The proposed work aims to create a statistical method to evaluate electrical distribution in automotive vehicles with the help of digital optimization (Process based) techniques. Key advancements in EDS architecture include the adoption of distributed power distribution strategies, such as multi-level power distribution units (PDUs) and intelligent load management systems, to enhance system flexibility and fault tolerance. For the work proposed three tests were simulated, fast blow fuse test where wiper motor had actual value of 0.097 Sec and the simulation results achieved was 0.030 Sec which is an acceptable value and is much less than the threshold of 0.050 sec, for Slow blow fuse the battery had actual value of 0.17 Sec and the simulation results achieved was 0.146 Sec which is an borderline value but is still acceptable as it is approximately equal to the threshold of 0.1 sec and for voltage drop test washer motor had load end voltage of 13.29 V and the simulation results achieved was 13.83 V which is an acceptable and in line with expected results. Work done provides an optimum platform for conduct-ing simulated tests for the vehicles and hence will help reduce physical proto development and electrical validation at the initial project level.

关键词： Fault tolerance Fuses Simulation Fault tolerant systems Computer architecture Motors Load management Threshold voltage Batteries Next generation networking

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A Deep Reinforcement Learning-based Contention Window Avoidance Scheme for Distinguishing Network Service Priorities

A Deep Reinforcement Learning-based Contention Window Avoida...

引用

international conference on computing, networking and Communications (ICNC)

作者： Xiaowei Nie Demin Wang Yuqing Zhang Xiaoduo Pan Zhibin Zuo National Tibetan Plateau Data Center (TPDC) State Key Laboratory of Tibetan Plateau Earth System Environment and Resources (TPESER) Institute of Tibetan Plateau Research Chinese Academy of Sciences Beijing China Science Center of Lingshan Forum of Guangdong Province Guangzhou China School of Ecology and Environment Tibet University Lhasa China College of Information Science and Engineering Henan University of Technology Zhengzhou China Henan International Joint Laboratory of Grain Information Processing Zhengzhou China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350370997

ISBN: (纸本)9798350371000

This study introduces a Deep Reinforcement Learning-based Contention Window Avoidance Scheme aimed at improving service priority differentiation and network throughput under high load conditions within wireless networks that utilize the Network distributed Coordination Function (DCF) mechanism. This novel approach involves two key strategies: firstly, it enables the differentiation of service priorities within DCF-based channel access by categorizing them in accordance with the varying data types and streams transmitted by different stations; secondly, it leverages Deep Reinforcement Learning to dynamically evaluate the network state through analysis of collision probabilities derived from channel observations. By doing so, the scheme dynamically modulates the ranges of contention windows in alignment with the service priorities, responding to the immediate network circumstances. Empirical evidence indicates that the proposed scheme substantially mitigates collision frequencies and augments data transmission throughput, thus significantly advancing the overall performance of networks that require service priority differentiation.

关键词： Frequency modulation Wireless networks Throughput Deep reinforcement learning Data communication Streams Optimization

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Lossless Compression of Medical Volume Images using distributed Source Coding with Prior Knowledge

Lossless Compression of Medical Volume Images using Distribu...

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international conference on computing and networking Technology (ICCNT)

作者： A. Amudha Amritpal Sidhu S. Rathika Harsimrat Kandhari B K Sunitha Sachin R. Sakhare Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome chitkara university Rajpura Punjab India Department of Business Administration Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement-UG JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Dispensed source coding with previous knowledge (DSPK) is a method used in the lossless compression of scientific volume photos. DSPK strategies make use of predictive earlier know-how to improve the compression of clinical extent pics and reduce the bit rate of lossless compression. DSPK works by way of combining techniques of records compression, specifically distributed supply coding and asymptotic Wyner-Ziv coding. With DSPK, the supply coding block is divided into two parts: a predictive stage and a coding level. Within the predictive degree, earlier expertise is used to reduce the amount of statistics to be compressed, even as in the coding degree, a Wyner-Ziv code is used to compress the ultimate information. The predictive degree makes use of a studying algorithm to construct a dictionary for each block, primarily based on facts. It allows the set of rules to leverage redundancy within the facts better and acquires higher compression ratios. Moreover, the bigger the source block size, the higher the advantage of the earlier understanding inside the predictive stage. Subsequently, the coded block is output as a binary movement, taking into account quick and green transmission and storage. Standard, DSPK can drastically lessen the bit fee for lossless compression of scientific quantity pictures, resulting in stepped-forward storage and transmission quotes.

关键词： Knowledge engineering Image coding Shape Source coding Scalability Redundancy Propagation losses Prediction algorithms Standards Biomedical imaging

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Hierarchical Caching for Digital Twins Communications Over mURLLC-Based 6G distributed-computing Mobile Networks Using FBC

Hierarchical Caching for Digital Twins Communications Over m...

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international conference on distributed computing Systems Workshop

作者： Xi Zhang Qixuan Zhu Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA

ISBN: (数字)9798350354713

ISBN: (纸本)9798350354720

The massive ultra-reliable and low-latency communications (mURLLC) services are emerging as a new traffic type to support massive numbers of mobile users (MUs) demanding the stringent delay and error-rate bounded quality-of-services (QoS) requirements over 6G. Among multiple 6G mURLLC services, digital twins (DT) has been widely envisioned as a major intelligent application to support efficient interactions between physical and virtual objects. Moreover, multi-tier caching, which is one of the key distributed computing techniques, stores the frequently-demanded data items at different wireless network tiers to efficiently reduce mURLLC streaming delay and data move. However, how to efficiently cache mURLLC-based DT data items at different caching tiers of wireless networks and how to statistically upper-bound both delay and error-rate for DT communications remain challenging problems. To overcome these difficulties, in this paper we propose a multi-tier caching mechanism to support DT communications over 6 G mobile networks. First, we propose the DT data adaptive collection scheme applying finite blocklength coding (FBC) to dynamically encode a physical object into its virtual representation according to the current network and wireless channel statuses. Second, we develop inter-tier and intra-tier collaborative caching mechanisms, where DT data items are selectively cached at different wireless network caching tiers according to their popularities including: router tier, massive-multiple-input-multiple-output (MIMO) basestation tier, and mobile device tier. Third, our proposed intertier collaborative caching mechanisms maximize the aggregate $\epsilon$ effective capacity across all three caching tiers, and our proposed intra-tier collaborative caching mechanisms minimize the sum of data transmission delay for all DT data items cached in each caching tier. Finally, we numerically validate and evaluate our developed multi-tier hierarchical caching schemes over 6G DTe

关键词： 6G mobile communication Wireless networks Aggregates Collaboration distributed databases Quality of service Encoding

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Message from the conference Chair

Proceedings - 2022 23rd ACIS International Summer Virtual Co...

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Proceedings - 2022 23rd ACIS international Summer Virtual conference on Software Engineering, Artificial Intelligence, networking and Parallel/distributed computing, SNPD-Summer 2022 2022年 XI页

作者： Hirata, Hiroaki Kyoto Institute of Technology Japan

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Cyber-Phishing Analysis offering Cyber Security for Social Networks

Cyber-Phishing Analysis offering Cyber Security for Social N...

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Blockchain and distributed Systems Security (ICBDS), IEEE international conference on

作者： Mohd Khaja Moizuddin M. Kabeer Mohammed Misbahuddin Department of CSE B.S. Abdur Rahman Crescent Institute of Science & Technology Chennai India Department of IT B.S. Abdur Rahman Crescent Institute of Science & Technology Chennai India Scientist’F’ & Head ACTS and BD Centre for Development of Advanced Computing Bangalore India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

Social phishers cleverly devise stringent methods to secretly obtain login_names and passwords by forming seemingly friendly connections through microblogs and various social media platforms. They adapt these techniques to gain financial benefits. The 2024 APWG latest report shows that Cyber-Phishing attacks are increasing using different methods like fake websites, fake email links, and social media content. Despite efforts to defend against these attacks, there are still problems, especially with compromised microblogs in social network environments (SNEs). This puts confidential information at risk. It's crucial to stop Cyber-Phishing attacks on SNEs, and this paper looks at different ways to detect and prevent Social Cyber-Phishing specifically for these environments.

关键词： Social networking (online) Blogs Passwords Blockchains Security Computer crime

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Federated Learning for distributed Reasoning on Edge computing

Federated Learning for Distributed Reasoning on Edge Computi...

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12th international conference on Ambient Systems, Networks and Technologies (ANT) / 4th international conference on Emerging Data and Industry 4.0 (EDI40)

作者： Firouzi, Ramin Rahmani, Rahim Kanter, Theo Stockholm Univ Dept Comp & Syst Sci Stockholm Sweden

The development of the Internet of Things over the last decade has led to large amounts of data being generated at the network edge. This highlights the importance of local data processing and reasoning. Machine learning is most commonly used to automate tasks and perform complex data processing and reasoning. Collecting such data in a centralized location has become increasingly problematic in recent years due to network bandwidth and data privacy concerns. The easy-to-change behavior of edge infrastructure enabled by software-defined networking (SDN) allows IoT data to be gathered on edge servers and gateways, where federated learning (FL) can be performed: creating a centralized model without uploading data to the cloud. In this paper, we analyze the use of edge computing and federated learning, a decentralized machine learning methodology that increases the amount and variety of data used to train deep learning models. To the best of our knowledge, this paper reports the first use of federated learning to help the Microgrid Energy Management System (EMS) predict load and obtain promising results. Simulations were performed using TensorFlow Federated with data from a modified version of the Dataport site. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： distributed Reasoning SDN Federated Learning Edge computing Internet of Things LSTM Smart Grid

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Access optimization for small files in HDFS: Challenges and Solutions

Access optimization for small files in HDFS: Challenges and ...

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Image Processing, Computer Vision and Machine Learning (ICICML), international conference on

作者： Xiaolong Li Congfeng Jiang Junming Liu Weijing Xu Xiaoyu Wang Shijie Chen School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

The Hadoop distributed File System (HDFS) is an open-source distributed file system optimized for data appending and reading, offering portability, high fault tolerance, and scalability. After more than a decade of development, HDFS has become widely used for big data storage, supporting both structured and unstructured data at a massive scale. It also enables a variety of applications, such as complex query analysis, interactive analytics, detailed billing queries, Key-Value read/write operations, and iterative computations. However, with the rapid growth of internet applications and scientific computing, the number of small files—such as images and text—has surged. Since HDFS was originally designed for large file storage, it encounters issues when handling large volumes of small files, including inefficient metadata structures, master server bottlenecks, and network latency. As a result, efficiently storing and accessing large numbers of small files has become a significant challenge for HDFS. This paper introduces the principles and features of HDFS, and summarizes existing solutions for managing massive small files in HDFS, including Hadoop’s built-in approaches and user-customized solutions. It also reviews related research conducted in recent years. Looking forward, with ongoing technological advancements, indexing, networking, and caching technologies are expected to become key strategies for optimizing HDFS for small file storage.

关键词： File systems Scientific computing Reviews Scalability distributed databases Big Data Throughput Servers Optimization Periodic structures

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A Moving Target Defense Approach for the distributed Dynamic Network

A Moving Target Defense Approach for the Distributed Dynamic...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Lin Zhang Yunchuan Guo Siyuan Leng Zifu Li Fenghua Li Liang Fang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

The distributed dynamic network is vulnerable to scanning attacks due to the openness of wireless channels. Traditional defense systems tend to be passive and exhibit delayed responses. A moving target defense approach, namely distributed Network Address Shuffling (DNAS), is proposed to thwart attackers’ network scanning through the shuffling of network addresses. To resolve address conflicts resulting from this shuffling, DNAS employs a dynamic diffusion method of allocated addresses before the shuffling process to reduce the probability of conflict generation, and utilizes a passive detection based conflict elimination algorithm after the shuffling process to eliminate any generated conflicts. To select low-risk addresses, DNAS leverages an artificial feature selection based Fully Connected Neural Network (FCNN) to recognize the attacker’s scanning policy, and identifies low-risk addresses based on the scanning range of the policy. Empirical experiments and theoretical analysis indicate that DNAS significantly reduces the probability of address conflict generation at a minimal cost. It effectively eliminates all generated address conflicts within an average conflict resolution time of less than 500ms. Furthermore, DNAS exhibits an accuracy of 99.45% in recognizing scanning policies, surpassing pseudorandom address hopping in diminishing the success rate of sequential, local random, and mixed scanning.

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