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7th international conference on Information, Control, and Communication Technologies, ICCT 2023

作者： Blinova, Olga Kuprikov, Oleg Farkhadov, Mais Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Lab. 17 Moscow Russia

ISBN: (纸本)9798350340945

The article discusses the main metrics for evaluating the performance of big data warehouses, compares the characteristics of the most popular modern frameworks. When considering distributed storage of large amounts of information, it is impossible to use the same indicators of speed and efficiency that are used in local databases. Qualitative and quantitative evaluation is complicated by the inability to reproduce the same queries in the same conditions when using distributed storage. This is caused by the constantly changing state of the transmitting environment and computing resources. When choosing a framework, technology stack, file system, distributed database architecture for a specific task, it is necessary to take into account a large number of factors, as well as the totality and frequency of operations and treatments that will be required in this system. The features and advantages of using containers and various file systems for storing data in distributed systems are presented, as well as other technologies that allow increasing the speed and efficiency of data processing in large arrays of heterogeneous data. At the moment, there is no single solution that would allow the best way to organize the storage of large volumes of heterogeneous data, but there are trends in the development of technologies for their processing. Many of them are related to the ability to apply to big data the same methods that are used in relational databases, but taking into account specific features.∗∗This study was conducted within the framework of the scientific program of the National Center for Physics and Mathematics, section Artificial intelligence and big data in technical, industrial, natural and social systems © 2023 ieee.

关键词： Data warehouses

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A Granular computing Approach for Multi-Labelled Sequences Classification in ieee 802.11 Networks

A Granular Computing Approach for Multi-Labelled Sequences C...

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ieee international conference on Fuzzy systems (FUZZ-ieee) / ieee World Congress on Computational Intelligence (ieee WCCI) / international Joint conference on Neural Networks (IJCNN) / ieee Congress on Evolutionary Computation (ieee CEC)

作者： Granato, Giuseppe Martino, Alessio Rizzi, Antonello Univ Roma La Sapienza Dept Informat Engn Elect & Telecommun Via Eudossiana 18 I-00184 Rome Italy LUISS Univ Dept Business & Management Viale Romania 32 I-00197 Rome Italy

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Recent developments in communication networks have made new kind of services possible, which are even more connected due to smart Internet of Things devices and sensor networks. This complex integration of technologies had increased the attack surface on networks that are spread across multiple environments, with different protocols and services, usually based on the wireless medium. Network Intrusion Detection systems aim to overcome these security issues, analyzing network traffic and applying multiple reconnaissance techniques. For this purpose, state-of-the-art research activities have been focused on Artificial Intelligence and Machine Learning-based approaches in order to scale more efficiently the capabilities of this kind of systems (in terms of both complexity, speed and training data required). Our work further analyzes these security issues, in particular, by introducing a consolidated approach based on the Granular computing information processing paradigm applied to sequences of WiFi frames. We focused on a subset of attacks collected in the Aegean WiFi Intrusion Detection dataset, that consists in complex multi-step attacks or simpler control frames flooding attacks, not recognizable using a single-frame processing strategy. This kind of structured data heterogeneity implies a non-exclusive labelling strategy, as each frames' sequence could bring information related to different attack classes, making the whole supervised problem more challenging. We show that the proposed solution provides interesting results in terms of classification performances, limited embedding complexity and peculiar white-box trained models.

关键词： Machine Learning Granular computing Knowledge Discovery Explainable Artificial Intelligence Intrusion Detection Communication Networks WiFi

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A Literature Review on Data Transmission Reduction Techniques for Wireless Multimedia sensor Networks 14

A Literature Review on Data Transmission Reduction Technique...

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14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Abbood, Iman K. Idrees, Ali Kadhum University of Babylon Dept. of Software Babylon Iraq University of Babylon Dept. of Information Networks Babylon Iraq

ISBN: (纸本)9798350335095

The potential of IoT (Internet of Things) and WSN (Wireless sensor Networks) technologies can be used to build a picture of a future intelligent surveillance system. Because of the small size of the sensor nodes and their ability to transmit data remotely, they can be deployed at locations that are difficult or impossible to access. The Wireless Multimedia sensor Network (WMSN) has become a special field of WSN. This has led to many uses in the smart cities of the future, such as monitoring health care, home automation, transportation, and vehicle networks. A WMSN is a resource-constrained network in which the nodes are small battery-powered devices. In addition, sending a large amount of collected data by WMSNs across the IoT network imposes important challenges in terms of bandwidth, storage, processing, energy consumption, and WMSN lifespan. One of the solutions to these kinds of problems is data transmission reduction. In this paper, we present a detailed roadmap for scholars exploring video transmission in WMSN, with a specific emphasis on data reduction techniques for intelligent surveillance systems in IoT networks. The primary objective is to examine the current challenges and propose an effective solution based on the authors' research findings. Additionally, the paper provides an extensive survey of video transmission techniques in WSNs, offering valuable insights for further investigations in this field. © 2023 ieee.

关键词： Internet of things

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Design and Analysis of Proposed Smartphone-based distributed Parallel Processing System 9

Design and Analysis of Proposed Smartphone-based Distributed...

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9th international Engineering conference on Sustainable Technology and Development, IEC 2023

作者： Muhammed, Nashma Taha Rashid, Zryan Najat Zeebaree, Subhi R. M. Jghef, Yousif Sufyan Ibrahim, Rowaida Khalil Sami, Teba Mohammed Ghazi Sulaimani Polytechnic University It Department Sulaymani Iraq Sulaimani Polytechnic University Computer Network Department Sulaymani Iraq Duhok Polytechnic University Energy Eng. Department Duhok Iraq Knowledge University Computer Eng. Department Erbil Iraq University of Zakho Computer Science Department Duhok Iraq

ISBN: (纸本)9798350335064

Online storage and use of digital assets and applications are both aspects of cloud computing. Through a computer network, distributed information systems store and transmit data. Data and effort have both grown for studying and keeping track of outcomes. Cloud-based parallel processing techniques must be improved. Our research hastens the resolution of interactive composite jobs. In this innovative architecture, cell phones act as servers for distributed parallel processing and cloud computing. Users with restricted availability could find the update useful for completing challenging jobs. The recommended strategy is supported by cloud computing, which may expand to infinity servers (webserver). Servers between clients and servers are automatically registered by web servers. Cloud storage and client-side matrices (web server). To ensure timely completion, the server distributes matrix algebra assignments to all subscribed workstations. Before transmitting results to the user, the server uploads them to the cloud. A large number of sets of two-dimensional matrices were requested from the user, and the responses were compared to the total number of servers that were available. © 2023 ieee.

关键词： Smartphones

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A Knowledge Graph-Based Deep Learning Framework for Efficient Content Similarity Search of Sustainable Development Goals Data

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Data Intelligence 2023年第3期5卷 663-684页

作者： Irene Kilanioti George A.Papadopoulos School of Electrical and Computer Engineering National Technical University of Athens9 Heroon Polytechneiou St.Zografou Campus Athens15780Greece Department of Computer Science University of Cyprus1 University AvenueAglantziaCY-2109NicosiaCyprus

Sustainable development denotes the enhancement ofliving standards in the present without compromising future generations'*** Development Goals(SDGs)quantify the accomplishment of sustainable development and pave the way for a world worth living in for future *** can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of SDG data,as intended by this *** propose a framework of algorithms based on dimensionality reduction methods with the use of Hilbert Space Filling Curves(HSFCs)in order to semantically cluster new uncategorised SDG data and novel indicators,and efficiently place them in the environment of a distributed knowledge graph ***,a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment,for retrieval of indicators and loadbalancing along with an approach for data classification of entrant-indicators is ***,a thorough case study in a distributed knowledge graph environment experimentally evaluates our *** results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data,including intergovernmental organizations,government agencies and social welfare *** approach empowers SDG knowledge graphs for causal analysis,inference,and manifold interpretations of the societal implications of SDG-related actions,as data are accessed in reduced retrieval *** facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching,as semantic cohesion of data is preserved.

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Wireless sensor Network Access Control Technology with Joint Optimization of Delay and Power Consumption 3

Wireless Sensor Network Access Control Technology with Joint...

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3rd international conference on Intelligent computing, Communication and Convergence, ICI3C 2023

作者： Lv, Jun Zhou, Hongyu Zhang, Hejun Fan, Pengzhan Dai, Feng Chen, Xiao State Grid Corporation of China Beijing100031 China State Grid Jiangsu Electric Power Co. Ltd Nanjing210018 China

ISBN: (纸本)9798350343922

Nowadays is the era of interconnected everything, and the entire network system is becoming increasingly large. At this time, the carrying capacity of the network will gradually become a constraint. Therefore, research on access control for large-scale wireless sensor networks (WSNs) is urgent. With the rapid increase in the number of network service terminal sites, the continuous improvement of network service latency requirements and security performance, and the significant increase in network energy consumption, the new generation of Internet of Things (IOT) systems are facing a series of new technological challenges. In WSN, latency and power performance are often a pair of indicators that cannot be achieved simultaneously. How to optimize and balance these two indicators is a challenge in routing and MAC algorithms. We choose sensor nodes with different specifications and performance, and build a practical WSN test platform. On this platform, we measure the delay and power consumption data of the algorithm in the actual transmission process. The experimental results show that compared with the simulation research, the proposed algorithm also shows lower delay and power consumption on real hardware, which verifies the practicability of the algorithm. © 2023 ieee.

关键词： Delay tolerant networks

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DRACo-SLAM: distributed Robust Acoustic Communication-efficient SLAM for Imaging Sonar Equipped Underwater Robot Teams

DRACo-SLAM: Distributed Robust Acoustic Communication-effici...

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ieee/RSJ international conference on Intelligent Robots and systems (IROS)

作者： McConnell, John Huang, Yewei Szenher, Paul Collado-Gonzalez, Ivana Englot, Brendan Stevens Inst Technol Hoboken NJ 07030 USA

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

An essential task for a multi-robot system is generating a common understanding of the environment and relative poses between robots. Cooperative tasks can be executed only when a vehicle has knowledge of its own state and the states of the team members. However, this has primarily been achieved with direct rendezvous between underwater robots, via inter-robot ranging. We propose a novel distributed multi-robot simultaneous localization and mapping (SLAM) framework for underwater robots using imaging sonar-based perception. By passing only scene descriptors between robots, we do not need to pass raw sensor data unless there is a likelihood of inter-robot loop closure. We utilize pairwise consistent measurement set maximization (PCM), making our system robust to erroneous loop closures. The functionality of our system is demonstrated using two real-world datasets, one with three robots and another with two robots. We show that our system effectively estimates the trajectories of the multi-robot system and keeps the bandwidth requirements of inter-robot communication low. To our knowledge, this paper describes the first instance of multi-robot SLAM using real imaging sonar data (which we implement offline, using simulated communication). Code link: https://***/jake3991/DRACo-SLAM.

关键词： Point cloud compression Autonomous underwater vehicles Simultaneous localization and mapping Imaging Sonar Real-time systems Trajectory

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Multi-Agent systems for Autonomous IoT Network Management Using distributed Reinforcement Learning 3

Multi-Agent Systems for Autonomous IoT Network Management Us...

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3rd international conference on Intelligent systems, Advanced computing, and Communication, ISACC 2025

作者： Neelamegam, G. Venkatesan, Rajaram Ramya, S.R. Ramya, R.S. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak VSB College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Builders Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Tirupur India Dr NGP Institute of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. The framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. The proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. The system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. The proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems. © 2025 ieee.

关键词： Resource allocation

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Routing Protocols in Wireless sensor Networks: A Comprehensive Review and Future Perspectives 7

Routing Protocols in Wireless Sensor Networks: A Comprehensi...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Kaur, Puneet Kaur, Kiranbir Verma, Deepak Kumar Singh, Kuldeep Kaushik, Keshav Singh, Vineeta Guru Nanak Dev University Department of Computer Engineering & Technology Punjab Amritsar India Department of Computer Science and Engineering Uttar Pradesh Kalyanpur India Guru Nanak Dev University Department of Electronics Technology Punjab Amritsar India Amity University Amity School of Engineering and Technology Punjab Mohali India

ISBN: (纸本)9798350350067

Wireless sensor Networks (WSNs) have significantly transformed diverse domains such as industrial automation, healthcare, disaster management, and environmental monitoring. The complex interaction of routing protocols, essential for enabling smooth data transport despite the specific difficulties in WSN environments, is the core of their success. The objective of this study is to get a accurate understanding of routing algorithms specifically projected for wireless sensor networks. Expanding upon the essential features and significant disputes faced in WSN systems, admitting limited energy resources, bandwidth constraints, and node processing capabilities, the discussion accentuates the significance of effective routing protocols. Furthermore, it thoroughly analyzes many forms of routing protocols, including basic hierarchical and flat schemes, as well as advanced location-based and data-centric protocols. The protocol category is thoroughly examined, uncovering its unique benefits, limitations, and suitability for various WSN applications. Furthermore, the review reveals the latest developments and upcoming trends in WSN routing protocols, brooding areas such as energy efficiency, dependability, scalability, and adaptive resilience to changing network considerations. In conclusion, the evaluation furnishes a forward-looking view by outlining potential research areas and possible pathways for meliorating the designs of WSN routing protocols. The main goal is to heighten the efficacy and practicality of WSNs. In this work, we examined a number of routing algorithms according to important criteria including mobility, scalability, energy efficiency, and Quality of Service (QoS). We looked at how each method handles node mobility, performs as the network expands, guarantees dependable data transmission, and optimizes energy use. We also looked at ways for choosing the cluster head (CH), energy-saving data aggregation strategies, redundancy-reduction mechanisms, and the

关键词： Routing algorithms

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2023 6th international conference on Software Engineering and Computer Science, CSECS 2023

2023 6th International Conference on Software Engineering an...

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6th international conference on Software Engineering and Computer Science, CSECS 2023

ISBN: (纸本)9798350306378

The proceedings contain 71 papers. The topics discussed include: multi-class failure prediction for distributed systems based on KPI data and feature selection;accelerating high-precision vulnerability detection in c programs with parallel graph summarization;PointStack: a point cloud processing network with enhanced global feature aggregation;improving visual speech recognition for small-scale datasets via speaker embedding;GNN and encoder integrated model for distributed solar and wind power forecasting;automatic detection method for software requirements text with language processing model;MAFF: a novel MobileNetV3 attention feature fusion network for automatic vehicle classification;and interval-valued Z-number aggregation operators and its application in airbus type selection problem.

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