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检索条件"任意字段=6th IEEE International Conference on Distributed Computing in Sensor Systems"

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A Novel distributed Tree-Based One-Hot Encoding (DTOHE) Technique for Intrusion Detection 6th

A Novel Distributed Tree-Based One-Hot Encoding (DTOHE) Tech...

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6th international conference on Recent Innovations in computing, ICRIC 2023

作者： Abirami, K. Samraj, Jasmine Chennai India

ISBN: (纸本)9789819728381

In the realm of cloud security, intrusion detection systems (IDSs) play a pivotal role as they are instrumental in identifying vulnerable activities occurring across networks and computer systems. Developing a novel IDS for Internet of things (IoT)-enabled networks is a complex undertaking beset by numerous challenges, primarily stemming from difficulties in data access. Moreover, the specific constraints imposed by IoT devices further complicate the task of safeguarding the network against external threats and analyzing these threats effectively. In this current study, a range of existing methodologies has been explored to enhance the efficiency of intrusion detection systems, despite facing certain limitations and resource constraints. the principal objective of this research paper is to investigate the efficacy of existing intrusion detection systems integrated with genetic algorithm decision-making and coding techniques. By incorporating these techniques, the aim is to bolster the performance of intrusion detection systems and fortify them against external intrusion attempts. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network security

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Weak Network Oriented Mobile distributed Storage: A Hybrid Fault-Tolerance Scheme Based on Potential Replicas 24

Weak Network Oriented Mobile Distributed Storage: A Hybrid F...

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24th ieee international conference on High Performance computing and Communications, 8th ieee international conference on Data Science and systems, 20th ieee international conference on Smart City and 8th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Wei, Xinlei Tan, Yujuan Liu, Duo Wu, Daitao Wu, Yu Chen, Xianzhang Li, Jian College of Computer Science Chongqing University Chongqing China School of Big Data and Software Engineering Chongqing University Chongqing China School of Electrical Engineering Chongqing University Chongqing China

ISBN: (纸本)9798350319934

Node failure is one of the most typical issues in distributed storage systems. the classic fault-tolerance methods can meet the fault tolerance needs of systems deployed in edge storage, 5G IoT, and other high-performance data centers. However, mobile distributed systems contend with inconsistent network signals and relatively low bandwidth in weak network environments. the traditional fault-tolerance schemes have difficulty ensuring distributed storage and data repair simultaneously, posing a considerable challenge to data storage reliability. To address this problem, we proposed a hybrid fault-tolerance scheme based on potential replicas, called HFPR, which trades off storage overhead and performance for weak network mobile distributed storage systems. HFPR first stores data with the erasure code redundancy and then gradually increases data redundancy by reserving potential replicas, enabling the system to disperse network transmission pressure and reduce network transmission. To maximize replica utilization and control the storage overhead of potential replicas, we design a node state prediction mechanism and a file lifecycle management mechanism. Evaluation results show that, compared with the traditional hybrid fault-tolerant scheme with the same storage overhead, HFPR achieves better replica-level repair efficiency in data repair and a reduced probability of degraded reads by approximately 50%. During data distribution, HFPR can minimize data write amplification by up to 1.1× and additional network transfers by up to 77.5%. © 2022 ieee.

关键词： Fault tolerance

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distributed data reduction with decision-making for IoT-based smart farming irrigation systems 14

Distributed data reduction with decision-making for IoT-base...

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

作者： Hasan, Zahraa Yaseen Kadhum Idrees, Ali University of Babylon Department of Software Babylon Iraq University of Babylon Department of Information Networks Babylon Iraq

ISBN: (纸本)9798350335095

the more crucial process and one of the world's major consumers of water in agriculture is irrigation, which has been growing as a result of population growth and the resulting rise in demand for food. the irrigation system is one of the most important components of a system of agriculture. the crop's productivity will be directly influenced by the efficiency and applicability of the irrigation system. Water scarcity is one of the greatest challenges for an irrigation system. the efficient use of land, water, and energy resources depends on Internet of things (IoT)-based smart agriculture systems. In this article, a distributed data reduction and decision-making (DiDaReD) approach for IoT-based smart farming irrigation systems is proposed. the DiDaReD approach is implemented on two levels: sensor devices and edge gateway. At the sensor device level, we implemented a lightweight scoring method for removing the redundant collected soil moisture readings before sending them to the edge gateway. At the edge of the gateway, a voting technique is applied to the scores of received readings from sensor devices to produce a decision regarding irrigation of the monitored farming field according to the soil moisture state of this field. Finally, since the DiDaReD approach is periodic and works in real-time, we implement a decision reduction algorithm to prevent sending the same decision notifications to the actuator, thus saving the energy of the IoT network. Several experiments are implemented using real sensed data from the farming field, and the conducted results show that the proposed DiDaReD outperforms other methods in terms of data reduction ratio, number of sent readings, energy consumption, network lifetime, data integrity, decisions, and total energy consumed by the edge-gateway node. © 2023 ieee.

关键词： Internet of things

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An Ultra-Wide Band Multi-Layered Hatch-Mounted UHF Partial Discharge sensor for High-Voltage Power Transformers 6

An Ultra-Wide Band Multi-Layered Hatch-Mounted UHF Partial D...

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ieee 6th international conference on Condition Assessment Techniques in Electrical systems (ieee CATCON)

作者： Dhara, Sourav Koley, Chiranjib Chakravorti, Sivaji Natl Inst Technol Durgapur Dept Elect Engn Durgapur India Jadavpur Univ Dept Elect Engn Kolkata India

ISBN: (纸本)9781665473804

Monitoring of partial discharges (PD) activities within high-voltage power transformers are necessary for assessing insulation health conditions and early prediction of insulation failures. Hatch-mounted UHF PD sensors are used for non-contact monitoring of PD activities within power transformers on a continuous basis. the design of the UHF PD sensors is intended to capture electromagnetic waves emitted from the PD sources in the UHF frequency band (300-3000 MHz). Conventional hatch-mounted UHF PD sensors are mechanically robust but lag in sensitivity for their limited bandwidth. this paper presents a multi-layered UHF PD sensor design compatible with mounting over the inspection hatch windows of the high-voltage power transformers. the proposed design offers an ultra-wide bandwidth of 2572 MHz (95.26% of the UHF band) and an operating frequency range of 303 MHz to 3000 MHz.

关键词： partial discharge sensor Archimedean Spiral condition monitoring ultra-wide band sensor ultra-high frequency measurement power transformer

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BioDSSL: A Domain Specific sensor Language for Global, distributed, Biometric Identification systems 12

BioDSSL: A Domain Specific Sensor Language for Global, Distr...

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12th ieee international conference on Intelligent systems, IS 2024

作者： Hofer, Philipp Roland, Michael Mayrhofer, René Johannes Kepler University Linz Institute of Networks and Security Linz Austria

ISBN: (纸本)9798350350982

With biometric identification systems becoming increasingly ubiquitous, their complexity is escalating due to the integration of diverse sensors and modalities, aimed at minimizing error rates. the current paradigm for these systems involves hard-coded aggregation instructions, presenting challenges in system maintenance, scalability, and adaptability. these challenges become particularly prominent when deploying new sensors or adjusting security levels to respond to evolving threat models. To address these concerns, this research introduces BioDSSL, a Domain Specific sensor Language to simplify the integration and dynamic adjustment of security levels in biometric identification systems. Designed to address the increasing complexity due to diverse sensors and modalities, BioDSSL promotes system maintainability and resilience while ensuring a balance between usability and security for specific scenarios. Furthermore, it facilitates decentralization of biometric identification systems, by improving interoperability and abstraction. Decentralization inherently disperses the concentration of sensitive biometric data across various nodes, which could indirectly enhance privacy protection and limit the potential damage from localized security breaches. therefore, BioDSSL is not just a technical improvement, but a step towards decentralized, resilient, and more secure biometric identification systems. this approach holds the promise of indirectly improving privacy while enhancing the reliability and adaptability of these systems amidst evolving threat landscapes and technological advancements. © 2024 ieee.

关键词： Decentralized systems

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Hybrid Parameter Update: Alleviating Imbalance Impacts for distributed Deep Learning 24

Hybrid Parameter Update: Alleviating Imbalance Impacts for D...

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作者： Li, Hongliang Xu, Dong Xu, Zhewen Li, Xiang College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Changchun China

ISBN: (纸本)9798350319934

Nowadays, data parallelism has been widely applied to train large datasets on distributed deep learning clusters, but it has suffered from costly global parameter updates at batch barriers. Performance imbalance among worker instances, introduced by uneven workload partitioning or biased resource allocation, can cause straggly workers, which can lead to severe impacts on both training speed and result accuracy. this paper studies the issue focusing on the tradeoff between training speed and result accuracy. We propose Cooperate Grouping Parallel (CGP), a hybrid parameter update solution that allows the flexibility of both synchronous and asynchronous update schemes. We introduce a novel Cooperate Worker Grouping Problem (CWGP) that seeks a task grouping configuration that leads to maximum model accuracy and holds customized training speed guarantees. We propose an evolution-based Pareto local searching algorithm to compute efficient worker grouping configurations. Comprehensive evaluation results are presented to demonstrate the effectiveness of CGP under both persistent and fluctuant imbalances. the proposed method alleviates the imbalance impacts without introducing extra adjustment over-heads. © 2022 ieee.

关键词： Large dataset

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ZombieCoin3.0: On the Looming of a Novel Botnet Fortified by distributed Ledger Technology and Internet of things 23

ZombieCoin3.0: On the Looming of a Novel Botnet Fortified by...

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23rd ieee international conference on High Performance computing and Communications, 7th ieee international conference on Data Science and systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Gao, Haoyu Li, Leixiao Lin, Hao Chang, Xiangyang Wan, Jianxiong Li, Jie Zhu, Fangyuan College of Data Science and Application Inner Mongolia University of Technology China Inner Mongolia Autonomous Reg. Eng. and Technol. Research Center of Big Data Based Software Service China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665494571

P2P Botnet is famous for the resilience against termination. However, its dependence on Neighbor List (NL) makes it susceptible to infiltration and poison, also leading to a dearth of adequate protection of Botmaster's identity. So as to overcome these weaknesses, the novel botnet infrastructure combined with blockchain emerges. But due to some flaws such like high latency and expensive transactions fees on blockchain, these new schemes can't satisfy the need of constructing hyper large-scale botnet in practice. We propose ZombieCoin3.0, a Botnet underlying overlay leveraging IoT devices to be maintainers and integrated with distributed Ledger Technology (DLT), which is able to deploy bots fully on blockchain. And it is versatile for file transfer, data feedback and other command and control scenarios that many contemporaries are impotent to cope with. Our proposal eliminates the P2P botnets' dependence on NL and the bottlenecks that other blockchain combined botnets have. Experiments' results authenticated that the effectiveness of our proposal as well as low latency of communication and nearly zero overhead of cost other comparatives unable to attain are achieved. © 2021 ieee.

关键词： Costs distributed ledger Botnet Blockchains Proposals Internet of things Low latency communication

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Embracing Emerging Technologies: Insights from the 6th Workshop for Young Scientists in Computer Science & Software Engineering 6

Embracing Emerging Technologies: Insights from the 6th Works...

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6th Workshop for Young Scientists in Computer Science and Software Engineering, CS and SE@SW 2023

作者： Semerikov, Serhiy O. Striuk, Andrii M. Kryvyi Rih State Pedagogical University 54 Universytetskyi Ave. Kryvyi Rih50086 Ukraine Institute for Digitalisation of Education The NAES of Ukraine 9 M. Berlynskoho Str. Kyiv04060 Ukraine Zhytomyr Polytechnic State University 103 Chudnivsyka Str. Zhytomyr10005 Ukraine Kryvyi Rih National University 11 Vitalii Matusevych Str. Kryvyi Rih50027 Ukraine Academy of Cognitive and Natural Sciences 54 Gagarin Ave. Kryvyi Rih50086 Ukraine

the 6th Workshop for Young Scientists in Computer Science & Software Engineering showcases cutting-edge research from emerging talents. this volume comprises diverse papers illuminating emerging technologies' profound impact across various domains. Several contributions underscore the pivotal role of telemetry, graph theory, and machine learning in optimising distributed systems, detecting anomalies, and streamlining processes. Others delve into acoustic surveillance techniques for UAV detection, genetic algorithms for university scheduling, and neural network-driven optimisation of chemical synthesis. the proceedings also highlight novel approaches to assessing software architecture reliability, implementing ERP systems, and designing information systems for viral infection data analysis. thermal resistance calculation software, multimodal distribution data processing methods, and high-performance computing energy consumption modelling are also explored. Moreover, the importance of user experience research in cross-platform application development is emphasised, alongside the design of virtual physics laboratories and Python learning game applications. Notably, predatory conferences are addressed, proposing robust conference management platforms to uphold research integrity. Collectively, these papers exemplify young scientists' innovative spirit and determination to tackle real-world challenges and push the boundaries of their disciplines. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Software reliability

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Predictive Analytics for Hospital Readmissions Using Logistic Regression and IoT sensor Data 6

Predictive Analytics for Hospital Readmissions Using Logisti...

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6th international conference on Energy, Power and Environment, ICEPE 2024

作者： Kiruthiga, B. Riazulhameed, Arshadh Ariff Mohamed Abuthahir Rajapriya, M. Sarasu, R. Mohankumar, N. Kasthuri, A. Srm Valliammai Engineering College Department of Management Studies Tamil Nadu Chennai India Blackstraw Technologies Pvt Ltd Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Management Studies Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Spatial Informatics Tamil Nadu Chennai India Maharashtra Nagpur India Vinayaka Mission's Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India

ISBN: (纸本)9798350379297

Hospital readmissions affect healthcare systems globally, increasing costs and morbidity. Readmissions must be predicted and prevented to improve patient outcomes and save healthcare costs. this research leverages the Internet of things (IoT) sensor data and Logistic Regression (LR) to reduce hospital readmissions using predictive analytics. Patient health data from IoT devices during and after hospitalization is used in the paper. Heart rate, blood pressure, and oxygen saturation are constantly monitored. LR is used to examine this dataset and create a prediction model. the LR algorithm trains us to recognize IoT sensor data patterns and correlations that indicate readmission risks. Our methodology provides developed risk estimates incorporating patient-specific parameters, including age, multiple illnesses, and medical history. the temporal data permits dynamic predictions, considering patient health changes over time. this research detected hospital readmission risk with promising accuracy. these predictions allow healthcare practitioners to proactively intervene and deliver targeted treatments, including prescription modifications, lifestyle advice, and follow-up consultations to decrease readmission. this proactive strategy may improve patient outcomes and save healthcare expenses. © 2024 ieee.

关键词： Logistic regression

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OMN-IBUS Type of Networking System for the Communication System in 6G 4

OMN-IBUS Type of Networking System for the Communication Sys...

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4th ieee international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Devi, A. Sangeetha Bhatt, Deepanshu Bansal, Saloni Keerthika, A. Mohanraj, T. Santhosh, S. Tamil Nadu T.M.Palayam India Ies Institute of Technology and Management Ies University Department of Mechanical Engineering M.P Bhopal India Gla University Department of Computer Engineering and Applications Mathura India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering TN India TN India Einstein College of Engineering Department of Electronic and Communication Engineering Tamil nadu India

ISBN: (纸本)9798350360165

the scientific community and industry have been working together in recent years to shift computational resources off centralized cloud systems to decentralized computing, or 'edge' computing. this change is a result of the cloud system's inability to keep up with the growing demands of emerging relationships, which are marked by the spread of time-sensitive applications like sophisticated robotics, augmented reality/virtual the truth technologies, self-driving cars, and vital remote control of Internet of things applications. Decentralized computing at the border is still in its infancy and lacks a full platform, despite the critical role that it will play in future distributed systems. this article presents OMNIBUS, a novel decentralized edge architecture. this approach uses moving vehicles as resources for processing and storage, enabling the continuous delivery of computational capability to end-devices across many locations. Scalability and flexibility are two characteristics that set OMNIBUS distinct from other edge computing solutions. the suggested method, which is based on mathematical models of prediction, determines computational capacity for particular locations by learning the direction and time of vehicle movements. It also presents a theoretical foundation for learning, improvement, and caching amid dynamic conditions caused by moving vehicles, specifically for sequential concurrent conversion. OMNIBUS's scalability allows for almost infinite growth, which might result in a significant increase in network speed. © 2024 ieee.

关键词： Scalability

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