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

共 30762 条记录，以下是591-600 订阅

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distributed Data Storage Architecture Model and Data Platform Design for Power Grid Dispatching 3

Distributed Data Storage Architecture Model and Data Platfor...

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3rd international conference on Energy and Electrical Power systems (ICEEPS)

作者： Xu, Yuan Su, Yinsheng Zhao, Ligang Zhou, Baorong Tang, Yingqi Huang, Guanbiao Hu, Jiaming Li, Kaihang CSG Elect Power Res Inst State Key Lab HVDC Technol Guangzhou Peoples R China CSG Power Dispatching & Control Ctr Guangzhou Peoples R China South China Univ Technol Sch Elect Power Engn Guangzhou Peoples R China

ISBN: (纸本)9798350375145;9798350375138

The construction of a distributed heterogeneous data platform for power grid dispatching faces challenges of diversity, large scale, and high performance. However, existing data platform design methods in both the power and computer science fields struggle to meet practical production requirements effectively. This paper constructs a distributed data storage architecture model for power grid dispatching, defining the elements and their relationships within the architecture. Additionally, it proposes methods for managing massive source data and distributed heterogeneous database clusters. Based on these findings, a power grid dispatching business data platform is designed. Test results indicate that the proposed architecture effectively supports the efficient execution of power grid dispatching business, providing a specialized data platform design paradigm for the power industry.

关键词： data architecture model distributed heterogeneous database cluster power grid dispatching data platform design metadata

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Energy-Centric Clustering and Routing Using Seagull Optimization Algorithm in Wireless sensor Networks 3

Energy-Centric Clustering and Routing Using Seagull Optimiza...

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3rd ieee international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Anitha, R. Al-Attabi, Kassem Sathyavani, B. Janardhana, D.R. Dineshkumar, R. B. S. Abdur Rahman Crescent Institute Of Science And Technology Department Of Electronics & Communication Engineering Chennai Vandalur India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Dept. Of Cse Bengaluru India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Electronics And Communication Engineering Chennai India

ISBN: (纸本)9798350318609

The Wireless sensor Networks (WSN), the imperative for energy-efficient clustering and routing protocols is indisputable. This protocol, designed to meet this critical demand, integrates advanced optimization techniques, aiming to augment network performance and extend system lifespan. Existing methods, while attempting to strike a balance between energy consumption and network lifetime, often encounter challenges, a refined solution. The proposed energy-centric clustering and routing protocol in WSN leverages the Seagull Optimization Algorithm (SOA). This innovative approach optimizes cluster formation and routing dynamics by giving precedence to nodes with higher residual energy, thereby enhancing overall network efficiency. By systematically addressing existing limitations, the SOA-based protocol attains commendable performance metrics, including energy consumption at 0.1435J, End-to-End Delay of 10.4566 sec, Throughput of 0.97498 Mbps, and Network Lifetime of 19,857 rounds. In a comparative analysis with MFA-SSO, HPSO-CSSO, DECR-GOA, and FMCB-ER, the proposed method emerges as superior, effectively overcoming challenges and optimizing operations in WSN. © 2024 ieee.

关键词： Energy utilization

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In-Situ Model Validation for Continuous Processes Using In-Network computing 7

In-Situ Model Validation for Continuous Processes Using In-N...

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ieee 7th international conference on Industrial Cyber-Physical systems (ICPS)

作者： Kunze, Ike Scheurenberg, Dominik Tirpitz, Liam Geisler, Sandra Wehrle, Klaus Rhein Westfal TH Aachen Commun & Distributed Syst Aachen Germany Rhein Westfal TH Aachen Inst Automat Control Aachen Germany Rhein Westfal TH Aachen Data Stream Management & Anal Aachen Germany

ISBN: (纸本)9798350363029;9798350363012

The advancing industrial digitalization enables evolved process control schemes that rely on accurate models learned through data-driven approaches. While they provide high control performance and are robust to smaller deviations, a larger change in process behavior can pose significant challenges, in the worst case even leading to a damaged process plant. Hence, it is important to frequently assess the fit between the model and the actual process behavior. As the number of controlled processes and associated data volumes increase, the need for lightweight and fast reacting assessment solutions also increases. In this paper, we propose CIVIC, an in-network computing-based solution for Continuous In-situ Validation of Industrial Control models. In short, CIVIC monitors relevant process variables and detects different process states through comparison with a priori knowledge about the desired process behavior. This detection can then be leveraged to, e.g., shut down the process or trigger a reconfiguration. We prototype CIVIC on an Intel Tofino-based switch and apply it to a lab-scale water treatment plant. Our results show that we can achieve a high detection accuracy, proving that such monitoring systems are feasible and sensible.

关键词： Plant shutdowns

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Measuring a Soft Resistive Strain sensor Array by Solving the Resistor Network Inverse Problem

Measuring a Soft Resistive Strain Sensor Array by Solving th...

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ieee international conference on Soft Robotics (RoboSoft)

作者： Zhao, Yuchen Khaw, Choo Kean Wang, Yifan Nanyang Technol Univ Sch Mech & Aerosp Engn 50 Nanyang Ave Singapore 639798 Singapore

ISBN: (纸本)9798350332223

Soft robotics is applicable to a variety of domains due to the adaptability offered by the soft and compliant materials. To develop future intelligent soft robots, soft sensors that can capture deformations with nearly infinite degrees of freedom are necessary. Soft sensor networks can address this problem, however, measuring all sensor values throughout the body requires excessive wiring and complex fabrication that may hinder robot performance. We circumvent these challenges by developing a non-invasive measurement technique, which is based on an algorithm that solves the inverse problem of resistor network, and implement this algorithm on a soft resistive, strain sensor network. Our algorithm works by iteratively computing the resistor values based on the applied boundary voltage and current responses, and we analyze the reconstruction error of the algorithm as a function of network size and measurement error. We further develop electronics setup to implement our algorithm on a stretchable resistive strain sensor network made of soft conductive silicone, and show the response of the measured network to different deformation modes. Our work opens a new path to addressing the challenge of measuring many sensor values in soft sensors, and could be applied to soft robotic sensor systems.

关键词： Inverse problems

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A Deep Assessmen of Smart Logistics Kind of Parks with the Fog Based and Enabled sensor Cloud Type Platforms 1

A Deep Assessmen of Smart Logistics Kind of Parks with the F...

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1st international conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart systems, ISTEMS 2024

作者： Yadav, Savita Gupta, Ashish Preethi, D. Noida Institute of Engineering and Technology Department of Internet of Things Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350384246

The purpose of this research is to compare, in the environment of a clever logistics park, the effectiveness of fog-enabled sensor clouds (FSC) with traditional cloud computing with fog computing models. Expanding on our earlier work, we describe the physical instrument virtualization framework and scheme that are essential to the envisioned FSC. There is a careful construction of the network model and a mathematical explanation of the FSC parameters. We use a well-known logistics company in China is a case study to assess the efficacy of the suggested platform and clarify the network architecture in a real-world logistics situation. The experiment's results confirm that, with regard to bandwidth usage and service delay, the FSC for logistics parks is demonstrably superior to standard internet-based and fog computing models. © 2024 ieee.

关键词： Fog

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EMDRIVE Architecture: Embedded distributed computing and Diagnostics from sensor to Edge

EMDRIVE Architecture: Embedded Distributed Computing and Dia...

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27th Design, Automation and Test in Europe conference and Exhibition (DATE)

作者： Schmidt, Patrick Topko, Iuliia Stammler, Matthias Harbaum, Tanja Becker, Juergen Berner, Rico Ahmed, Omar Jagielski, Jakub Seidler, Thomas Abel, Markus Kreutzer, Marius Kirschner, Maximilian Betancourt, Victor Pazmino Sehm, Robin Groth, Lukas Neskovic, Andrija Meyer, Rolf Mulhem, Saleh Berekovic, Mladen Probst, Matthias Brosch, Manuel Sigl, Georg Wild, Thomas Ernst, Matthias Herkersdorf, Andreas Aigner, Florian Hommes, Stefan Lauer, Sebastian Seidler, Maximilian Raste, Thomas Bozic, Gasper Skvarc Ceberio, Ibai Irigoyen Hassan, Muhammad Mayer, Albrecht Karlsruhe Inst Technol KIT Karlsruhe Germany Ambrosys GmbH Potsdam Germany FZI Res Ctr Informat Technol Karlsruhe Germany Univ Lubeck Lubeck Germany Tech Univ Munich Munich Germany Elektrobit Automot GmbH Erlangen Germany ZF Friedrichshafen AG Friedrichshafen Germany Siemens AG Technol Erlangen Germany Continental Automot Technol GmbH Frankfurt Germany Infineon Technol AG Munich Germany

ISBN: (纸本)9798350348606;9783981926385

Future automotive architectures are expected to transition from a network-centric to a domain-centered architecture featuring central compute units. Powerful domain controllers or smart sensors alleviate the load on these central units and communication systems. These controllers execute tasks with varying criticalities on heterogeneous multicore processors, and are ideally capable of dynamically balancing the computing load between the central unit and sensors. Here, Artificial Intelligence (AI) capabilities play a crucial role, as it is in high demand for such an automotive architecture. However, AI still requires specialized accelerators to improve their computation performance. Task-oriented distributed computing with criticalities up to ASIL-D necessitates the development and utilization of specialized methodologies, such as safety, through the isolation and abstraction of low-level hardware concepts. Meanwhile, online monitoring and diagnostics become vital features to detect errors during operation. The EMDRIVE architecture includes methods, components, and strategies to enhance the performance, safety, and security of such distributed computing platforms. The nationally funded EMDRIVE project connects its twelve partners from academia and industry and is currently in its intermediate stage.

关键词： automotive safety monitoring load balancing intrusion detection AI accelerator

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distributed Fusion Estimation for Nonlinear systems under Denial-of-Service Attacks using the Try-Once-Discard Protocol 6

Distributed Fusion Estimation for Nonlinear Systems under De...

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6th international conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Zhu, Guangyuan Chen, Xin College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798331541699

This paper tackles the challenge of distributed state fusion estimation in nonlinear, time-varying systems subjected to external denial-of-service (DoS) attacks. Given the susceptibility of network channels to such attacks, a transmission strategy employing the try-once-discard (TOD) protocol was introduced to reduce their impact. In this approach, each sensor in the network is linked to local estimators through multiple channels, with the TOD protocol determining the transmission priority. Data transmitted via the highest priority channel is retained, while data from other channels are discarded. A local estimator was constructed using the cubature Kalman filter (CKF), and a consensus-based approach was utilized to merge the local estimates, forming a distributed fusion estimation algorithm. The algorithm’s convergence was analyzed, and sufficient conditions for maintaining bounded filter error covariance were established, accounting for network disruptions and random nonlinearity. The proposed algorithm’s performance was demonstrated through numerical simulations. © 2024 ieee.

关键词： Kalman filters

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A Strategy for Implementing Gaussian distributed Chaotic Signals Based on the Lorenz System 9

A Strategy for Implementing Gaussian Distributed Chaotic Sig...

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9th international conference on Intelligent computing and Signal Processing, ICSP 2024

作者： Yang, Zaixue Liu, Bin Chen, Yanming School of Electrical Engineering Guangxi University Guangxi Zhuang Autonomous Region Nanning China

ISBN: (纸本)9798350376548

Gaussian distributed random signal has broad prospects in various applications. Random signal generators make it easy to generate Gaussian distributed random signals, greatly promoting the widespread application of Gaussian distributed random signals. Research has shown that chaotic signals not only possess randomness but also other characteristics that random signals do not have. In some application scenarios, chaotic signals are superior to random signals. However, chaotic signals are typically not in accordance with Gaussian distribution due to the invariant distribution of chaotic systems. Therefore, this paper proposes a strategy for implementing Gaussian-type chaotic signals based on the Lorenz system to address the acquisition problem of Gaussian distributed chaotic signals. Firstly, the Lorenz chaotic system is selected as the intrinsic signal source, and a non-parametric estimation algorithm is used to solve its probability density function and probability distribution function. Then, a transformation algorithm is proposed to transform the intrinsic signal into Gaussian distributed chaotic signals. Finally, the effectiveness of the proposed algorithm is verified by implementing Gaussian distributed chaotic signals with different parameters. © 2024 ieee.

关键词： Probability density function

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Knowledge Extraction from distributed Heterogeneous Data Sources 5

Knowledge Extraction from Distributed Heterogeneous Data Sou...

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5th ieee international conference for Emerging Technology, INCET 2024

作者： Ramachandran, Raji Abhijith, K. Karthik, J.J. Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361155

The growing volume and complexity of unstructured and semi-structured data pose a significant challenge in extracting meaningful and relevant information. Information Extraction (IE) emerges as a powerful technique to transform unstructured data into structured format, facilitating further analysis and knowledge discovery. IE techniques can effectively extract information from unstructured data in the form of entities, relationships, objects, and events. Our objective is to develop a method for extracting or filtering out the most relevant information from data sources which are heterogeneous in nature as well as they are distributed over many systems and hence build a unified schema based on the collective knowledge extracted from the data sources. Several existing methods have been developed to extract knowledge from heterogeneous datasets. We focuses on datasets in the medical field that are distributed over multiple systems. Specifically, we aim to extract valuable information from patient records, including patient details, medical conditions and medications consumed. The unified schema can be utilized to study a new patient's condition and assist doctors in making a pre-examination of a patient by querying it. © 2024 ieee.

关键词： Data mining

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An LWE-Based Multi-Key Privacy-Preserving distributed Deep Learning∗ 23

An LWE-Based Multi-Key Privacy-Preserving Distributed Deep L...

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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： Antwi-Boasiako, Emmanuel Zhou, Shijie Liao, Yongjian Danso, Ebenezer Kwaku School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Ghana Institute of Management and Public Administration Computer Science Department Accra Ghana China University for Development Studies Ghana

ISBN: (纸本)9781665494571

Privacy preservation in distributed deep learning (DDL) has received a lot of attention recently. One key approach to preserving privacy for DDL is the use of Homomorphic Encryption (HE), which allows computation on ciphertexts without decryption. Although current applications of HE provide some level of privacy to DDL, they are not post-quantum resistant and multi-key compliant. The use of same key by all participants could lead to collusion attacks and possible leakage of sensitive information from a participant's local dataset in the distributed deep learning setting. Furthermore, most of the privacy solutions provided for the DDL assume that all learning participants must be present before the start of the protocol. Hence, such solutions do not allow a new participant to benefit from the utility of the joint learning of other participants, for which it was not present. To address the above problems, we propose a new privacy preserving solution for DDL that uses an LWE-based Multi-key approach. Our proposed privacy preserving DDL solution is multi-key compliant, post-quantum resistant and has an additional property of allowing a new participant to benefit from the utility of an already trained collaborative deep learning system. We prove that our privacy-preserving distributed deep learning system is secured. We note that our proposed solution generally provides a robust privacy-preserving solution for the DDL and at the same time has great potential to reduce communication and computation costs associated with similar systems. © 2021 ieee.

关键词： Deep learning Resistance Privacy Protocols Costs Collaboration Cryptography

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