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

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Edge computing and AI for IoT: Opportunities and Challenges

Edge Computing and AI for IoT: Opportunities and Challenges

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2023 international conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Lim, Ean Heng Yuen Chai, Tong Muniandy, Manoranjitham A.-P. Fui Yong, Tien Ooi, Boon Yaik Lin, Jim-Min Quest International University School of Computing Ipoh Malaysia Quest International University School of Engineering Ipoh Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar Malaysia Universiti Tunku Abdul Rahman Department of Information Systems Kampar Malaysia Feng Chia University Dept of Information Engineering and Computer Science Taichung Taiwan

ISBN: (纸本)9798350324174

The proliferation of Internet of Things (IoT) devices has led to an exponential growth in data generated at the edge of the network. Edge computing, a distributed computing paradigm that enables computation and data storage at the network edge, has emerged as a promising solution for managing this data deluge. With the integration of Artificial Intelligence (AI) technologies, edge computing can provide real-time insights and decision-making capabilities, making it a powerful tool for a variety of IoT applications which poses both opportunities and challenges. © 2023 ieee.

关键词： Edge computing

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3D Marketplace: distributed Attestation of 3D Designs on Blockchain 8

3D Marketplace: Distributed Attestation of 3D Designs on Blo...

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8th ieee international conference on Smart computing (SMARTCOMP)

作者： Tapas, Nachiket Belikovetsky, Sofia Longo, Francesco Puliafito, Antonio Shabtai, Asaf Elovici, Yuval Univ Messina Dept Engn Messina Italy Ben Gurion Univ Negev Dept Software & Informat Syst Engn Beer Sheva Israel

ISBN: (纸本)9781665481526

Industry 4.0 encourages the integration of intelligent technology with manufacturing systems. Among them, additive manufacturing (AM) is critical to solving some of the fourth industrial revolution's most pressing needs. With AM gaining popularity, the need for the validation of 3D designs grows. In this paper, we introduce a novel concept of a distributed marketplace that will support the attestation of 3D printing designs. We build a mathematical trust model that ensures truthfulness among rational, selfish, and independent agents, which is based on a reward/penalty system. The payment for participating in the evaluation is calculated by factoring in agents' reputations and peer feedback. Moreover, we describe the architecture and the implementation of the trust model on the blockchain using smart contracts for the creation of a distributed marketplace. Our model relies both on theoretical and practical best practices to create a unique platform that elicits effort and truthfulness from the participants. Finally, we present a performance evaluation and cost analysis of the proposed architecture to evaluate scalability and financial viability.

关键词： Additive Manufacturing Blockchain 3D Printing Truthfulness distributed Market IIoT Industry 4.0

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Virtual sensor for Efficient Soil Fertilizer and Water Content Differentiation 4

Virtual Sensor for Efficient Soil Fertilizer and Water Conte...

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4th international conference on Embedded and distributed systems, EDiS 2024

作者： Diaz, Francisco Javier Ahmad, Ali Parra, Lorena Heydari, Arman Sendra, Sandra Lloret, Jaime Universitat Politècnica de València Valencia Spain

ISBN: (纸本)9798331519872

Soil degradation issues, such as salinization, waterlogging, and erosion, pose significant challenges in agricultural irrigation and fertigation. Conventional laboratory methods for analyzing soil conditions are often cost-prohibitive, time-intensive, and require specialized personnel. This study investigates the application of a virtual sensor, incorporating RGB and LDR modules, to measure fertilizer concentration and water content in soil, as well as to differentiate between water and fertilizer. Statistical analysis of the acquired data using ANOVA revealed significant differences between samples under blue, yellow, and magenta lights, with p-values of 0.0425, 0.018, and 0.0425, respectively. Machine Learning (ML) techniques were employed to enhance the system's performance, yielding an 87.5% accuracy in binary classification using the K-Nearest Neighbors (KNN) and Euclidean models. Additionally, regression models demonstrated R2 values of 0.802 and 0.891 for irrigated and ferti-irrigated soils, respectively. These results suggest that the proposed optical sensor system is a promising, cost-effective alternative to traditional methods, though further validation in real-world applications is necessary. © 2024 ieee.

关键词： Water content

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Demo Abstract: A distributed Power Side-channel Auditing System for Online IoT Intrusion Detection 21

Demo Abstract: A Distributed Power Side-channel Auditing Sys...

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21st ACM/ieee international conference on Information Processing in sensor Networks (IPSN)

作者： Jung, Woosub Feng, Yizhou Khan, Sabbir A. Xin, Chunsheng Zhao, Danella Zhou, Gang William & Mary Williamsburg VA 23185 USA Old Dominion Univ Norfolk VA 23529 USA

ISBN: (纸本)9781665496247

As the number of IoT devices has increased rapidly, IoT botnets have exploited the vulnerabilities of IoT devices. However, it is still challenging to detect the initial intrusion on IoT devices prior to massive attacks. Thus, a new approach that monitors these initial intrusions is needed. Power side-channel information can be used because it does not require any modification in programming languages or operating systems on diverse IoT devices. We propose a distributed power side-channel auditing system for online IoT intrusion detection. To meet the real-time requirement, we develop a lightweight power auditing device. We then design a distributed CNN classifier for online inference in a laboratory setting. Two distributed protocols are also proposed in order to protect data leakage and reduce networking redundancy. In this work, we demonstrate the feasibility of our real-time distributed system for intrusion detection on IoT devices.

关键词： Power Intrusion Detection System distributed Online System Privacy-preservation Internet of Things

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Real-Time Diabetes Detection Using Machine Learning and Apache Spark 4

Real-Time Diabetes Detection Using Machine Learning and Apac...

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4th international conference on Emerging Smart Technologies and Applications (eSmarTA)

作者： Saeed, Mohammed A. Saeed, Mogeeb A. Taiz Univ Dept Al Saeed Comp Networks & Distributed Syst Fac Engn & IT Taizi Yemen

ISBN: (纸本)9798350354140;9798350354133

The convergence of contemporary and cuttingedge technologies, including big data analytics and machine learning, is reshaping the scale and efficiency of healthcare systems. This work presents distinct approaches and contributions, addressing specific challenges in healthcare and disease and diabetes analytics. The real-time healthcare for disease diabetes and the integration of big data analytics, machine learning, and real-time processing have paved the way for innovative solutions to address disease diabetes prediction and monitoring. It's explored innovative solutions to overcome challenges in healthcare analytics, offering real-time predictions and continuous monitoring for improved patient care. The realtime Healthcare-Diabetes dataset was analyzed using various machine learning models, and processing in real-time has led to innovative solutions to address diabetes prediction and monitoring. The Gradient Boosted Tree Classifier emerged as the most accurate model with an accuracy of 90.14%, followed by the Decision Tree Classifier at 84.62%, the Random Forest Classifier at 82.84%, the Linear Support Vector Classifier at 78.70%, and Logistic Regression at 64.69%. These results demonstrate the system's robustness and efficiency in real-time data collection, processing, and prediction. Leveraging Apache Spark and opensource big data technologies, specify data challenges and advocate for scalable, efficient, and cost-effective healthcare analytics. It contributes to the ongoing transformation of healthcare systems, demonstrating the effectiveness of advanced technologies in enhancing disease prediction, monitoring, and overall healthcare services.

关键词： Healthcare analytics real-time prediction Apache Spark machine learning distributed computing personalized healthcare

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distributed Traffic Congestion Classification in Intelligent Transportation systems based on SDN and Fog computing 7

Distributed Traffic Congestion Classification in Intelligent...

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7th international conference on Internet of Things and Applications, IoT 2023

作者： Nourani, Naserali Seno, Seyed Amin Hosseini Etehadi, Seno Sina Samadzad Hosseinpour, Mahsa Yaghmaee, Mohammad Hossein Ferdowsi University of Mashhad Department of Computer Engineering Mashhad Iran

ISBN: (纸本)9798350369410

In recent years, in addition to the significant growth in intelligent transportation systems with new concepts such as SDN (Software Defined Networks), fog computing, and new machine learning methods, the advancement of communication technologies and automotive industries has been considerable. Vehicular Adhoc NETworks (VANETs) can effectively detect traffic congestion, which can be classified into Repetitive Congestion (RC) and Non-Repetitive Congestion (NRC). In particular, NRC in an urban area is mainly due to accidents, congested areas with high loads, special events, and weather conditions. This paper presents a new architecture using two concepts, SDN and fog computing, called SDN and Fog computing-based VANET (SFVN), to classify the causes of congestion in VANET. This architecture is proposed for distributed and real-time congestion classification, considering its components in an urban road network in VANET. The proposed scheme has been evaluated with CCSANN, FogJam, C4.5 classification tree, and the Naïve Bayes method, which indicates the effectiveness of the proposed method. By knowing the leading causes of congestion, this scheme can help transport companies develop effective policies to reduce urban congestion. © 2023 ieee.

关键词： Traffic congestion

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Improvement of Task Scheduling with Energy Efficiency including Fault Tolerant with Resilient computing System in Parallel and distributed Communication Network 2

Improvement of Task Scheduling with Energy Efficiency includ...

引用

2nd international conference on Augmented Intelligence and Sustainable systems, ICAISS 2023

作者： Kumar, Vijay Madhuri, P. Yamini, R. Sravani, Palla Muthukumar, K. Graphic Era Hill University Department of Physics Dehradun India Hyderabad501401 India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India Raghu Engineering College Department of Computer Science and Engineering Andhra Pradesh Visakhapatnam531162 India Sri Krishna College of Engineering and Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798350325799

In parallel and distributed communication networks, task scheduling is essential for attaining the best system performance. Innovative ways that may intelligently distribute computing resources while minimizing energy consumption and maintaining system resilience are required due to the rising need for energy efficiency and fault tolerance. This system, offers a unique approach to job scheduling in parallel and distributed communication networks that integrate genetic algorithms (GA) and machine learning (ML).Using ML approaches, proposed method analyses historical task execution data to discover patterns that may be applied to task execution time prediction in various computing systems. As a result, this can assign tasks to resources effectively, maximize resource usage, and cut down on execution time as a whole. In addition, using predefined objectives like energy efficiency and fault tolerance, GA is used to find the best task-resource assignments. This applies resilient computing approaches to the job scheduling procedure to overcome problems with fault tolerance. This prevents the execution of crucial operations from being hampered by system faults or resource shortages by utilizing fault detection and recovery technologies. This system can adapt dynamically to shifting network conditions through the combination of ML, GA, and resilient computing, which raises the entire system's reliability and availability. © 2023 ieee.

关键词： Machine learning

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Improving the Performance of distributed Model Predictive Control by Applying Graph Partitioning Methods 26

Improving the Performance of Distributed Model Predictive Co...

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26th international conference on System Theory, Control and computing (ICSTCC)

作者： Burk, Daniel Voelz, Andreas Graichen, Knut FAU Erlangen Nurnberg Chair Automat Control Erlangen Germany

ISBN: (数字)9781665467469

ISBN: (纸本)9781665467469

The major part of the execution time of distributed algorithms is required for the communication between agents. This paper approaches a reduction of the communication effort by reducing the number of edges in the considered graph. This is achieved by partitioning the graph and formulating a super graph. At first, the computational and communication effort is evaluated on an abstract level independent of the distributed algorithm, before the Alternating Direction Method of Multipliers (ADMM) is applied to a system of coupled water tanks. This allows to outline the trade-off between computation and communication time and to evaluate an optimal number of partitions that minimizes the execution time. The influence of the partitioning on the convergence behavior of the distributed algorithm is studied and compared with the concept of neighbor approximation.

关键词： distributed systems predictive control nonlinear systems

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Flex sensor Enabled Glove based Manual Wheelchair for Elderly and Speech-Impaired Assistance 3

Flex Sensor Enabled Glove based Manual Wheelchair for Elderl...

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

作者： Tripathi, Abhishek Gopi Reddy, Kasu V S Amarnath, K. Bavesh, C. Vardhan, Bestha Singh, Sumitra Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar India Central Electronics Engineering Research Institute Semiconductor Devices Design Group Pilani India

ISBN: (纸本)9798331523893

This paper presents a cost-effective manual wheelchair system designed to enhance mobility and communication for deaf-mute, disabled, and elderly individuals. The system employs a gesture-based mechanism using a glove with embedded flex sensors to detect finger movements, translating them into actionable commands. These commands are processed by a microcontroller to provide real-time feedback through three channels: visual messages displayed on an LCD, wireless communication with a caretaker's device, and audio feedback via a speaker. Testing demonstrates reliable gesture detection and response generation, highlighting the system's effectiveness in meeting the diverse needs of users. The design emphasizes affordability, ease of use, and adaptability, offering a practical and inclusive solution for individuals with varying levels of mobility and communication challenges. © 2025 ieee.

关键词： Signaling

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2023 ieee international conference on Omni-Layer Intelligent systems, COINS 2023

2023 IEEE International Conference on Omni-Layer Intelligent...

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2023 ieee international conference on Omni-Layer Intelligent systems, COINS 2023

ISBN: (纸本)9798350346473

The proceedings contain 89 papers. The topics discussed include: performance comparison of post-quantum signature algorithms through an android email application plug-in;sensors at the edge: towards micromechanical in-memory computing;optimal fractional order PID control of sensorless BLDC motor;heuristics-enabled high-performance application mapping in network-on-chip based multicore systems;quantity analysis of solar modules based on aerial images;addressing non-intervention challenges via resilient robotics utilizing a digital twin;predicting asset returns with self-attentive networks;weather visibility sensor network: platform characterization;artificial intelligence approaches for fast and portable traceability assessment of EVOO;computer vision based auto-id for optimizing logistics operations;and chatbot integration for metaverse - a university platform prototype.

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