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Edge-Cloud Infrastructure for Mushroom Growth Monitoring and Analysis

Edge-Cloud Infrastructure for Mushroom Growth Monitoring and...

引用

international conference on Computational Cybernetics (ICCC)

作者： Sándor Tarsoly Artúr I. Károly Tamás Csibrák Péter Galambos Antal Bejczy Center for Intelligent Robotics University Research and Innovation Center Obuda University Budapest Hungary

ISBN: (数字)9798331502461

ISBN: (纸本)9798331502478

This paper presents the development and implementation of an edge-cloud infrastructure for monitoring and analyzing white and brown button mushroom growth. The system includes edge devices with sensors and cameras for onsite data collection and a cloud platform for data storage and processing. The collected data is processed using a Mask R-CNN model trained on synthetic data to identify and analyze individual mushrooms. Over two years of monitoring, it has demonstrated reliability in capturing and processing data in challenging agricultural environments. These results highlight the potential of integrating advanced machine learning with edge-cloud systems to enhance precision agriculture and resource management.

关键词： Precision agriculture Memory Machine learning Sensor systems data models Sensors Resource management Reliability Monitoring Synthetic data

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Design and Simulation of an intelligent Traffic Management System Using Vehicular Networks

Design and Simulation of an Intelligent Traffic Management S...

引用

intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： P. Tapan Datta D.Lalith Kumar S. Tanoj Shinu M Rajagopal Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

In this study , the reasearch is focused on the simulation of vehicular communication networks, especially intelligent traffic systems, to enhance dependability, safety, and optimality in communication. The simulation is performed using the OMNeT++ simulator and is integrated with the Veins framework that uses the ieee 802.11p protocol for V2V and V2I communications. Importing real-world traffic scenarios into the framework is obtained via the use of SUMO through TraCIScenarioManager. Other features of the framework include the definition of mobility models, dynamic obstacles, and detailed radio propagation modeling. TraCIDemo11p is used for mobility and communication by both RSUs and vehicles with applications in beaconing and channel switching. The whole framework analyzes smart traffic management solutions, providing as much insight into their performance based upon the communication reliability and overall efficiency in vehicular networks.

关键词： Protocols Vehicle-to-infrastructure Simulation Vehicular ad hoc networks Switches Radio propagation Safety Security Reliability Vehicle dynamics

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Dynamic Quality Management in Wind Power Construction: Integrating Digital Twins with BIM and IoT

Dynamic Quality Management in Wind Power Construction: Integ...

引用

Electronics, Energy systems and Power Engineering (EESPE), ieee international conference on

作者： Xiaodan Yuan Silu Wang Xu Zhao Jinzhou Electric Power Supply Company State Grid Liaoning Electric Power Supply Co. Ltd Jinzhou China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

The increasing complexity of wind power construction projects necessitates advanced quality management systems to address real-time challenges effectively. This paper introduces a digital twin-based framework integrating Building Information Modeling (BIM) and Internet of Things (IoT) technologies to enhance quality management. IoT sensors provide continuous, real-time data acquisition from personnel, materials, equipment, inspection, methods, and the environment, while BIM offers a spatial platform for visualization and analysis. The system's architecture includes automated data collection, parametric information integration, and predictive analytics, enabling comprehensive and proactive quality control. Preliminary results from pilot implementations highlight significant improvements in material efficiency, response times to quality issues, and stakeholder collaboration, demonstrating the system's potential for scalable and adaptive applications in wind power construction.

关键词： Scalability Collaboration Wind power generation Real-time systems Digital twins Internet of Things Time factors Stakeholders Predictive analytics Quality management

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A Big data Analytic and IoT-Based Approach for Addressing Water Scarcity in Jordan

A Big Data Analytic and IoT-Based Approach for Addressing Wa...

引用

Computational Intelligence Approaches and Applications (ICCIAA), international conference on

作者： Abdelraouf Ishtaiwi Nuha El-Khalili May Alnashashibi Mohammad Abu Arqoub Dept. of Data Science and Artificial Intelligence University of Petra Amman Jordan Department of Software Engineering University of Petra Amman Jordan Department of Computer Science University of Petra Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Water scarcity critically threatens agricultural sus-tainability in Jordan, necessitating innovative technological solutions. This study presents a comprehensive framework that integrates Internet of Things (IoT) sensors with Big data Analytics to optimize water usage in agriculture. Employing a multi-stage approach, we conducted real-time data acquisition from diverse farms, processed the data using clustering algorithms, and implemented intelligent decision-making systems. Our analysis focused on key parameters such as soil moisture, temperature, rainfall, and wind speed across various farming locations. The methodology encompassed extensive field data collection, advanced analytics, and the deployment of smart irrigation systems. Results demonstrated a significant 25 % reduction in water consumption while maintaining optimal crop yields. Additionally, clustering identified three distinct farm categories, each requiring tailored water management strategies. This research contributes to the field of smart agriculture by providing practical, scalable solutions for regions facing severe water scarcity. The findings highlight the potential of advanced technologies to enhance agricultural sustainability and address critical water management challenges.

关键词： Temperature sensors Smart agriculture Irrigation Wind speed Soil moisture Big data Real-time systems Internet of Things Sustainable development Water resources

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Design of Cloud-Edge-End Collaborative Framework for Power Supply intelligent Inspection Operation Scenario Based on Unmanned Aerial Vehicle

Design of Cloud-Edge-End Collaborative Framework for Power S...

引用

ieee Information Technology and Mechatronics Engineering conference (ITOEC)

作者： Wei Wang Ping Qiu State Grid Zhejiang Electric Power Co. Ltd. Hangzhou China Zhejiang International Studies University Hangzhou China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

As a new type of tool with powerful perception, monitoring, and emergency response capabilities, unmanned aerial vehicles (UAVs) have the characteristics of flexibility, maneuverability, and high-altitude inspection. UAVs can greatly improve the efficiency of power facility maintenance and repairing, reduce personnel risks, and have gradually become an important auxiliary means for power supply operation and management. Currently, research on cloud-edge collaborative computing using UAVs has been applied to power systems, especially in their daily maintenance. However, there is not much discussion on building a systematic UAV-based cloud-edge collaboration system, especially to meet the requirements of power supply companies. Aiming at the problems such as the limited distance of image transmission, the lack of timeliness and visualization of image data processing in the application process of UAVs, in this paper, a "cloud-edge-end" collaborative framework has been proposed to achieve fast data return over the distance under signal coverage, and use the characteristics of local edge computing to quickly solve image preprocessing.

关键词： Power supplies Clouds Collaboration Inspection Autonomous aerial vehicles Cameras Electricity supply industry Real-time systems Maintenance Edge computing

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Time Series data Classification using the Knowledge-Augmented Transformer Model

Time Series Data Classification using the Knowledge-Augmente...

引用

international conference on Big data and Smart computing (BIGCOMP)

作者： Hyun-Seok Chung Sun-Young Hyun Chang-Eun Lee Young-Guk Ha SmartLabs Seoul Republic of Korea ETRI Daejeon Republic of Korea Department of Computer Science and Engineering Konkuk University Seoul Republic of Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Time Series Classification predicts outcomes by analyzing sequential data that changes over time. As the complexity of time series data increases, particularly in scenarios involving dynamic relationships, existing models face limitations. This paper proposes a classification model using an encoder-based transformer with knowledge augmentation to address these challenges. The model preprocesses time-series data into a graph structure, enabling effective learning of intricate relationships. Relational attention and temporal attention enhance focus on critical relationships and time points, respectively. Additionally, symbolic reasoning through knowledge augmentation allows the model to capture complex, implicit information. Experimental results show that the proposed model outperforms existing SOTA model on complex datasets like SC2EGSet.

关键词： Computational modeling Time series analysis Knowledge based systems Games Predictive models Transformers data models Cognition Complexity theory Faces

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Distributed Photovoltaic Island Monitoring Method Based on Gated Loop Unit

Distributed Photovoltaic Island Monitoring Method Based on G...

引用

ieee Information Technology and Mechatronics Engineering conference (ITOEC)

作者： Jianyong Ao Yanping Li Zhukun Li Yangjiang Power Supply Bureau of Guangdong Power Grid Co. Ltd. Yangjiang China NARI Technology Co. Ltd. Nanjing China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

The rapid deployment of distributed photovoltaic (PV) systems has introduced new challenges in islanding detection, a critical task for grid stability and safety. This paper presents a novel Distributed Photovoltaic Island Monitoring Method Based on Gated Loop Unit (GLU). The GLU leverages advanced signal processing and dynamic response mechanisms to enhance detection sensitivity and accuracy. The proposed distributed architecture enables real-time data acquisition and collaborative decision-making across PV nodes, ensuring reliable monitoring under diverse grid conditions. Simulations and case studies validate the method’s effectiveness, demonstrating superior performance in terms of detection accuracy, low latency, and robustness when compared to conventional techniques. The proposed approach provides a scalable and efficient solution for improving islanding detection in distributed PV systems, contributing to the development of resilient and intelligent power grids.

关键词： Photovoltaic systems Accuracy Islanding Logic gates Power system stability Signal processing Stability analysis Time factors Springs Monitoring

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Research on Multi-Sensor Parameter Updating and Firmware Upgrading Method Based on Modbus

Research on Multi-Sensor Parameter Updating and Firmware Upg...

引用

Consumer Electronics and Computer Engineering (ICCECE), 2021 ieee international conference on

作者： Kai Ding Chaoyue Jiang Ming Tao Renping Xie Jiaxue Li School of Computer Science and Technology Dongguan University of Technology Dongguan China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

In industrial systems, sensors are used as key data acquisition devices. Optimizing their performance and functionality is essential for ensuring stable operation and improving system productivity. In order to realize function expansion, performance optimization and security guarantee, parameter updates, and firmware upgrades are indispensable operations. However, traditional methods for parameter updates and firmware upgrades typically rely on manual operations, which has many drawbacks such as low efficiency, high maintenance cost, and a significant risk of errors. These limitations make it difficult to meet the demands for high efficiency and intelligent equipment management in modern industrial systems. We propose a solution for parameter updating and firmware upgrading based on Modbus protocol, designing and realizing related hardware and Supervisory Control Software systems. The system communicates with the Supervisory Control Software through the LoRa module, utilizing the reliability and standardization characteristics of the Modbus protocol to improve the efficiency of parameter updates and firmware upgrades effectively. The research in this paper provides an efficient and reliable realization path for the intelligent management of sensors in industrial systems and provides an important reference for research and application in related fields.

关键词： Protocols Supervisory control Software Hardware Sensor systems Software reliability Maintenance intelligent sensors Thermal stability Microprogramming

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Skill Learning Using a Single Demonstration for Hierarchical Robotic Navigation

Skill Learning Using a Single Demonstration for Hierarchical...

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ieee international conference on Autonomous Robot systems and Competitions (ICARSC)

作者： João Bernardo Alves Nuno Lau Filipe Silva IEETA DETI University of Aveiro Aveiro Portugal Intelligent System Associate Laboratory LASI Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Training robots to perform complex navigation tasks traditionally requires extensive datasets of expert demonstrations, which are expensive and time-consuming to collect. This paper introduces a hierarchical reinforcement learning approach that distils meaningful skills using a single expert trajectory. By combining reverse curriculum learning and goal-conditioned reinforcement learning, our method segments complex tasks into manageable sub-tasks and learns these sub-tasks using sparse rewards. A learned skill selection mechanism then dynamically chains the skills based on their estimated values. We evaluate our approach in a high-dimensional autonomous driving scenario, demonstrating skill acquisition and task completion rates that surpass a state-of-the-art baseline that also uses a single demonstration. Our results show that flexible skill extraction and chaining using a reduced amount of data can effectively tackle the challenges of sparse rewards and planning in a robotic navigation environment.

关键词： Training Navigation Reinforcement learning Trajectory Planning data mining Autonomous vehicles Autonomous robots

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Quantum Neural Network Extraction Attack via Split Co-Teaching

Quantum Neural Network Extraction Attack via Split Co-Teachi...

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), ieee international conference on

作者： Zhenxiao Fu Fan Chen Department of Intelligent Systems Engineering Indiana University Bloomington IN USA

ISBN: (数字)9798331519315

ISBN: (纸本)9798331519322

Quantum Neural Networks (QNNs), now offered as QNN-as-a-Service (QNNaaS), have become key targets for model extraction attacks. Existing methods use ensemble learning to train substitute QNNs, but our analysis reveals significant limitations in real-world environments, where noise and cost constraints undermine their effectiveness. In this work, we introduce a novel attack, split co-teaching, which uses label variations to split queried data by noise sensitivity and employs co-teaching schemes to enhance extraction accuracy. The experimental results show that our approach outperforms classical extraction attacks by 6.5%~9.5% and existing QNN extraction methods by 0.1%~3.7% across various tasks.

关键词： Sensitivity Quantum computing Accuracy Computational modeling Noise Neural networks data mining Noise measurement Ensemble learning Speech processing

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