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检索条件"任意字段=6th International Conference on Distributed Computing and Internet Technology"

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distributed resource low-delay interaction method for low-voltage substation based on communication quality-aware reinforcement learning 6

Distributed resource low-delay interaction method for low-vo...

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6th international conference on Energy, Power and Grid, ICEPG 2024

作者： Shi, Zhan Guangdong Power Grid Co. Ltd Power Dispatching and Control Center Guangdong China

ISBN: (纸本)9798350377798

In the distribution substation, edge computing can optimize the allocation and utilization of overall distributed resources, which is essential for ensuring efficient energy utilization and stable operation of the power grid. However, because of electromagnetic interference during data transmission at the device, the traditional upper confidence bound (UCB) algorithm neglects the impact of communication quality when estimating confidence bounds, leading to a deviation in the value estimation of actions and an increase in data processing delay. To tackle this issue, a distributed resource low-delay optimization algorithm for substations based on communication quality-aware UCB is proposed. Firstly, we propose the low-delay interaction architecture for low-voltage substations with communication quality-aware distributed resources. Moreover, by optimizing the data migration strategy, the problem of minimizing edge-edge collaborative data processing delay is formulated. To address this issue, we convert it into a multi-armed bandit (MAB) problem and solve it by using the proposed algorithm. this algorithm incorporates communication quality weight into the calculation formula for edge server preference value, enabling real-time awareness of migration channel quality during the data migration process and improving the efficiency of distributed resource interaction in the distribution substation. Finally, the proposed algorithm's effectiveness is confirmed through simulation. © 2024 IEEE.

关键词： Reinforcement learning

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Requirements, Technologies, Applications and Challenges of 6G Communication: A Review 5

Requirements, Technologies, Applications and Challenges of 6...

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5th IEEE international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Singh, Gambhir Verma, Indradeep Bhardwaj, Shivam Sharma, Rohit Ahmed, Waseem Department of CSE-IoT UP Greater Noida India

ISBN: (纸本)9798350383522

With the advent of internet & wireless network connectivity, the data usage has increased very rapidly in the past few years. Fourth-gen (4G) internet communication is still in scaling period but 5G is been rolled out in few countries. Now with almost 8-billion people on planet, AI, ML, and other technologies are in greater demand. the search for 6G has already began and is thought to be deployed worldwide soon. With more & more research in this area, the speed of internet is ultra-fast and with 6thGen it will be even faster. the 6G will revolutionize the IT & technology industry in less than a decade from now. Nevertheless, there are some underlying challenges that occur with more speed and need to be resolved before providing it to masses. the issues are higher losses for small distance, lower latency, more system capacity etc. this paper helps in understanding the goal of 6thGen wireless network communication in future and its network's structure. Below we have described those new budding technologies like artificial intelligence, virtual reality, automated cars, block chain, three-dimensional networking, quantum communication, cloud computing, information transfer, remote access and analyzing big data that will help the 6G internet communication architecture development with the guaranteed transfer-rate we aim to achieve. All other technologies are also heighted alongside the expected applications of 6th Gen network communication. We have highlighted the hidden agendas and research related problems for attaining this target. © 2024 IEEE.

关键词： 5G network 6G network wireless connectivity

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State-of-the-art Analysis on the Design Parameters of distributed Transformer using IoT 6

State-of-the-art Analysis on the Design Parameters of Distri...

引用

6th international conference on Intelligent computing and Control Systems, ICICCS 2022

作者： Rao, D. S. Naga Malleswara Sundara, Rishi Krishna, R. Vamshi Ayyan Varma, M. Srujan, S. Prasad, B. Sai Gokaraju Rangaraju Institute of Engineering and Technology Department of Electrical and Electronics Engineering Telangana Hyderabad India

ISBN: (纸本)9781665410359

the internet of things (IoT), which allows autonomous items to share data over the internet, has created a lot of interest in Machine-to-Machine communication. the design and implementation of real-time transformer monitoring and fault detection, as well as the recording of critical dispersion in the transformer operating indicators such as current, voltage, oil level, transformer temperature, and frequency, are described in this study. With this project, you may reduce working effort while improving accuracy, stability, and efficiency. Sensors are used in this project to monitor critical equipment aspects including fire, gas, and temperature. this data is sent to a microcontroller, which checks for parameter limits before transmitting it to the Adafruit IoT web server over Wi-Fi. the substation operator may be prompted to take suitable steps if a warning or alert indicates that the transformer is overloaded and at risk of excessive insulation ageing or loss of life. © 2022 IEEE.

关键词： internet of things

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Towards Reliable distributed Quantum computing on Quantum Interconnects 23

Towards Reliable Distributed Quantum Computing on Quantum In...

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7th international conference on the Art, Science, and Engineering of Programming (Programming)

作者： Wakizaka, Ryo Kyoto Univ Kyoto Japan

ISBN: (纸本)9798400707551

Quantum interconnects (QuICs), which connect multiple quantum devices to build distributed quantum systems, are essential for large-scale quantum information processing. While various software tools have been developed to analyze quantum programs running on QuICs, there are very few frameworks for formal reasoning about them on actual quantum devices. the lack of such tools may make distributed systems unreliable because of the complicated behavior of concurrent quantum processes and the physical constraints of quantum devices. this paper discusses the need for an infrastructure, including a distributed quantum programming language, to realize reliable distributed quantum computing.

关键词： quantum interconnects quantum internet distributed quantum computing quantum programming language formal verification

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Sustainable Industrial Symbiosis using IoT and Gradient Boosting for Efficient Waste Management 6

Sustainable Industrial Symbiosis using IoT and Gradient Boos...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Azath, H. Ranganathan, Chitra Sabapathy Arunsankar, G. Reena Roy, R. Rajmohan, M. Murugan, S. School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Mphasis Corporation Tamil Nadu Chennai India Department of Electronics and Communication Engineering Gojan School of Business and Technology Tamil Nadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798331522667

Many sectors have formed Sustainable Industrial Symbiosis (SIS) to improve resource efficiency and environmental sustainability. this study explores how SIS frameworks may optimize waste management by combining internet of things (IoT) technologies with gradient-boosting algorithms. Using sensors and IoT devices allows for real-time data gathering on waste production, composition, and processing steps. Gradient Boosting models are used to optimize recycling operations, reduce waste, and forecast the best use of resources. the objective is to improve SIS with integration of IoT and Gradient Boosting, enabling effective waste management, improving resource reuse, and reducing environmental impact. With this connectivity, adaptive management solutions may be used, which in turn helps to improve resource utilization and decrease environmental impact, thereby supporting a sustainable circular economy. As shown in case studies and simulations, the proposed IoTGradient Boosting method successfully achieves cost savings and environmental advantages in industrial environments. the results show how IoT and advanced analytics can revolutionize waste management, leading to more environmentally friendly manufacturing processes and better sustainability initiatives. By using IoT and machine learning (ML) methods to solve difficult problems in waste management and resource optimization, this study helps move the discipline of SIS forward. © 2025 IEEE.

关键词： Circular economy

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DRL-Based Dependent Task Offloading in Collaborative Vehicular Networks 6

DRL-Based Dependent Task Offloading in Collaborative Vehicul...

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6th international conference on internet of things, Automation and Artificial Intelligence, IoTAAI 2024

作者： Zhao, Gaoyu Cui, Haixia South China Normal University School of Electronics and Information Engineering Foshan China South China Normal University School of Electronic Science and Engineering Foshan China

ISBN: (纸本)9798350386974

Collaborative vehicular networks (CVNs) can provide the data processing and task offloading services with the seamless cooperation of edge and fog computing for user vehicles (UVs). However, intelligent task offloading in CVNs will raise numerous concerns, including the long-term quality of service (QoS) requirements and the environmental information utilization efficiency. In response to the aforementioned questions, we present a delay-energy-aware dependent task offloading algorithm (DEADTO) based on DRL to optimize the comprehensive throughput of UVs while ensuring the preservation of quality of service (QoS) stability with the utmost diligence. Compared with the existing works, our proposed algorithm demonstrates superior performance, delivering enhanced throughput, reduced latency and optimized energy consumption. © 2024 IEEE.

关键词： Fog computing

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Enhancing Data Consistency in Distribution Networks Leveraging Graph Contrastive Representation Learning 6

Enhancing Data Consistency in Distribution Networks Leveragi...

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6th international conference on Power and Energy technology, ICPET 2024

作者： Liu, Siyan Shi, Danni Tang, Zhuang State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co.Ltd. Beijing China

ISBN: (纸本)9798350375855

the rapid integration of distributed renewable energy sources and the diversification of loads have precipitated profound changes in the configuration and operation of power distribution networks in the 21st century, underscoring the necessity for accurate and standardized power grid topology model data. this manuscript introduces a novel graph contrastive learning-based approach for the representation of node features in distribution networks, specifically targeting the alignment of identical nodes across different Common Information Model (CIM) documents. By combining self-supervised learning with graph neural networks, a feature extraction framework that integrates topological structural features and attribute features of the distribution network is proposed in the paper. Besides, the study designs a graph view generation strategy considering distribution network characteristics. the proposed method is validated by an 11-feeder distribution network dataset, and the results show the effectivity of the graph contrastive learning. © 2024 IEEE.

关键词： Contrastive Learning

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Optimizing Latency and Communication in Federated Edge computing with LAFEO and Gradient Compression for Real-Time Edge Analytics 6

Optimizing Latency and Communication in Federated Edge Compu...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Namratha, P. Chaganti, Koushik Reddy Elicherla, Sivananda Lahari Reddy Guddati, Surekha Swarna, Akarapu thiramal Reddy, P. Department of CSE GATES Institute of Technology and Science Anantapur India Dept. of Computer and Information Systems University of the Cumberlands KY United States Dept of CSE Dayananda Sagar University Bangalore India Dept of CSE Vidya Jyothi Institute of Technology Hyderabad India

ISBN: (纸本)9798331522667

the increasing need for real-time data analytics in edge computing is furthered by the latency pressures from computational and communication bottlenecks, thus demanding novel solutions. Fundamental approaches to federated learning and distributed machine learning do not accommodate changes in dynamic latency conditions very well, thereby being inefficient in terms of synchronization, communication overhead, and delay in task execution. this paper presents a holistic framework consisting of three innovative approaches to solve these problems: Latency-Aware Federated Edge-Aware Optimization (LAFEO), Latency-Driven Gradient Compression with Adaptive Sparsity (LAGCAS), and Predictive Latency Compensation with Dynamic Replication (PLCDR). the LAFEO framework consists of a double-layer optimization framework, combining latency-weighted scheduling with reinforcement learning to dynamically adapt the synchronization frequencies. this results in 25-30% end-to-end latency reduction with an accuracy deviation that is capped at 2% centralized models. LAGCAS reduces communication overhead by applying adaptive gradient scarification methods, bringing compressions of gradients under real-time accuracy thresholds: it reduces 40% communication volume and accelerates convergence by 20%. PLCDR uses predictive modelling to predict latency bottlenecks and dynamically replicates critical computations across the edge nodes. It hence offers 15-25% decrease in the completion time of tasks and up to 30% enhancement in the resilience of system sets. the existing methods will be addressed with the key limitations due to the introduction of latency-aware mechanisms that adapt to real-time edge conditions along with high accuracy and efficiency. Simulation results have shown marked scalability improvements, latency reductions, and resilience;in its positioning, this work's effort is a transformative advancement in the latency-sensitive edge computing process. © 2025 IEEE.

关键词： Federated learning

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A Life-long Learning Intrusion Detection System for 6G-Enabled IoV 20

A Life-long Learning Intrusion Detection System for 6G-Enabl...

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20th IEEE international Wireless Communications and Mobile computing (IEEE IWCMC) - Green and Intelligent Communications

作者： Korba, Abdelaziz Amara Sebaa, Souad Mabrouki, Malik Ghamri-Doudane, Yacine Benatchba, Karima Ecole Natl Super Informat Oued Smar Alger Algeria Badji Mokhtar Univ Annaba LRS Annaba Algeria Univ La Rochelle L3I La Rochelle France

ISBN: (纸本)9798350361261;9798350361278

the introduction of 6G technology into the internet of Vehicles (IoV) promises to revolutionize connectivity with ultra-high data rates and seamless network coverage. However, this technological leap also brings significant challenges, particularly for the dynamic and diverse IoV landscape, which must meet the rigorous reliability and security requirements of 6G networks. Furthermore, integrating 6G will likely increase the IoV's susceptibility to a spectrum of emerging cyber threats. therefore, it is crucial for security mechanisms to dynamically adapt and learn new attack patterns, keeping pace with the rapid evolution and diversification of these threats - a capability currently lacking in existing systems. this paper presents a novel intrusion detection system leveraging the paradigm of life-long (or continual) learning. Our methodology combines class-incremental learning with federated learning, an approach ideally suited to the distributed nature of the IoV. this strategy effectively harnesses the collective intelligence of Connected and Automated Vehicles (CAVs) and edge computing capabilities to train the detection system. To the best of our knowledge, this study is the first to synergize class-incremental learning with federated learning specifically for cyber attack detection. through comprehensive experiments on a recent network traffic dataset, our system has exhibited a robust adaptability in learning new cyber attack patterns, while effectively retaining knowledge of previously encountered ones. Additionally, it has proven to maintain high accuracy and a low false positive rate.

关键词： 6G IoV Security intrusion detection Continual Learning Life-long Learning Federated Learning

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Mutual information higher-order link prediction based on Naive Bayes 6

Mutual information higher-order link prediction based on Nai...

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6th international conference on internet of things, Automation and Artificial Intelligence, IoTAAI 2024

作者： Xu, Zhipeng Yao, Yabing Ma, Ning Lanzhou University of Technology School of Computer and Communication Gansu Lanzhou730050 China Northwest Minzu University The Key Laboratory of Linguistic and Cultural Computing Ministry of Education Gansu Lanzhou730030 China

ISBN: (纸本)9798350386974

Traditional link prediction primarily focuses on dual node interactions to forecast missing links or detect erroneous interactions, a method with extensive real-world applications. Nevertheless, as network scale expands and complexity increases, networks exhibit not only dual interactions between two nodes but also higher-order interactions encompassing multiple nodes. Traditional link prediction cannot directly predict these higher-order structures. this paper models the higher-order structures in the network through cliques. In order to better quantify and balance the contribution of common neighbor nodes of the clique to be predicted, this paper considers the interaction between common neighbors and proposes a mutual information higher-order link prediction method based on naive Bayes (MINB), used for directly predicting the higher-order structures in the network. through experimental comparison with several benchmark algorithms on eight real networks, the results prove that this method effectively improves the prediction accuracy of 3-clique and has good stability. © 2024 IEEE.

关键词： Prediction models

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