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Adaptive Model Transfers and Aggregations for Efficient Federated Learning in IoT Edge Systems with Non-IID Data 6

Adaptive Model Transfers and Aggregations for Efficient Fede...

引用

6th international conference on Data Science and Information technology, DSIT 2023

作者： Lin, Jenn-Wei Kim, Taewoon Tu, Hung-Jen Kuo, Po-Hsien Fu Jen Catholic University Department of Computer Science and Information Engineering Taiwan Pusan National University School of Computer Science and Engineering Korea Republic of

ISBN: (纸本)9798350304442

Federated learning (FL) can associate with edge computing system to perform distributed and privacy-preserving learning for intelligent IoT applications. With individually collecting the data in each FL node, the training data may exhibit non-independent and identically distributed (non-IID) property, which may seriously impair the FL performance. To mitigate the impact of non-IID training data, we propose a virtual non-IID transformation approach by combing local datasets among FL nodes to form an IID dataset group for tuning each local training model. First, each local dataset is examined and the data label distribution is uploaded. then, a perfect data label distribution is made as the best IID benchmark. For each communication round of FL, the local training model is verified to find lacking data label distributions. In next several rounds, the locally trained model will be sent to assistant FL nodes for compensating its uneven data label distribution based on the perfect IID benchmark. thereafter all FL participating clients join the parameter aggregation at the end of each round same as the traditional FL approaches. the selection of assistant nodes will be transformed into the minimum-cost maximum-matches (MCMM) problem, and then we use integer linear programming (ILP) to obtain the optimal solution. Finally, we perform extensive simulation experiments to evaluate the effectiveness of the proposed approach. © 2023 IEEE.

关键词： Edge computing

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Workload Allocation in Fog Environment Using Multi-Objective Evolutionary Algorithms for internet of things 6

Workload Allocation in Fog Environment Using Multi-Objective...

引用

6th international conference on Advanced Communication Technologies and Networking, CommNet 2023

作者： Raissouli, Hafsa Ariffin, Ahmad Alauddin Bin Belhaouari, Samir Brahim University Putra Malaysia Computer Science and Information Technology Serdang Malaysia Hamad Bin Khalifa University College of Science and Engineering Doha Qatar

ISBN: (纸本)9798350329391

the continuous rise in the number of IoT devices has led to an increasing importance of the fog computing paradigm. Part of the workload that should be processed is executed locally on the IoT device and the rest is offloaded and allocated to the fog nodes. this workload allocation decision should be done in a way that provides the lowest delay but while taking into account the energy consumption as well. this study presents an optimization of the workload allocation that minimizes delay and power consumption using the multi-objective evolutionary algorithms, namely, NSGA II, R-NSGA II, NSGA III, R-NSGA III and CTAEA. the experiments involve two scenarios, full transmission power of the IoT device, and half of its transmission power with varying workload sizes. the results manifested the superior performance of NSGA III and CTAEA in optimizing the allocation of tasks in fog computing environments. By demonstrating NSGA III and CTAEA's effectiveness, this findings not only advance the understanding of evolutionary algorithms but also provide practical insights for optimizing fog computing systems. this research has broader implications for improving the efficiency and performance of fog computing applications, with potential applications across various scenarios in the field. © 2023 IEEE.

关键词： Multiobjective optimization

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Advanced Techniques to Predict and Detect Cloud System Failure: A Survey 6

Advanced Techniques to Predict and Detect Cloud System Failu...

引用

6th international conference on Electronics, Communication and Aerospace technology, ICECA 2022

作者： Ramoliya, Dipak Patel, Akash Patel, Khushi Patel, Gaurang Vaghela, Priyalba Budhrani, Aishwariya Department of Computer Science and Engineering Gujarat Changa388421 India Department of Information Technology Gujarat Changa388421 India Department of Computer Engineering Gujarat Changa388421 India

ISBN: (纸本)9781665482714

Cloud computing is a widely used technology in today's rapidly advancing world. Cloud computing allows access to different computational resources over the internet like storage, bandwidth, and computational power. With the increase in the number of devices across the globe, cloud computing has been widely adopted to deliver services across the internet. Due to the increased demand, the data centers grow in terms of size and complexity. In large data centers, failures are common. Hence, failure management becomes crucial. In this review, different unsupervised learning techniques that can detect failures and supervised learning techniques and predict failures are discussed. © 2022 IEEE.

关键词： Cloud computing

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Multi-Class Failure Prediction for distributed Systems Based on KPI Data and Feature Selection 6

Multi-Class Failure Prediction for Distributed Systems Based...

引用

6th international conference on Software Engineering and Computer Science, CSECS 2023

作者： Yuan, Zhidan Zhu, Mingliang Qian, Haizhong Lv, Taizhi Huang, Tao College of Information Engineering Jiangsu Maritime Institute Nanjing China School of Computer Science and Technology Silicon Lake College Suzhou China

ISBN: (纸本)9798350306378

In the field of distributed system failure prediction, most existing research only focuses on specific categories of failure prediction and cannot predict multiple types of failures simultaneously. In this article, we propose a novel multi-class failure prediction method KFPCIFS. KFPCIFS is built based on historical KPI metrics data. Due to not all KPI metrics being necessarily relevant to a specific type of failure, we use feature selection methods to improve the multi-class OvR strategy in KFPCIFS. Because of the limited number of failure cases, the dataset is often imbalanced. In KFPCIFS, we use the SMOTE method to address the issue of class imbalance in the dataset. Empirical studies on the dataset show the effective of KFPCIFS after comparing with eleven baselines. © 2023 IEEE.

关键词： Forecasting

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Blockchain-based Edge Intelligence Enabled by AI Large Models for Future internet of things 12

Blockchain-based Edge Intelligence Enabled by AI Large Model...

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12th IEEE international conference on Information and Communication Networks, ICICN 2024

作者： Zhang, Dajun Shi, Wei School of Information Technology Carleton University OttawaON Canada

ISBN: (纸本)9798350355802

In recent years, the integration of internet of things (IoT) and Artificial Intelligence (AI) technologies has become a key factor in the development of modern communication systems. through the deep integration of AI and IoT, the intelligence level of the network has been greatly improved, achieving higher data transmission speed, lower latency, and higher reliability to meet the growing communication needs. Especially in the context of intelligence networking and edge computing, large-scale language models (LLM) such as the Generative Pretrained Transformer (GPT) series have extended the capabilities to handle complex tasks and predict user intentions, programming, and planning. these capabilities provide new possibilities for optimizing communication systems, reducing semantic communication costs, and customizing services according to user preferences. this article explores the application of blockchain and AI large models in the edge intelligence of the internet of things, and proposes a distributed, non-tamperable knowledge and learning achievement recording system based on blockchain technology and AI large models. the system is designed to automatically generate code to train new models in a privacy-preserving manner. Experimental results show that the system can accurately understand user needs, efficiently execute, and create high-performance artificial intelligence models at minimal cost on edge servers. © 2024 IEEE.

关键词： AI large models blockchain edge intelligence internet of things

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distributed Data Sharing and Access Control in Industrial IoT Using Blockchain technology 5

Distributed Data Sharing and Access Control in Industrial Io...

引用

5th international conference on Computer Engineering and Intelligent Control, ICCEIC 2024

作者： Li, Rui Sun, Yan Liu, Caiyun Wen, Ying Liu, Yitong Fujian Provincial Industrial and Information Industry Development Research Center Fujian Fuzhou China China Industrial Control Systems Cyber Emergency Reponse Team Beijing China

ISBN: (纸本)9798331507992

With the rapid development of the Industrial internet of things (IIoT), secure and efficient data sharing has become crucial for enabling industrial automation and smart transformation. However, existing centralized solutions face limitations in scalability, latency, and data privacy. To address these challenges, this paper proposes a blockchain-based distributed data-sharing and access control system tailored for resource-constrained industrial edge networks. the system adopts a three-layer architecture that integrates edge computing and blockchain technology to provide decentralized, low-latency data management. A novel smart contract framework is introduced to enforce multi-level access control and support fine-grained permissions for intra-domain and cross-domain data sharing. Experimental results show the system's improved performance in data access efficiency and throughput, offering a robust solution for real-time, secure IIoT applications. © 2024 IEEE.

关键词： Smart contract

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Integrated internet of things with Blockchains for Vendor-Management Inventory System 6

Integrated Internet of Things with Blockchains for Vendor-Ma...

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6th international conference on computing Methodologies and Communication, ICCMC 2022

作者： Atyam, Nageswara Rao Babu, Ramesh P Ponmurugan, P. Senthamil, L. Sahaya John Augustine, P. Govindarajan, A. CMR Institute of Technology Department of Electrical and Electronics Engineering Bengaluru India Wollega University College of Engineering Technology Department of Computer Science Nekemte Ethiopia Sengunthar Engineering College Department of EEE Tamil Nadu Tiruchengode India PSNA College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Dindigul India Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India Koneru Lakshmaiah Educational Foundation KL University Green Fields Vaddeswaram KL Business School Andhra Pradesh Guntur India

ISBN: (纸本)9781665410281

In recent era, the supply chain management is becoming a complex valued network that offers a major competitive advantage over logistics and supply chain management. Despite its advantages, the complexity is becoming a challenge that should provide verification of sources, maintaining product visibility and monitoring while moving via supply chains. the adoption of internet of things (IoT) can support all these movements that tends to track and monitor the activities of the products moving in the chain network. Such optimization enables the operations to get optimized that includes manufacturing, warehousing and transportation. In addition, the transparency of the supply chains can be maintained via blockchain and when combined with IoT, it increases the effectiveness and efficacy of the supply chain © 2022 IEEE.

关键词： internet of things

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Capacity Compensation Mechanism of Independent Energy Storage Devices Considering Investment Recovery 6

Capacity Compensation Mechanism of Independent Energy Storag...

引用

6th IEEE international Electrical and Energy conference, CIEEC 2023

作者： Li, Yang Xu, Binkun Gong, Anrong Meng, Ke Yang, Hongming Liu, Yuqing State Grid Hunan Electric Power Company Hunan Changsha China Min. of Educ. Intl. Coop. Jt. Lab. of Ener. Internet Oper. and Plan. Based on Distributed Optical Storage Changsha Univ. of Sci. and Technol. Changsha University of Science and Technology Hunan Changsha China

ISBN: (纸本)9798350346671

As important flexible resources, independent energy storage devices can be employed to maintain the long-term abundant capacity of the renewable-dominated power system. However, the investment recovery of independent energy storage devices is almost impossible to achieve, which limits their development and application. therefore, this paper focuses on the capacity compensation mechanism of independent energy storage devices to achieve investment recovery. Firstly, different compensation mechanisms worldwide are compared. Secondly, the costs and incomes of independent energy storage devices are analyzed detailedly. Finally, according to the situation in China, suggestions about the capacity compensation mechanisms for independent energy storage devices are proposed to support the achievement of investment recovery. © 2023 IEEE.

关键词： Energy storage

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internet Of Secure Elements: A New Direction For Secure Elements Use In 6G Landscape 8

Internet Of Secure Elements: A New Direction For Secure Elem...

引用

8th international conference On Mobile And Secure Services (MobiSecServ)

作者： Urien, Pascal Telecom Paris 19 Pl Marguerite Perey F-91120 Palaiseau France

ISBN: (纸本)9798350316490

this talk presents a new and emerging open technology named internet of Secure Elements (IOSE), whose goal is to enforce trust and security for digital platforms such as internet of things (IoT), blockchain, cloud computing or edge computing, thanks to on-line secure elements identified by Uniform Resource Identifiers (URIs). A Secure Element is a tamper resistant microcontroller equipped with host interfaces such as ISO7816, I2C, or SPI. Most of secure elements include a Java Virtual Machine (JVM) and execute embedded program written in the JAVACARD language. More than 10 billion secure elements are manufactured every year.

关键词： Secure Element HSM Blockchain IoT

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Synergistic Integration of Blockchain and Software-Defined Networking in the internet of Energy Systems 6

Synergistic Integration of Blockchain and Software-Defined N...

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6th international conference on Blockchain computing and Applications, BCCA 2024

作者： Hayyolalam, Vahideh Zekiye, Abdulrezzak Abuzahra, Hamza Özkasap, Öznur Karakus, Murat Guler, Evrim Uludag, Suleyman Department of Computer Engineering Koç University İstanbul Turkey Department of Software Engineering Ankara University Ankara Turkey Department of Computer Engineering Bartin University Bartin Turkey Department of Computer Science University of Michigan FlintMI United States

ISBN: (纸本)9798350351538

Peer-to-peer (P2P) energy trading, Smart Grids (SG), and electric vehicle energy management are integral components of the internet of Energy (IoE) field. the integration of Software-Defined Networks (SDNs) and Blockchain (BC) technologies into the IoE domain offers potential benefits that have only been studied in the literature in a few works. In this paper, we investigate the state-of-art solutions that leverage both SDNs and blockchain within the realm of the IoE. We categorize these solutions based on the method of integrating SDN and BC into two categories. the first category is the blockchain for SDN, where blockchain enhances the SDN directly. the second category is blockchain and SDN, where both technologies are used to enhance the proposed solutions. We identify three distinct blockchain applications based on their usage: decentralizing the SDN control plane, serving as a decentralized platform, and improving security measures. Similarly, we observe that SDN serves as a performance enhancer, a substitute for traditional networking, and solely as a control and management framework. It is posited that integrating SDNs and blockchain into IoE leads to performance enhancements, improves security, enables decentralized operations, and eliminates single points of failure in the SDN control plane. Additionally, some unaddressed issues, such as energy efficiency, smart contract management, and scalability, are discussed as potential future directions. © 2024 IEEE.

关键词： internet of Energy

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