IEEE Internet of Things Magazine最新文献_第4页

SDCL: A Framework for Secure, Distributed, and Collaborative Learning in Smart Grids SDCL：智能电网中的安全、分布式协作学习框架

IEEE Internet of Things Magazine Pub Date : 2024-05-01 DOI: 10.1109/IOTM.001.2300059

A. Abdellatif, K. Shaban, Ahmed Massoud

{"title":"SDCL: A Framework for Secure, Distributed, and Collaborative Learning in Smart Grids","authors":"A. Abdellatif, K. Shaban, Ahmed Massoud","doi":"10.1109/IOTM.001.2300059","DOIUrl":"https://doi.org/10.1109/IOTM.001.2300059","url":null,"abstract":"The future of electric grids is undergoing a remarkable transformation driven by the increasing adoption of emerging technologies, notably Artificial Intelligence (AI) and Blockchain. These innovative technologies are revolutionizing smart grid management by introducing novel approaches that enhance efficiency, reliability, and sustainability, all while securing information across distributed grid components. AI empowers predictive analytics and real-time optimization, while Blockchain ensures secure and transparent transactions, laying the foundation for a more resilient and adaptive electrical grid system. This article introduces a novel Secure, Distributed, and Collaborative Learning (SDCL) framework for the smart grid. The SDCL framework leverages advances in distributed learning and blockchain technologies to provide scalability, secure data exchange, and rapid response capabilities. The proposed architecture not only enables secure data and model exchange among different microgrids but also facilitates the integration of multiple microgrids and distributed network operators. This integration enables the correlation of unforeseen events and enhances the management and control of emerging failures. Our resilient, blockchain-based architecture optimizes information sharing and security levels within the blockchain, accommodating diverse requirements for smart grid services. Finally, we highlight the advantages of the proposed SDCL framework and outline future research directions that warrant further investigation.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"4 26","pages":"84-90"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141041949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing EEG Signal Classifier Robustness Against Adversarial Attacks Using a Generative Adversarial Network Approach 利用生成式对抗网络方法增强脑电信号分类器的鲁棒性，抵御对抗性攻击

IEEE Internet of Things Magazine Pub Date : 2024-05-01 DOI: 10.1109/IOTM.001.2300262

Nour El-Houda Sayah Ben Aissa, C. A. Kerrache, Ahmed Korichi, Abderrahmane Lakas, Abdelkader Nasreddine Belkacem

{"title":"Enhancing EEG Signal Classifier Robustness Against Adversarial Attacks Using a Generative Adversarial Network Approach","authors":"Nour El-Houda Sayah Ben Aissa, C. A. Kerrache, Ahmed Korichi, Abderrahmane Lakas, Abdelkader Nasreddine Belkacem","doi":"10.1109/IOTM.001.2300262","DOIUrl":"https://doi.org/10.1109/IOTM.001.2300262","url":null,"abstract":"Electroencephalogram (EEG) based brain computer interfaces (BCIs) have particularly benefited from deep learning models thanks to their remarkable performance for classification purposes. Despite their success, these models have shown to be vulnerable to adversarial attacks, which are attacks that manipulate EEG signals to cause misclassification. Adversarial training, where models are trained on both normal and adversarial examples, has been proposed to address this issue. However, overfitting on adversarial examples can lead to reduced performance. To overcome this challenge, we present a new approach of adversarial training based on a generative adversarial network (GAN). In particular, we first generate real adversarial examples using fast gradient sign method, Then, Our GAN generates new adversarial EEG signals using real adversarial examples as a validation set. By incorporating both real and generated adversarial examples during training, we enhance the EEG model performance. Finally, we evaluate our approach on BCI competition 2a dataset showing that it achieves a statistically significant performance improvement and enhances the robustness to adversarial attacks.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"20 23","pages":"44-49"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141048765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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IEEE App IEEE 应用程序

IEEE Internet of Things Magazine Pub Date : 2024-05-01 DOI: 10.1109/miot.2024.10517514

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Cover 3 封面 3

IEEE Internet of Things Magazine Pub Date : 2024-01-01 DOI: 10.1109/miot.2024.10397566

引用次数: 0

Optimization Design in RIS-Assisted Integrated Satellite-UAV-Served 6G IoT: A Deep Reinforcement Learning Approach RIS 辅助集成卫星-无人机服务 6G 物联网中的优化设计：深度强化学习方法

IEEE Internet of Things Magazine Pub Date : 2024-01-01 DOI: 10.1109/IOTM.001.2300111

Min Wu, K. Guo, Xingwang Li, Ali Nauman, Kang An, Ji Wang

{"title":"Optimization Design in RIS-Assisted Integrated Satellite-UAV-Served 6G IoT: A Deep Reinforcement Learning Approach","authors":"Min Wu, K. Guo, Xingwang Li, Ali Nauman, Kang An, Ji Wang","doi":"10.1109/IOTM.001.2300111","DOIUrl":"https://doi.org/10.1109/IOTM.001.2300111","url":null,"abstract":"Satellite networks have been emerged as a critical part of the next-generation wireless networks. However, the high transmission latency, highly dynamic channel conditions and energy resource constraints of Internet of Things (IoT) devices pose a challenge to performance improvements. To tackle above issues, technologies such as integrated satellite-unmanned aerial vehicle-terrestrial networks (IS-UAV-TNs), deep reinforcement learning (DRL), reconfigurable intelligent surface (RIS) are highly anticipated in 6G IoT. In this article, we consider the application of RIS to IS-UAV-TNs to reshape wireless channels by controlling the phase shift of the scattering elements. The dynamic configuration of the RIS reflection unit poses a high-dimensional problem, making beamforming optimization challenging. We focus on discussing the optimization method of integrating DRL in RIS-assisted IS-UAV-TNs, which offers flexibility in scenarios where precise channel state information (CSI) is unknown. To illustrate the advantage of the DRL framework in RIS-assisted IS-UAV-TNs, we design a representative communication scenario, where the results are provided according to the considered scenario. Finally, potential future research directions and challenges are presented.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"4 1","pages":"12-18"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139457794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sensor Clouds: Recent Advancements, Use Cases and Open Challenges 传感器云：最新进展、使用案例和公开挑战

IEEE Internet of Things Magazine Pub Date : 2024-01-01 DOI: 10.1109/IOTM.001.2300028

Ihsan Ali, Hasniuj Zahan, Spyridon Mastorakis

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A Data-Driven Framework for Air Quality Sensor Networks 空气质量传感器网络的数据驱动框架

IEEE Internet of Things Magazine Pub Date : 2024-01-01 DOI: 10.1109/IOTM.001.2300112

Pau Ferrer-Cid, J.A. Paredes-Ahumada, Xhensilda Allka, Manel Guerrero-Zapata, J. Barceló-Ordinas, J. García-Vidal

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Blockchain-Empowered Vehicular Intelligence: A Perspective of Asynchronous Federated Learning 区块链驱动的车载智能：异步联合学习的视角

IEEE Internet of Things Magazine Pub Date : 2024-01-01 DOI: 10.1109/IOTM.001.2300092

Jiancong Zhang, Shining Li

{"title":"Blockchain-Empowered Vehicular Intelligence: A Perspective of Asynchronous Federated Learning","authors":"Jiancong Zhang, Shining Li","doi":"10.1109/IOTM.001.2300092","DOIUrl":"https://doi.org/10.1109/IOTM.001.2300092","url":null,"abstract":"Blockchain-empowered federated learning is a promising learning framework, which mitigates several potential security threats in learning. However, in the Internet of Vehicles, the asynchronous network puts higher requirements on blockchains. Specifically, due to the asynchronous transaction updates, traditional consensus mechanisms require nodes to frequently coordinate to reach a consensus on the global order of transactions. This strong consistency brings excessive computing time and low efficiency to federated learning. Existing solutions completely relax the consistency of transactions, which, however, reduces the persistence and traceability. Therefore, we propose a lightweight permissioned blockchain with partial consensus, which reduce the coordination among nodes to reduce the system overhead. First, we run the consensus of transactions for global models and relax the strong consistency for local models, which are stored in parallel in real-time without coordination among nodes. Accordingly, we provide relative persistence to ensure the traceability of local models. Then, due to the orderless transactions, we use smart contracts, instead of time stamps, to control the staleness weight of local models in aggregation to reduce the vulnerability. Experimental results show that our scheme effectively improves the performance of blockchain-empowered systems and overcomes the challenges of asynchrony to the security of vehicular networks.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"4 3","pages":"74-80"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139458391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design Methodology for Robust, Distributed Time-Sensitive Applications 稳健分布式时间敏感型应用的设计方法

IEEE Internet of Things Magazine Pub Date : 2024-01-01 DOI: 10.1109/IOTM.001.2300048

Aviral Shrivastava, M. Khayatian, Bob Iannucci

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IEEE Foundation 电气和电子工程师学会基金会

IEEE Internet of Things Magazine Pub Date : 2024-01-01 DOI: 10.1109/miot.2024.10397592

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