IEEE Internet of Things Magazine最新文献

Integrating Sensing and Communication for IoT Systems: Task-Oriented Control Perspective 为物联网系统整合传感与通信：面向任务的控制视角

IEEE Internet of Things Magazine Pub Date : 2024-07-01 DOI: 10.1109/IOTM.001.2300210

Dongxuan He, Huazhou Hou, Rongkun Jiang, Xinghuo Yu, Zhongyuan Zhao, Yuanqiu Mo, Yongming Huang, Wenwu Yu, Tony Q. S. Quek

{"title":"Integrating Sensing and Communication for IoT Systems: Task-Oriented Control Perspective","authors":"Dongxuan He, Huazhou Hou, Rongkun Jiang, Xinghuo Yu, Zhongyuan Zhao, Yuanqiu Mo, Yongming Huang, Wenwu Yu, Tony Q. S. Quek","doi":"10.1109/IOTM.001.2300210","DOIUrl":"https://doi.org/10.1109/IOTM.001.2300210","url":null,"abstract":"The Internet of Things (IoT) is widely acknowledged as an innovative paradigm that engenders profound alterations along with society's development due to its inevitability and universality. To effectively control IoT systems for specific tasks, it is important to tackle their emerging challenges, such as information transmission and sensing for the environment or specific task. Integrating sensing and communication (ISAC), which achieves the communication and sensing functionalities over one hardware platform, has significant advantages over dedicated sensing and communication, and has been regarded as a key technique for IoT ecosystems with the upcoming 6G communication revolution. Bearing this in mind, we provide an overview of ISAC-enabled IoT systems in terms of their framework and key technologies. In particular, we first give a basic introduction to control systems, which guides the design of our considered system structure. Then, ISAC and other enabling technologies are illustrated to facilitate our proposed system. Benefiting from the communication and sensing functionalities, our proposed ISAC-enabled IoT systems enable task-oriented control. Future challenges and directions for more efficient task-oriented IoT are also discussed.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"59 5","pages":"76-83"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141697769","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

IEEE App IEEE 应用程序

IEEE Internet of Things Magazine Pub Date : 2024-07-01 DOI: 10.1109/miot.2024.10574242

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AI for Critical Infrastructure Security: Concepts, Challenges, and Future Directions 关键基础设施安全的人工智能：概念、挑战和未来方向

IEEE Internet of Things Magazine Pub Date : 2024-07-01 DOI: 10.1109/IOTM.001.2300181

Muna Al-Hawawreh, Zubair A. Baig, S. Zeadally

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Cover 4 封面 4

IEEE Internet of Things Magazine Pub Date : 2024-07-01 DOI: 10.1109/miot.2024.10574238

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RIS-Assisted Integrated Sensing and Backscatter Communications for Future IoT Networks 面向未来物联网网络的 RIS 辅助集成传感和反向散射通信

IEEE Internet of Things Magazine Pub Date : 2024-07-01 DOI: 10.1109/IOTM.001.2300184

Nan Wu, Xinyi Wang, Zesong Fei, Fanghao Xia, Jingxuan Huang, Arumugam Nallanathan

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Reconfigurable Intelligent Surface for Sensing, Communication, and Computation: Perspectives, Challenges, and Opportunities 用于传感、通信和计算的可重构智能表面：视角、挑战和机遇

IEEE Internet of Things Magazine Pub Date : 2024-07-01 DOI: 10.1109/IOTM.001.2300177

Bin Li, Wancheng Xie, Zesong Fei

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Cover 2 封二

IEEE Internet of Things Magazine Pub Date : 2024-07-01 DOI: 10.1109/miot.2024.10574193

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Mentor's Musings on Integrated Sensing & Communication - A Major Leap Towards an Ubiquitous IoT Paradigm Mentor 关于集成传感与通信的思考 - 迈向无所不在的物联网范式的重大飞跃

IEEE Internet of Things Magazine Pub Date : 2024-07-01 DOI: 10.1109/miot.2024.10574241

N. Narang

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Workload Allocation for Distributed Coded Machine Learning: From Offline Model-Based to Online Model-Free 分布式编码机器学习的工作量分配：从基于离线模型到无在线模型

IEEE Internet of Things Magazine Pub Date : 2024-07-01 DOI: 10.1109/IOTM.001.2300247

Yuxuan Jiang, Qiang Ye, E. T. Fapi, Wenting Sun, Fudong Li

{"title":"Workload Allocation for Distributed Coded Machine Learning: From Offline Model-Based to Online Model-Free","authors":"Yuxuan Jiang, Qiang Ye, E. T. Fapi, Wenting Sun, Fudong Li","doi":"10.1109/IOTM.001.2300247","DOIUrl":"https://doi.org/10.1109/IOTM.001.2300247","url":null,"abstract":"Distributed machine learning (ML) is an important Internet-of-Things (IoT) application. In traditional partitioned learning (PL) paradigm, a coordinator divides a high-dimensional dataset into subsets, which are processed on IoT devices. The execution time of PL can be seriously bottlenecked by slow devices named stragglers. To mitigate the negative impact of stragglers, distributed coded machine learning (DCML) was recently proposed to inject redundancy into the subsets using coding techniques. With this redundancy, the coordinator no longer requires the processing results from all devices, but only from a subgroup, where stragglers can be eliminated. This article aims to bring the burgeoning field of DCML to the wider community. After outlining the principles of DCML, we focus on its workload allocation, which addresses the appropriate level of injected redundancy to minimize the overall execution time. We highlight the fundamental trade-off and point out two critical design choices in workload allocation: model-based versus model-free, and offline versus online. Despite the predominance of offline model-based approaches in the literature, online model-based approaches also have a wide array of use case scenarios, but remain largely unexplored. At the end of the article, we propose the first online model-free workload allocation scheme for DCML, and identify future paths and opportunities along this direction.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"44 1","pages":"100-106"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141706352","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

ODL: Opportunistic Distributed Learning for Intelligent IoT Systems ODL：面向智能物联网系统的机会性分布式学习

IEEE Internet of Things Magazine Pub Date : 2024-07-01 DOI: 10.1109/IOTM.001.2300187

A. Abdellatif, Noor Khial, Menna Helmy, Amr Mohamed, A. Erbad, K. Shaban

引用次数: 1