Pub Date : 2024-07-01DOI: 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
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.
{"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}
Pub Date : 2024-07-01DOI: 10.1109/miot.2024.10574242
{"title":"IEEE App","authors":"","doi":"10.1109/miot.2024.10574242","DOIUrl":"https://doi.org/10.1109/miot.2024.10574242","url":null,"abstract":"","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"44 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141704525","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}
Pub Date : 2024-07-01DOI: 10.1109/IOTM.001.2300181
Muna Al-Hawawreh, Zubair A. Baig, S. Zeadally
Artificial intelligence (AI) plays an increasingly important role in security, particularly in view of constantly evolving adversarial tactics and techniques. For Critical Infrastructures (CIs), the demand for AI-based security solutions is essential in an increasingly connected world of heterogeneous CI devices and through evolution of attack vectors. We study how AI could provide better CI security and prevent cyber-attacks. An extensive survey of popular AI models currently adopted for intrusion/attack detection, privacy, trust management, and authentication for CIs is presented. We also propose use cases to describe how AI is used to enable zero trust in industrial control systems and to provide cyber resilience. Based on the study, we also elaborate upon some prominent challenges which must be addressed in the future for adopting reliable and trusted AI for CI security.
人工智能（AI）在安全领域发挥着越来越重要的作用，特别是考虑到对手不断演变的战术和技术。对于关键基础设施（CI）来说，在异构 CI 设备连接日益紧密、攻击载体不断演变的世界中，对基于人工智能的安全解决方案的需求至关重要。我们研究了人工智能如何提供更好的 CI 安全和预防网络攻击。我们对目前用于入侵/攻击检测、隐私保护、信任管理和 CI 身份验证的流行人工智能模型进行了广泛调查。我们还提出了使用案例，描述如何利用人工智能实现工业控制系统的零信任，并提供网络弹性。在研究的基础上，我们还阐述了未来在采用可靠、可信的人工智能确保 CI 安全方面必须应对的一些突出挑战。
{"title":"AI for Critical Infrastructure Security: Concepts, Challenges, and Future Directions","authors":"Muna Al-Hawawreh, Zubair A. Baig, S. Zeadally","doi":"10.1109/IOTM.001.2300181","DOIUrl":"https://doi.org/10.1109/IOTM.001.2300181","url":null,"abstract":"Artificial intelligence (AI) plays an increasingly important role in security, particularly in view of constantly evolving adversarial tactics and techniques. For Critical Infrastructures (CIs), the demand for AI-based security solutions is essential in an increasingly connected world of heterogeneous CI devices and through evolution of attack vectors. We study how AI could provide better CI security and prevent cyber-attacks. An extensive survey of popular AI models currently adopted for intrusion/attack detection, privacy, trust management, and authentication for CIs is presented. We also propose use cases to describe how AI is used to enable zero trust in industrial control systems and to provide cyber resilience. Based on the study, we also elaborate upon some prominent challenges which must be addressed in the future for adopting reliable and trusted AI for CI security.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"23 2","pages":"136-142"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141691321","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}
Pub Date : 2024-07-01DOI: 10.1109/miot.2024.10574238
{"title":"Cover 4","authors":"","doi":"10.1109/miot.2024.10574238","DOIUrl":"https://doi.org/10.1109/miot.2024.10574238","url":null,"abstract":"","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"32 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141695271","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}
Reconfigurable intelligent surface (RIS), by intelligently manipulating the incident waveform, offers a spectral and energy efficient capability for improving sensing and communication performance. In this article, we introduce a novel concept of RIS-assisted integrated sensing and backscatter communication (ISABC) system, by introducing RIS as either helper or transceiver to resolve the energy constraint of devices in internet of things (IoT) network and enable non line-of-sight (NLoS) sensing. We first introduce the RIS-assisted ISABC framework, including the system architecture and realization of RIS. Three potential applications are then discussed, with the analysis on their requirements. The research on several critical techniques for the RIS-assisted ISABC system is then discussed. Finally, we provide our vision of the challenges and future research directions to facilitate the development of the RIS-assisted ISABC systems.
{"title":"RIS-Assisted Integrated Sensing and Backscatter Communications for Future IoT Networks","authors":"Nan Wu, Xinyi Wang, Zesong Fei, Fanghao Xia, Jingxuan Huang, Arumugam Nallanathan","doi":"10.1109/IOTM.001.2300184","DOIUrl":"https://doi.org/10.1109/IOTM.001.2300184","url":null,"abstract":"Reconfigurable intelligent surface (RIS), by intelligently manipulating the incident waveform, offers a spectral and energy efficient capability for improving sensing and communication performance. In this article, we introduce a novel concept of RIS-assisted integrated sensing and backscatter communication (ISABC) system, by introducing RIS as either helper or transceiver to resolve the energy constraint of devices in internet of things (IoT) network and enable non line-of-sight (NLoS) sensing. We first introduce the RIS-assisted ISABC framework, including the system architecture and realization of RIS. Three potential applications are then discussed, with the analysis on their requirements. The research on several critical techniques for the RIS-assisted ISABC system is then discussed. Finally, we provide our vision of the challenges and future research directions to facilitate the development of the RIS-assisted ISABC systems.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"106 1","pages":"44-50"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141711674","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}
Pub Date : 2024-07-01DOI: 10.1109/IOTM.001.2300177
Bin Li, Wancheng Xie, Zesong Fei
Forthcoming 6G networks have two predominant features of wide coverage and sufficient computation capability. To support the promising applications, Integrated Sensing, Communication, and Computation (ISCC) has been considered as a vital enabler by completing the computation of raw data to achieve accurate environmental sensing. To help the ISCC networks better support the comprehensive services of radar detection, data transmission and edge computing, Reconfigurable Intelligent Surface (RIS) can be employed to boost the transmission rate and the wireless coverage by smartly tuning the electromagnetic characteristics of the environment. In this article, we propose an RIS-assisted ISCC framework and exploit the RIS benefits for improving radar sensing, communication and computing functionalities via cross-layer design, while discussing the key challenges. Then, two generic application scenarios are presented, i.e., unmanned aerial vehicles and Internet of vehicles. Finally, numerical results demonstrate a superiority of RIS-assisted ISCC, followed by a range of future research directions.
{"title":"Reconfigurable Intelligent Surface for Sensing, Communication, and Computation: Perspectives, Challenges, and Opportunities","authors":"Bin Li, Wancheng Xie, Zesong Fei","doi":"10.1109/IOTM.001.2300177","DOIUrl":"https://doi.org/10.1109/IOTM.001.2300177","url":null,"abstract":"Forthcoming 6G networks have two predominant features of wide coverage and sufficient computation capability. To support the promising applications, Integrated Sensing, Communication, and Computation (ISCC) has been considered as a vital enabler by completing the computation of raw data to achieve accurate environmental sensing. To help the ISCC networks better support the comprehensive services of radar detection, data transmission and edge computing, Reconfigurable Intelligent Surface (RIS) can be employed to boost the transmission rate and the wireless coverage by smartly tuning the electromagnetic characteristics of the environment. In this article, we propose an RIS-assisted ISCC framework and exploit the RIS benefits for improving radar sensing, communication and computing functionalities via cross-layer design, while discussing the key challenges. Then, two generic application scenarios are presented, i.e., unmanned aerial vehicles and Internet of vehicles. Finally, numerical results demonstrate a superiority of RIS-assisted ISCC, followed by a range of future research directions.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"56 2","pages":"36-42"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141710850","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}
Pub Date : 2024-07-01DOI: 10.1109/miot.2024.10574193
{"title":"Cover 2","authors":"","doi":"10.1109/miot.2024.10574193","DOIUrl":"https://doi.org/10.1109/miot.2024.10574193","url":null,"abstract":"","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"2010 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141707077","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}
Pub Date : 2024-07-01DOI: 10.1109/miot.2024.10574241
N. Narang
{"title":"Mentor's Musings on Integrated Sensing & Communication - A Major Leap Towards an Ubiquitous IoT Paradigm","authors":"N. Narang","doi":"10.1109/miot.2024.10574241","DOIUrl":"https://doi.org/10.1109/miot.2024.10574241","url":null,"abstract":"","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"339 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141691783","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}
Pub Date : 2024-07-01DOI: 10.1109/IOTM.001.2300247
Yuxuan Jiang, Qiang Ye, E. T. Fapi, Wenting Sun, Fudong Li
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.
{"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}
Pub Date : 2024-07-01DOI: 10.1109/IOTM.001.2300187
A. Abdellatif, Noor Khial, Menna Helmy, Amr Mohamed, A. Erbad, K. Shaban
As we transition from centralized machine learning to distributed learning, new practices can significantly enhance intelligent Internet of Things (IoT) systems. This article introduces the concept of Opportunistic Distributed Learning (ODL), a general framework that enables any node in a network to initiates learning tasks by leveraging local, unused distributed resources collaboratively. ODL, facilitated by edge intelligence, promotes collective responsibility, pervasive and flexible distributed learning, allowing participating nodes to freely move, group, and regroup based on their conditions and benefits. The article discusses key research challenges of ODL in intelligent IoT systems, presents the ODL framework, proposes a reputation-based node selection scheme, and highlights the benefits and future research directions of the ODL system.
{"title":"ODL: Opportunistic Distributed Learning for Intelligent IoT Systems","authors":"A. Abdellatif, Noor Khial, Menna Helmy, Amr Mohamed, A. Erbad, K. Shaban","doi":"10.1109/IOTM.001.2300187","DOIUrl":"https://doi.org/10.1109/IOTM.001.2300187","url":null,"abstract":"As we transition from centralized machine learning to distributed learning, new practices can significantly enhance intelligent Internet of Things (IoT) systems. This article introduces the concept of Opportunistic Distributed Learning (ODL), a general framework that enables any node in a network to initiates learning tasks by leveraging local, unused distributed resources collaboratively. ODL, facilitated by edge intelligence, promotes collective responsibility, pervasive and flexible distributed learning, allowing participating nodes to freely move, group, and regroup based on their conditions and benefits. The article discusses key research challenges of ODL in intelligent IoT systems, presents the ODL framework, proposes a reputation-based node selection scheme, and highlights the benefits and future research directions of the ODL system.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"39 1","pages":"92-99"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141705881","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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