Pub Date : 2024-03-01DOI: 10.1109/IOTM.001.2300119
Lina S. Mohjazi, Bassant Selim, Mallik Tatipamula, Muhammad Ali Imran
While the fifth-generation (5G) is bringing an innovative fabric of breakthrough technologies, enabling smart factories, cities, and Internet-of-Things (IoT), the unprecedented strain on communication networks put by these applications, in terms of highly cognitive, agile architectures and the support of massive connectivity, energy efficiency, and extreme ultra-low latency, is pushing 5G to their limits. As such, the focus of academic and industrial efforts has shifted toward beyond 5G (B5G) and the conceptualization of sixth-generation (6G) systems. This article discusses four main digital and societal use cases (UCs) that will drive the need to reconcile a new breed of network requirements. Based on this, we provide our vision of the fundamental architectural ingredients that will enable the promise of 6G networks of bringing the unification of experiences across the digital, physical, and human worlds. We outline key disruptive technological paradigms that will support 6G materialize a bouquet of unique expectations and redefine how we live and protect our planet. Finally, we adopt the recently envisaged ecosystem of the Internet-of-Musical Things (IoMusT) to depict how the discussed UCs and technological paradigms may be exploited to realize this ecosystem.
{"title":"The Journey Toward 6G: A Digital and Societal Revolution in the Making","authors":"Lina S. Mohjazi, Bassant Selim, Mallik Tatipamula, Muhammad Ali Imran","doi":"10.1109/IOTM.001.2300119","DOIUrl":"https://doi.org/10.1109/IOTM.001.2300119","url":null,"abstract":"While the fifth-generation (5G) is bringing an innovative fabric of breakthrough technologies, enabling smart factories, cities, and Internet-of-Things (IoT), the unprecedented strain on communication networks put by these applications, in terms of highly cognitive, agile architectures and the support of massive connectivity, energy efficiency, and extreme ultra-low latency, is pushing 5G to their limits. As such, the focus of academic and industrial efforts has shifted toward beyond 5G (B5G) and the conceptualization of sixth-generation (6G) systems. This article discusses four main digital and societal use cases (UCs) that will drive the need to reconcile a new breed of network requirements. Based on this, we provide our vision of the fundamental architectural ingredients that will enable the promise of 6G networks of bringing the unification of experiences across the digital, physical, and human worlds. We outline key disruptive technological paradigms that will support 6G materialize a bouquet of unique expectations and redefine how we live and protect our planet. Finally, we adopt the recently envisaged ecosystem of the Internet-of-Musical Things (IoMusT) to depict how the discussed UCs and technological paradigms may be exploited to realize this ecosystem.","PeriodicalId":502997,"journal":{"name":"IEEE Internet of Things Magazine","volume":"658 2","pages":"119-128"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140273400","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}
This article presents an in-depth examination of aerial reconfigurable intelligent surface (ARIS) technology, an innovative development in wireless communication networks facilitated by unmanned aerial vehicles (UAVs). ARIS introduces a dynamic approach to augmenting communication links, particularly vital in environments characterized by rapid change and complexity. We focus on key technical aspects such as the strategic 3D placement of ARIS for optimal signal coverage, intricate real-time channel estimation techniques, and the engineering challenges in hardware implementation. A detailed case study demonstrates the application of ARIS in a UAV-mounted relay system, aimed at enhancing the signal range and quality between a terrestrial base station and multiple ground users. This investigation encompasses the technical nuances of deploying ARIS, with an emphasis on the optimization of beamforming algorithms, evaluation of environmental influences on ARIS's operational stability and signal integrity, and a comparative analysis of beamforming strategies under variable operational parameters. The results underscore the efficacy of ARIS in elevating terrestrial network performance, offering technical strategies to address UAV-related dynamism and environmental variability. Additionally, the article explores diverse applications of ARIS, underscoring its critical role in scenarios spanning from emergency communications to advanced urban network infrastructure.
{"title":"Aerial Reconfigurable Intelligent Surface-Assisted Terrestrial Communications","authors":"Xiaohui Gu, Wei Duan, Guoan Zhang, Miaowen Wen, Jaeho Choi, Pin-Han Ho","doi":"10.1109/IOTM.001.2300141","DOIUrl":"https://doi.org/10.1109/IOTM.001.2300141","url":null,"abstract":"This article presents an in-depth examination of aerial reconfigurable intelligent surface (ARIS) technology, an innovative development in wireless communication networks facilitated by unmanned aerial vehicles (UAVs). ARIS introduces a dynamic approach to augmenting communication links, particularly vital in environments characterized by rapid change and complexity. We focus on key technical aspects such as the strategic 3D placement of ARIS for optimal signal coverage, intricate real-time channel estimation techniques, and the engineering challenges in hardware implementation. A detailed case study demonstrates the application of ARIS in a UAV-mounted relay system, aimed at enhancing the signal range and quality between a terrestrial base station and multiple ground users. This investigation encompasses the technical nuances of deploying ARIS, with an emphasis on the optimization of beamforming algorithms, evaluation of environmental influences on ARIS's operational stability and signal integrity, and a comparative analysis of beamforming strategies under variable operational parameters. The results underscore the efficacy of ARIS in elevating terrestrial network performance, offering technical strategies to address UAV-related dynamism and environmental variability. Additionally, the article explores diverse applications of ARIS, underscoring its critical role in scenarios spanning from emergency communications to advanced urban network infrastructure.","PeriodicalId":502997,"journal":{"name":"IEEE Internet of Things Magazine","volume":"175 ","pages":"54-60"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140276152","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 : 2024-03-01DOI: 10.1109/IOTM.001.2300129
Xinnan Yuan, Shuyan Hu, Wei Ni, Xin Wang, Abbas Jamalipour
Reconfigurable intelligent surfaces (RISs) and unmanned aerial vehicles (UAVs) have the potential to play a significant role in enhancing the security of the Internet-of-Things (IoT). RISs can be deployed as intelligent reflectors to augment wireless coverage passively. UAVs offer flexible and dynamic IoT platforms for communication, sensing, and monitoring. In this article, a particular interest is given to RIS-assisted, anti-jamming, UAV communication and radio surveillance, which are generally nonconvex and difficult to solve using traditional optimization tools. New artificial intelligence (AI) tools, more specifically, deep reinforcement learning (DRL), are developed to tackle the problems of UAV and RIS design. The use of DRL allows a UAV to learn its trajectory and RIS configuration to diffuse jamming signals and maximize its communication rate based on its received data rate. It also allows the UAV to maximize its eavesdropping rate based on the transmit rate of a suspicious transmitter that the UAV observes when conducting radio surveillance. The UAVs no longer rely on explicit knowledge of the channel state information, and can learn through trial and error. Simulations confirm the effectiveness of using UAVs, RISs, and AI to enhance the security of air-to-ground IoT networks, compared to baseline schemes without RIS or with non-AI-based RIS configurations.
{"title":"Empowering Reconfigurable Intelligent Surfaces with Artificial Intelligence to Secure Air-To-Ground Internet-of-Things","authors":"Xinnan Yuan, Shuyan Hu, Wei Ni, Xin Wang, Abbas Jamalipour","doi":"10.1109/IOTM.001.2300129","DOIUrl":"https://doi.org/10.1109/IOTM.001.2300129","url":null,"abstract":"Reconfigurable intelligent surfaces (RISs) and unmanned aerial vehicles (UAVs) have the potential to play a significant role in enhancing the security of the Internet-of-Things (IoT). RISs can be deployed as intelligent reflectors to augment wireless coverage passively. UAVs offer flexible and dynamic IoT platforms for communication, sensing, and monitoring. In this article, a particular interest is given to RIS-assisted, anti-jamming, UAV communication and radio surveillance, which are generally nonconvex and difficult to solve using traditional optimization tools. New artificial intelligence (AI) tools, more specifically, deep reinforcement learning (DRL), are developed to tackle the problems of UAV and RIS design. The use of DRL allows a UAV to learn its trajectory and RIS configuration to diffuse jamming signals and maximize its communication rate based on its received data rate. It also allows the UAV to maximize its eavesdropping rate based on the transmit rate of a suspicious transmitter that the UAV observes when conducting radio surveillance. The UAVs no longer rely on explicit knowledge of the channel state information, and can learn through trial and error. Simulations confirm the effectiveness of using UAVs, RISs, and AI to enhance the security of air-to-ground IoT networks, compared to baseline schemes without RIS or with non-AI-based RIS configurations.","PeriodicalId":502997,"journal":{"name":"IEEE Internet of Things Magazine","volume":"143 ","pages":"14-21"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140272594","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 : 2024-01-01DOI: 10.1109/miot.2024.10397561
{"title":"Comsoc Membershipt","authors":"","doi":"10.1109/miot.2024.10397561","DOIUrl":"https://doi.org/10.1109/miot.2024.10397561","url":null,"abstract":"","PeriodicalId":502997,"journal":{"name":"IEEE Internet of Things Magazine","volume":"55 33","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139634311","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 : 2024-01-01DOI: 10.1109/miot.2024.10397588
{"title":"Comsoc Technical Committees","authors":"","doi":"10.1109/miot.2024.10397588","DOIUrl":"https://doi.org/10.1109/miot.2024.10397588","url":null,"abstract":"","PeriodicalId":502997,"journal":{"name":"IEEE Internet of Things Magazine","volume":"182 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139638021","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 : 2024-01-01DOI: 10.1109/miot.2024.10397563
{"title":"Cover 2","authors":"","doi":"10.1109/miot.2024.10397563","DOIUrl":"https://doi.org/10.1109/miot.2024.10397563","url":null,"abstract":"","PeriodicalId":502997,"journal":{"name":"IEEE Internet of Things Magazine","volume":"242 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139636470","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 : 2024-01-01DOI: 10.1109/miot.2024.10397578
{"title":"IEEE App","authors":"","doi":"10.1109/miot.2024.10397578","DOIUrl":"https://doi.org/10.1109/miot.2024.10397578","url":null,"abstract":"","PeriodicalId":502997,"journal":{"name":"IEEE Internet of Things Magazine","volume":"5 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139631972","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 : 2024-01-01DOI: 10.1109/miot.2024.10397590
{"title":"IEEE Collabratec","authors":"","doi":"10.1109/miot.2024.10397590","DOIUrl":"https://doi.org/10.1109/miot.2024.10397590","url":null,"abstract":"","PeriodicalId":502997,"journal":{"name":"IEEE Internet of Things Magazine","volume":"341 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139632197","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 : 2020-03-01DOI: 10.1109/miot.2020.9063409
{"title":"Comsoc Conferences","authors":"","doi":"10.1109/miot.2020.9063409","DOIUrl":"https://doi.org/10.1109/miot.2020.9063409","url":null,"abstract":"","PeriodicalId":502997,"journal":{"name":"IEEE Internet of Things Magazine","volume":"27 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141226615","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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