Pub Date : 2023-12-01DOI: 10.1109/MCOMSTD.0003.2200066
Matteo Repetto
Recent management paradigms for software-defined infrastructures bring more agility to the creation and operation of digital services, but also introduce new cyber-security issues due to fast-changing environments, dynamic topologies, and wider attack surfaces. Rigid and statically-configured architectures are no longer suitable for the detection of cyber-attacks in mixed cloud/6G/IoT environments, hence new frameworks must be designed that are more flexible and adaptable to become cognitive. A fundamental step in this direction is represented by the adoption of common interfaces to orchestrate heterogeneous and multi-vendor security functions in a homogeneous way. In this article, we consider two recent interfaces to security functions that are representative of different approaches and industrial domains, namely I2NSF and OpenC2. We briefly review the latest advances in their definition, provide a deep comparison, and outline major limitations and research challenges for concrete application scenarios. The main purpose of our work is to make an unbiased evaluation of the current status of these standards and to encourage researchers to actively contribute to the development of the standards by adopting them and proposing further extensions and refinements.
{"title":"Interface To Security Functions: An Overview And Comparison Of I2nsf And Openc2","authors":"Matteo Repetto","doi":"10.1109/MCOMSTD.0003.2200066","DOIUrl":"https://doi.org/10.1109/MCOMSTD.0003.2200066","url":null,"abstract":"Recent management paradigms for software-defined infrastructures bring more agility to the creation and operation of digital services, but also introduce new cyber-security issues due to fast-changing environments, dynamic topologies, and wider attack surfaces. Rigid and statically-configured architectures are no longer suitable for the detection of cyber-attacks in mixed cloud/6G/IoT environments, hence new frameworks must be designed that are more flexible and adaptable to become cognitive. A fundamental step in this direction is represented by the adoption of common interfaces to orchestrate heterogeneous and multi-vendor security functions in a homogeneous way. In this article, we consider two recent interfaces to security functions that are representative of different approaches and industrial domains, namely I2NSF and OpenC2. We briefly review the latest advances in their definition, provide a deep comparison, and outline major limitations and research challenges for concrete application scenarios. The main purpose of our work is to make an unbiased evaluation of the current status of these standards and to encourage researchers to actively contribute to the development of the standards by adopting them and proposing further extensions and refinements.","PeriodicalId":36719,"journal":{"name":"IEEE Communications Standards Magazine","volume":"14 2","pages":"60-67"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138992868","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}
{"title":"Towards Trustworthy Digital Media In The Aigc Era: An Introduction To The Upcoming IsoJpegTrust Standard","authors":"Jiayun Mo, Xin Kang, Ziyuan Hu, Haibo Zhou, Tieyan Li, Xiaojun Gu","doi":"10.1109/mcomstd.2023.10353009","DOIUrl":"https://doi.org/10.1109/mcomstd.2023.10353009","url":null,"abstract":"","PeriodicalId":36719,"journal":{"name":"IEEE Communications Standards Magazine","volume":"46 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139017590","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 : 2023-09-01DOI: 10.1109/mcomstd.0006.2200060
Hao Ran Chi, Ayman Radwan, Chunjiong Zhang, Abd-Elhamid M. Taha
While great advances have been made in vehicular networks, especially in terms of softwarization and dynamic infrastructure, increasing dependence on Artificial Intelligence (AI) continues to challenge optimizations of the Energy Efficiency (EE)-Quality of Experience (QoE) tradeoffs. Moreover, optimal achievement of trade-off between EE and QoE will be put under great challenge in upcoming emerging 6G applications, resulting from identifying EE as a quantitative requirement for the first time in 6G. In this article, we present a comprehensive overview for the requirements of QoE and EE, throughout 4G, 5G, and beyond 5G. We summarize the mutual and conflicted perspectives of achieving high QoE and EE, considering the standardizations of the selected scenarios: industrial-based vehicular network and smart transportation. We also provide an insight into the potential challenges and opportunities, for future AI-based 6G vehicular networks, regarding QoE and EE.
{"title":"Managing Energy-Experience Trade-Off with AI Towards 6G Vehicular Networks","authors":"Hao Ran Chi, Ayman Radwan, Chunjiong Zhang, Abd-Elhamid M. Taha","doi":"10.1109/mcomstd.0006.2200060","DOIUrl":"https://doi.org/10.1109/mcomstd.0006.2200060","url":null,"abstract":"While great advances have been made in vehicular networks, especially in terms of softwarization and dynamic infrastructure, increasing dependence on Artificial Intelligence (AI) continues to challenge optimizations of the Energy Efficiency (EE)-Quality of Experience (QoE) tradeoffs. Moreover, optimal achievement of trade-off between EE and QoE will be put under great challenge in upcoming emerging 6G applications, resulting from identifying EE as a quantitative requirement for the first time in 6G. In this article, we present a comprehensive overview for the requirements of QoE and EE, throughout 4G, 5G, and beyond 5G. We summarize the mutual and conflicted perspectives of achieving high QoE and EE, considering the standardizations of the selected scenarios: industrial-based vehicular network and smart transportation. We also provide an insight into the potential challenges and opportunities, for future AI-based 6G vehicular networks, regarding QoE and EE.","PeriodicalId":36719,"journal":{"name":"IEEE Communications Standards Magazine","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135735640","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 : 2023-09-01DOI: 10.1109/mcomstd.0002.2200007
Sohail Ahmad, Saud Khan, Komal S. Khan, Faisal Naeem, Muhammad Tariq
In a wireless communication network, the propagation medium has been perceived as a randomly behaving entity between the transmitter and receiver for a long time. However future wireless networks, such as 5G and beyond, rely on Intelligent Reflecting Surfaces (IRS) for promising energy efficiency and spectrum improvement as it enables the proactive control of the wireless channel. In this article, we take a step further and discuss the role of Deep Reinforcement Learning (DRL)-based IRS-assisted network in delivering performance enhancement in a wireless communication network. We provide an insight into DRL followed by the challenges in place for an IRS-assisted wireless network. We then lay out a case study to show the channel processing using two state-of-the-art DRL algorithms, that is, deep Q-network (DQN) and deep deterministic policy gradient (DDPG), in an IRS-assisted network and work on jointly optimizing the transmit beamforming and the IRS phase angles for sum-rate maximization. The sum-rate improvement is shown in reference to the number of reflecting IRS elements. Opportunities in DRL-based IRS-assisted wireless networks are then discussed to showcase the exciting future opportunities in this domain.
{"title":"Resource Allocation for IRS-Assisted Networks: A Deep Reinforcement Learning Approach","authors":"Sohail Ahmad, Saud Khan, Komal S. Khan, Faisal Naeem, Muhammad Tariq","doi":"10.1109/mcomstd.0002.2200007","DOIUrl":"https://doi.org/10.1109/mcomstd.0002.2200007","url":null,"abstract":"In a wireless communication network, the propagation medium has been perceived as a randomly behaving entity between the transmitter and receiver for a long time. However future wireless networks, such as 5G and beyond, rely on Intelligent Reflecting Surfaces (IRS) for promising energy efficiency and spectrum improvement as it enables the proactive control of the wireless channel. In this article, we take a step further and discuss the role of Deep Reinforcement Learning (DRL)-based IRS-assisted network in delivering performance enhancement in a wireless communication network. We provide an insight into DRL followed by the challenges in place for an IRS-assisted wireless network. We then lay out a case study to show the channel processing using two state-of-the-art DRL algorithms, that is, deep Q-network (DQN) and deep deterministic policy gradient (DDPG), in an IRS-assisted network and work on jointly optimizing the transmit beamforming and the IRS phase angles for sum-rate maximization. The sum-rate improvement is shown in reference to the number of reflecting IRS elements. Opportunities in DRL-based IRS-assisted wireless networks are then discussed to showcase the exciting future opportunities in this domain.","PeriodicalId":36719,"journal":{"name":"IEEE Communications Standards Magazine","volume":"32 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135735453","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 : 2023-09-01DOI: 10.1109/mcomstd.0008.2200067
Ahmad Ahilal, Tristan Braud, Lik-Hang Lee, Hang Chen, Pan Hui
The emergence of the Metaverse enables the creation of alternative spaces at the intersection between digital and physical through the replication of physical events and objects within physical-digital twins. In this article, we apply such twins to connected vehicles within a Traffic Metaverse as an intermediate platform for the shared perception of the road environment. The platform builds a real-time digital copy of the road conditions to serve vehicular applications on a city scale through ubiquitous sensing, reliable and low-latency communications, artificial intelligence, and extended reality. This article focuses on collaboratively building virtual 3D maps of road networks that provide road users with a pervasive view of the road conditions to increase situational awareness. Through vehicle-to-everything (V2X) cooperative perception, such a Metaverse platform expands the driver's visibility beyond the vehicle's line of sight. The platform also enables virtual remote access to monitor and forecast traffic ahead of space and time.
{"title":"Toward A Traffic Metaverse With Shared Vehicle Perception","authors":"Ahmad Ahilal, Tristan Braud, Lik-Hang Lee, Hang Chen, Pan Hui","doi":"10.1109/mcomstd.0008.2200067","DOIUrl":"https://doi.org/10.1109/mcomstd.0008.2200067","url":null,"abstract":"The emergence of the Metaverse enables the creation of alternative spaces at the intersection between digital and physical through the replication of physical events and objects within physical-digital twins. In this article, we apply such twins to connected vehicles within a Traffic Metaverse as an intermediate platform for the shared perception of the road environment. The platform builds a real-time digital copy of the road conditions to serve vehicular applications on a city scale through ubiquitous sensing, reliable and low-latency communications, artificial intelligence, and extended reality. This article focuses on collaboratively building virtual 3D maps of road networks that provide road users with a pervasive view of the road conditions to increase situational awareness. Through vehicle-to-everything (V2X) cooperative perception, such a Metaverse platform expands the driver's visibility beyond the vehicle's line of sight. The platform also enables virtual remote access to monitor and forecast traffic ahead of space and time.","PeriodicalId":36719,"journal":{"name":"IEEE Communications Standards Magazine","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135735684","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}
Integrated access and backhaul (IAB) is an innovative wireless backhaul solution to provide cost-efficient deployment of small cells for successful 5G adoption. Besides, IAB can utilize the same spectrum for access and backhaul purposes. The 3GPP has standardized IAB in Release 16 and may incorporate a few enhancements in the upcoming releases. The 3GPP IAB architecture, however, suffers from some limitations, such as it does not support mobile relays or dual-connectivity. This article presents a novel IAB architecture that addresses these limitations and is transparent to legacy operations of the 5G system. The architecture also supports multi-RAT coexistence where access and backhaul may belong to different RATs. These factors (and many others) enable operators to capitalize on the architecture for deploying IAB anywhere in a plug-and-play manner. We also show the merits of the architecture by evaluating its capacity and mobility robustness compared to the 3GPP architecture. Simulation results corroborate our design approach. Owing to its robust design, the architecture can contend for standardization in B5G system.
{"title":"A Flexible IAB Architecture for Beyond 5G Network","authors":"Shashi Ranjan, Pranav Jha, Abhay Karandikar, Prasanna Chaporkar","doi":"10.1109/mcomstd.0009.2200018","DOIUrl":"https://doi.org/10.1109/mcomstd.0009.2200018","url":null,"abstract":"Integrated access and backhaul (IAB) is an innovative wireless backhaul solution to provide cost-efficient deployment of small cells for successful 5G adoption. Besides, IAB can utilize the same spectrum for access and backhaul purposes. The 3GPP has standardized IAB in Release 16 and may incorporate a few enhancements in the upcoming releases. The 3GPP IAB architecture, however, suffers from some limitations, such as it does not support mobile relays or dual-connectivity. This article presents a novel IAB architecture that addresses these limitations and is transparent to legacy operations of the 5G system. The architecture also supports multi-RAT coexistence where access and backhaul may belong to different RATs. These factors (and many others) enable operators to capitalize on the architecture for deploying IAB anywhere in a plug-and-play manner. We also show the merits of the architecture by evaluating its capacity and mobility robustness compared to the 3GPP architecture. Simulation results corroborate our design approach. Owing to its robust design, the architecture can contend for standardization in B5G system.","PeriodicalId":36719,"journal":{"name":"IEEE Communications Standards Magazine","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135735693","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 : 2023-09-01DOI: 10.1109/mcomstd.2023.10287317
Anwer Al-Dulaimi, Xiaodong Lin
In the dynamic realm of technology, vehicular networking stands as a testament to human ingenuity, transforming the way vehicles communicate and operate. Our September issue of IEEE Communication Standards Magazine dives deep into the pivotal theme of Ultra-Low Latency and Reliable Communications for Future Wireless Networks.
{"title":"Vehicular Networking: Ultra-Low Latency and Reliable Communications For Future Wireless Networks","authors":"Anwer Al-Dulaimi, Xiaodong Lin","doi":"10.1109/mcomstd.2023.10287317","DOIUrl":"https://doi.org/10.1109/mcomstd.2023.10287317","url":null,"abstract":"In the dynamic realm of technology, vehicular networking stands as a testament to human ingenuity, transforming the way vehicles communicate and operate. Our September issue of IEEE Communication Standards Magazine dives deep into the pivotal theme of Ultra-Low Latency and Reliable Communications for Future Wireless Networks.","PeriodicalId":36719,"journal":{"name":"IEEE Communications Standards Magazine","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135735683","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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