In the era of sixth-generation (6G) wireless communications, integrated sensing and communications (ISAC) is recognized as a promising solution to upgrade the physical system by endowing wireless communications with sensing capability. Existing ISAC is mainly oriented to static scenarios with radio-frequency (RF) sensors being the primary participants, thus lacking a comprehensive environment feature characterization and facing a severe performance bottleneck in dynamic environments. To date, extensive surveys on ISAC have been conducted but are limited to summarizing RF-based radar sensing. Currently, some research efforts have been devoted to exploring multi-modal sensing-communication integration but still lack a comprehensive review. To fill the gap, we embark on an initial endeavor with the goal of establishing a unified framework of intelligent multi-modal sensing-communication integration by generalizing the concept of ISAC and providing a comprehensive review under this framework. Inspired by the human synesthesia, the so-termed Synesthesia of Machines (SoM) gives the clearest cognition of such an intelligent integration and details its paradigm for the first time. We commence by justifying the necessity and potential of the new paradigm. Subsequently, we offer a rigorous definition of SoM and zoom into the detailed paradigm, which is summarized as three operational modes realizing the integration. To facilitate SoM research, we overview the prerequisite of SoM research, that is, mixed multi-modal (MMM) datasets, and introduce our work. Built upon the MMM datasets, we introduce the mapping relationships between multi-modal sensing and communications, and discuss how channel modeling can be customized to support the exploration of such relationships. Afterward, aiming at giving a comprehensive survey on the current research status of multi-modal sensing-communication integration, we cover the technological review on SoM-enhance-based and SoM-concert-based applications in transceiver design and environment sensing. To corroborate the rationality and superiority of SoM, we also present simulation results related to dual-function waveform and predictive beamforming design tailored for dynamic scenarios. Finally, we propose some open issues and potential directions to inspire future research efforts on SoM.
在第六代（6G）无线通信时代，综合传感与通信（ISAC）被认为是通过赋予无线通信以传感能力来升级物理系统的一种有前途的解决方案。现有的 ISAC 主要面向静态场景，以射频（RF）传感器为主要参与者，因此缺乏全面的环境特征描述，在动态环境中面临严重的性能瓶颈。迄今为止，关于 ISAC 的研究已经进行了大量调查，但仅限于对基于射频的雷达传感进行总结。目前，一些研究工作致力于探索多模式传感-通信集成，但仍缺乏全面的综述。为了填补这一空白，我们开始了初步的尝试，目标是通过概括 ISAC 的概念，建立智能多模态传感-通信集成的统一框架，并在此框架下提供全面的综述。受人类联觉的启发，所谓的机器联觉（SoM）对这种智能集成给出了最清晰的认知，并首次详细介绍了其范式。我们首先论证了新范式的必要性和潜力。随后，我们给出了 SoM 的严格定义，并详细介绍了其范式，概括为实现集成的三种运行模式。为了促进 SoM 研究，我们概述了 SoM 研究的先决条件，即混合多模态（MMM）数据集，并介绍了我们的工作。在多模式混合数据集的基础上，我们介绍了多模式传感与通信之间的映射关系，并讨论了如何定制信道建模以支持对这种关系的探索。随后，为了全面考察多模态传感与通信集成的研究现状，我们对收发器设计和环境感知中基于SoM增强和SoM增强的应用进行了技术综述。为了证实 SoM 的合理性和优越性，我们还介绍了针对动态场景的双功能波形和预测波束成形设计的相关仿真结果。最后，我们提出了一些有待解决的问题和潜在的研究方向，以激励未来的 SoM 研究工作。
{"title":"Intelligent Multi-Modal Sensing-Communication Integration: Synesthesia of Machines","authors":"Xiang Cheng;Haotian Zhang;Jianan Zhang;Shijian Gao;Sijiang Li;Ziwei Huang;Lu Bai;Zonghui Yang;Xinhu Zheng;Liuqing Yang","doi":"10.1109/COMST.2023.3336917","DOIUrl":"https://doi.org/10.1109/COMST.2023.3336917","url":null,"abstract":"In the era of sixth-generation (6G) wireless communications, integrated sensing and communications (ISAC) is recognized as a promising solution to upgrade the physical system by endowing wireless communications with sensing capability. Existing ISAC is mainly oriented to static scenarios with radio-frequency (RF) sensors being the primary participants, thus lacking a comprehensive environment feature characterization and facing a severe performance bottleneck in dynamic environments. To date, extensive surveys on ISAC have been conducted but are limited to summarizing RF-based radar sensing. Currently, some research efforts have been devoted to exploring multi-modal sensing-communication integration but still lack a comprehensive review. To fill the gap, we embark on an initial endeavor with the goal of establishing a unified framework of intelligent multi-modal sensing-communication integration by generalizing the concept of ISAC and providing a comprehensive review under this framework. Inspired by the human synesthesia, the so-termed Synesthesia of Machines (SoM) gives the clearest cognition of such an intelligent integration and details its paradigm for the first time. We commence by justifying the necessity and potential of the new paradigm. Subsequently, we offer a rigorous definition of SoM and zoom into the detailed paradigm, which is summarized as three operational modes realizing the integration. To facilitate SoM research, we overview the prerequisite of SoM research, that is, mixed multi-modal (MMM) datasets, and introduce our work. Built upon the MMM datasets, we introduce the mapping relationships between multi-modal sensing and communications, and discuss how channel modeling can be customized to support the exploration of such relationships. Afterward, aiming at giving a comprehensive survey on the current research status of multi-modal sensing-communication integration, we cover the technological review on SoM-enhance-based and SoM-concert-based applications in transceiver design and environment sensing. To corroborate the rationality and superiority of SoM, we also present simulation results related to dual-function waveform and predictive beamforming design tailored for dynamic scenarios. Finally, we propose some open issues and potential directions to inspire future research efforts on SoM.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"26 1","pages":"258-301"},"PeriodicalIF":35.6,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139976287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-21DOI: 10.1109/COMST.2023.3330559
Dusit Niyato
I welcome you to the fourth issue of the IEEE Communications Surveys and Tutorials in 2023. This issue includes 25 papers covering different aspects of communication networks. In particular, these articles survey and tutor various issues in “Wireless Communications”, “Cyber Security”, “IoT and M2M”, “Vehicular and Sensor Communications”, “Internet Technologies”, and “Network and Service Management and Green Communications”. A brief account for each of these papers is given below.
{"title":"Editorial: Fourth Quarter 2023 IEEE Communications Surveys and Tutorials","authors":"Dusit Niyato","doi":"10.1109/COMST.2023.3330559","DOIUrl":"https://doi.org/10.1109/COMST.2023.3330559","url":null,"abstract":"I welcome you to the fourth issue of the IEEE Communications Surveys and Tutorials in 2023. This issue includes 25 papers covering different aspects of communication networks. In particular, these articles survey and tutor various issues in “Wireless Communications”, “Cyber Security”, “IoT and M2M”, “Vehicular and Sensor Communications”, “Internet Technologies”, and “Network and Service Management and Green Communications”. A brief account for each of these papers is given below.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"25 4","pages":"i-viii"},"PeriodicalIF":35.6,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10325334","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138431085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-20DOI: 10.1109/COMST.2023.3334269
Shahzad Ahmed;Sung Ho Cho
The unprecedented non-contact, non-invasive, and privacy-preserving nature of radar sensors has enabled various healthcare applications, including vital sign monitoring, fall detection, gait analysis, activity recognition, fitness evaluation, and sleep monitoring. Machine learning (ML) is revolutionizing every domain, with radar-based healthcare being no exception. Progress in the field of healthcare radars and ML is complementing the existing radar-based healthcare industry. This article provides an overview of ML usage for two major healthcare applications: vital sign monitoring and activity recognition. Vital sign monitoring is the most promising healthcare application of radar, as it can predict several chronic cardiac and respiratory diseases. Activity recognition is also a prominent application since the inability to perform activities may result in critical suffering. The article presents an overview of commercial radars, radar hardware, and historical progress of healthcare radars, followed by the usage of ML for healthcare radars. Subsequently, the paper discusses how ML can overcome the limitations of conventional radar data processing chains for healthcare radars. The article also touches upon recent generative ML concepts used in healthcare radars. Among several interesting findings, it was discovered that ML does not completely replace existing vital sign monitoring algorithms; rather, ML is deployed to overcome the limitations of traditional algorithms. On the other hand, activity recognition always relies on ML approaches. The most widely used algorithms for both applications are Convolutional Neural Network (CNN) followed by Support Vector Machine (SVM). Generative AI has the capability to augment data and is expected to have a significant impact soon. Recent trends, lessons learned from these trends, and future directions for both healthcare applications are presented in detail. Finally, the future work section discusses a wide range of healthcare topics for humans, ranging from neonates to elderly individuals.
雷达传感器具有前所未有的非接触、非侵入和保护隐私的特性，使各种医疗保健应用成为可能，包括生命体征监测、跌倒检测、步态分析、活动识别、体能评估和睡眠监测。机器学习（ML）正在彻底改变各个领域，基于雷达的医疗保健领域也不例外。医疗雷达和 ML 领域的进步正在补充现有的雷达医疗行业。本文概述了 ML 在生命体征监测和活动识别这两大医疗应用中的应用。生命体征监测是雷达最有前途的医疗应用，因为它可以预测多种慢性心脏病和呼吸系统疾病。活动识别也是一个突出的应用，因为无法进行活动可能会导致严重的痛苦。文章概述了商用雷达、雷达硬件和医疗雷达的历史进展，然后介绍了 ML 在医疗雷达中的应用。随后，文章讨论了 ML 如何克服医疗雷达传统雷达数据处理链的局限性。文章还谈到了最近用于医疗雷达的生成式 ML 概念。在几个有趣的发现中，我们发现 ML 并不能完全取代现有的生命体征监测算法；相反，部署 ML 是为了克服传统算法的局限性。另一方面，活动识别始终依赖于 ML 方法。这两种应用中最广泛使用的算法是卷积神经网络（CNN），其次是支持向量机（SVM）。生成式人工智能具有增强数据的能力，预计不久将产生重大影响。本文详细介绍了这两种医疗应用的最新趋势、从这些趋势中吸取的经验教训以及未来发展方向。最后，未来工作部分讨论了从新生儿到老年人的各种人类医疗保健主题。
{"title":"Machine Learning for Healthcare Radars: Recent Progresses in Human Vital Sign Measurement and Activity Recognition","authors":"Shahzad Ahmed;Sung Ho Cho","doi":"10.1109/COMST.2023.3334269","DOIUrl":"https://doi.org/10.1109/COMST.2023.3334269","url":null,"abstract":"The unprecedented non-contact, non-invasive, and privacy-preserving nature of radar sensors has enabled various healthcare applications, including vital sign monitoring, fall detection, gait analysis, activity recognition, fitness evaluation, and sleep monitoring. Machine learning (ML) is revolutionizing every domain, with radar-based healthcare being no exception. Progress in the field of healthcare radars and ML is complementing the existing radar-based healthcare industry. This article provides an overview of ML usage for two major healthcare applications: vital sign monitoring and activity recognition. Vital sign monitoring is the most promising healthcare application of radar, as it can predict several chronic cardiac and respiratory diseases. Activity recognition is also a prominent application since the inability to perform activities may result in critical suffering. The article presents an overview of commercial radars, radar hardware, and historical progress of healthcare radars, followed by the usage of ML for healthcare radars. Subsequently, the paper discusses how ML can overcome the limitations of conventional radar data processing chains for healthcare radars. The article also touches upon recent generative ML concepts used in healthcare radars. Among several interesting findings, it was discovered that ML does not completely replace existing vital sign monitoring algorithms; rather, ML is deployed to overcome the limitations of traditional algorithms. On the other hand, activity recognition always relies on ML approaches. The most widely used algorithms for both applications are Convolutional Neural Network (CNN) followed by Support Vector Machine (SVM). Generative AI has the capability to augment data and is expected to have a significant impact soon. Recent trends, lessons learned from these trends, and future directions for both healthcare applications are presented in detail. Finally, the future work section discusses a wide range of healthcare topics for humans, ranging from neonates to elderly individuals.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"26 1","pages":"461-495"},"PeriodicalIF":35.6,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10322785","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139976189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Along with the springing up of the semantics-empowered communication (SemCom) research, it is now witnessing an unprecedentedly growing interest towards a wide range of aspects (e.g., theories, applications, metrics and implementations) in both academia and industry. In this work, we primarily aim to provide a comprehensive survey on both the background and research taxonomy, as well as a detailed technical tutorial. Specifically, we start by reviewing the literature and answering the “what” and “why” questions in semantic transmissions. Afterwards, we present the ecosystems of SemCom, including history, theories, metrics, datasets and toolkits, on top of which the taxonomy for research directions is presented. Furthermore, we propose to categorize the critical enabling techniques by explicit and implicit reasoning-based methods, and elaborate on how they evolve and contribute to modern content & channel semantics-empowered communications. Besides reviewing and summarizing the latest efforts in SemCom, we discuss the relations with other communication levels (e.g., conventional communications) from a holistic and unified viewpoint. Subsequently, in order to facilitate future developments and industrial applications, we also highlight advanced practical techniques for boosting semantic accuracy, robustness, and large-scale scalability, just to mention a few. Finally, we discuss the technical challenges that shed light on future research opportunities.
{"title":"Semantics-Empowered Communications: A Tutorial-Cum-Survey","authors":"Zhilin Lu;Rongpeng Li;Kun Lu;Xianfu Chen;Ekram Hossain;Zhifeng Zhao;Honggang Zhang","doi":"10.1109/COMST.2023.3333342","DOIUrl":"https://doi.org/10.1109/COMST.2023.3333342","url":null,"abstract":"Along with the springing up of the semantics-empowered communication (SemCom) research, it is now witnessing an unprecedentedly growing interest towards a wide range of aspects (e.g., theories, applications, metrics and implementations) in both academia and industry. In this work, we primarily aim to provide a comprehensive survey on both the background and research taxonomy, as well as a detailed technical tutorial. Specifically, we start by reviewing the literature and answering the “what” and “why” questions in semantic transmissions. Afterwards, we present the ecosystems of SemCom, including history, theories, metrics, datasets and toolkits, on top of which the taxonomy for research directions is presented. Furthermore, we propose to categorize the critical enabling techniques by explicit and implicit reasoning-based methods, and elaborate on how they evolve and contribute to modern content & channel semantics-empowered communications. Besides reviewing and summarizing the latest efforts in SemCom, we discuss the relations with other communication levels (e.g., conventional communications) from a holistic and unified viewpoint. Subsequently, in order to facilitate future developments and industrial applications, we also highlight advanced practical techniques for boosting semantic accuracy, robustness, and large-scale scalability, just to mention a few. Finally, we discuss the technical challenges that shed light on future research opportunities.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"26 1","pages":"41-79"},"PeriodicalIF":35.6,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139976220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Over the past few years, significant advancements have been made in the field of machine learning (ML) to address resource management, interference management, autonomy, and decision-making in wireless networks. Traditional ML approaches rely on centralized methods, where data is collected at a central server for training. However, this approach poses a challenge in terms of preserving the data privacy of devices. To address this issue, federated learning (FL) has emerged as an effective solution that allows edge devices to collaboratively train ML models without compromising data privacy. In FL, local datasets are not shared, and the focus is on learning a global model for a specific task involving all devices. However, FL has limitations when it comes to adapting the model to devices with different data distributions. In such cases, meta learning is considered, as it enables the adaptation of learning models to different data distributions using only a few data samples. In this tutorial, we present a comprehensive review of FL, meta learning, and federated meta learning (FedMeta). Unlike other tutorial papers, our objective is to explore how FL, meta learning, and FedMeta methodologies can be designed, optimized, and evolved, and their applications over wireless networks. We also analyze the relationships among these learning algorithms and examine their advantages and disadvantages in real-world applications.
在过去几年中，机器学习（ML）领域在解决无线网络中的资源管理、干扰管理、自主性和决策方面取得了重大进展。传统的 ML 方法依赖于集中式方法，即在中央服务器上收集数据进行训练。然而，这种方法在保护设备数据隐私方面提出了挑战。为了解决这个问题，联合学习（FL）作为一种有效的解决方案应运而生，它允许边缘设备在不损害数据隐私的情况下协作训练 ML 模型。在联合学习中，本地数据集不共享，重点是为涉及所有设备的特定任务学习全局模型。然而，FL 在使模型适应不同数据分布的设备时存在局限性。在这种情况下，就需要考虑元学习，因为元学习只需使用少量数据样本，就能使学习模型适应不同的数据分布。在本教程中，我们将全面回顾 FL、元学习和联合元学习（FedMeta）。与其他教程不同的是，我们的目标是探讨如何设计、优化和发展 FL、元学习和 FedMeta 方法，以及它们在无线网络中的应用。我们还分析了这些学习算法之间的关系，并研究了它们在实际应用中的优缺点。
{"title":"Federated Learning and Meta Learning: Approaches, Applications, and Directions","authors":"Xiaonan Liu;Yansha Deng;Arumugam Nallanathan;Mehdi Bennis","doi":"10.1109/COMST.2023.3330910","DOIUrl":"10.1109/COMST.2023.3330910","url":null,"abstract":"Over the past few years, significant advancements have been made in the field of machine learning (ML) to address resource management, interference management, autonomy, and decision-making in wireless networks. Traditional ML approaches rely on centralized methods, where data is collected at a central server for training. However, this approach poses a challenge in terms of preserving the data privacy of devices. To address this issue, federated learning (FL) has emerged as an effective solution that allows edge devices to collaboratively train ML models without compromising data privacy. In FL, local datasets are not shared, and the focus is on learning a global model for a specific task involving all devices. However, FL has limitations when it comes to adapting the model to devices with different data distributions. In such cases, meta learning is considered, as it enables the adaptation of learning models to different data distributions using only a few data samples. In this tutorial, we present a comprehensive review of FL, meta learning, and federated meta learning (FedMeta). Unlike other tutorial papers, our objective is to explore how FL, meta learning, and FedMeta methodologies can be designed, optimized, and evolved, and their applications over wireless networks. We also analyze the relationships among these learning algorithms and examine their advantages and disadvantages in real-world applications.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"26 1","pages":"571-618"},"PeriodicalIF":35.6,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135507428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-07DOI: 10.1109/COMST.2023.3330953
Espen Volnes;Thomas Plagemann;Vera Goebel
One of the most important issues in distributed data stream processing systems is using operator migration to handle highly variable workloads cost-efficiently and adapt to the needs at any given time on demand. Operator migration is a complex process involving changes in the state and stream management of a running query, typically without any data loss, and with as little disruption to the execution as possible. This tutorial aims to introduce operator migration, explain the core elements of operator migration, and provide the reader with a good understanding of the design alternatives used in existing solutions. We developed a conceptual model to explain the fundamentals of operator migration and introduce a unified terminology, leading to a taxonomy of existing solutions. The conceptual model separates mechanisms, i.e., how to migrate, and policy, i.e., when to migrate. This separation is further applied to structure the description of existing solutions, offering the reader an algorithmic perspective on various design alternatives. To enhance our understanding of the impact of various design alternatives on migration mechanisms, we also conducted an empirical study that provides quantitative insights. The operator downtime for the naïve migration approach is almost 20 times longer than when applying an incremental checkpoint-based approach.
{"title":"To Migrate or Not to Migrate: An Analysis of Operator Migration in Distributed Stream Processing","authors":"Espen Volnes;Thomas Plagemann;Vera Goebel","doi":"10.1109/COMST.2023.3330953","DOIUrl":"10.1109/COMST.2023.3330953","url":null,"abstract":"One of the most important issues in distributed data stream processing systems is using operator migration to handle highly variable workloads cost-efficiently and adapt to the needs at any given time on demand. Operator migration is a complex process involving changes in the state and stream management of a running query, typically without any data loss, and with as little disruption to the execution as possible. This tutorial aims to introduce operator migration, explain the core elements of operator migration, and provide the reader with a good understanding of the design alternatives used in existing solutions. We developed a conceptual model to explain the fundamentals of operator migration and introduce a unified terminology, leading to a taxonomy of existing solutions. The conceptual model separates mechanisms, i.e., how to migrate, and policy, i.e., when to migrate. This separation is further applied to structure the description of existing solutions, offering the reader an algorithmic perspective on various design alternatives. To enhance our understanding of the impact of various design alternatives on migration mechanisms, we also conducted an empirical study that provides quantitative insights. The operator downtime for the naïve migration approach is almost 20 times longer than when applying an incremental checkpoint-based approach.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"26 1","pages":"670-705"},"PeriodicalIF":35.6,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10310197","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135507416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This century has been a major avenue for revolutionary changes in technology and industry. Industries have transitioned towards intelligent automation, relying less on human intervention, resulting in the fourth industrial revolution, Industry 4.0. That is why IoT has been the researcher’s arena for quite some time. With Industry 4.0 still in motion, the world is on the verge of the $5^{textit {th}}$ industrial revolution, a relatively new concept with many unclear opinions regarding its potential benefits, challenges, opportunities, trends, and impact on society. There is a dire need for a broader and more critical perspective. This research paints a bigger picture of “What is happening?” and “What to expect?” during the transition phase of Industry 5.0. In this comprehensive review, we have addressed the state-of-the-art practices in Industry 4.0 and the transitional phase of Industry 5.0. We have highlighted the most promising key enabling technologies, trends, research topics, rising challenges, and unfolding opportunities that can help prepare society for this paradigm shift. The paper then surveys the work toward the outstanding key enablers, challenges, trends, and opportunities in the IoT evolution for Industry 5.0. To spur further avenues for researchers and industrialists, the paper offers conclusive insights at the end. In addition, the article has a precise set of research questions answered in consequent sections and subsections for the reader’s clarity.
{"title":"Navigating Industry 5.0: A Survey of Key Enabling Technologies, Trends, Challenges, and Opportunities","authors":"Raiha Tallat;Ammar Hawbani;Xingfu Wang;Ahmed Al-Dubai;Liang Zhao;Zhi Liu;Geyong Min;Albert Y. Zomaya;Saeed Hamood Alsamhi","doi":"10.1109/COMST.2023.3329472","DOIUrl":"10.1109/COMST.2023.3329472","url":null,"abstract":"This century has been a major avenue for revolutionary changes in technology and industry. Industries have transitioned towards intelligent automation, relying less on human intervention, resulting in the fourth industrial revolution, Industry 4.0. That is why IoT has been the researcher’s arena for quite some time. With Industry 4.0 still in motion, the world is on the verge of the \u0000<inline-formula> <tex-math>$5^{textit {th}}$ </tex-math></inline-formula>\u0000 industrial revolution, a relatively new concept with many unclear opinions regarding its potential benefits, challenges, opportunities, trends, and impact on society. There is a dire need for a broader and more critical perspective. This research paints a bigger picture of “What is happening?” and “What to expect?” during the transition phase of Industry 5.0. In this comprehensive review, we have addressed the state-of-the-art practices in Industry 4.0 and the transitional phase of Industry 5.0. We have highlighted the most promising key enabling technologies, trends, research topics, rising challenges, and unfolding opportunities that can help prepare society for this paradigm shift. The paper then surveys the work toward the outstanding key enablers, challenges, trends, and opportunities in the IoT evolution for Industry 5.0. To spur further avenues for researchers and industrialists, the paper offers conclusive insights at the end. In addition, the article has a precise set of research questions answered in consequent sections and subsections for the reader’s clarity.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"26 2","pages":"1080-1126"},"PeriodicalIF":35.6,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134891017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The computing demand for massive applications has led to the ubiquitous deployment of computing power. This trend results in the urgent need for higher-level computing resource scheduling services. The Computing and Network Convergence (CNC), a new type of infrastructure, has become a hot topic. To realize the visions of CNC, such as computing-network integration, ubiquitous collaboration, latency-free, and ready-to-use, an intelligent scheduling strategy for CNC should integrate and collaborate with the network. However, the Computing and Network Convergence is built on the cloud, edge, and endless terminals, making the scheduling problem more difficult due to its wide-area requests, available flexibility arrangements, interconnections, and resource adaptations. In view of this, in this survey, we comprehensively review the literature on scheduling in various scenarios. We cover the scheduling problem of Computing and Network Convergence from heterogeneous resources, multiple-objective optimization, and diverse tasks. Possible explanations and implications are discussed. Finally, we point out important challenges for future work.
{"title":"A Survey on Scheduling Techniques in Computing and Network Convergence","authors":"Shujiong Tang;Yue Yu;Hui Wang;Guiliang Wang;Wuhui Chen;Zenglin Xu;Song Guo;Wen Gao","doi":"10.1109/COMST.2023.3329027","DOIUrl":"10.1109/COMST.2023.3329027","url":null,"abstract":"The computing demand for massive applications has led to the ubiquitous deployment of computing power. This trend results in the urgent need for higher-level computing resource scheduling services. The Computing and Network Convergence (CNC), a new type of infrastructure, has become a hot topic. To realize the visions of CNC, such as computing-network integration, ubiquitous collaboration, latency-free, and ready-to-use, an intelligent scheduling strategy for CNC should integrate and collaborate with the network. However, the Computing and Network Convergence is built on the cloud, edge, and endless terminals, making the scheduling problem more difficult due to its wide-area requests, available flexibility arrangements, interconnections, and resource adaptations. In view of this, in this survey, we comprehensively review the literature on scheduling in various scenarios. We cover the scheduling problem of Computing and Network Convergence from heterogeneous resources, multiple-objective optimization, and diverse tasks. Possible explanations and implications are discussed. Finally, we point out important challenges for future work.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"26 1","pages":"160-195"},"PeriodicalIF":35.6,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134884066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-27DOI: 10.1109/COMST.2023.3327321
Moisés V. Ribeiro;Mateus de L. Filomeno;Ândrei Camponogara;Thiago Rodrigues Oliveira;Túlio F. Moreira;Stefano Galli;H. Vincent Poor
Aimed at inspiring researchers and practitioners, this paper presents comprehensive and multidimensional discussions of the potential and benefits of integrating power line and wireless communication systems to enhance data communication in both indoor and outdoor environments. By systematically examining the strengths and weaknesses of power line and wireless communication systems, we first elucidate the rationale behind their integration—hybrid communication systems. Then we categorize these hybrid communication systems and provide an in-depth analysis of each category, considering technical, chronological, industrial, and standardization aspects. Moreover, we discuss strategies for designing hybrid communication devices. Next, a qualitative assessment of hybrid communication systems follows, offering guidance for advancing their use, while a concise summary of key lessons imparts valuable insights for future endeavors. Lastly, the paper identifies a range of pertinent topics for further exploration and, consequently, advancing hybrid communication systems for smart grid, Internet of Things, and Industry 4.0 or 5.0 applications.
{"title":"Seamless Connectivity: The Power of Integrating Power Line and Wireless Communications","authors":"Moisés V. Ribeiro;Mateus de L. Filomeno;Ândrei Camponogara;Thiago Rodrigues Oliveira;Túlio F. Moreira;Stefano Galli;H. Vincent Poor","doi":"10.1109/COMST.2023.3327321","DOIUrl":"10.1109/COMST.2023.3327321","url":null,"abstract":"Aimed at inspiring researchers and practitioners, this paper presents comprehensive and multidimensional discussions of the potential and benefits of integrating power line and wireless communication systems to enhance data communication in both indoor and outdoor environments. By systematically examining the strengths and weaknesses of power line and wireless communication systems, we first elucidate the rationale behind their integration—hybrid communication systems. Then we categorize these hybrid communication systems and provide an in-depth analysis of each category, considering technical, chronological, industrial, and standardization aspects. Moreover, we discuss strategies for designing hybrid communication devices. Next, a qualitative assessment of hybrid communication systems follows, offering guidance for advancing their use, while a concise summary of key lessons imparts valuable insights for future endeavors. Lastly, the paper identifies a range of pertinent topics for further exploration and, consequently, advancing hybrid communication systems for smart grid, Internet of Things, and Industry 4.0 or 5.0 applications.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"26 1","pages":"1-40"},"PeriodicalIF":35.6,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135214243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The pervasiveness of commercial Internet of Things (IoT) around the globe is expected to reach significant levels with the upcoming sixth generation of mobile networks (6G). Throughout the past years, wireless standardization units worldwide have been prominently active in the deployment and performance optimization of such IoT networks and fusing them with current and futuristic cellular networks. Nonetheless, the openness of wireless transmissions and the forecasted overwhelm in connected devices will provoke unprecedented security leakages and vulnerabilities. In addition to the key targets of the 6G and IoT, it has been of paramount importance to cater to decent and lightweight security mechanisms in ultra-massively connected heterogeneous networks. Recently, significant efforts have been made to pave the way for the integration of physical layer security (PLS) in contemporary and futuristic networks. The primary motivation behind its deployment resides in its low complexity and ability to provide information-theoretic secure transmissions, which alleviates the complexity burden caused by implementing complex cryptographic schemes. This survey overviews the recent advancement in PLS techniques with a particular interest in its application to the Internet of Things (IoT). We review essentially recent PLS techniques aiming at ensuring message confidentiality along with node/message authentication and malicious nodes’ detection, where their corresponding application scenarios and underlying pros and cons are discussed. On top of that, we explore recent findings in the incorporation of cutting-edge technologies at the physical layer, such as non-orthogonal multiple-access, reconfigurable intelligent surfaces, joint communication and sensing, and optical wireless/Terahertz communications in boosting confidentiality and authentication at the physical layer. Lastly, promising extensions and future directions are discussed based on the quantified pros and cons of each PLS category, opening up ways for timely research directions within the topic and current/future challenges faced by PLS.
{"title":"Physical Layer Security for Authentication, Confidentiality, and Malicious Node Detection: A Paradigm Shift in Securing IoT Networks","authors":"Elmehdi Illi;Marwa Qaraqe;Saud Althunibat;Abdullah Alhasanat;Moath Alsafasfeh;Marcus de Ree;Georgios Mantas;Jonathan Rodriguez;Waqas Aman;Saif Al-Kuwari","doi":"10.1109/COMST.2023.3327327","DOIUrl":"10.1109/COMST.2023.3327327","url":null,"abstract":"The pervasiveness of commercial Internet of Things (IoT) around the globe is expected to reach significant levels with the upcoming sixth generation of mobile networks (6G). Throughout the past years, wireless standardization units worldwide have been prominently active in the deployment and performance optimization of such IoT networks and fusing them with current and futuristic cellular networks. Nonetheless, the openness of wireless transmissions and the forecasted overwhelm in connected devices will provoke unprecedented security leakages and vulnerabilities. In addition to the key targets of the 6G and IoT, it has been of paramount importance to cater to decent and lightweight security mechanisms in ultra-massively connected heterogeneous networks. Recently, significant efforts have been made to pave the way for the integration of physical layer security (PLS) in contemporary and futuristic networks. The primary motivation behind its deployment resides in its low complexity and ability to provide information-theoretic secure transmissions, which alleviates the complexity burden caused by implementing complex cryptographic schemes. This survey overviews the recent advancement in PLS techniques with a particular interest in its application to the Internet of Things (IoT). We review essentially recent PLS techniques aiming at ensuring message confidentiality along with node/message authentication and malicious nodes’ detection, where their corresponding application scenarios and underlying pros and cons are discussed. On top of that, we explore recent findings in the incorporation of cutting-edge technologies at the physical layer, such as non-orthogonal multiple-access, reconfigurable intelligent surfaces, joint communication and sensing, and optical wireless/Terahertz communications in boosting confidentiality and authentication at the physical layer. Lastly, promising extensions and future directions are discussed based on the quantified pros and cons of each PLS category, opening up ways for timely research directions within the topic and current/future challenges faced by PLS.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"26 1","pages":"347-388"},"PeriodicalIF":35.6,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135158607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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