Pub Date : 2024-01-01DOI: 10.1109/miot.2024.10397579
{"title":"Comsoc Publications","authors":"","doi":"10.1109/miot.2024.10397579","DOIUrl":"https://doi.org/10.1109/miot.2024.10397579","url":null,"abstract":"","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"33 42","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139455766","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/IOTM.001.2300078
Latif U. Khan, Ahmed Elhagry, Mohsen Guizani, Abdulmotaleb El Saddik
Emerging intelligent transportation system applications witnessed significantly different requirements and performance metrics (e.g., latency, reliability, and quality of experience). To meet the diverse requirements, one can use a convergence of the metaverse with vehicular networks at the network edge which offers proactive analysis and efficient real-time control for the management of vehicular network resources. Therefore, in this article, we present key design aspects of an edge intelligence-enabled vehicular metaverse. We also present a high-level architecture for an edge intelligence-based vehicular metaverse that has three main aspects: a metaverse engine, offline learning, and online real-time control. Moreover, we present two case studies: joint sampling and packet error rate minimization and object detection task at the network edge. Finally, we conclude the article.
{"title":"Edge Intelligence Empowered Vehicular Metaverse: Key Design Aspects and Future Directions","authors":"Latif U. Khan, Ahmed Elhagry, Mohsen Guizani, Abdulmotaleb El Saddik","doi":"10.1109/IOTM.001.2300078","DOIUrl":"https://doi.org/10.1109/IOTM.001.2300078","url":null,"abstract":"Emerging intelligent transportation system applications witnessed significantly different requirements and performance metrics (e.g., latency, reliability, and quality of experience). To meet the diverse requirements, one can use a convergence of the metaverse with vehicular networks at the network edge which offers proactive analysis and efficient real-time control for the management of vehicular network resources. Therefore, in this article, we present key design aspects of an edge intelligence-enabled vehicular metaverse. We also present a high-level architecture for an edge intelligence-based vehicular metaverse that has three main aspects: a metaverse engine, offline learning, and online real-time control. Moreover, we present two case studies: joint sampling and packet error rate minimization and object detection task at the network edge. Finally, we conclude the article.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"32 6","pages":"120-126"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139455014","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.2024.10397574
N. Narang
{"title":"Mentor's Musings on Architectural & Standardization Imperatives for NTN to Enable Ubiquitous Global Connectivity","authors":"N. Narang","doi":"10.1109/miot.2024.10397574","DOIUrl":"https://doi.org/10.1109/miot.2024.10397574","url":null,"abstract":"","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"31 45","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139539508","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 : 2023-10-21DOI: 10.1109/IOTM.001.2300163
P. Lorenzo, M. Merluzzi, Francesco Binucci, Claudio Battiloro, P. Banelli, E. Strinati, S. Barbarossa
Internet of Things (IoT) applications combine sensing, wireless communication, intelligence, and actuation, enabling the interaction among heterogeneous devices that collect and process considerable amounts of data. However, the effectiveness of IoT applications needs to face the limitation of available resources, including spectrum, energy, computing, learning and inference capabilities. This article challenges the prevailing approach to IoT communication, which prioritizes the usage of resources in order to guarantee perfect recovery, at the bit level, of the data transmitted by the sensors to the central unit. We propose a novel approach, called goal-oriented (GO) IoT system design, that transcends traditional bit-related metrics and focuses directly on the fulfillment of the goal motivating the exchange of data. The improve-ment is then achieved through a comprehensive system optimization, integrating sensing, communication, computation, learning, and control. We provide numerical results demonstrating the practical applications of our methodology in compelling use cases such as edge inference, cooperative sensing, and federated learning. These examples highlight the effectiveness and real-world implications of our pro-posed approach, with the potential to revolutionize IoT systems.
{"title":"Goal-Oriented Communications for the IoT: System Design and Adaptive Resource Optimization","authors":"P. Lorenzo, M. Merluzzi, Francesco Binucci, Claudio Battiloro, P. Banelli, E. Strinati, S. Barbarossa","doi":"10.1109/IOTM.001.2300163","DOIUrl":"https://doi.org/10.1109/IOTM.001.2300163","url":null,"abstract":"Internet of Things (IoT) applications combine sensing, wireless communication, intelligence, and actuation, enabling the interaction among heterogeneous devices that collect and process considerable amounts of data. However, the effectiveness of IoT applications needs to face the limitation of available resources, including spectrum, energy, computing, learning and inference capabilities. This article challenges the prevailing approach to IoT communication, which prioritizes the usage of resources in order to guarantee perfect recovery, at the bit level, of the data transmitted by the sensors to the central unit. We propose a novel approach, called goal-oriented (GO) IoT system design, that transcends traditional bit-related metrics and focuses directly on the fulfillment of the goal motivating the exchange of data. The improve-ment is then achieved through a comprehensive system optimization, integrating sensing, communication, computation, learning, and control. We provide numerical results demonstrating the practical applications of our methodology in compelling use cases such as edge inference, cooperative sensing, and federated learning. These examples highlight the effectiveness and real-world implications of our pro-posed approach, with the potential to revolutionize IoT systems.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"72 1","pages":"26-32"},"PeriodicalIF":0.0,"publicationDate":"2023-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139315724","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 : 2023-09-12DOI: 10.1109/IOTM.001.2300102
Marwa Chafii, Salmane Naoumi, Réda Alami, Ebtesam Almazrouei, M. Bennis, M. Debbah
In different wireless network scenarios, multiple network entities need to cooperate in order to achieve a common task with minimum delay and energy consumption. Future wireless networks mandate exchanging high dimensional data in dynamic and uncertain environments, therefore implementing communication control tasks becomes challenging and highly complex. Multi-agent reinforcement learning with emergent communication (EC-MARL) is a promising solution to address high dimensional continuous control problems with partially observable states in a cooperative fashion where agents build an emergent communication protocol to solve complex tasks. This article articulates the importance of EC-MARL within the context of future 6G wireless networks, which imbues autonomous decision-making capabilities into network entities to solve complex tasks such as autonomous driving, robot navigation, flying base stations network planning, and smart city applications. An overview of EC-MARL algorithms and their design criteria are provided while presenting use cases and research opportuni-ties on this emerging topic.
{"title":"Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks","authors":"Marwa Chafii, Salmane Naoumi, Réda Alami, Ebtesam Almazrouei, M. Bennis, M. Debbah","doi":"10.1109/IOTM.001.2300102","DOIUrl":"https://doi.org/10.1109/IOTM.001.2300102","url":null,"abstract":"In different wireless network scenarios, multiple network entities need to cooperate in order to achieve a common task with minimum delay and energy consumption. Future wireless networks mandate exchanging high dimensional data in dynamic and uncertain environments, therefore implementing communication control tasks becomes challenging and highly complex. Multi-agent reinforcement learning with emergent communication (EC-MARL) is a promising solution to address high dimensional continuous control problems with partially observable states in a cooperative fashion where agents build an emergent communication protocol to solve complex tasks. This article articulates the importance of EC-MARL within the context of future 6G wireless networks, which imbues autonomous decision-making capabilities into network entities to solve complex tasks such as autonomous driving, robot navigation, flying base stations network planning, and smart city applications. An overview of EC-MARL algorithms and their design criteria are provided while presenting use cases and research opportuni-ties on this emerging topic.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"53 1","pages":"18-24"},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139340963","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/miot.2023.10255790
{"title":"Comsoc Training","authors":"","doi":"10.1109/miot.2023.10255790","DOIUrl":"https://doi.org/10.1109/miot.2023.10255790","url":null,"abstract":"","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"78 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":"135388590","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/miot.2023.10255785
{"title":"Comsoc Membership","authors":"","doi":"10.1109/miot.2023.10255785","DOIUrl":"https://doi.org/10.1109/miot.2023.10255785","url":null,"abstract":"","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"7 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":"135388592","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/iotm.001.2300080
Ganggui Wang, Celimuge Wu, Zhaoyang Du, Tsutomu Yoshinaga, Rui Yin, Lei Zhong
Federated learning, a distributed machine learning framework, can be used in many Internet of Vehicles (IoV) scenarios to enable privacy-preserving distributed intelligence. While federated learning avoids transmitting raw data in the learning process, it also requires to transmit learning models between clients and server, where the limited wireless resources is always the bottleneck for performance. In this paper, we propose a deep reinforcement learning (DRL) based approach for selecting the best wireless network in a multi-access environment to improve the performance of federated learning. The proposed approach can enhance the overall robustness of the network with efficient network switching based on network environment. We conduct realistic computer simulations to show that the proposed approach exhibits significant performance advantages over existing baselines.
{"title":"DRL-Assisted Network Selection for Federated IoV","authors":"Ganggui Wang, Celimuge Wu, Zhaoyang Du, Tsutomu Yoshinaga, Rui Yin, Lei Zhong","doi":"10.1109/iotm.001.2300080","DOIUrl":"https://doi.org/10.1109/iotm.001.2300080","url":null,"abstract":"Federated learning, a distributed machine learning framework, can be used in many Internet of Vehicles (IoV) scenarios to enable privacy-preserving distributed intelligence. While federated learning avoids transmitting raw data in the learning process, it also requires to transmit learning models between clients and server, where the limited wireless resources is always the bottleneck for performance. In this paper, we propose a deep reinforcement learning (DRL) based approach for selecting the best wireless network in a multi-access environment to improve the performance of federated learning. The proposed approach can enhance the overall robustness of the network with efficient network switching based on network environment. We conduct realistic computer simulations to show that the proposed approach exhibits significant performance advantages over existing baselines.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"95 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":"135388595","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/iotm.001.2200266
Wang, Yuntao, Su, Zhou, Yan, Miao
Social metaverse is a shared digital space combining a series of interconnected virtual worlds for users to play, shop, work, and socialize. In parallel with the advances of artificial intelligence (AI) and growing awareness of data privacy concerns, federated learning (FL) is promoted as a paradigm shift towards privacy-preserving AI-empowered social metaverse. However, challenges including privacy-utility tradeoff, learning reliability, and AI model thefts hinder the deployment of FL in real metaverse applications. In this article, we exploit the pervasive social ties among users/avatars to advance a social-aware hierarchical FL framework, i.e., SocialFL for a better privacy-utility tradeoff in the social metaverse. Then, an aggregator-free robust FL mechanism based on blockchain is devised with a new block structure and an improved consensus protocol featured with on/off-chain collaboration. Furthermore, based on digital watermarks, an automatic federated AI (FedAI) model ownership provenance mechanism is designed to prevent AI model thefts and collusive avatars in social metaverse. Experimental findings validate the feasibility and effectiveness of proposed framework. Finally, we envision promising future research directions in this emerging area.
{"title":"Social Metaverse: Challenges and Solutions","authors":"Wang, Yuntao, Su, Zhou, Yan, Miao","doi":"10.1109/iotm.001.2200266","DOIUrl":"https://doi.org/10.1109/iotm.001.2200266","url":null,"abstract":"Social metaverse is a shared digital space combining a series of interconnected virtual worlds for users to play, shop, work, and socialize. In parallel with the advances of artificial intelligence (AI) and growing awareness of data privacy concerns, federated learning (FL) is promoted as a paradigm shift towards privacy-preserving AI-empowered social metaverse. However, challenges including privacy-utility tradeoff, learning reliability, and AI model thefts hinder the deployment of FL in real metaverse applications. In this article, we exploit the pervasive social ties among users/avatars to advance a social-aware hierarchical FL framework, i.e., SocialFL for a better privacy-utility tradeoff in the social metaverse. Then, an aggregator-free robust FL mechanism based on blockchain is devised with a new block structure and an improved consensus protocol featured with on/off-chain collaboration. Furthermore, based on digital watermarks, an automatic federated AI (FedAI) model ownership provenance mechanism is designed to prevent AI model thefts and collusive avatars in social metaverse. Experimental findings validate the feasibility and effectiveness of proposed framework. Finally, we envision promising future research directions in this emerging area.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"37 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":"135895726","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/iotm.001.2200235
Shimaa Naser, Lina Bariah, Sami Muhaidat, Ertugrul Basar
The sixth generation (6G) of wireless networks are envisioned to support a plethora of human-centric applications and offer connectivity to a massive number of devices with diverse requirements. Nevertheless, with the rapid growth of the number of connected devices as well as the ever-increasing network traffic, network energy consumption has become a major challenge. Additionally, 6G is expected to catalyze the emergence of new applications that are characterized by their harsh environmental conditions, with ultra-small and low-cost wireless devices. Therefore, there is a pressing need for developing sustainable solutions that take into consideration all these requirements in order to realize the full potential of 6G networks. Within this context, zero-energy devices (ZEDs) have emerged as a prominent solution for the next generation green communication architecture. Such devices eliminate the need for recharging plugins and replacing batteries by integrating disruptive technologies, such as radio frequency energy harvesting, backscatter communications, low power computing, and ultra-low power receivers. Motivated by this, this article provides an in-depth review of the existing literature on the newly emerging ZEDs for future networks. We further identify different relevant use cases and provide an extensive overview on the key enabling technologies and their requirements for realizing ZEDs-empowered networks. Finally, we discuss potential future research directions and challenges that are envisioned to enhance the performance and efficiency of ZEDs-empowered networks.
{"title":"Zero-Energy Devices Empowered 6G Networks: Opportunities, Key Technologies, and Challenges","authors":"Shimaa Naser, Lina Bariah, Sami Muhaidat, Ertugrul Basar","doi":"10.1109/iotm.001.2200235","DOIUrl":"https://doi.org/10.1109/iotm.001.2200235","url":null,"abstract":"The sixth generation (6G) of wireless networks are envisioned to support a plethora of human-centric applications and offer connectivity to a massive number of devices with diverse requirements. Nevertheless, with the rapid growth of the number of connected devices as well as the ever-increasing network traffic, network energy consumption has become a major challenge. Additionally, 6G is expected to catalyze the emergence of new applications that are characterized by their harsh environmental conditions, with ultra-small and low-cost wireless devices. Therefore, there is a pressing need for developing sustainable solutions that take into consideration all these requirements in order to realize the full potential of 6G networks. Within this context, zero-energy devices (ZEDs) have emerged as a prominent solution for the next generation green communication architecture. Such devices eliminate the need for recharging plugins and replacing batteries by integrating disruptive technologies, such as radio frequency energy harvesting, backscatter communications, low power computing, and ultra-low power receivers. Motivated by this, this article provides an in-depth review of the existing literature on the newly emerging ZEDs for future networks. We further identify different relevant use cases and provide an extensive overview on the key enabling technologies and their requirements for realizing ZEDs-empowered networks. Finally, we discuss potential future research directions and challenges that are envisioned to enhance the performance and efficiency of ZEDs-empowered networks.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"78 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":"135388229","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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