Pub Date : 2023-09-01DOI: 10.1109/miot.2023.10255784
{"title":"Cover 4","authors":"","doi":"10.1109/miot.2023.10255784","DOIUrl":"https://doi.org/10.1109/miot.2023.10255784","url":null,"abstract":"","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"198 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":"135388591","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.001.2300032
Rafael Kaliski, Shin-Ming Cheng, Cheng-Feng Hung
In the era of 6G, cellular networks will no longer be locked into a small set of equipment manufacturers; instead, cellular networks will be disaggregated and support open interfaces. Thus, there is an inherent need for networking functions to be softwarized and virtualized so that customers can apply different vendors' solutions. 6G mission-critical networks must be dependable and secure, ultra-reliable and low latency, and support high connectivity, all while being flexible enough to support custom user deployments. 6G will integrate Artificial Intelligence (AI) into the network architecture to meet the diverse user requirements of 3 rd party solutions. A possible 6G candidate capable of supporting the requirements above is Open Radio Access Networks (O-RAN). O-RAN enables multiple levels of AI-based control for RAN Intelligent Controllers (RICs). RICs facilitate real-time sensing, reaction, policy determination, and management of radio resources. When coupled with Multi-access Edge Computing (MEC), O-RAN enables customized per-device AI service chains that can address the needs of dynamic, diverse 6G networks in real-time. This article presents an O-RAN architecture that supports split-plane multi-component cooperative AI models that utilize multiple RIC-centric and MEC-centric control loops. Through multiple example applications and O-RAN testbeds, we demonstrate the efficacy of our proposed architecture and how it can address the multitude of 6G requirements as necessitated for mission-critical Internet of Things applications.
{"title":"Supporting 6G Mission-Critical Services on O-RAN","authors":"Rafael Kaliski, Shin-Ming Cheng, Cheng-Feng Hung","doi":"10.1109/iotm.001.2300032","DOIUrl":"https://doi.org/10.1109/iotm.001.2300032","url":null,"abstract":"In the era of 6G, cellular networks will no longer be locked into a small set of equipment manufacturers; instead, cellular networks will be disaggregated and support open interfaces. Thus, there is an inherent need for networking functions to be softwarized and virtualized so that customers can apply different vendors' solutions. 6G mission-critical networks must be dependable and secure, ultra-reliable and low latency, and support high connectivity, all while being flexible enough to support custom user deployments. 6G will integrate Artificial Intelligence (AI) into the network architecture to meet the diverse user requirements of 3 <sup xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">rd</sup> party solutions. A possible 6G candidate capable of supporting the requirements above is Open Radio Access Networks (O-RAN). O-RAN enables multiple levels of AI-based control for RAN Intelligent Controllers (RICs). RICs facilitate real-time sensing, reaction, policy determination, and management of radio resources. When coupled with Multi-access Edge Computing (MEC), O-RAN enables customized per-device AI service chains that can address the needs of dynamic, diverse 6G networks in real-time. This article presents an O-RAN architecture that supports split-plane multi-component cooperative AI models that utilize multiple RIC-centric and MEC-centric control loops. Through multiple example applications and O-RAN testbeds, we demonstrate the efficacy of our proposed architecture and how it can address the multitude of 6G requirements as necessitated for mission-critical Internet of Things applications.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"164 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":"135388601","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.2023.10255781
{"title":"IEEE Collabratec","authors":"","doi":"10.1109/miot.2023.10255781","DOIUrl":"https://doi.org/10.1109/miot.2023.10255781","url":null,"abstract":"","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"30 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":"135388388","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.001.2200273
Yuhan Su, Minghui Liwang, Zhong Chen, Xianbin Wang
Benefited from their flexibility and on-demand deployment capability, unmanned aerial vehicles (UAVs) have emerged as critical aerial communication platforms in future Internet of Vehicles (IoV). However, limited spectrum resources can lead to unsatisfying data rate of IoV, which thus incur large latency, especially under congested IoV network conditions. Although UAVs and road side units (RSUs) can work within the same spectrum and increase spectral efficiency, mutual interference becomes unavoidable. To this end, this article develops a heterogeneous network architecture, in which a UAV-assisted IoV system coexists with a Wi-Fi system: the RSUs can properly occupy unlicensed spectrum to increase the capacity of the UAV-assisted IoV system while mitigating interference, without affecting the performance of the Wi-Fi system. A case study of resource management over licensed and unlicensed spectrum is investigated under the proposed architecture, where time and power are jointly optimized to maximize the sum user satisfaction of the system. We further provide an intelligent solution to tackle the problem in the considered case study. Simulations demonstrate that our proposed case can efficiently improve the sum user satisfaction of the system. Key challenges and opportunities for UAV-assisted IoV over licensed and unlicensed spectrum are discussed, while recommendable future research directions are investigated.
{"title":"UAV-Assisted Internet of Vehicles Over Licensed and Unlicensed Spectrum: Architecture, Intelligent Resource Management, and Challenges","authors":"Yuhan Su, Minghui Liwang, Zhong Chen, Xianbin Wang","doi":"10.1109/iotm.001.2200273","DOIUrl":"https://doi.org/10.1109/iotm.001.2200273","url":null,"abstract":"Benefited from their flexibility and on-demand deployment capability, unmanned aerial vehicles (UAVs) have emerged as critical aerial communication platforms in future Internet of Vehicles (IoV). However, limited spectrum resources can lead to unsatisfying data rate of IoV, which thus incur large latency, especially under congested IoV network conditions. Although UAVs and road side units (RSUs) can work within the same spectrum and increase spectral efficiency, mutual interference becomes unavoidable. To this end, this article develops a heterogeneous network architecture, in which a UAV-assisted IoV system coexists with a Wi-Fi system: the RSUs can properly occupy unlicensed spectrum to increase the capacity of the UAV-assisted IoV system while mitigating interference, without affecting the performance of the Wi-Fi system. A case study of resource management over licensed and unlicensed spectrum is investigated under the proposed architecture, where time and power are jointly optimized to maximize the sum user satisfaction of the system. We further provide an intelligent solution to tackle the problem in the considered case study. Simulations demonstrate that our proposed case can efficiently improve the sum user satisfaction of the system. Key challenges and opportunities for UAV-assisted IoV over licensed and unlicensed spectrum are discussed, while recommendable future research directions are investigated.","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":"135388394","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.001.2300035
Dimitrios Michael Manias, Ali Chouman, Anwer Al-Dulaimi, Abdallah Shami
The intricate web of vehicles connected in the fifth-generation (5G) wireless infrastructure forms the Internet of Vehicles (IoV) and enabling technologies, such as Multi-Access Edge Computing (MEC) and network slicing, are employed in guaranteeing application requirements in the IoV and optimizing network resource allocation. In particular, network slicing allows mobile network operators to support virtualized end-to-end networks with diverse slice requirements that are typically grouped into use case classes such as Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (uRLLC), and Massive Machine-Type Communication (mMTC). These enabling technologies rely on Performance Metrics, monitored and gathered within the network, in order to evaluate and suggest improvement for slice configuration. As such, this article considers a forecasting model for Performance Metrics at the network slice level by leveraging the use of the Network Data Analytics Function (NWDAF) and its edge placements. The results and analysis, including the scalability of the forecasting model, are assessed as a step towards total automation of network slice management within the 5G network. This evaluation is later illustrated using an end-to-end IoV use case incorporating the edge NWDAF placements to guide decision-making regarding management and orchestration for future networks.
{"title":"Slice-Level Performance Metric Forecasting in Intelligent Transportation Systems and the Internet of Vehicles","authors":"Dimitrios Michael Manias, Ali Chouman, Anwer Al-Dulaimi, Abdallah Shami","doi":"10.1109/iotm.001.2300035","DOIUrl":"https://doi.org/10.1109/iotm.001.2300035","url":null,"abstract":"The intricate web of vehicles connected in the fifth-generation (5G) wireless infrastructure forms the Internet of Vehicles (IoV) and enabling technologies, such as Multi-Access Edge Computing (MEC) and network slicing, are employed in guaranteeing application requirements in the IoV and optimizing network resource allocation. In particular, network slicing allows mobile network operators to support virtualized end-to-end networks with diverse slice requirements that are typically grouped into use case classes such as Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (uRLLC), and Massive Machine-Type Communication (mMTC). These enabling technologies rely on Performance Metrics, monitored and gathered within the network, in order to evaluate and suggest improvement for slice configuration. As such, this article considers a forecasting model for Performance Metrics at the network slice level by leveraging the use of the Network Data Analytics Function (NWDAF) and its edge placements. The results and analysis, including the scalability of the forecasting model, are assessed as a step towards total automation of network slice management within the 5G network. This evaluation is later illustrated using an end-to-end IoV use case incorporating the edge NWDAF placements to guide decision-making regarding management and orchestration for future networks.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"22 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":"135388230","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.001.2200260
Alisson R. Svaigen, Azzedine Boukerche, Linnyer B. Ruiz, Antonio A. F. Loureiro
The Industrial Internet of Things (IIoT) has played a key role in enabling an efficient and interconnected industry through real-time communication and processing systems, thereby building on the principles of Industry 4.0. Nowadays, industrial systems are in the process of transitioning towards Industry 5.0, where humans will once again take center stage in decision-making, supported by Artificial Intelligence (AI)-based methods. In this context, drones have emerged as a feasible device for enhancing environmental sensing tasks, reducing operational costs, alleviating communication bottlenecks, and cooperating with humans through the use of Virtual Reality (VR) and Augmented Reality (AR) platforms. Therefore, Internet of Drones (IoD) network paradigm has been adopted in the industry, giving rise to the Industrial Internet of Drones (IIoD). Given these aspects, there have been changes in the privacy and security requirements for this network environment, which demands a thorough analysis of these modifications, including the challenges that arise and possible solutions to overcome them. Consequently, this study analyzes the privacy and security issues related to IIoD. Namely, we highlight the elements of IIoD which require protection, the threats and the countermeasures. We also present how these aspects differ from the general IoD environment. Lastly, we discuss the challenges regarding IIoD security and privacy, leveraging the future directions to address Industry 5.0 aspects.
工业物联网(IIoT)通过实时通信和处理系统在实现高效互联工业方面发挥了关键作用，从而建立在工业4.0的原则之上。如今，工业系统正处于向工业5.0过渡的过程中，在基于人工智能(AI)的方法的支持下，人类将再次成为决策的中心。在这种情况下，无人机通过使用虚拟现实(VR)和增强现实(AR)平台，成为增强环境感知任务、降低运营成本、缓解通信瓶颈以及与人类合作的可行设备。因此，无人机互联网(Internet of Drones, IoD)的网络模式在业界被采用，从而产生了工业无人机互联网(Industrial Internet of Drones, IIoD)。考虑到这些方面，这个网络环境的隐私和安全要求已经发生了变化，这需要对这些变化进行彻底的分析，包括出现的挑战和可能的解决方案。因此，本研究分析了与IIoD相关的隐私和安全问题。也就是说，我们强调了IIoD需要保护的要素，威胁和对策。我们还介绍了这些方面与一般IoD环境的不同之处。最后，我们讨论了IIoD安全和隐私方面的挑战，利用未来的方向来解决工业5.0方面的问题。
{"title":"Security in the Industrial Internet of Drones","authors":"Alisson R. Svaigen, Azzedine Boukerche, Linnyer B. Ruiz, Antonio A. F. Loureiro","doi":"10.1109/iotm.001.2200260","DOIUrl":"https://doi.org/10.1109/iotm.001.2200260","url":null,"abstract":"The Industrial Internet of Things (IIoT) has played a key role in enabling an efficient and interconnected industry through real-time communication and processing systems, thereby building on the principles of Industry 4.0. Nowadays, industrial systems are in the process of transitioning towards Industry 5.0, where humans will once again take center stage in decision-making, supported by Artificial Intelligence (AI)-based methods. In this context, drones have emerged as a feasible device for enhancing environmental sensing tasks, reducing operational costs, alleviating communication bottlenecks, and cooperating with humans through the use of Virtual Reality (VR) and Augmented Reality (AR) platforms. Therefore, Internet of Drones (IoD) network paradigm has been adopted in the industry, giving rise to the Industrial Internet of Drones (IIoD). Given these aspects, there have been changes in the privacy and security requirements for this network environment, which demands a thorough analysis of these modifications, including the challenges that arise and possible solutions to overcome them. Consequently, this study analyzes the privacy and security issues related to IIoD. Namely, we highlight the elements of IIoD which require protection, the threats and the countermeasures. We also present how these aspects differ from the general IoD environment. Lastly, we discuss the challenges regarding IIoD security and privacy, leveraging the future directions to address Industry 5.0 aspects.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"21 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":"135388380","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.2023.10255788
{"title":"Comsoc Training","authors":"","doi":"10.1109/miot.2023.10255788","DOIUrl":"https://doi.org/10.1109/miot.2023.10255788","url":null,"abstract":"","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"50 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":"135388386","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.001.2300073
Mina Zamanirafe, Pegah Mansourian, Ning Zhang
The Internet of Vehicles (IoV) has emerged as a promising technology for transforming transportation systems by leveraging intelligent services and data-driven decision-making. Leveraging machine learning (ML) techniques, IoV data offers various benefits, including enhanced traffic management, improved road safety, and personalized user experiences. However, centralized ML methods face challenges in scalability and security, hampering their effectiveness in large-scale IoV deployments. This article presents a scalable and secure framework that incorporates distributed machine learning and blockchain technologies into the IoV ecosystem to overcome these limitations. The proposed framework enables the distribution of ML algorithms among participating vehicles, with each vehicle training a local model using its data. By executing a consensus algorithm, Roadside Units (RSUs) aggregate local models to provide more personalized and intelligent services in a scalable manner. Furthermore, the integration of blockchain ensures safety, transparency, and untampered features, thereby enhancing the overall security of the IoV system. This framework holds the potential to advance the efficiency, scalability, and security of IoV applications, paving the way for the widespread adoption of intelligent services in the transportation domain.
{"title":"Blockchain and Machine Learning in Internet of Vehicles: Applications, Challenges, and Opportunities","authors":"Mina Zamanirafe, Pegah Mansourian, Ning Zhang","doi":"10.1109/iotm.001.2300073","DOIUrl":"https://doi.org/10.1109/iotm.001.2300073","url":null,"abstract":"The Internet of Vehicles (IoV) has emerged as a promising technology for transforming transportation systems by leveraging intelligent services and data-driven decision-making. Leveraging machine learning (ML) techniques, IoV data offers various benefits, including enhanced traffic management, improved road safety, and personalized user experiences. However, centralized ML methods face challenges in scalability and security, hampering their effectiveness in large-scale IoV deployments. This article presents a scalable and secure framework that incorporates distributed machine learning and blockchain technologies into the IoV ecosystem to overcome these limitations. The proposed framework enables the distribution of ML algorithms among participating vehicles, with each vehicle training a local model using its data. By executing a consensus algorithm, Roadside Units (RSUs) aggregate local models to provide more personalized and intelligent services in a scalable manner. Furthermore, the integration of blockchain ensures safety, transparency, and untampered features, thereby enhancing the overall security of the IoV system. This framework holds the potential to advance the efficiency, scalability, and security of IoV applications, paving the way for the widespread adoption of intelligent services in the transportation domain.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"60 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":"135388398","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 : 2023-06-01DOI: 10.1109/IOTM.001.2200271
Xinchen Xu, Hong Wen, Huan-huan Song, Yingwei Zhao
Satellite orbit prediction has important applications in the field of space situation awareness, such as space collision warning and observation scheduling. The expansion of space information network challenges the low delay, high-accuracy transmission and real-time response of satellite orbit prediction tasks. The traditional orbit prediction process is affected by the measurement error, the estimation error, the unmodeled orbit perturbation and other factors, resulting in low accuracy orbit prediction results. In order to meet the requirements of high accuracy requirements, we built a satellite digital twin system based on the Docker container to predict, optimize and control the satellite orbit status in low consumption. The proposed digital twin system uses the container technology to build each module, which makes the updating of the orbit prediction model more convenient. In addition, in the designed digital twin system, we present an orbit error prediction model based on machine learning. Compared with the traditional physical dynamic model, the proposed machine learning model can effectively correct the error value of orbit prediction and improve the accuracy of orbit prediction. Finally, the validity of the corresponding model is verified in the simulation environment.
{"title":"A DT Machine Learning-Based Satellite Orbit Prediction for IoT Applications","authors":"Xinchen Xu, Hong Wen, Huan-huan Song, Yingwei Zhao","doi":"10.1109/IOTM.001.2200271","DOIUrl":"https://doi.org/10.1109/IOTM.001.2200271","url":null,"abstract":"Satellite orbit prediction has important applications in the field of space situation awareness, such as space collision warning and observation scheduling. The expansion of space information network challenges the low delay, high-accuracy transmission and real-time response of satellite orbit prediction tasks. The traditional orbit prediction process is affected by the measurement error, the estimation error, the unmodeled orbit perturbation and other factors, resulting in low accuracy orbit prediction results. In order to meet the requirements of high accuracy requirements, we built a satellite digital twin system based on the Docker container to predict, optimize and control the satellite orbit status in low consumption. The proposed digital twin system uses the container technology to build each module, which makes the updating of the orbit prediction model more convenient. In addition, in the designed digital twin system, we present an orbit error prediction model based on machine learning. Compared with the traditional physical dynamic model, the proposed machine learning model can effectively correct the error value of orbit prediction and improve the accuracy of orbit prediction. Finally, the validity of the corresponding model is verified in the simulation environment.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124950115","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 : 2023-06-01DOI: 10.1109/IOTM.001.2200199
Omar Rinchi, Hakim Ghazzai, Ahmad Alsharoa, Y. Massoud
The smart and autonomous learning and recognition of human activities will certainly lead to an incredible progression for several applications and services in public healthcare, education, entertainment, safety and security, and more. With the recent advances in artificial intelligence, signal processing, and computational capabilities, light detection and ranging (LiDAR) technology can play an instrumental role to revamp current human activity recognition (HAR) systems. In this magazine, we investigate the potential of using LiDAR sensors as a novel technology enabling complex and real-time HAR applications. We first overview the HAR categories and the existing state-of-the-art technologies: video-based cameras, depth sensors, wearable devices, and WiFi. Afterward, we delve into the integration of LiDAR technology to perform HAR applications. Then, we discuss the advantages and drawbacks of utilizing LiDAR technology for HAR and compare it with the existing techniques. Finally, we discuss the challenges that need to be addressed to enable advanced LiDAR-based HAR applications.
{"title":"LiDAR Technology for Human Activity Recognition: Outlooks and Challenges","authors":"Omar Rinchi, Hakim Ghazzai, Ahmad Alsharoa, Y. Massoud","doi":"10.1109/IOTM.001.2200199","DOIUrl":"https://doi.org/10.1109/IOTM.001.2200199","url":null,"abstract":"The smart and autonomous learning and recognition of human activities will certainly lead to an incredible progression for several applications and services in public healthcare, education, entertainment, safety and security, and more. With the recent advances in artificial intelligence, signal processing, and computational capabilities, light detection and ranging (LiDAR) technology can play an instrumental role to revamp current human activity recognition (HAR) systems. In this magazine, we investigate the potential of using LiDAR sensors as a novel technology enabling complex and real-time HAR applications. We first overview the HAR categories and the existing state-of-the-art technologies: video-based cameras, depth sensors, wearable devices, and WiFi. Afterward, we delve into the integration of LiDAR technology to perform HAR applications. Then, we discuss the advantages and drawbacks of utilizing LiDAR technology for HAR and compare it with the existing techniques. Finally, we discuss the challenges that need to be addressed to enable advanced LiDAR-based HAR applications.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128485066","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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