Pub Date : 2024-07-01DOI: 10.1109/IOTM.001.2300201
Li You, Yongxiang Zhu, Xiao-Hong Qiang, Christos G. Tsinos, Wenjin Wang, Ziqi Gao, B. Ottersten
The next sixth generation (6G) networks are envisioned to integrate sensing and communications in a single system, thus greatly improving spectrum utilization and reducing hardware costs. Low earth orbit (LEO) satellite communications combined with massive multiple-input multiple-output (MIMO) technology holds significant promise in offering ubiquitous and seamless connectivity with high data rates. Existing integrated sensing and communications (ISAC) studies mainly focus on terrestrial systems, while operating ISAC in massive MIMO LEO satellite systems is promising to provide high-capacity communication and flexible sensing ubiquitously. In this article, we first give an overview of LEO satellite systems and ISAC and consider adopting ISAC in the massive MIMO LEO satellite systems. Then, the recent research advances are presented. A discussion on related challenges and key enabling technologies follows. Finally, we point out some open issues and promising research directions.
{"title":"Ubiquitous Integrated Sensing and Communications for Massive MIMO LEO Satellite Systems","authors":"Li You, Yongxiang Zhu, Xiao-Hong Qiang, Christos G. Tsinos, Wenjin Wang, Ziqi Gao, B. Ottersten","doi":"10.1109/IOTM.001.2300201","DOIUrl":"https://doi.org/10.1109/IOTM.001.2300201","url":null,"abstract":"The next sixth generation (6G) networks are envisioned to integrate sensing and communications in a single system, thus greatly improving spectrum utilization and reducing hardware costs. Low earth orbit (LEO) satellite communications combined with massive multiple-input multiple-output (MIMO) technology holds significant promise in offering ubiquitous and seamless connectivity with high data rates. Existing integrated sensing and communications (ISAC) studies mainly focus on terrestrial systems, while operating ISAC in massive MIMO LEO satellite systems is promising to provide high-capacity communication and flexible sensing ubiquitously. In this article, we first give an overview of LEO satellite systems and ISAC and consider adopting ISAC in the massive MIMO LEO satellite systems. Then, the recent research advances are presented. A discussion on related challenges and key enabling technologies follows. Finally, we point out some open issues and promising research directions.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"20 4","pages":"30-35"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141689016","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 : 2024-07-01DOI: 10.1109/IOTM.001.2400021
K. T. Pauu, Qianqian Pan, Jun Wu, Ali Kashif Bashir, Mafua-‘i-Vai’utukakau Maka, Marwan Omar
The unforeseen events of natural disasters often devastate critical infrastructure and disrupt communication. The use of unmanned aerial vehicles (UAVs) in emergency response scenarios offers significant potential for delivering real-time information and assisting emergency response efforts. However, challenges such as physical barriers to communication not only hinder transmission performance by obstructing established line-of-sight (LoS) links but also pose risks to the privacy of sensitive information exchanged across these links. To address these challenges, we propose a novel IRS-aided UAV secure communications framework aimed to enhance communication efficiency while ensuring privacy preservation in emergency response scenarios. The framework consists of three stages: (i) local model training with dynamic differential privacy mechanism using stochastic gradient descent (SGD), with adaptive learning rate adjustment based on validation performance, (ii) decentralized federated learning (FL) with intelligent reflective surfaces (IRS) incorporation to improve communication and information exchange between UAV-to-UAV and UAV-to-ground station, and (iii) selection of a UAV header based on operational characteristics and connectivity to aid UAV-to-ground station communication.Furthermore, we evaluated our proposed framework through experimental simulations and achieved 0.91 accuracy after 50 federated learning rounds underscoring the efficacy of our dynamic noise and learning rate adjustment mechanism. Additionally, our integration of IRS led to lower communication latency, highlighting the effectiveness of our approach. This framework adeptly balances privacy protection with model accuracy.
{"title":"IRS-Aided Federated Learning with Dynamic Differential Privacy for UAVs in Emergency Response","authors":"K. T. Pauu, Qianqian Pan, Jun Wu, Ali Kashif Bashir, Mafua-‘i-Vai’utukakau Maka, Marwan Omar","doi":"10.1109/IOTM.001.2400021","DOIUrl":"https://doi.org/10.1109/IOTM.001.2400021","url":null,"abstract":"The unforeseen events of natural disasters often devastate critical infrastructure and disrupt communication. The use of unmanned aerial vehicles (UAVs) in emergency response scenarios offers significant potential for delivering real-time information and assisting emergency response efforts. However, challenges such as physical barriers to communication not only hinder transmission performance by obstructing established line-of-sight (LoS) links but also pose risks to the privacy of sensitive information exchanged across these links. To address these challenges, we propose a novel IRS-aided UAV secure communications framework aimed to enhance communication efficiency while ensuring privacy preservation in emergency response scenarios. The framework consists of three stages: (i) local model training with dynamic differential privacy mechanism using stochastic gradient descent (SGD), with adaptive learning rate adjustment based on validation performance, (ii) decentralized federated learning (FL) with intelligent reflective surfaces (IRS) incorporation to improve communication and information exchange between UAV-to-UAV and UAV-to-ground station, and (iii) selection of a UAV header based on operational characteristics and connectivity to aid UAV-to-ground station communication.Furthermore, we evaluated our proposed framework through experimental simulations and achieved 0.91 accuracy after 50 federated learning rounds underscoring the efficacy of our dynamic noise and learning rate adjustment mechanism. Additionally, our integration of IRS led to lower communication latency, highlighting the effectiveness of our approach. This framework adeptly balances privacy protection with model accuracy.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"135 34","pages":"108-115"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141714155","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 : 2024-07-01DOI: 10.1109/IOTM.001.2300251
Yun-Wei Lin, Yi-Bing Lin, Wen-Liang Chen, Chia-Hui Chang, Han-Kuan Li
During the commercial production of watermelons, farmers must swiftly assess fruit ripeness post-harvest to minimize losses through sorting based on edibility time. This process enhances marketability and productivity but is often very tedious in traditional approaches. This article delves into the multifaceted realm of Internet of Things (IoT) based real-time watermelon ripeness evaluation. Watermelons, subject to diverse degrees of ripeness, significantly impact the fruit's taste and texture. Notably, watermelons cease to mature after detachment from the vine, underscoring the importance of selecting the ripest specimens at purchase. Prompt post-harvest fruit ripeness assessment is pivotal to mitigate losses, ensuring accurate sorting based on edibility timeline. Consequently, diligent watermelon ripeness assessment by farmers gains importance for enhanced marketability and productivity. While manual techniques like tapping, color examination, and day counting serve practical purposes, their accuracy relies on subjective judgment. Currently, the prevailing method for assessing watermelon ripeness is the sound test. This tapping technique surprisingly rests on logical grounds, as the resulting sounds offer an adequate ripeness indicator. However, personal interpretations of these sounds are influenced by subjective experiences and traditional wisdom. This article investigates non-destructive methodologies for evaluating watermelon ripeness. Then we propose WatermelonTalk, an IoT based real-time deep learning platform designed for acoustic watermelon testing. We also introduce the concept of the “tapping ensemble,” not previously found in the literature, which significantly enhances prediction accuracy. The article's contributions encompass the most comprehensive categorization of watermelons in the literature, specifically categorizing 1698 watermelons across 343 varieties by ripeness. Previous studies have considered either the 2-level test (unripe and ripe) or the 3-level test (unripe, ripe, and overripe). This article explores the 4-level test, where the unripe category from the 3-level test is further divided into the unripe class and the half-ripe class. In this test, the farmer pays more attention to the half-ripe class to ensure it undergoes more frequent testing than the unripe class. This precaution is taken to prevent these half-ripe watermelons from becoming overripe in the subsequent test. Our study achieved an enhanced testing accuracy of 97.64% for the three-level test and a notable accuracy of 94.07% for the four-level test, standing as the best result within the acoustic framework. The three-level test can be utilized by customers when purchasing watermelons, while the four-level test serves as a tool for farmers engaged in professional production.
{"title":"Watermelons Talk: Predicting Ripeness through Tapping","authors":"Yun-Wei Lin, Yi-Bing Lin, Wen-Liang Chen, Chia-Hui Chang, Han-Kuan Li","doi":"10.1109/IOTM.001.2300251","DOIUrl":"https://doi.org/10.1109/IOTM.001.2300251","url":null,"abstract":"During the commercial production of watermelons, farmers must swiftly assess fruit ripeness post-harvest to minimize losses through sorting based on edibility time. This process enhances marketability and productivity but is often very tedious in traditional approaches. This article delves into the multifaceted realm of Internet of Things (IoT) based real-time watermelon ripeness evaluation. Watermelons, subject to diverse degrees of ripeness, significantly impact the fruit's taste and texture. Notably, watermelons cease to mature after detachment from the vine, underscoring the importance of selecting the ripest specimens at purchase. Prompt post-harvest fruit ripeness assessment is pivotal to mitigate losses, ensuring accurate sorting based on edibility timeline. Consequently, diligent watermelon ripeness assessment by farmers gains importance for enhanced marketability and productivity. While manual techniques like tapping, color examination, and day counting serve practical purposes, their accuracy relies on subjective judgment. Currently, the prevailing method for assessing watermelon ripeness is the sound test. This tapping technique surprisingly rests on logical grounds, as the resulting sounds offer an adequate ripeness indicator. However, personal interpretations of these sounds are influenced by subjective experiences and traditional wisdom. This article investigates non-destructive methodologies for evaluating watermelon ripeness. Then we propose WatermelonTalk, an IoT based real-time deep learning platform designed for acoustic watermelon testing. We also introduce the concept of the “tapping ensemble,” not previously found in the literature, which significantly enhances prediction accuracy. The article's contributions encompass the most comprehensive categorization of watermelons in the literature, specifically categorizing 1698 watermelons across 343 varieties by ripeness. Previous studies have considered either the 2-level test (unripe and ripe) or the 3-level test (unripe, ripe, and overripe). This article explores the 4-level test, where the unripe category from the 3-level test is further divided into the unripe class and the half-ripe class. In this test, the farmer pays more attention to the half-ripe class to ensure it undergoes more frequent testing than the unripe class. This precaution is taken to prevent these half-ripe watermelons from becoming overripe in the subsequent test. Our study achieved an enhanced testing accuracy of 97.64% for the three-level test and a notable accuracy of 94.07% for the four-level test, standing as the best result within the acoustic framework. The three-level test can be utilized by customers when purchasing watermelons, while the four-level test serves as a tool for farmers engaged in professional production.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"100 2","pages":"154-161"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141714397","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 : 2024-07-01DOI: 10.1109/miot.2024.10574264
{"title":"IEEE Medala of Honor","authors":"","doi":"10.1109/miot.2024.10574264","DOIUrl":"https://doi.org/10.1109/miot.2024.10574264","url":null,"abstract":"","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"142 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141694936","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 : 2024-07-01DOI: 10.1109/miot.2024.10574235
{"title":"Cover 3","authors":"","doi":"10.1109/miot.2024.10574235","DOIUrl":"https://doi.org/10.1109/miot.2024.10574235","url":null,"abstract":"","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"12 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141700294","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}
The metaverse's rise brings unique privacy challenges. While existing research has broadly surveyed security and privacy in this domain, our article provides a targeted analysis of user privacy within notable VR/MR devices and platforms. We critically evaluate their stated policies, specifically examining practices of data handling, tracking, identity management, and consent. Additionally, we analyze the mechanisms of cross-platform data sharing and software integrations. Moving beyond previous works, we dissect the practical application of these policies, providing a granular look at the intersection of personalization and privacy. Our study also reviews privacy-enhancing technologies and the implications of legal regulations to advance the state of the art in metaverse privacy discourse.
{"title":"Navigating Privacy Challenges in the Metaverse: A Comprehensive Examination of Current Technologies and Platforms","authors":"Lamiaa Basyoni, Aliya Tabassum, Khaled Shaban, Ezieddin Elmahjub, Osama Halabi, Junaid Qadir","doi":"10.1109/IOTM.001.2300197","DOIUrl":"https://doi.org/10.1109/IOTM.001.2300197","url":null,"abstract":"The metaverse's rise brings unique privacy challenges. While existing research has broadly surveyed security and privacy in this domain, our article provides a targeted analysis of user privacy within notable VR/MR devices and platforms. We critically evaluate their stated policies, specifically examining practices of data handling, tracking, identity management, and consent. Additionally, we analyze the mechanisms of cross-platform data sharing and software integrations. Moving beyond previous works, we dissect the practical application of these policies, providing a granular look at the intersection of personalization and privacy. Our study also reviews privacy-enhancing technologies and the implications of legal regulations to advance the state of the art in metaverse privacy discourse.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"9 6","pages":"144-152"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141711087","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 : 2024-05-01DOI: 10.1109/miot.2024.10517522
{"title":"Comsoc Membership","authors":"","doi":"10.1109/miot.2024.10517522","DOIUrl":"https://doi.org/10.1109/miot.2024.10517522","url":null,"abstract":"","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"15 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141036546","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 : 2024-05-01DOI: 10.1109/IOTM.001.2400008
P. M. Rao, Anusha Vangala, Saraswathi Pedada, Ashok Kumar Das, Athanasios V. Vasilakos
With the rapid advancements in the Internet of Things (IoT), edge computing has a significant role in many eHealth applications. However, security and data privacy are major challenges due to the widespread popularity of the digital health domain. The network and data storage should withstand adversarial entities and allow access to only legitimate users. Medical users and servers must be registered with a trusted third-party authority to obtain permissions to authenticate remaining users. Millions of smart medical devices connect online to collect critical patient information, analyze reports, and perform meaningful decisions without human interaction. In this scenario, standard security is essential to safeguard eHealth applications. This research provides a secured, lightweight mobile edge computing framework to address these issues. The empirical results show that our framework mitigates computational overheads. The informal and formal analysis shows that our framework withstands potential attacks.
{"title":"Privacy-Preserving Lightweight Authentication for Location-Aware Edge-Enabled eHealth Systems","authors":"P. M. Rao, Anusha Vangala, Saraswathi Pedada, Ashok Kumar Das, Athanasios V. Vasilakos","doi":"10.1109/IOTM.001.2400008","DOIUrl":"https://doi.org/10.1109/IOTM.001.2400008","url":null,"abstract":"With the rapid advancements in the Internet of Things (IoT), edge computing has a significant role in many eHealth applications. However, security and data privacy are major challenges due to the widespread popularity of the digital health domain. The network and data storage should withstand adversarial entities and allow access to only legitimate users. Medical users and servers must be registered with a trusted third-party authority to obtain permissions to authenticate remaining users. Millions of smart medical devices connect online to collect critical patient information, analyze reports, and perform meaningful decisions without human interaction. In this scenario, standard security is essential to safeguard eHealth applications. This research provides a secured, lightweight mobile edge computing framework to address these issues. The empirical results show that our framework mitigates computational overheads. The informal and formal analysis shows that our framework withstands potential attacks.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"17 6","pages":"76-82"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141044987","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 : 2024-05-01DOI: 10.1109/miot.2024.10517513
{"title":"Cover 3","authors":"","doi":"10.1109/miot.2024.10517513","DOIUrl":"https://doi.org/10.1109/miot.2024.10517513","url":null,"abstract":"","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141048311","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 : 2024-05-01DOI: 10.1109/IOTM.001.2300246
Siva Sai, Mizaan Kanadia, Vinay Chamola
The rise of the Generative Pre-Trained Transformer(GPT) language model, more commonly used as ChatGPT has brought a spotlight on the ever-developing field of Generative AI (GAI).} With current advancements in graphics processing units (GPUs), it has become easier to train and use deep generative models. Similarly, the developments in edge computing have made it possible for us to make the most of GAI's potential for numerous use cases in IoT. In this article, we explore the prospects of combining GAI with Internet of Things (IoT) technology to create innovative solutions for several areas where these devices fall short. Specifically, we dive into how GAI can help address the challenges posed by data insufficiency and incompleteness in IoT systems, by generating synthetic data that can be used to train other deep models. We also discuss how GAI can be used to personalize content generated by IoT devices along with other applications of the synergy. Additionally, we also delve into the real-world scenarios where the technology shall be implemented. We conclude with the limitations of GAI technology for IoT applications which can be worked upon in the future.
生成式预训练变换器（GPT）语言模型的兴起（更常用的名称是 ChatGPT）使不断发展的生成式人工智能（GAI）领域成为焦点。随着图形处理器（GPU）的不断进步，训练和使用深度生成模型变得更加容易。同样，边缘计算的发展也让我们有可能在物联网的众多用例中充分利用 GAI 的潜力。在本文中，我们将探讨将 GAI 与物联网（IoT）技术相结合的前景，以便为这些设备所欠缺的几个领域创建创新解决方案。具体来说，我们将深入探讨 GAI 如何通过生成可用于训练其他深度模型的合成数据，帮助解决物联网系统中数据不足和不完整所带来的挑战。我们还讨论了 GAI 如何用于个性化物联网设备生成的内容，以及协同效应的其他应用。此外，我们还深入探讨了该技术在现实世界中的应用场景。最后，我们总结了 GAI 技术在物联网应用中的局限性，这些局限性可以在未来加以改进。
{"title":"Empowering IoT with Generative AI: Applications, Case Studies, and Limitations","authors":"Siva Sai, Mizaan Kanadia, Vinay Chamola","doi":"10.1109/IOTM.001.2300246","DOIUrl":"https://doi.org/10.1109/IOTM.001.2300246","url":null,"abstract":"The rise of the Generative Pre-Trained Transformer(GPT) language model, more commonly used as ChatGPT has brought a spotlight on the ever-developing field of Generative AI (GAI).} With current advancements in graphics processing units (GPUs), it has become easier to train and use deep generative models. Similarly, the developments in edge computing have made it possible for us to make the most of GAI's potential for numerous use cases in IoT. In this article, we explore the prospects of combining GAI with Internet of Things (IoT) technology to create innovative solutions for several areas where these devices fall short. Specifically, we dive into how GAI can help address the challenges posed by data insufficiency and incompleteness in IoT systems, by generating synthetic data that can be used to train other deep models. We also discuss how GAI can be used to personalize content generated by IoT devices along with other applications of the synergy. Additionally, we also delve into the real-world scenarios where the technology shall be implemented. We conclude with the limitations of GAI technology for IoT applications which can be worked upon in the future.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"31 5","pages":"38-43"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141032575","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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