The primary role of a reconfigurable intelligent surface (RIS) is to restore the propagation path between the access point (AP) and the user equipment (UE) in a non-line-of-sight communication link. Depending on the RIS placement with respect to the AP and UE positions, different power levels can reach the UE, thus affecting the quality of the communication. Particularly when the UE moves freely, the RIS position that maximizes the received signal will depend strongly on the UE location. In this context, we use an analytical model to assess the decisions that have to be made concerning the positioning of the RIS, which are determined by the interplay of three crucial quantities, namely (a) the available AP gain, (b) the available positions for the AP and RIS placement, and (c) the minimum desired power levels at the UE. The impact of the AP antenna gain tunability on the RIS placement selection is assessed and illustrated in D-band indoor scenarios.
{"title":"An analytical framework for Reconfigurable Intelligent Surfaces placement in a mobile user environment","authors":"Giorgos Stratidakis, S. Droulias, A. Alexiou","doi":"10.1145/3485730.3494038","DOIUrl":"https://doi.org/10.1145/3485730.3494038","url":null,"abstract":"The primary role of a reconfigurable intelligent surface (RIS) is to restore the propagation path between the access point (AP) and the user equipment (UE) in a non-line-of-sight communication link. Depending on the RIS placement with respect to the AP and UE positions, different power levels can reach the UE, thus affecting the quality of the communication. Particularly when the UE moves freely, the RIS position that maximizes the received signal will depend strongly on the UE location. In this context, we use an analytical model to assess the decisions that have to be made concerning the positioning of the RIS, which are determined by the interplay of three crucial quantities, namely (a) the available AP gain, (b) the available positions for the AP and RIS placement, and (c) the minimum desired power levels at the UE. The impact of the AP antenna gain tunability on the RIS placement selection is assessed and illustrated in D-band indoor scenarios.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126760574","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}
We are witnessing a trend of users owning multiple data-generating wearable and IoT devices that continuously capture sensor data pertaining to a user's activities and context. Federated Learning is a potential technique to derive meaningful insights from this sensor data in a privacy-preserving way without revealing the raw sensor data to a central server. In this paper, we introduce a new problem setting in this multi-device context called Federated Learning in Multi-Device Local Networks (FL-MDLN). We identify core challenges for FL-MDLN in relation to its federation architecture, and statistical and systems heterogeneity across multiple users and multiple devices. Then, we introduce a new user-as-client (UAC) federation architecture, and propose various device selection strategies to counter statistical and systems heterogeneity in FL-MDLN. Early empirical findings show that our proposed techniques improve model test accuracy as well as battery power efficiency in FL. Based on these findings, we elucidate open research questions and future work in FL-MDLN.
{"title":"Device or User: Rethinking Federated Learning in Personal-Scale Multi-Device Environments","authors":"Hyunsung Cho, Akhil Mathur, F. Kawsar","doi":"10.1145/3485730.3493449","DOIUrl":"https://doi.org/10.1145/3485730.3493449","url":null,"abstract":"We are witnessing a trend of users owning multiple data-generating wearable and IoT devices that continuously capture sensor data pertaining to a user's activities and context. Federated Learning is a potential technique to derive meaningful insights from this sensor data in a privacy-preserving way without revealing the raw sensor data to a central server. In this paper, we introduce a new problem setting in this multi-device context called Federated Learning in Multi-Device Local Networks (FL-MDLN). We identify core challenges for FL-MDLN in relation to its federation architecture, and statistical and systems heterogeneity across multiple users and multiple devices. Then, we introduce a new user-as-client (UAC) federation architecture, and propose various device selection strategies to counter statistical and systems heterogeneity in FL-MDLN. Early empirical findings show that our proposed techniques improve model test accuracy as well as battery power efficiency in FL. Based on these findings, we elucidate open research questions and future work in FL-MDLN.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127511539","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}
Physical-layer identification using hardware imperfections, known as radiometric fingerprinting, has existed for some time, but little focus has been put on Bluetooth Low Energy (BLE). This work systematically explores features for physical-layer identification of BLE. We evaluate the fingerprinting performance on different feature sets, and we discuss the potential issues with the robustness that may arise in a practical environment. Accuracy results are achieved in excess of 99%, showing potential for the system to work in security settings to safeguard a Bluetooth network.
{"title":"Identifying Bluetooth Low Energy Devices","authors":"Daniel Nilsson, Wenqing Yan","doi":"10.1145/3485730.3492880","DOIUrl":"https://doi.org/10.1145/3485730.3492880","url":null,"abstract":"Physical-layer identification using hardware imperfections, known as radiometric fingerprinting, has existed for some time, but little focus has been put on Bluetooth Low Energy (BLE). This work systematically explores features for physical-layer identification of BLE. We evaluate the fingerprinting performance on different feature sets, and we discuss the potential issues with the robustness that may arise in a practical environment. Accuracy results are achieved in excess of 99%, showing potential for the system to work in security settings to safeguard a Bluetooth network.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130404319","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 large-scale Bluetooth low energy (BLE) location-based services (LBS) are challenging due to the requirement of additional Bluetooth beacons, which inevitably incur tremendous hardware and maintenance cost. To alleviate this issue, this work presents WiBeacon which repurposes ubiquitously deployed WiFi access points into virtual beacons via cross-technology communication (CTC). WiBeacon only requires moderate software updates in APs, thus enabling fast deployment with zero additional hardware and also low maintenance cost via the remote Internet access. We implement it on COTS WiFi APs and evaluate it in various scenarios including a real-world commercial BLE LBS application as the pilot study. During this two-week pilot study, our WiBeacon provides reliable LBS, e.g., as robust as conventional BLE beacons, for 512 users with 150 types of smartphones. The full paper of this work [2] was published in MobiCom 2021.
{"title":"BLE Location-based Services via WiFi","authors":"Ruofeng Liu, Zhimeng Yin, Wenchao Jiang, Tian He","doi":"10.1145/3485730.3492891","DOIUrl":"https://doi.org/10.1145/3485730.3492891","url":null,"abstract":"The large-scale Bluetooth low energy (BLE) location-based services (LBS) are challenging due to the requirement of additional Bluetooth beacons, which inevitably incur tremendous hardware and maintenance cost. To alleviate this issue, this work presents WiBeacon which repurposes ubiquitously deployed WiFi access points into virtual beacons via cross-technology communication (CTC). WiBeacon only requires moderate software updates in APs, thus enabling fast deployment with zero additional hardware and also low maintenance cost via the remote Internet access. We implement it on COTS WiFi APs and evaluate it in various scenarios including a real-world commercial BLE LBS application as the pilot study. During this two-week pilot study, our WiBeacon provides reliable LBS, e.g., as robust as conventional BLE beacons, for 512 users with 150 types of smartphones. The full paper of this work [2] was published in MobiCom 2021.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"71 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130671383","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}
J. T. Gómez, Anke Kuestner, Ketki Pitke, Jennifer Simonjan, B. Unluturk, F. Dressler
Early detection of diseases in the human body is of utmost importance for the diagnosis and medical treatment of patients. Supported by recent advancements in nanotechnology, diseases may be detected by patrolling nanosensors, even before symptoms appear. This paper explores the detection capabilities of nanosensors flowing through the human circulatory system (HCS). We model the HCS through a Markov chain and propose the use of machine learning (ML) methods to learn the corresponding transition probabilities. Doing so, we propose a methodology to develop an early detection mechanism of quorum sensing (QS) molecules released by bacteria. Simulation results indicate the suitability of our machine learning approach as a basis for in-body precision medicine.
{"title":"A Machine Learning Approach for Abnormality Detection in Blood Vessels via Mobile Nanosensors","authors":"J. T. Gómez, Anke Kuestner, Ketki Pitke, Jennifer Simonjan, B. Unluturk, F. Dressler","doi":"10.1145/3485730.3494037","DOIUrl":"https://doi.org/10.1145/3485730.3494037","url":null,"abstract":"Early detection of diseases in the human body is of utmost importance for the diagnosis and medical treatment of patients. Supported by recent advancements in nanotechnology, diseases may be detected by patrolling nanosensors, even before symptoms appear. This paper explores the detection capabilities of nanosensors flowing through the human circulatory system (HCS). We model the HCS through a Markov chain and propose the use of machine learning (ML) methods to learn the corresponding transition probabilities. Doing so, we propose a methodology to develop an early detection mechanism of quorum sensing (QS) molecules released by bacteria. Simulation results indicate the suitability of our machine learning approach as a basis for in-body precision medicine.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116911611","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}
Video cameras are becoming ubiquitous in our daily lives. With the recent advancement of Artificial Intelligence (AI), live video analytics are enabling various useful services, including traffic monitoring and campus surveillance. However, current video analytics systems are highly limited in leveraging the enormous opportunities of the deployed cameras due to (i) centralized processing architecture (i.e., cameras are treated as dumb streaming-only sensors), (ii) hard-coded analytics capabilities from tightly coupled hardware and software, (iii) isolated and fragmented camera deployment from different service providers, and (iv) independent processing of camera streams without any collaboration. In this paper, we envision a full-fledged system for software-defined video analytics with cross-camera collaboration that overcomes the aforementioned limitations. We illustrate its detailed system architecture, carefully analyze the key system requirements with representative app scenarios, and derive potential research issues along with a summary of the status quo of existing works.
{"title":"Vision Paper: Towards Software-Defined Video Analytics with Cross-Camera Collaboration","authors":"Juheon Yi, Chulhong Min, F. Kawsar","doi":"10.1145/3485730.3493453","DOIUrl":"https://doi.org/10.1145/3485730.3493453","url":null,"abstract":"Video cameras are becoming ubiquitous in our daily lives. With the recent advancement of Artificial Intelligence (AI), live video analytics are enabling various useful services, including traffic monitoring and campus surveillance. However, current video analytics systems are highly limited in leveraging the enormous opportunities of the deployed cameras due to (i) centralized processing architecture (i.e., cameras are treated as dumb streaming-only sensors), (ii) hard-coded analytics capabilities from tightly coupled hardware and software, (iii) isolated and fragmented camera deployment from different service providers, and (iv) independent processing of camera streams without any collaboration. In this paper, we envision a full-fledged system for software-defined video analytics with cross-camera collaboration that overcomes the aforementioned limitations. We illustrate its detailed system architecture, carefully analyze the key system requirements with representative app scenarios, and derive potential research issues along with a summary of the status quo of existing works.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127875243","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}
Fan Yang, A. Thangarajan, Sam Michiels, W. Joosen, D. Hughes
Recent innovations in energy harvesting promise extended operational life and reduced maintenance costs for the next generation of Internet of Things (IoT) platforms. However, energy management in these platforms remains problematic due to dynamism in energy supply and demand, inefficiency in storing and converting energy and a lack of per-task charge isolation. This paper tackles this problem by proposing a software defined charge storage module called Morphy, which combines a polymorphic capacitor array with intelligent power management software. Morphy delivers energy to application tasks in a flexible, efficient, and isolated manner. Morphy provides two software extensions to the Operating System scheduler: the energy semaphore blocks the execution of tasks until sufficient charge is available to safely run them, and the energy watchdog monitors and mitigates energy management bugs. We have realized a prototype of Morphy with the hardware form factor of a standard 9V (PP3) battery package and a software library that integrates with the FreeRTOS scheduler. Our evaluation shows that, in comparison to standard energy storage and management approaches, our prototype reaches an operational voltage more quickly, sustains operation longer in the case of power failure and effectively isolates charge storage for dedicated tasks with minimal compute, memory and energy overhead.
{"title":"Morphy","authors":"Fan Yang, A. Thangarajan, Sam Michiels, W. Joosen, D. Hughes","doi":"10.1145/3485730.3485947","DOIUrl":"https://doi.org/10.1145/3485730.3485947","url":null,"abstract":"Recent innovations in energy harvesting promise extended operational life and reduced maintenance costs for the next generation of Internet of Things (IoT) platforms. However, energy management in these platforms remains problematic due to dynamism in energy supply and demand, inefficiency in storing and converting energy and a lack of per-task charge isolation. This paper tackles this problem by proposing a software defined charge storage module called Morphy, which combines a polymorphic capacitor array with intelligent power management software. Morphy delivers energy to application tasks in a flexible, efficient, and isolated manner. Morphy provides two software extensions to the Operating System scheduler: the energy semaphore blocks the execution of tasks until sufficient charge is available to safely run them, and the energy watchdog monitors and mitigates energy management bugs. We have realized a prototype of Morphy with the hardware form factor of a standard 9V (PP3) battery package and a software library that integrates with the FreeRTOS scheduler. Our evaluation shows that, in comparison to standard energy storage and management approaches, our prototype reaches an operational voltage more quickly, sustains operation longer in the case of power failure and effectively isolates charge storage for dedicated tasks with minimal compute, memory and energy overhead.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116875955","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 ECCO-Box is a framework to establish connectivity of heterogeneous devices and sensors and distribute computing on the computing continuum dynamically at runtime. The goal of the dissertation is to design an architecture concept and develop a working prototype as a proof of concept. There are several requirements to the application which have to be fulfilled and will be evaluated in the form of an experimental study in the end. The main use case of the application will be processing of sensor data and analysis with artificial intelligence in an industrial environment.
{"title":"ECCO-Box: An Edge Computing and Connectivity Framework","authors":"Jannik Blähser","doi":"10.1145/3485730.3492898","DOIUrl":"https://doi.org/10.1145/3485730.3492898","url":null,"abstract":"The ECCO-Box is a framework to establish connectivity of heterogeneous devices and sensors and distribute computing on the computing continuum dynamically at runtime. The goal of the dissertation is to design an architecture concept and develop a working prototype as a proof of concept. There are several requirements to the application which have to be fulfilled and will be evaluated in the form of an experimental study in the end. The main use case of the application will be processing of sensor data and analysis with artificial intelligence in an industrial environment.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114348822","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}
Wiebke Toussaint, Akhil Mathur, A. Ding, F. Kawsar
When deploying machine learning (ML) models on embedded and IoT devices, performance encompasses more than an accuracy metric: inference latency, energy consumption, and model fairness are necessary to ensure reliable performance under heterogeneous and resource-constrained operating conditions. To this end, prior research has studied model-centric approaches, such as tuning the hyperparameters of the model during training and later applying model compression techniques to tailor the model to the resource needs of an embedded device. In this paper, we take a data-centric view of embedded ML and study the role that pre-processing parameters in the data pipeline can play in balancing the various performance metrics of an embedded ML system. Through an in-depth case study with audio-based keyword spotting (KWS) models, we show that pre-processing parameter tuning is a remarkable tool that model developers can adopt to trade-off between a model's accuracy, fairness, and system efficiency, as well as to make an embedded ML model resilient to unseen deployment conditions.
{"title":"Characterising the Role of Pre-Processing Parameters in Audio-based Embedded Machine Learning","authors":"Wiebke Toussaint, Akhil Mathur, A. Ding, F. Kawsar","doi":"10.1145/3485730.3493448","DOIUrl":"https://doi.org/10.1145/3485730.3493448","url":null,"abstract":"When deploying machine learning (ML) models on embedded and IoT devices, performance encompasses more than an accuracy metric: inference latency, energy consumption, and model fairness are necessary to ensure reliable performance under heterogeneous and resource-constrained operating conditions. To this end, prior research has studied model-centric approaches, such as tuning the hyperparameters of the model during training and later applying model compression techniques to tailor the model to the resource needs of an embedded device. In this paper, we take a data-centric view of embedded ML and study the role that pre-processing parameters in the data pipeline can play in balancing the various performance metrics of an embedded ML system. Through an in-depth case study with audio-based keyword spotting (KWS) models, we show that pre-processing parameter tuning is a remarkable tool that model developers can adopt to trade-off between a model's accuracy, fairness, and system efficiency, as well as to make an embedded ML model resilient to unseen deployment conditions.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123371028","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}
Jian Xu, A. Bhattacharya, A. Balasubramanian, Donald E. Porter
Users are surrounded by sensors that are available through various devices beyond their smartphones. However, these sensors are not fully utilized by current end-user applications. A key reason sensor use is so limited is that application developers must exactly identify how the sensor data can be used by smartphone apps. To mitigate this problem, we present SenseWear, a sensor-sharing platform that extends the functionality of a smartphone to use remote sensors with limited additional developer effort. Sensor sharing has several uses, including augmenting the hardware in smartphones, creating new gestural interactions with smartphone applications, and improving application's Quality of Experience via higher-quality sensors from other devices, such as wearables. We developed and present six use cases that use remote sensors in various smartphone applications. Each extension requires adding fewer than 20 lines of code on average. Furthermore, using remote sensors did not introduce a perceptible increase in latency, and creates more convenient interaction options for smartphone apps.
{"title":"Sensor Virtualization for Efficient Sharing of Mobile and Wearable Sensors","authors":"Jian Xu, A. Bhattacharya, A. Balasubramanian, Donald E. Porter","doi":"10.1145/3485730.3493451","DOIUrl":"https://doi.org/10.1145/3485730.3493451","url":null,"abstract":"Users are surrounded by sensors that are available through various devices beyond their smartphones. However, these sensors are not fully utilized by current end-user applications. A key reason sensor use is so limited is that application developers must exactly identify how the sensor data can be used by smartphone apps. To mitigate this problem, we present SenseWear, a sensor-sharing platform that extends the functionality of a smartphone to use remote sensors with limited additional developer effort. Sensor sharing has several uses, including augmenting the hardware in smartphones, creating new gestural interactions with smartphone applications, and improving application's Quality of Experience via higher-quality sensors from other devices, such as wearables. We developed and present six use cases that use remote sensors in various smartphone applications. Each extension requires adding fewer than 20 lines of code on average. Furthermore, using remote sensors did not introduce a perceptible increase in latency, and creates more convenient interaction options for smartphone apps.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131598485","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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