With the advent of edge computing, the Internet of Things (IoT) environment has the ability to process data locally. The complexity of the context reasoning process can be scattered across several edge nodes that are physically placed at the source of the qualitative information by moving the processing and knowledge inference to the edge of the IoT network. This facilitates the real-time processing of a large range of rich data sources that would be less complex and expensive compare to the traditional centralized cloud system. In this paper, we propose a novel approach to provide low-level intelligence for IoT applications through an IoT edge controller that is leveraging the Fuzzy Logic Controller along with edge computing. This low-level intelligence, together with cloud-based intelligence, forms the distributed IoT intelligence. The proposed controller allows distributed IoT gateway to manage input uncertainties; besides, by interacting with its environment, the learning system can enhance its performance over time, which leads to improving the reliability of the IoT gateway. Therefore, such a controller is able to offer different context-aware reasoning to alleviate the distributed IoT. A simulated smart home scenario has been done to prove the plausibility of the low-level intelligence concerning reducing latency and more accurate prediction through learning experiences at the edge.
{"title":"Context-based Reasoning through Fuzzy Logic for Edge Intelligence","authors":"Ramin Firouzi, R. Rahmani, T. Kanter","doi":"10.5383/JUSPN.15.01.003","DOIUrl":"https://doi.org/10.5383/JUSPN.15.01.003","url":null,"abstract":"With the advent of edge computing, the Internet of Things (IoT) environment has the ability to process data locally. The complexity of the context reasoning process can be scattered across several edge nodes that are physically placed at the source of the qualitative information by moving the processing and knowledge inference to the edge of the IoT network. This facilitates the real-time processing of a large range of rich data sources that would be less complex and expensive compare to the traditional centralized cloud system. In this paper, we propose a novel approach to provide low-level intelligence for IoT applications through an IoT edge controller that is leveraging the Fuzzy Logic Controller along with edge computing. This low-level intelligence, together with cloud-based intelligence, forms the distributed IoT intelligence. The proposed controller allows distributed IoT gateway to manage input uncertainties; besides, by interacting with its environment, the learning system can enhance its performance over time, which leads to improving the reliability of the IoT gateway. Therefore, such a controller is able to offer different context-aware reasoning to alleviate the distributed IoT. A simulated smart home scenario has been done to prove the plausibility of the low-level intelligence concerning reducing latency and more accurate prediction through learning experiences at the edge.","PeriodicalId":376249,"journal":{"name":"J. Ubiquitous Syst. Pervasive Networks","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131907865","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}
In recent years, Deep Learning has become a critical success factor for Machine Learning. In the present study, we introduced a Deep Learning model to network attack detection, by using Hidden Markov Model and Artificial Neural Networks. We used a model aggregation technique to find a single consolidated Deep Learning model for better data fitting. The model selection technique is applied to optimize the bias-variance trade-off of the expected prediction. We demonstrate its ability to reduce the convergence, reach the optimal solution and obtain more cluttered decision boundaries. Experimental studies conducted on attack detection indicate that our proposed model outperformed existing Deep Learning models and gives an enhanced generalization.
{"title":"An Enhanced Deep Learning Model to Network Attack Detection, by using Parameter Tuning, Hidden Markov Model and Neural Network","authors":"Choukri Djellali, Mehdi Adda","doi":"10.5383/JUSPN.15.01.005","DOIUrl":"https://doi.org/10.5383/JUSPN.15.01.005","url":null,"abstract":"In recent years, Deep Learning has become a critical success factor for Machine Learning. In the present study, we introduced a Deep Learning model to network attack detection, by using Hidden Markov Model and Artificial Neural Networks. We used a model aggregation technique to find a single consolidated Deep Learning model for better data fitting. The model selection technique is applied to optimize the bias-variance trade-off of the expected prediction. We demonstrate its ability to reduce the convergence, reach the optimal solution and obtain more cluttered decision boundaries. Experimental studies conducted on attack detection indicate that our proposed model outperformed existing Deep Learning models and gives an enhanced generalization.","PeriodicalId":376249,"journal":{"name":"J. Ubiquitous Syst. Pervasive Networks","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129755870","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}
Olivier Debauche, M. Elmoulat, S. Mahmoudi, S. Mahmoudi, Adriano Guttadauria, P. Manneback, F. Lebeau
Landslides are phenomena that cause significant human and economic losses. Researchers have investigated the prediction of high landslides susceptibility with various methodologies based upon statistical and mathematical models, in addition to artificial intelligence tools. These methodologies allow to determine the areas that could present a serious risk of landslides. Monitoring these risky areas is particularly important for developing an Early Warning Systems (EWS). As matter of fact, the variety of landslides’ types make their monitoring a sophisticated task to accomplish. Indeed, each landslide area has its own specificities and potential triggering factors; therefore, there is no single device that can monitor all types of landslides. Consequently, Wireless Sensor Networks (WSN) combined with Internet of Things (IoT) allow to set up large-scale data acquisition systems. In addition, recent advances in Artificial Intelligence (AI) and Federated Learning (FL) allow to develop performant algorithms to analyze this data and predict early landslides events at edge level (on gateways). These algorithms are trained in this case at fog level on specific hardware. The novelty of the work proposed in this paper is the integration of Federated Learning based on Fog-Edge approaches to continuously improve prediction models.
{"title":"Towards Landslides Early Warning System With Fog - Edge Computing And Artificial Intelligence*","authors":"Olivier Debauche, M. Elmoulat, S. Mahmoudi, S. Mahmoudi, Adriano Guttadauria, P. Manneback, F. Lebeau","doi":"10.5383/JUSPN.15.02.002","DOIUrl":"https://doi.org/10.5383/JUSPN.15.02.002","url":null,"abstract":"Landslides are phenomena that cause significant human and economic losses. Researchers have investigated the prediction of high landslides susceptibility with various methodologies based upon statistical and mathematical models, in addition to artificial intelligence tools. These methodologies allow to determine the areas that could present a serious risk of landslides. Monitoring these risky areas is particularly important for developing an Early Warning Systems (EWS). As matter of fact, the variety of landslides’ types make their monitoring a sophisticated task to accomplish. Indeed, each landslide area has its own specificities and potential triggering factors; therefore, there is no single device that can monitor all types of landslides. Consequently, Wireless Sensor Networks (WSN) combined with Internet of Things (IoT) allow to set up large-scale data acquisition systems. In addition, recent advances in Artificial Intelligence (AI) and Federated Learning (FL) allow to develop performant algorithms to analyze this data and predict early landslides events at edge level (on gateways). These algorithms are trained in this case at fog level on specific hardware. The novelty of the work proposed in this paper is the integration of Federated Learning based on Fog-Edge approaches to continuously improve prediction models.","PeriodicalId":376249,"journal":{"name":"J. Ubiquitous Syst. Pervasive Networks","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116882343","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 deployment of the Internet of Things (IoT) is empowering Smart City tasks and activities everywhere throughout the world. Items utilized in day-by-day life are outfitted with IoT devices and sensors to make them interconnected and connected with the internet. Internet of Things (IoT) is a vital piece of a smart city that tremendously impact on all the city sectors, for example, governance, healthcare, mobility, pollution, and transportation. This all connected IoT devices will make the cities smart. As different smart city activities and undertakings have been propelled in recent times, we have seen the benefits as well as the risks. This paper depicts the primary challenges and weaknesses of applying IoT innovations dependent on smart city standards. Moreover, this paper points the outline of the technologies and applications of the smart cities.
{"title":"Emerging Technologies and Applications for Smart Cities","authors":"Vishva Patel, Devansh Shah, Nishant Doshi","doi":"10.5383/JUSPN.15.02.003","DOIUrl":"https://doi.org/10.5383/JUSPN.15.02.003","url":null,"abstract":"The large deployment of the Internet of Things (IoT) is empowering Smart City tasks and activities everywhere throughout the world. Items utilized in day-by-day life are outfitted with IoT devices and sensors to make them interconnected and connected with the internet. Internet of Things (IoT) is a vital piece of a smart city that tremendously impact on all the city sectors, for example, governance, healthcare, mobility, pollution, and transportation. This all connected IoT devices will make the cities smart. As different smart city activities and undertakings have been propelled in recent times, we have seen the benefits as well as the risks. This paper depicts the primary challenges and weaknesses of applying IoT innovations dependent on smart city standards. Moreover, this paper points the outline of the technologies and applications of the smart cities.","PeriodicalId":376249,"journal":{"name":"J. Ubiquitous Syst. Pervasive Networks","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124830713","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}
This paper explores a low-cost portable visible light communication (VLC) system to support the increasing needs of lightweight mobile applications. VLC grows rapidly in the past decade for many applications (e.g., indoor data transmission, human sensing, and visual MIMO) due to its RF interference immunity and inherent high security. However, most existing VLC systems heavily rely on fixed infrastructures with less adaptability to emerging lightweight mobile applications. This work proposes Light Storage, a portable VLC system takes the advantage of commercial smartphone flashlights as the transmitter and a solar panel equipped with both data reception and energy harvesting modules as the receiver. Light Storage can achieve concurrent data transmission and energy harvesting from the visible light signals. It develops multi-level light intensity data modulation to increase data throughput and integrates the noise reduction functionality to allow portability under various lighting conditions. The system supports synchronization together with adaptive error correction to overcome both the linear and non-linear signal offsets caused by the low time-control ability from the commercial smartphones. Finally, the energy harvesting capability in Light Storage provides sufficient energy support for efficient short range communication. Light Storage is validated in both indoor and outdoor environments and can achieve over 98% data decoding accuracy, demonstrating the potential as an important alternative to support low-cost and portable short range communication.
{"title":"VLC-based Data Transfer and Energy Harvesting Mobile System","authors":"Yingying Chen, Bo Liu, Hongbo Liu, Yudong Yao","doi":"10.5383/JUSPN.15.01.001","DOIUrl":"https://doi.org/10.5383/JUSPN.15.01.001","url":null,"abstract":"This paper explores a low-cost portable visible light communication (VLC) system to support the increasing needs of lightweight mobile applications. VLC grows rapidly in the past decade for many applications (e.g., indoor data transmission, human sensing, and visual MIMO) due to its RF interference immunity and inherent high security. However, most existing VLC systems heavily rely on fixed infrastructures with less adaptability to emerging lightweight mobile applications. This work proposes Light Storage, a portable VLC system takes the advantage of commercial smartphone flashlights as the transmitter and a solar panel equipped with both data reception and energy harvesting modules as the receiver. Light Storage can achieve concurrent data transmission and energy harvesting from the visible light signals. It develops multi-level light intensity data modulation to increase data throughput and integrates the noise reduction functionality to allow portability under various lighting conditions. The system supports synchronization together with adaptive error correction to overcome both the linear and non-linear signal offsets caused by the low time-control ability from the commercial smartphones. Finally, the energy harvesting capability in Light Storage provides sufficient energy support for efficient short range communication. Light Storage is validated in both indoor and outdoor environments and can achieve over 98% data decoding accuracy, demonstrating the potential as an important alternative to support low-cost and portable short range communication.","PeriodicalId":376249,"journal":{"name":"J. Ubiquitous Syst. Pervasive Networks","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125133893","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}
M. E. Khaili, Redouane Marhoum, Chaimaa Fouhad, H. Ouajji
Global demand for primary fossil energy continues to increase. However, the production of energy from fossil fuels, in addition to depleting available reserves, releases millions of tons of Greenhouse Gas (GHG) into the atmosphere. Thus, it is obvious that the high concentration of GHGs in the air disrupts the natural greenhouse effect and consequently causes global warming. The implementation of action plans aimed at reducing greenhouse gas emissions has led all countries to use clean energy sources (sun, earth, wind) called renewable energies and also to rationalize the use of energies whether based on fossil fuels or renewable. Our paper presents a modeling of the demand and its management to ensure an optimization of the energy consumption and the reduction of its bill
{"title":"Contribution to Multi-Energy Flow Management for Building Energy Hub","authors":"M. E. Khaili, Redouane Marhoum, Chaimaa Fouhad, H. Ouajji","doi":"10.5383/JUSPN.15.01.004","DOIUrl":"https://doi.org/10.5383/JUSPN.15.01.004","url":null,"abstract":"Global demand for primary fossil energy continues to increase. However, the production of energy from fossil fuels, in addition to depleting available reserves, releases millions of tons of Greenhouse Gas (GHG) into the atmosphere. Thus, it is obvious that the high concentration of GHGs in the air disrupts the natural greenhouse effect and consequently causes global warming. The implementation of action plans aimed at reducing greenhouse gas emissions has led all countries to use clean energy sources (sun, earth, wind) called renewable energies and also to rationalize the use of energies whether based on fossil fuels or renewable. Our paper presents a modeling of the demand and its management to ensure an optimization of the energy consumption and the reduction of its bill","PeriodicalId":376249,"journal":{"name":"J. Ubiquitous Syst. Pervasive Networks","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114832564","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}
Karim Haricha, Azeddine Khiat, Y. Issaoui, Ayoub Bahnasse, H. Ouajji
Production activities is generating a large amount of data in different types (i.e., text, images), that is not well exploited. This data can be translated easily to knowledge that can help to predict all the risks that can impact the business, solve problems, promote efficiency of the manufacture to the maximum, make the production more flexible and improving the quality of making smart decisions, however, implementing the Smart Manufacturing(SM) concept provides this opportunity supported by the new generation of the technologies. Internet Of Things (IoT) for more connectivity and getting data in real time, Big Data to store the huge volume of data and Deep Learning algorithms(DL) to learn from the historical and real time data to generate knowledge, that can be used, predict all the risks, problem solving, and better decision-making. In this paper, we will introduce SM and the main technologies to success the implementation, the benefits, and the challenges.
{"title":"Towards smart manufacturing: Implementation and benefits","authors":"Karim Haricha, Azeddine Khiat, Y. Issaoui, Ayoub Bahnasse, H. Ouajji","doi":"10.5383/JUSPN.15.02.004","DOIUrl":"https://doi.org/10.5383/JUSPN.15.02.004","url":null,"abstract":"Production activities is generating a large amount of data in different types (i.e., text, images), that is not well exploited. This data can be translated easily to knowledge that can help to predict all the risks that can impact the business, solve problems, promote efficiency of the manufacture to the maximum, make the production more flexible and improving the quality of making smart decisions, however, implementing the Smart Manufacturing(SM) concept provides this opportunity supported by the new generation of the technologies. Internet Of Things (IoT) for more connectivity and getting data in real time, Big Data to store the huge volume of data and Deep Learning algorithms(DL) to learn from the historical and real time data to generate knowledge, that can be used, predict all the risks, problem solving, and better decision-making. In this paper, we will introduce SM and the main technologies to success the implementation, the benefits, and the challenges.","PeriodicalId":376249,"journal":{"name":"J. Ubiquitous Syst. Pervasive Networks","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134536629","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}
With the aim of investigating the effect of climate on the heating in buildings and human productivity, the data of thermal sensors distributed at rooms and buildings level were sent to an implemented analytic management system. This latter integrated an Internet of Things (IoT) open-source software. As part of the smart buildings project, the challenge here is to analyze the aggregated data. Therefore, statistical methods were applied to study the relationship between climate and environmental parameters of the HFT University buildings in the city Stuttgart (Germany) during 2016. Moreover, we studied the effect of indoor temperature on the thermal sensation at the same location. To optimize the result, this study was limited to workdays and cold seasons.
{"title":"IOT Analysis and Management System for Improving Work Performance with an IOT Open Software in Smart Buildings","authors":"S. Traboulsi, S. Knauth","doi":"10.5383/juspn.14.01.001","DOIUrl":"https://doi.org/10.5383/juspn.14.01.001","url":null,"abstract":"With the aim of investigating the effect of climate on the heating in buildings and human productivity, the data of thermal sensors distributed at rooms and buildings level were sent to an implemented analytic management system. This latter integrated an Internet of Things (IoT) open-source software. As part of the smart buildings project, the challenge here is to analyze the aggregated data. Therefore, statistical methods were applied to study the relationship between climate and environmental parameters of the HFT University buildings in the city Stuttgart (Germany) during 2016. Moreover, we studied the effect of indoor temperature on the thermal sensation at the same location. To optimize the result, this study was limited to workdays and cold seasons.","PeriodicalId":376249,"journal":{"name":"J. Ubiquitous Syst. Pervasive Networks","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121542619","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 advances made in the sensing and communications technologies over the last few years have made the deployment of IoT solutions possible on a massive scale. The wide deployment of IoT sensors and devices has resulted in the development of smart services that were not possible before. These services typically rely on cloud services for processing IoT data streams, given that edge devices have limited computing and storage capabilities. However, time-sensitive IoT applications and services do not tolerate the high latency they can encounter when sending IoT data streams to the cloud. Fog computing-based solutions for these services are increasingly becoming attractive because of the low latency they can guarantee. With increasing deployments of fog nodes and fog clusters, we propose an architecture for the placement of IoT applications tasks on a cluster of fog nodes in the vicinity of the application’s data sources. The Fog Broker component can implement various scheduling policies to help IoT applications meet their quality-of-service (QoS) requirements. Our simulations show that it is possible to maintain low application latency and distribute the load between the fog nodes of the cluster by using simple scheduling strategies.
{"title":"QoS-Aware Placement of Tasks on a Fog Cluster in an Edge Computing Environment","authors":"E. Badidi","doi":"10.5383/juspn.13.01.002","DOIUrl":"https://doi.org/10.5383/juspn.13.01.002","url":null,"abstract":"The advances made in the sensing and communications technologies over the last few years have made the deployment of IoT solutions possible on a massive scale. The wide deployment of IoT sensors and devices has resulted in the development of smart services that were not possible before. These services typically rely on cloud services for processing IoT data streams, given that edge devices have limited computing and storage capabilities. However, time-sensitive IoT applications and services do not tolerate the high latency they can encounter when sending IoT data streams to the cloud. Fog computing-based solutions for these services are increasingly becoming attractive because of the low latency they can guarantee. With increasing deployments of fog nodes and fog clusters, we propose an architecture for the placement of IoT applications tasks on a cluster of fog nodes in the vicinity of the application’s data sources. The Fog Broker component can implement various scheduling policies to help IoT applications meet their quality-of-service (QoS) requirements. Our simulations show that it is possible to maintain low application latency and distribute the load between the fog nodes of the cluster by using simple scheduling strategies.","PeriodicalId":376249,"journal":{"name":"J. Ubiquitous Syst. Pervasive Networks","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133942032","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}
{"title":"Student orientation using machine learning under MapReduce with Hadoop","authors":"Farouk Ouatik, M. Erritali, M. Jourhmane","doi":"10.5383/juspn.13.01.003","DOIUrl":"https://doi.org/10.5383/juspn.13.01.003","url":null,"abstract":"","PeriodicalId":376249,"journal":{"name":"J. Ubiquitous Syst. Pervasive Networks","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125657410","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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