Pub Date : 2020-08-01DOI: 10.1109/SmartIoT49966.2020.00024
De-gan Zhang, Jing-yu Du, Ting Zhang, Hong-rui Fan
We propose a new method of traffic flow forecasting based on quantum particle swarm optimization strategy (QPSO) for Internet of Vehicles (IOV). Establish a corresponding model based on the characteristics of the traffic flow data. The genetic simulated annealing method is applied to the quantum particle swarm method to obtain the optimized initial cluster center, and is applied to the parameter optimization of the radial basis neural network prediction model. The function approximation of radial basis neural network can be used to obtain the required data. In addition, in order to compare the performance of the methods, a comparison study with other related methods such as QPSO-RBF is also performed. Our method can reduce prediction errors and get better and more stable prediction results.
{"title":"New Method of Traffic Flow Forecasting Based on QPSO Strategy for Internet of Vehicles","authors":"De-gan Zhang, Jing-yu Du, Ting Zhang, Hong-rui Fan","doi":"10.1109/SmartIoT49966.2020.00024","DOIUrl":"https://doi.org/10.1109/SmartIoT49966.2020.00024","url":null,"abstract":"We propose a new method of traffic flow forecasting based on quantum particle swarm optimization strategy (QPSO) for Internet of Vehicles (IOV). Establish a corresponding model based on the characteristics of the traffic flow data. The genetic simulated annealing method is applied to the quantum particle swarm method to obtain the optimized initial cluster center, and is applied to the parameter optimization of the radial basis neural network prediction model. The function approximation of radial basis neural network can be used to obtain the required data. In addition, in order to compare the performance of the methods, a comparison study with other related methods such as QPSO-RBF is also performed. Our method can reduce prediction errors and get better and more stable prediction results.","PeriodicalId":399187,"journal":{"name":"2020 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121058477","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 : 2020-08-01DOI: 10.1109/SmartIoT49966.2020.00051
Annisa Sarah, Theresia Ghozali, Geraldo Giano, M. Mulyadi, Sandra Octaviani, A. Hikmaturokhman
Extensive implementation of Internet-of-Things (IoT) systems increases the needs of IoT engineers. However, some available IoT-trainer modules, which needed to equipped engineers with IoT skills, are quite expensive and exclusive for private institutions. This research proposes an IoT-trainer design that offers seven simple experiments to understand IoT concepts, which classified into three aspects: IoT devices, connectivity, and cloud or application system. All devices are available and purchasable from the market, low-price, and easy to configure: ESP8266 (NodeMCU-12E, and ESP-01), Arduino Uno, and Xbee. Moreover, the platforms that we use to tunnel a network and processing data are also open source: Ngrok, ThingSpeak, and 000webhost. By exploiting this simple, inexpensive IoT-trainer design and experimenting with those seven scenarios, learners can study the basic concept of IoT as infrastructure.
{"title":"Learning IoT: Basic Experiments of Home Automation using ESP8266, Arduino and XBee","authors":"Annisa Sarah, Theresia Ghozali, Geraldo Giano, M. Mulyadi, Sandra Octaviani, A. Hikmaturokhman","doi":"10.1109/SmartIoT49966.2020.00051","DOIUrl":"https://doi.org/10.1109/SmartIoT49966.2020.00051","url":null,"abstract":"Extensive implementation of Internet-of-Things (IoT) systems increases the needs of IoT engineers. However, some available IoT-trainer modules, which needed to equipped engineers with IoT skills, are quite expensive and exclusive for private institutions. This research proposes an IoT-trainer design that offers seven simple experiments to understand IoT concepts, which classified into three aspects: IoT devices, connectivity, and cloud or application system. All devices are available and purchasable from the market, low-price, and easy to configure: ESP8266 (NodeMCU-12E, and ESP-01), Arduino Uno, and Xbee. Moreover, the platforms that we use to tunnel a network and processing data are also open source: Ngrok, ThingSpeak, and 000webhost. By exploiting this simple, inexpensive IoT-trainer design and experimenting with those seven scenarios, learners can study the basic concept of IoT as infrastructure.","PeriodicalId":399187,"journal":{"name":"2020 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127278859","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 : 2020-08-01DOI: 10.1109/SmartIoT49966.2020.00056
Batnyam Enkhtaivan, Akiko Inoue
In recent years, with the progress of data analysis methods utilizing artificial intelligence (AI) technology, concepts of smart cities collecting data from IoT devices and creating values by analyzing it have been proposed. However, making sure that the data is not tampered with is of the utmost importance. One way to do this is to utilize blockchain technology to record and trace the history of the data. Park and Kim proposed ensuring the trustworthiness of the data by utilizing an IoT device with a trusted execution environment (TEE). Also, Guan et al. proposed authenticating an IoT device and mediating data using a TEE. For the authentication, they use the physically unclonable function of the IoT device. Usually, IoT devices suffer from the lack of resources necessary for creating transactions for the blockchain ledger. In this paper, we present a secure protocol in which a TEE acts as a proxy to the IoT devices and creates the necessary transactions for the blockchain. We use an authenticated encryption method on the data transmission between the IoT device and TEE to authenticate the device and ensure the integrity and confidentiality of the data generated by the IoT devices.
{"title":"Mediating Data Trustworthiness by Using Trusted Hardware between IoT Devices and Blockchain","authors":"Batnyam Enkhtaivan, Akiko Inoue","doi":"10.1109/SmartIoT49966.2020.00056","DOIUrl":"https://doi.org/10.1109/SmartIoT49966.2020.00056","url":null,"abstract":"In recent years, with the progress of data analysis methods utilizing artificial intelligence (AI) technology, concepts of smart cities collecting data from IoT devices and creating values by analyzing it have been proposed. However, making sure that the data is not tampered with is of the utmost importance. One way to do this is to utilize blockchain technology to record and trace the history of the data. Park and Kim proposed ensuring the trustworthiness of the data by utilizing an IoT device with a trusted execution environment (TEE). Also, Guan et al. proposed authenticating an IoT device and mediating data using a TEE. For the authentication, they use the physically unclonable function of the IoT device. Usually, IoT devices suffer from the lack of resources necessary for creating transactions for the blockchain ledger. In this paper, we present a secure protocol in which a TEE acts as a proxy to the IoT devices and creates the necessary transactions for the blockchain. We use an authenticated encryption method on the data transmission between the IoT device and TEE to authenticate the device and ensure the integrity and confidentiality of the data generated by the IoT devices.","PeriodicalId":399187,"journal":{"name":"2020 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123440922","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 : 2020-08-01DOI: 10.1109/SmartIoT49966.2020.00038
Wenbing Zhao, Shunkun Yang, Xiong Luo
In this paper, we provide a survey on threat analysis of systems based on Internet of Things (IoT), and how the blockchain technology can help mitigate the threats. Although the topic of IoT security (and previously the security of wireless sensor networks) has been reviewed extensively, we believe that the topic deserves a more in-depth examination towards a more systematic threat analysis, which is not only necessary to better understand the threats against IoT-based systems, but also paves the way to effectively improve the security of IoT-based systems by integrating with the blockchain technology. The major research contributions of this paper include a new taxonomy of the threats against IoT-based systems, identify what threats can be effectively mitigated by integrating IoT with with blockchain technology, the challenges faced by the blockchain-enabled IoT-based systems, and the likely approaches to overcoming these challenges.
{"title":"On Threat Analysis of IoT-Based Systems: A Survey","authors":"Wenbing Zhao, Shunkun Yang, Xiong Luo","doi":"10.1109/SmartIoT49966.2020.00038","DOIUrl":"https://doi.org/10.1109/SmartIoT49966.2020.00038","url":null,"abstract":"In this paper, we provide a survey on threat analysis of systems based on Internet of Things (IoT), and how the blockchain technology can help mitigate the threats. Although the topic of IoT security (and previously the security of wireless sensor networks) has been reviewed extensively, we believe that the topic deserves a more in-depth examination towards a more systematic threat analysis, which is not only necessary to better understand the threats against IoT-based systems, but also paves the way to effectively improve the security of IoT-based systems by integrating with the blockchain technology. The major research contributions of this paper include a new taxonomy of the threats against IoT-based systems, identify what threats can be effectively mitigated by integrating IoT with with blockchain technology, the challenges faced by the blockchain-enabled IoT-based systems, and the likely approaches to overcoming these challenges.","PeriodicalId":399187,"journal":{"name":"2020 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127720994","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 : 2020-08-01DOI: 10.1109/SmartIoT49966.2020.00052
G. Ghantous, A. Gill
There is a growing interest to adopt vendor-driven DevOps tools in organizations. However, it is not clear which tools to use in a reference architecture which enables the deployment of the emerging IoT applications to multi-cloud environments. A research-based and vendor-neutral DevOps reference architecture (DRA) framework has been developed to address this critical challenge. The DRA framework can be utilized to architect and implement the DevOps environment that enables automation and continuous integration of software applications deployment to multi-cloud. This paper confers and discusses the evaluation outcomes of the DRA framework at the DigiSAS research Lab. The evaluation outcomes present practical evidence about the applicability of the DRA framework. The evaluation results also indicate that the DRA framework provides general knowledge-base to researchers and practitioners about the adoption DevOps approach in reference architecture design for deploying IoT-applications to multi-cloud environments.
{"title":"The DevOps Reference Architecture Evaluation : A Design Science Research Case Study","authors":"G. Ghantous, A. Gill","doi":"10.1109/SmartIoT49966.2020.00052","DOIUrl":"https://doi.org/10.1109/SmartIoT49966.2020.00052","url":null,"abstract":"There is a growing interest to adopt vendor-driven DevOps tools in organizations. However, it is not clear which tools to use in a reference architecture which enables the deployment of the emerging IoT applications to multi-cloud environments. A research-based and vendor-neutral DevOps reference architecture (DRA) framework has been developed to address this critical challenge. The DRA framework can be utilized to architect and implement the DevOps environment that enables automation and continuous integration of software applications deployment to multi-cloud. This paper confers and discusses the evaluation outcomes of the DRA framework at the DigiSAS research Lab. The evaluation outcomes present practical evidence about the applicability of the DRA framework. The evaluation results also indicate that the DRA framework provides general knowledge-base to researchers and practitioners about the adoption DevOps approach in reference architecture design for deploying IoT-applications to multi-cloud environments.","PeriodicalId":399187,"journal":{"name":"2020 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125742603","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}
Education is the foundation of our cities' and society's development. The realization of Smart City is inseparable from the construction of Smart Education. The development of science and technology has promoted the popularity and booming of information technology in the education field. With the rise of Smart Education, intelligent learning and evaluation have provided new ideas for in-class teaching evaluation. The model proposed in this article is based on existing multi-dimensional and multi-modal data set from in-class audio and video recognition, as well as movement perception and interaction analysis. We firstly designed a statistical model and an ensemble learning model for in-class teaching evaluation, which are based on Analytic Hierarchy Process - Entropy Weight Method and AdaBoost algorithm respectively. Then, we designed experiments to assess the performance of the proposed statistical model and ensemble learning model. Finally, we compared and selected better models through experiments in different evaluation indicators and combined them into our In-class Teaching Comprehensive Evaluation Model with outstanding performance.
{"title":"An In-class Teaching Comprehensive Evaluation Model Based on Statistical Modelling and Ensemble Learning","authors":"Ludi Bai, Zehui Yu, Shifeng Zhang, Kangying Hu, Zhan-yong Chen, Junqi Guo","doi":"10.1109/SmartIoT49966.2020.00045","DOIUrl":"https://doi.org/10.1109/SmartIoT49966.2020.00045","url":null,"abstract":"Education is the foundation of our cities' and society's development. The realization of Smart City is inseparable from the construction of Smart Education. The development of science and technology has promoted the popularity and booming of information technology in the education field. With the rise of Smart Education, intelligent learning and evaluation have provided new ideas for in-class teaching evaluation. The model proposed in this article is based on existing multi-dimensional and multi-modal data set from in-class audio and video recognition, as well as movement perception and interaction analysis. We firstly designed a statistical model and an ensemble learning model for in-class teaching evaluation, which are based on Analytic Hierarchy Process - Entropy Weight Method and AdaBoost algorithm respectively. Then, we designed experiments to assess the performance of the proposed statistical model and ensemble learning model. Finally, we compared and selected better models through experiments in different evaluation indicators and combined them into our In-class Teaching Comprehensive Evaluation Model with outstanding performance.","PeriodicalId":399187,"journal":{"name":"2020 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130708090","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 : 2020-08-01DOI: 10.1109/SmartIoT49966.2020.00026
Zhihan Xu, Shuja Ansari, Amir M. Abdulghani, Muhammad Ali Imran, Q. Abbasi
Recently, smart home design using Internet of Things (IoT) technology has become a growing industry. Since security is the most important element of the smart home design, the project aims to design a 3D printed smart home with a focus on the security features that would meet the security design of futuristic real homes. The surveillance system of traditional smart home is separated from the door lock system. This project innovatively integrates and coordinates them through the facial recognition algorithms, which forms the entry system of this design. The overall system can be divided into two subsystems (parts), which are the sensing and actuation system (PART I) and the entry system (PART II). PART I includes various sensors and actuators to ensure the security of home, including combustible gas sensor, air quality sensor and temperature & humidity sensor. When anomalies are detected by sensors, actuators such as ventilator, buzzer and LEDs start to work. In PART II, the PIR motion sensor is utilized to detect the person to activate the facial recognition step. Facial recognition algorithm (LBPH algorithm) is implemented for person classification, which is used in selecting the duration of recording for the surveillance system. The surveillance system could select not to record for the occupants or different levels of recording for each occupant based on the confidence of recognition. The project outcomes a 3D printed smart home with a door lock system, a surveillance system, and a sensing & actuation network, which accomplishes the security features in perception and network layer of IoT system design.
{"title":"IoT Enabled Smart Security Framework for 3D Printed Smart Home","authors":"Zhihan Xu, Shuja Ansari, Amir M. Abdulghani, Muhammad Ali Imran, Q. Abbasi","doi":"10.1109/SmartIoT49966.2020.00026","DOIUrl":"https://doi.org/10.1109/SmartIoT49966.2020.00026","url":null,"abstract":"Recently, smart home design using Internet of Things (IoT) technology has become a growing industry. Since security is the most important element of the smart home design, the project aims to design a 3D printed smart home with a focus on the security features that would meet the security design of futuristic real homes. The surveillance system of traditional smart home is separated from the door lock system. This project innovatively integrates and coordinates them through the facial recognition algorithms, which forms the entry system of this design. The overall system can be divided into two subsystems (parts), which are the sensing and actuation system (PART I) and the entry system (PART II). PART I includes various sensors and actuators to ensure the security of home, including combustible gas sensor, air quality sensor and temperature & humidity sensor. When anomalies are detected by sensors, actuators such as ventilator, buzzer and LEDs start to work. In PART II, the PIR motion sensor is utilized to detect the person to activate the facial recognition step. Facial recognition algorithm (LBPH algorithm) is implemented for person classification, which is used in selecting the duration of recording for the surveillance system. The surveillance system could select not to record for the occupants or different levels of recording for each occupant based on the confidence of recognition. The project outcomes a 3D printed smart home with a door lock system, a surveillance system, and a sensing & actuation network, which accomplishes the security features in perception and network layer of IoT system design.","PeriodicalId":399187,"journal":{"name":"2020 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126594629","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 ultra-short baseline (USBL) is an underwater localization method. It widely applied in the underwater localization system. However, as there are environmental interference and measurement error, the localization accuracy is poor, which can not meet the requirements. To order to enhance the localization accuracy, in this paper, an underwater accurate localization method exploring phase difference and Kalman filter is presented. In this method, we utilize a non-equidistant quaternary original array to receive acoustic signals from an underwater target. Then we explore the Kalman filtering algorithm to process the accurate acoustic signals and gain the phase difference. In the end, we get the localization result of the underwater target. Experimental results indicated the underwater localization method can improve the underwater localization performance.
{"title":"Accurate Underwater Localization Through Phase Difference","authors":"Qinghua Luo, Chunyu, Xiaozhen Yan, Cong Hu, Chuntao Wang, Jinfeng Ding","doi":"10.1109/SmartIoT49966.2020.00015","DOIUrl":"https://doi.org/10.1109/SmartIoT49966.2020.00015","url":null,"abstract":"The ultra-short baseline (USBL) is an underwater localization method. It widely applied in the underwater localization system. However, as there are environmental interference and measurement error, the localization accuracy is poor, which can not meet the requirements. To order to enhance the localization accuracy, in this paper, an underwater accurate localization method exploring phase difference and Kalman filter is presented. In this method, we utilize a non-equidistant quaternary original array to receive acoustic signals from an underwater target. Then we explore the Kalman filtering algorithm to process the accurate acoustic signals and gain the phase difference. In the end, we get the localization result of the underwater target. Experimental results indicated the underwater localization method can improve the underwater localization performance.","PeriodicalId":399187,"journal":{"name":"2020 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"212 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114286010","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 : 2020-08-01DOI: 10.1109/SmartIoT49966.2020.00041
Munmun Biswas, Tanni Dhoom, Refat Khan Pathan, Monisha Sen Chaiti
Visually impaired people face a lot of challenges in their day by day life. Due to blindness most of the time they depend on others for their daily movements. Many assistive technologies have been developed for blind people; most of them are expensive and designed in a complicated way. So in this paper, we represent a complete wearable navigation system for blind people based on the low expanse and truly subtle sensors, for example, Pi camera and Ultrasonic sensor. Live video analysis has been done to detect human faces and ultrasonic sensors are used to detect objects as obstacles. Raspberry Pi has been used as the main controller board. The indoor path has been pre-trained and saved in a database for blind assistance by voice command using Google Text To Speech (gTTS) API so that blind people can navigate independently. In an emergency, the blind person can seek help from the specific person by sending SOS short message service (SMS) through pressing an integrated button. This system has been tested continuously by both blindfolded and visually impaired people at various indoor locations. The outcome shows that it operates more efficiently than other assistive systems.
{"title":"Shortest Path Based Trained Indoor Smart Jacket Navigation System for Visually Impaired Person","authors":"Munmun Biswas, Tanni Dhoom, Refat Khan Pathan, Monisha Sen Chaiti","doi":"10.1109/SmartIoT49966.2020.00041","DOIUrl":"https://doi.org/10.1109/SmartIoT49966.2020.00041","url":null,"abstract":"Visually impaired people face a lot of challenges in their day by day life. Due to blindness most of the time they depend on others for their daily movements. Many assistive technologies have been developed for blind people; most of them are expensive and designed in a complicated way. So in this paper, we represent a complete wearable navigation system for blind people based on the low expanse and truly subtle sensors, for example, Pi camera and Ultrasonic sensor. Live video analysis has been done to detect human faces and ultrasonic sensors are used to detect objects as obstacles. Raspberry Pi has been used as the main controller board. The indoor path has been pre-trained and saved in a database for blind assistance by voice command using Google Text To Speech (gTTS) API so that blind people can navigate independently. In an emergency, the blind person can seek help from the specific person by sending SOS short message service (SMS) through pressing an integrated button. This system has been tested continuously by both blindfolded and visually impaired people at various indoor locations. The outcome shows that it operates more efficiently than other assistive systems.","PeriodicalId":399187,"journal":{"name":"2020 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123813394","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 : 2020-08-01DOI: 10.1109/smartiot49966.2020.00007
{"title":"SmartIoT 2020 Committees","authors":"","doi":"10.1109/smartiot49966.2020.00007","DOIUrl":"https://doi.org/10.1109/smartiot49966.2020.00007","url":null,"abstract":"","PeriodicalId":399187,"journal":{"name":"2020 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"294 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124222093","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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