Pub Date : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912904
Benedetta Bolis, Lorenzo Fratini, Mirko Salaris, M. Santambrogio
Several studies have shown stress to be associated with increased rates of heart attack, hypertension, and other disorders. In this regard, office workers are subjected to the dullness of their daily working routine which does nothing but increase their stress exposure. On the basis of these facts, our work acts as a proposal for a novel health-care-embedded system thought to detect the time course of a few vital signs, strictly related to stress, and to be a cost-effective solution for the market. The project, named GRETA (erGonomic stREss Tracking pAd), is based on a rubber-cork working pad provided with a set of photoplethysmography sensors that allow us to collect data about the ventral-wrist heart rate time evolution of average workers in an office setting environment. To this purpose, we designed our device in order to be as comfortable and noninvasive as possible and the implementation of the software and hardware part aims at reducing any environmental noise source, i.e., thermal noise, irregular detection, and sudden movements, in order to enable a cleaner data analysis.
{"title":"GRETA: erGonomic stREss Tracking pAd","authors":"Benedetta Bolis, Lorenzo Fratini, Mirko Salaris, M. Santambrogio","doi":"10.1109/ISCC55528.2022.9912904","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912904","url":null,"abstract":"Several studies have shown stress to be associated with increased rates of heart attack, hypertension, and other disorders. In this regard, office workers are subjected to the dullness of their daily working routine which does nothing but increase their stress exposure. On the basis of these facts, our work acts as a proposal for a novel health-care-embedded system thought to detect the time course of a few vital signs, strictly related to stress, and to be a cost-effective solution for the market. The project, named GRETA (erGonomic stREss Tracking pAd), is based on a rubber-cork working pad provided with a set of photoplethysmography sensors that allow us to collect data about the ventral-wrist heart rate time evolution of average workers in an office setting environment. To this purpose, we designed our device in order to be as comfortable and noninvasive as possible and the implementation of the software and hardware part aims at reducing any environmental noise source, i.e., thermal noise, irregular detection, and sudden movements, in order to enable a cleaner data analysis.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126440601","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 : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912829
Lorenzo De Lauretis, Fabio Persia, Stefania Costantini
Our work describes a smart-ecosystem able to mon-itor patients' health condition, even at home or at work, by ex-ploiting a creative blend of Medical Wearables, Intelligent Agents, Complex Event Processing and Image Processing. With the help of a smart application, that links together the Wearables and the power of Artificial Intelligence, patients will be continuously and actively supervised during their daily activities. This can even save their lives, in case sudden or gradual issues should occur. Using our system, patients with non-severe though potentially unstable chronic diseases will no longer overburden first aid services. This is also useful for containing the spread of COVID-19. Specifically, in this paper we focus on automated vitals monitoring, electrocardiogram (ECG) analysis, and Psoriasis detection.
{"title":"A Smart Ecosystem to improve Patient Monitoring using Wearables, Intelligent Agents, Complex Event Processing and Image Processing","authors":"Lorenzo De Lauretis, Fabio Persia, Stefania Costantini","doi":"10.1109/ISCC55528.2022.9912829","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912829","url":null,"abstract":"Our work describes a smart-ecosystem able to mon-itor patients' health condition, even at home or at work, by ex-ploiting a creative blend of Medical Wearables, Intelligent Agents, Complex Event Processing and Image Processing. With the help of a smart application, that links together the Wearables and the power of Artificial Intelligence, patients will be continuously and actively supervised during their daily activities. This can even save their lives, in case sudden or gradual issues should occur. Using our system, patients with non-severe though potentially unstable chronic diseases will no longer overburden first aid services. This is also useful for containing the spread of COVID-19. Specifically, in this paper we focus on automated vitals monitoring, electrocardiogram (ECG) analysis, and Psoriasis detection.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125704817","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 : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912931
Linda Senigagliesi, Manola Ricciuti, Gianluca Ciattaglia, E. Gambi
Sleep quality is an index of well-being, since sleep disorders, such as sleep apnea, may constitute a health risk. A constant monitoring of subjects, especially when there are heart or respiratory diseases, is essential. The present paper aims to offer a non-invasive and comfortable sleep monitoring, by employing a BallistoCardioGraphic (BCG) signal processing. In particular, with a BCG device located below the mattress, we are able to extract the heart rate, respiratory rate and, therefore, to exploit this information to develop an automatic sleep apnea recognition algorithm. The automatic approach presented has proven to achieve accuracy and reliability and could represent a valid resource to prevent serious damages during sleep.
{"title":"Physiological Parameters Extraction by Accelerometric Signal Analysis During Sleep","authors":"Linda Senigagliesi, Manola Ricciuti, Gianluca Ciattaglia, E. Gambi","doi":"10.1109/ISCC55528.2022.9912931","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912931","url":null,"abstract":"Sleep quality is an index of well-being, since sleep disorders, such as sleep apnea, may constitute a health risk. A constant monitoring of subjects, especially when there are heart or respiratory diseases, is essential. The present paper aims to offer a non-invasive and comfortable sleep monitoring, by employing a BallistoCardioGraphic (BCG) signal processing. In particular, with a BCG device located below the mattress, we are able to extract the heart rate, respiratory rate and, therefore, to exploit this information to develop an automatic sleep apnea recognition algorithm. The automatic approach presented has proven to achieve accuracy and reliability and could represent a valid resource to prevent serious damages during sleep.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120984288","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}
Due to the bulkiness and sophistication of the Distributed Deep Learning (DDL) systems, it leaves an enormous challenge for AI researchers and operation engineers to analyze, diagnose and locate the performance bottleneck during the training stage. Existing performance models and frameworks gain little insight on the performance reduction that a performance straggler induces. In this paper, we introduce MD-Roofline, a training performance analysis model, which extends the traditional rooftine model with communication dimension. The model considers the layer-wise attributes at application level, and a series of achievable peak performance metrics at hardware level. With the assistance of our MD-Roofline, the AI researchers and DDL operation engineers could locate the system bottleneck, which contains three dimensions: intra-GPU computation capacity, intra-GPU memory access bandwidth and inter-GPU communication bandwidth. We demonstrate that our performance analysis model provides great insights in bottleneck analysis when training 12 classic CNNs.
{"title":"MD-Roofline: A Training Performance Analysis Model for Distributed Deep Learning","authors":"Tianhao Miao, Qinghua Wu, Ting Liu, Penglai Cui, Rui Ren, Zhenyu Li, Gaogang Xie","doi":"10.1109/ISCC55528.2022.9912757","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912757","url":null,"abstract":"Due to the bulkiness and sophistication of the Distributed Deep Learning (DDL) systems, it leaves an enormous challenge for AI researchers and operation engineers to analyze, diagnose and locate the performance bottleneck during the training stage. Existing performance models and frameworks gain little insight on the performance reduction that a performance straggler induces. In this paper, we introduce MD-Roofline, a training performance analysis model, which extends the traditional rooftine model with communication dimension. The model considers the layer-wise attributes at application level, and a series of achievable peak performance metrics at hardware level. With the assistance of our MD-Roofline, the AI researchers and DDL operation engineers could locate the system bottleneck, which contains three dimensions: intra-GPU computation capacity, intra-GPU memory access bandwidth and inter-GPU communication bandwidth. We demonstrate that our performance analysis model provides great insights in bottleneck analysis when training 12 classic CNNs.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131364327","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 : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912811
Lijun Dong, Richard Li
Precise end-to-end latency guarantee is predicted to be required by many emerging applications. On the other hand, the network traffic will continue to be dominated by mobile devices. Therefore, the end-to-end latency is composed of the latency incurred in the Internet as well as in the mobile networks. In this paper, we target to address the end-to-end latency guarantee requirement for downlink traffic by leveraging the previously proposed 5G slice namely, Latency Guarantee Service (LGS) slice. The mechanisms and procedures are proposed by taking the compatibility of 5G architecture into consideration. The simulation results show that the downlink flows which are admitted by the LGS slices are verified to satisfy the end-to-end latency constraint consistently.
{"title":"Precise Latency Guarantee with Mobility and Handover in 5G and Beyond","authors":"Lijun Dong, Richard Li","doi":"10.1109/ISCC55528.2022.9912811","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912811","url":null,"abstract":"Precise end-to-end latency guarantee is predicted to be required by many emerging applications. On the other hand, the network traffic will continue to be dominated by mobile devices. Therefore, the end-to-end latency is composed of the latency incurred in the Internet as well as in the mobile networks. In this paper, we target to address the end-to-end latency guarantee requirement for downlink traffic by leveraging the previously proposed 5G slice namely, Latency Guarantee Service (LGS) slice. The mechanisms and procedures are proposed by taking the compatibility of 5G architecture into consideration. The simulation results show that the downlink flows which are admitted by the LGS slices are verified to satisfy the end-to-end latency constraint consistently.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132600478","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 : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912804
Zhiwei Wei, Bing Li, Rongqing Zhang, Xiang Cheng, Liuqing Yang
Vehicular fog computing (VFC) has emerged as a promising solution to relieve the overload in vehicular network. Since individual vehicular fog node is incapable of providing ultra-reliable and low-latency services constrained by limited resources, cooperation among vehicles becomes an attractive attempt to promote quality of service (QoS). In this paper, we propose a novel Overlapping-enabled Cooperative Vehicular Computing architecture in VFC, termed OCVC, to fully utilize vehicular fog nodes' local potential resources. The proposed OCVC architecture enables vehicles to participate in different fog groups simultaneously different from traditional cooperative computing architecture. In addition, we propose a distributed OCVC scheme to solve the complicated computing group for-mation, overlapping resource allocation, and task assignment problem based on overlapping coalition formation (OCF) game framework. We conduct experiments in several metrics and numerical results show that the proposed OCVC scheme per-forms at least 5 % better than other benchmarks under different conditions.
{"title":"OCVC: An Overlapping-Enabled Cooperative Computing Protocol in Vehicular Fog Computing","authors":"Zhiwei Wei, Bing Li, Rongqing Zhang, Xiang Cheng, Liuqing Yang","doi":"10.1109/ISCC55528.2022.9912804","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912804","url":null,"abstract":"Vehicular fog computing (VFC) has emerged as a promising solution to relieve the overload in vehicular network. Since individual vehicular fog node is incapable of providing ultra-reliable and low-latency services constrained by limited resources, cooperation among vehicles becomes an attractive attempt to promote quality of service (QoS). In this paper, we propose a novel Overlapping-enabled Cooperative Vehicular Computing architecture in VFC, termed OCVC, to fully utilize vehicular fog nodes' local potential resources. The proposed OCVC architecture enables vehicles to participate in different fog groups simultaneously different from traditional cooperative computing architecture. In addition, we propose a distributed OCVC scheme to solve the complicated computing group for-mation, overlapping resource allocation, and task assignment problem based on overlapping coalition formation (OCF) game framework. We conduct experiments in several metrics and numerical results show that the proposed OCVC scheme per-forms at least 5 % better than other benchmarks under different conditions.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114184438","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 : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912466
Masih Abedini, I. Al-Anbagi
Eavesdropping attacks can threaten the privacy, confidentiality, and authenticity of Wireless Sensor Networks (WSNs). Since the broadcast nature of the wireless channel is vulnerable to overhearing by adversaries, detection of the presence of eavesdroppers in wireless networks can mitigate the impacts of more harmful attacks. Traditionally, researchers have tried to decrease the risk of covert eavesdropping by cryptographic protocols, information-theoretic solutions, or controlling transmission range. These approaches are not suitable for the resource-limited WSNs. In this paper, we propose a novel Active Eavesdroppers Detection (AED) system for multi-hop WSNs. Our proposed system utilizes an out-of-band Unmanned Aerial Vehicle (UAV)-assisted monitoring system in WSNs to measure intranode delays. In addition, the detection system is equipped with a lightweight detection engine, which runs at edge devices, using the Z-test algorithm. We show the effectiveness of our proposed system through simulations. The results show a high detection rate and a low false-positive rate.
{"title":"Active Eavesdroppers Detection System in Multi-hop Wireless Sensor Networks","authors":"Masih Abedini, I. Al-Anbagi","doi":"10.1109/ISCC55528.2022.9912466","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912466","url":null,"abstract":"Eavesdropping attacks can threaten the privacy, confidentiality, and authenticity of Wireless Sensor Networks (WSNs). Since the broadcast nature of the wireless channel is vulnerable to overhearing by adversaries, detection of the presence of eavesdroppers in wireless networks can mitigate the impacts of more harmful attacks. Traditionally, researchers have tried to decrease the risk of covert eavesdropping by cryptographic protocols, information-theoretic solutions, or controlling transmission range. These approaches are not suitable for the resource-limited WSNs. In this paper, we propose a novel Active Eavesdroppers Detection (AED) system for multi-hop WSNs. Our proposed system utilizes an out-of-band Unmanned Aerial Vehicle (UAV)-assisted monitoring system in WSNs to measure intranode delays. In addition, the detection system is equipped with a lightweight detection engine, which runs at edge devices, using the Z-test algorithm. We show the effectiveness of our proposed system through simulations. The results show a high detection rate and a low false-positive rate.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114229375","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 : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912830
Gizem Sümen, B. Çelebi, G. Kurt, Ali̇ Görçi̇n, S. T. Basaran
Automatic modulation classification (AMC) with deep learning (DL) based methods has been studied in recent years and improvements have been shown in many studies; however, it has been difficult to design a classifier that can distinguish modulation orders such as 16-QAM and 64-QAM, with high accuracy. In this study, the distinction performance of 16-QAM and 64-QAM modulation orders increased by feeding the features obtained during the preprocessing stage to the multi-channel convolutional long short-term deep neural network (MCLDNN). Simulation results indicate performance improvements, particularly at the low SNR region. Furthermore, the proposed method can be extended for the separation of other orders of QAM and other digital modulations.
{"title":"Multi-Channel Learning with Preprocessing for Automatic Modulation Order Separation","authors":"Gizem Sümen, B. Çelebi, G. Kurt, Ali̇ Görçi̇n, S. T. Basaran","doi":"10.1109/ISCC55528.2022.9912830","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912830","url":null,"abstract":"Automatic modulation classification (AMC) with deep learning (DL) based methods has been studied in recent years and improvements have been shown in many studies; however, it has been difficult to design a classifier that can distinguish modulation orders such as 16-QAM and 64-QAM, with high accuracy. In this study, the distinction performance of 16-QAM and 64-QAM modulation orders increased by feeding the features obtained during the preprocessing stage to the multi-channel convolutional long short-term deep neural network (MCLDNN). Simulation results indicate performance improvements, particularly at the low SNR region. Furthermore, the proposed method can be extended for the separation of other orders of QAM and other digital modulations.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115835918","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 : 2022-06-30DOI: 10.1109/ISCC55528.2022.9912951
Valeria Lukaj, Francesco Martella, A. Celesti, M. Fazio, M. Villari
The innovation process for the management of a Water Distribution Network (WDN) in a Smart City starts from an efficient digital representation of the network itself. This paper presents a new visualization tool for WDN that overcomes current challenges and provides water companies with useful managing information. Existing visualization tools are self-contained systems that work independently from other visualization software and do not provide real-time analysis of the pipes and water flow status in the WDN. Using digital maps such as Google Maps it is possible to extend the traditional digital representation of the WDN based on EPANET software. Moreover, the WDN representation can be enriched with localized information (e.g. roads or buildings superimposed on the WDN), that is useful for planning maintenance and structural services. In presence of a WDN equipped with sensors and flowmeters, the proposed tool can be used for optimized visualization of the flow rate and the condition of the pipes in real-time. For these reasons, this tool can be a powerful instrument to help technicians quickly identify problems in the WDN. In this work, we used synthetic data generation techniques to obtain a data-set of values that updated over time. Finally, to evaluate the designed solution, we implemented the proposed visualization tool and performed some experiments to test its effectiveness.
{"title":"An Enriched Visualization Tool based on Google Maps for Water Distribution Networks in Smart Cities","authors":"Valeria Lukaj, Francesco Martella, A. Celesti, M. Fazio, M. Villari","doi":"10.1109/ISCC55528.2022.9912951","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912951","url":null,"abstract":"The innovation process for the management of a Water Distribution Network (WDN) in a Smart City starts from an efficient digital representation of the network itself. This paper presents a new visualization tool for WDN that overcomes current challenges and provides water companies with useful managing information. Existing visualization tools are self-contained systems that work independently from other visualization software and do not provide real-time analysis of the pipes and water flow status in the WDN. Using digital maps such as Google Maps it is possible to extend the traditional digital representation of the WDN based on EPANET software. Moreover, the WDN representation can be enriched with localized information (e.g. roads or buildings superimposed on the WDN), that is useful for planning maintenance and structural services. In presence of a WDN equipped with sensors and flowmeters, the proposed tool can be used for optimized visualization of the flow rate and the condition of the pipes in real-time. For these reasons, this tool can be a powerful instrument to help technicians quickly identify problems in the WDN. In this work, we used synthetic data generation techniques to obtain a data-set of values that updated over time. Finally, to evaluate the designed solution, we implemented the proposed visualization tool and performed some experiments to test its effectiveness.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123053661","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}
Radio frequency (RF) fingerprint representing the inherent hardware characteristics of mobile devices has been employed to classify and identify wireless devices for the security of Internet of Things (IoT). Existing works on RF fingerprinting are usually based on the amplitude or phase of RF signal envelope, which leads to relatively coarse features. Moreover, the classification performance over small sample dataset is poor. To solve the problem, a novel device identification method based on RF fingerprinting with on deep learning is proposed. In particular, the RF signal are transformed into two dimensional representations by image preprocessing. Then the gray images representing the RF fingerprints are classified by employing classical CNN. To verify the performance of the proposed approach, a testbed is constructed by using MATLAB build framework of gray image preprocessing. Extensive experiment results show that the identification accuracy can reach at least 90%. Even with the sample rate of 20Gsps. Particularly, the accuracy of iPhone can reach 100%. It is verified that the proposed method can effectively classify mobile devices even with small sample RF fingerprints represented two dimensional gray images,
{"title":"Mobile Device Identification Based on Two-dimensional Representation of RF Fingerprint with Deep Learning","authors":"Jing Li, Shunliang Zhang, Mengyan Xing, Zhuang Qiao, Xiaohui Zhang","doi":"10.1109/ISCC55528.2022.9913038","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9913038","url":null,"abstract":"Radio frequency (RF) fingerprint representing the inherent hardware characteristics of mobile devices has been employed to classify and identify wireless devices for the security of Internet of Things (IoT). Existing works on RF fingerprinting are usually based on the amplitude or phase of RF signal envelope, which leads to relatively coarse features. Moreover, the classification performance over small sample dataset is poor. To solve the problem, a novel device identification method based on RF fingerprinting with on deep learning is proposed. In particular, the RF signal are transformed into two dimensional representations by image preprocessing. Then the gray images representing the RF fingerprints are classified by employing classical CNN. To verify the performance of the proposed approach, a testbed is constructed by using MATLAB build framework of gray image preprocessing. Extensive experiment results show that the identification accuracy can reach at least 90%. Even with the sample rate of 20Gsps. Particularly, the accuracy of iPhone can reach 100%. It is verified that the proposed method can effectively classify mobile devices even with small sample RF fingerprints represented two dimensional gray images,","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122162774","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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