Yanchao Zhao, Yiming Zhao, Si Li, Hao Han, Linfu Xie
Keystroke snooping is an effective way to steal sensitive information from the victims. Recent research on acoustic emanation based techniques has greatly improved the accessibility by non-professional adversaries. However, these approaches either require multiple smartphones or require specific placement of the smartphone relative to the keyboards, which tremendously restrict the application scenarios. In this paper, we propose UltraSnoop, a training-free, transferable, and placement-agnostic scheme, which manages to infer user’s input using a single smartphone placed within the range covered by a microphone and speaker. The innovation of Ultrasnoop is that we propose an ultrasonic anchor-keystroke positioning method and an MFCCs clustering algorithm, synthesis of which could infer the relative position between the smartphone and the keyboard. Along with the keystroke TDoA, our method could infer the keystrokes and even gradually improve the accuracy as the snooping proceeds. Our real-world experiments show that UltraSnoop could achieve more than 85% top-3 snooping accuracy when the smartphone is placed within the range of 30-60cm from the keyboard.
{"title":"UltraSnoop: Placement-agnostic Keystroke Snooping via Smartphone-based Ultrasonic Sonar","authors":"Yanchao Zhao, Yiming Zhao, Si Li, Hao Han, Linfu Xie","doi":"10.1145/3614440","DOIUrl":"https://doi.org/10.1145/3614440","url":null,"abstract":"Keystroke snooping is an effective way to steal sensitive information from the victims. Recent research on acoustic emanation based techniques has greatly improved the accessibility by non-professional adversaries. However, these approaches either require multiple smartphones or require specific placement of the smartphone relative to the keyboards, which tremendously restrict the application scenarios. In this paper, we propose UltraSnoop, a training-free, transferable, and placement-agnostic scheme, which manages to infer user’s input using a single smartphone placed within the range covered by a microphone and speaker. The innovation of Ultrasnoop is that we propose an ultrasonic anchor-keystroke positioning method and an MFCCs clustering algorithm, synthesis of which could infer the relative position between the smartphone and the keyboard. Along with the keystroke TDoA, our method could infer the keystrokes and even gradually improve the accuracy as the snooping proceeds. Our real-world experiments show that UltraSnoop could achieve more than 85% top-3 snooping accuracy when the smartphone is placed within the range of 30-60cm from the keyboard.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"33 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78737930","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 presents EARFace, a system that shows the feasibility of tracking facial landmarks for 3D facial reconstruction using in-ear acoustic sensors embedded within smart earphones. This enables a number of applications in the areas of facial expression tracking, user-interfaces, AR/VR applications, affective computing, accessibility, etc. While conventional vision-based solutions break down under poor lighting, occlusions, and also suffer from privacy concerns, earphone platforms are robust to ambient conditions, while being privacy-preserving. In contrast to prior work on earable platforms that perform outer-ear sensing for facial motion tracking, EARFace shows the feasibility of completely in-ear sensing with a natural earphone form-factor, thus enhancing the comfort levels of wearing. The core intuition exploited by EARFace is that the shape of the ear canal changes due to the movement of facial muscles during facial motion. EARFace tracks the changes in shape of the ear canal by measuring ultrasonic channel frequency response (CFR) of the inner ear, ultimately resulting in tracking of the facial motion. A transformer based machine learning (ML) model is designed to exploit spectral and temporal relationships in the ultrasonic CFR data to predict the facial landmarks of the user with an accuracy of 1.83 mm. Using these predicted landmarks, a 3D graphical model of the face that replicates the precise facial motion of the user is then reconstructed. Domain adaptation is further performed by adapting the weights of layers using a group-wise and differential learning rate. This decreases the training overhead in EARFace. The transformer based ML model runs on smartphone devices with a processing latency of 13 ms and an overall low power consumption profile. Finally, usability studies indicate higher levels of comforts of wearing EARFace’s earphone platform in comparison with alternative form-factors.
{"title":"I am an Earphone and I can Hear my Users Face: Facial Landmark Tracking using Smart Earphones","authors":"Shijia Zhang, Taiting Lu, Hao Zhou, Yilin Liu, Runze Liu, Mahanth K. Gowda","doi":"10.1145/3614438","DOIUrl":"https://doi.org/10.1145/3614438","url":null,"abstract":"This paper presents EARFace, a system that shows the feasibility of tracking facial landmarks for 3D facial reconstruction using in-ear acoustic sensors embedded within smart earphones. This enables a number of applications in the areas of facial expression tracking, user-interfaces, AR/VR applications, affective computing, accessibility, etc. While conventional vision-based solutions break down under poor lighting, occlusions, and also suffer from privacy concerns, earphone platforms are robust to ambient conditions, while being privacy-preserving. In contrast to prior work on earable platforms that perform outer-ear sensing for facial motion tracking, EARFace shows the feasibility of completely in-ear sensing with a natural earphone form-factor, thus enhancing the comfort levels of wearing. The core intuition exploited by EARFace is that the shape of the ear canal changes due to the movement of facial muscles during facial motion. EARFace tracks the changes in shape of the ear canal by measuring ultrasonic channel frequency response (CFR) of the inner ear, ultimately resulting in tracking of the facial motion. A transformer based machine learning (ML) model is designed to exploit spectral and temporal relationships in the ultrasonic CFR data to predict the facial landmarks of the user with an accuracy of 1.83 mm. Using these predicted landmarks, a 3D graphical model of the face that replicates the precise facial motion of the user is then reconstructed. Domain adaptation is further performed by adapting the weights of layers using a group-wise and differential learning rate. This decreases the training overhead in EARFace. The transformer based ML model runs on smartphone devices with a processing latency of 13 ms and an overall low power consumption profile. Finally, usability studies indicate higher levels of comforts of wearing EARFace’s earphone platform in comparison with alternative form-factors.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"19 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85993332","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}
Yumeng Liang, Anfu Zhou, Xinzhe Wen, Wei Huang, Pu Shi, Lingyu Pu, Huanhuan Zhang, Huadong Ma
Blood pressure (BP), an important vital sign to assess human health, is expected to be monitored conveniently. The existing BP monitoring methods, either traditional cuff-based or newly-emerging wearable-based, all require skin contact, which may cause unpleasant user experience and is even injurious to certain users. In this paper, we explore contact-less BP monitoring and propose airBP, which emits millimeter-wave signals toward a user’s wrist, and captures the reflected signal bounded off from the pulsating artery underlying the wrist. By analyzing the reflected signal strength of the signal, airBP generates arterial pulse and further estimates BP by exploiting the relationship between the arterial pulse and BP. To realize airBP, we design a new beam-forming method to keep focusing on the tiny and hidden wrist artery, by leveraging the inherent periodicity of the arterial pulse. Moreover, we custom-design a pre-training and neural network architecture, to combat the challenges from the arterial pulse sparsity and ambiguity, so as to estimate BP accurately. We prototype airBP using a coin-size COTS mmWave radar and perform extensive experiments on 41 subjects. The results demonstrate that airBP accurately estimates systolic and diastolic BP, with the mean error of -0.30 mmHg and -0.23 mmHg, as well as the standard deviation error of 4.80 mmHg and 3.79 mmHg (within the acceptable range regulated by the FDA’s AAMI protocol), respectively, at a distance up to 26 cm.
{"title":"airBP: Monitor Your Blood Pressure with Millimeter-Wave in the Air","authors":"Yumeng Liang, Anfu Zhou, Xinzhe Wen, Wei Huang, Pu Shi, Lingyu Pu, Huanhuan Zhang, Huadong Ma","doi":"10.1145/3614439","DOIUrl":"https://doi.org/10.1145/3614439","url":null,"abstract":"Blood pressure (BP), an important vital sign to assess human health, is expected to be monitored conveniently. The existing BP monitoring methods, either traditional cuff-based or newly-emerging wearable-based, all require skin contact, which may cause unpleasant user experience and is even injurious to certain users. In this paper, we explore contact-less BP monitoring and propose airBP, which emits millimeter-wave signals toward a user’s wrist, and captures the reflected signal bounded off from the pulsating artery underlying the wrist. By analyzing the reflected signal strength of the signal, airBP generates arterial pulse and further estimates BP by exploiting the relationship between the arterial pulse and BP. To realize airBP, we design a new beam-forming method to keep focusing on the tiny and hidden wrist artery, by leveraging the inherent periodicity of the arterial pulse. Moreover, we custom-design a pre-training and neural network architecture, to combat the challenges from the arterial pulse sparsity and ambiguity, so as to estimate BP accurately. We prototype airBP using a coin-size COTS mmWave radar and perform extensive experiments on 41 subjects. The results demonstrate that airBP accurately estimates systolic and diastolic BP, with the mean error of -0.30 mmHg and -0.23 mmHg, as well as the standard deviation error of 4.80 mmHg and 3.79 mmHg (within the acceptable range regulated by the FDA’s AAMI protocol), respectively, at a distance up to 26 cm.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"22 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90865251","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}
Naeima Hamed, A. Gaglione, A. Gluhak, Omer F. Rana, Charith Perera
Cities are increasingly becoming augmented with sensors through public, private, and academic sector initiatives. Most of the time, these sensors are deployed with a primary purpose (objective) in mind (e.g., deploy sensors to understand noise pollution) by a sensor owner (i.e., the organization that invests in sensing hardware, e.g., a city council). Over the past few years, communities undertaking smart city development projects have understood the importance of making the sensor data available to a wider community—beyond their primary usage. Different business models have been proposed to achieve this, including creating data marketplaces. The vision is to encourage new startups and small and medium-scale businesses to create novel products and services using sensor data to generate additional economic value. Currently, data are sold as pre-defined independent datasets (e.g., noise level and parking status data may be sold separately). This approach creates several challenges, such as (i) difficulties in pricing, which leads to higher prices (per dataset); (ii) higher network communication and bandwidth requirements; and (iii) information overload for data consumers (i.e., those who purchase data). We investigate the benefit of semantic representation and its reasoning capabilities toward creating a business model that offers data on demand within smart city Internet of Things data marketplaces. The objective is to help data consumers (i.e., small and medium enterprises) acquire the most relevant data they need. We demonstrate the utility of our approach by integrating it into a real-world IoT data marketplace (developed by the synchronicity-iot.eu project). We discuss design decisions and their consequences (i.e., tradeoffs) on the choice and selection of datasets. Subsequently, we present a series of data modeling principles and recommendations for implementing IoT data marketplaces.
{"title":"Query Interface for Smart City Internet of Things Data Marketplaces: A Case Study","authors":"Naeima Hamed, A. Gaglione, A. Gluhak, Omer F. Rana, Charith Perera","doi":"10.1145/3609336","DOIUrl":"https://doi.org/10.1145/3609336","url":null,"abstract":"Cities are increasingly becoming augmented with sensors through public, private, and academic sector initiatives. Most of the time, these sensors are deployed with a primary purpose (objective) in mind (e.g., deploy sensors to understand noise pollution) by a sensor owner (i.e., the organization that invests in sensing hardware, e.g., a city council). Over the past few years, communities undertaking smart city development projects have understood the importance of making the sensor data available to a wider community—beyond their primary usage. Different business models have been proposed to achieve this, including creating data marketplaces. The vision is to encourage new startups and small and medium-scale businesses to create novel products and services using sensor data to generate additional economic value. Currently, data are sold as pre-defined independent datasets (e.g., noise level and parking status data may be sold separately). This approach creates several challenges, such as (i) difficulties in pricing, which leads to higher prices (per dataset); (ii) higher network communication and bandwidth requirements; and (iii) information overload for data consumers (i.e., those who purchase data). We investigate the benefit of semantic representation and its reasoning capabilities toward creating a business model that offers data on demand within smart city Internet of Things data marketplaces. The objective is to help data consumers (i.e., small and medium enterprises) acquire the most relevant data they need. We demonstrate the utility of our approach by integrating it into a real-world IoT data marketplace (developed by the synchronicity-iot.eu project). We discuss design decisions and their consequences (i.e., tradeoffs) on the choice and selection of datasets. Subsequently, we present a series of data modeling principles and recommendations for implementing IoT data marketplaces.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"52 1","pages":"1 - 39"},"PeriodicalIF":2.7,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85147445","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}
Unidentified devices in a network can result in devastating consequences. It is, therefore, necessary to fingerprint and identify IoT devices connected to private or critical networks. With the proliferation of massive but heterogeneous IoT devices, it is getting challenging to detect vulnerable devices connected to networks. Current machine learning-based techniques for fingerprinting and identifying devices necessitate a significant amount of data gathered from IoT networks that must be transmitted to a central cloud. Nevertheless, private IoT data cannot be shared with the central cloud in numerous sensitive scenarios. Federated learning (FL) has been regarded as a promising paradigm for decentralized learning and has been applied in many different use cases. It enables machine learning models to be trained in a privacy-preserving way. In this article, we propose a privacy-preserved IoT device fingerprinting and identification mechanisms using FL; we call it FL4IoT. FL4IoT is a two-phased system combining unsupervised-learning-based device fingerprinting and supervised-learning-based device identification. FL4IoT shows its practicality in different performance metrics in a federated and centralized setup. For instance, in the best cases, empirical results show that FL4IoT achieves ∼99% accuracy and F1-Score in identifying IoT devices using a federated setup without exposing any private data to a centralized cloud entity. In addition, FL4IoT can detect spoofed devices with over 99% accuracy.
{"title":"FL4IoT: IoT Device Fingerprinting and Identification Using Federated Learning","authors":"Han Wang, David Eklund, Alina Oprea, S. Raza","doi":"10.1145/3603257","DOIUrl":"https://doi.org/10.1145/3603257","url":null,"abstract":"Unidentified devices in a network can result in devastating consequences. It is, therefore, necessary to fingerprint and identify IoT devices connected to private or critical networks. With the proliferation of massive but heterogeneous IoT devices, it is getting challenging to detect vulnerable devices connected to networks. Current machine learning-based techniques for fingerprinting and identifying devices necessitate a significant amount of data gathered from IoT networks that must be transmitted to a central cloud. Nevertheless, private IoT data cannot be shared with the central cloud in numerous sensitive scenarios. Federated learning (FL) has been regarded as a promising paradigm for decentralized learning and has been applied in many different use cases. It enables machine learning models to be trained in a privacy-preserving way. In this article, we propose a privacy-preserved IoT device fingerprinting and identification mechanisms using FL; we call it FL4IoT. FL4IoT is a two-phased system combining unsupervised-learning-based device fingerprinting and supervised-learning-based device identification. FL4IoT shows its practicality in different performance metrics in a federated and centralized setup. For instance, in the best cases, empirical results show that FL4IoT achieves ∼99% accuracy and F1-Score in identifying IoT devices using a federated setup without exposing any private data to a centralized cloud entity. In addition, FL4IoT can detect spoofed devices with over 99% accuracy.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"129 1","pages":"1 - 24"},"PeriodicalIF":2.7,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79886241","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}
Bayan AL MUHANDER, Jason Wiese, Omer F. Rana, Charith Perera
The balance between protecting user privacy while providing cost-effective devices that are functional and usable is a key challenge in the burgeoning Internet of Things (IoT). In traditional desktop and mobile contexts, the primary user interface is a screen; however, in IoT devices, screens are rare or very small, invalidating many existing approaches to protecting user privacy. Privacy visualizations are a common approach for assisting users in understanding the privacy implications of web and mobile services. To gain a thorough understanding of IoT privacy, we examine existing web, mobile, and IoT visualization approaches. Following that, we define five major privacy factors in the IoT context: type, usage, storage, retention period, and access. We then describe notification methods used in various contexts as reported in the literature. We aim to highlight key approaches that developers and researchers can use for creating effective IoT privacy notices that improve user privacy management (awareness and control). Using a toolkit, a use case scenario, and two examples from the literature, we demonstrate how privacy visualization approaches can be supported in practice.
{"title":"Interactive Privacy Management: Toward Enhancing Privacy Awareness and Control in the Internet of Things","authors":"Bayan AL MUHANDER, Jason Wiese, Omer F. Rana, Charith Perera","doi":"10.1145/3600096","DOIUrl":"https://doi.org/10.1145/3600096","url":null,"abstract":"The balance between protecting user privacy while providing cost-effective devices that are functional and usable is a key challenge in the burgeoning Internet of Things (IoT). In traditional desktop and mobile contexts, the primary user interface is a screen; however, in IoT devices, screens are rare or very small, invalidating many existing approaches to protecting user privacy. Privacy visualizations are a common approach for assisting users in understanding the privacy implications of web and mobile services. To gain a thorough understanding of IoT privacy, we examine existing web, mobile, and IoT visualization approaches. Following that, we define five major privacy factors in the IoT context: type, usage, storage, retention period, and access. We then describe notification methods used in various contexts as reported in the literature. We aim to highlight key approaches that developers and researchers can use for creating effective IoT privacy notices that improve user privacy management (awareness and control). Using a toolkit, a use case scenario, and two examples from the literature, we demonstrate how privacy visualization approaches can be supported in practice.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"50 1","pages":"1 - 34"},"PeriodicalIF":2.7,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84464387","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}
Ivonne Andrea Mantilla Gonzalez, Florian Meyer, V. Turau
In the Industrial Internet of Things (i.e., IIoT), the standardization of open technologies and protocols has achieved seamless data exchange between machines and other physical systems from different manufacturers. At the MAC sublayer, the industry-standard protocols IEEE 802.15.4 Time Slot Channel Hopping (TSCH) and Deterministic and Synchronous Multi-channel Extension (DSME) show promising properties for high adaptability and dynamically changing traffic. However, performance comparison between these MAC protocols rarely has gone beyond a simulation phase. This work presents the results of such a comparison on physically deployed networks using the facilities of the FIT-IoTLab. The evaluation includes fully implementing an IIoT protocol stack based on MQTT in Contiki-NG. It comprises the integration of DSME as part of Contiki-NG’s software stack through OpenDSME, the only publicly available implementation of DSME. Results show that both protocols suit IIoT applications, particularly for data collection. The comparison between TSCH and DSME also includes an evaluation of distributed schedulers for both MAC modes and one autonomous scheduler for TSCH within a UDP protocol stack.
{"title":"A Comprehensive Performance Comparison of IEEE 802.15.4 DSME and TSCH in a Realistic IoT Scenario for Industrial Applications","authors":"Ivonne Andrea Mantilla Gonzalez, Florian Meyer, V. Turau","doi":"10.1145/3595188","DOIUrl":"https://doi.org/10.1145/3595188","url":null,"abstract":"In the Industrial Internet of Things (i.e., IIoT), the standardization of open technologies and protocols has achieved seamless data exchange between machines and other physical systems from different manufacturers. At the MAC sublayer, the industry-standard protocols IEEE 802.15.4 Time Slot Channel Hopping (TSCH) and Deterministic and Synchronous Multi-channel Extension (DSME) show promising properties for high adaptability and dynamically changing traffic. However, performance comparison between these MAC protocols rarely has gone beyond a simulation phase. This work presents the results of such a comparison on physically deployed networks using the facilities of the FIT-IoTLab. The evaluation includes fully implementing an IIoT protocol stack based on MQTT in Contiki-NG. It comprises the integration of DSME as part of Contiki-NG’s software stack through OpenDSME, the only publicly available implementation of DSME. Results show that both protocols suit IIoT applications, particularly for data collection. The comparison between TSCH and DSME also includes an evaluation of distributed schedulers for both MAC modes and one autonomous scheduler for TSCH within a UDP protocol stack.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"40 1","pages":"1 - 30"},"PeriodicalIF":2.7,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85202341","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}
Bo-yan Zhang, Boyu Jiang, Rong Zheng, Xiaoping Zhang, Jun Yu Li, Q. Xu
Continuous monitoring of human vital signs using non-contact mmWave radars is attractive due to their ability to penetrate garments and operate under different lighting conditions. Unfortunately, most prior research requires subjects to stay at a fixed distance from radar sensors and to remain still during monitoring. These restrictions limit the applications of radar vital sign monitoring in real life scenarios. In this article, we address these limitations and present Pi-ViMo, a non-contact Physiology-inspired Robust Vital Sign Monitoring system, using mmWave radars. We first derive a multi-scattering point model for the human body, and introduce a coherent combining of multiple scatterings to enhance the quality of estimated chest-wall movements. It enables vital sign estimations of subjects at any location in a radar’s field of view (FoV). We then propose a template matching method to extract human vital signs by adopting physical models of respiration and cardiac activities. The proposed method is capable to separate respiration and heartbeat in the presence of micro-level random body movements (RBM) when a subject is at any location within the field of view of a radar. Experiments in a radar testbed show average respiration rate errors of 6% and heart rate errors of 11.9% for the stationary subjects, and average errors of 13.5% for respiration rate and 13.6% for heart rate for subjects under different RBMs.
{"title":"Pi-ViMo: Physiology-inspired Robust Vital Sign Monitoring using mmWave Radars","authors":"Bo-yan Zhang, Boyu Jiang, Rong Zheng, Xiaoping Zhang, Jun Yu Li, Q. Xu","doi":"10.1145/3589347","DOIUrl":"https://doi.org/10.1145/3589347","url":null,"abstract":"Continuous monitoring of human vital signs using non-contact mmWave radars is attractive due to their ability to penetrate garments and operate under different lighting conditions. Unfortunately, most prior research requires subjects to stay at a fixed distance from radar sensors and to remain still during monitoring. These restrictions limit the applications of radar vital sign monitoring in real life scenarios. In this article, we address these limitations and present Pi-ViMo, a non-contact Physiology-inspired Robust Vital Sign Monitoring system, using mmWave radars. We first derive a multi-scattering point model for the human body, and introduce a coherent combining of multiple scatterings to enhance the quality of estimated chest-wall movements. It enables vital sign estimations of subjects at any location in a radar’s field of view (FoV). We then propose a template matching method to extract human vital signs by adopting physical models of respiration and cardiac activities. The proposed method is capable to separate respiration and heartbeat in the presence of micro-level random body movements (RBM) when a subject is at any location within the field of view of a radar. Experiments in a radar testbed show average respiration rate errors of 6% and heart rate errors of 11.9% for the stationary subjects, and average errors of 13.5% for respiration rate and 13.6% for heart rate for subjects under different RBMs.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"14 1","pages":"1 - 27"},"PeriodicalIF":2.7,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89614177","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 widespread use of Internet of Things (IoT) devices and the arrival of the 5G era, edge computing has become an attractive paradigm to serve end-users and provide better QoS. Many efforts have been paid to provision some merging public network services at the network edge. We reveal that it is very common that specific users call for private and isolated edge services to preserve data privacy and enable other security intentions. However, it still remains open to fulfill such kind of mixed requests in edge computing. In this article, we propose a cooperative edge computing framework, i.e., HyEdge, to offer both public and private edge services systematically. To fully exploit the benefits of this novel framework, we define the problem of optimal request scheduling over a given placement solution of hybrid edge servers to minimize the response delay. This problem is further modeled as a mixed integer non-linear programming problem (MINLP), which is typically NP-hard. Accordingly, we propose the partition-based optimization method, which can efficiently solve this NP-hard problem via the problem decomposition and the branch and bound strategies. We finally conduct extensive evaluations with a real-world dataset to measure the performance of our method. The results indicate that the proposed method achieves elegant performance with low computation complexity.
{"title":"HyEdge: A Cooperative Edge Computing Framework for Provisioning Private and Public Services","authors":"Siyuan Gu, Deke Guo, Guoming Tang, Lailong Luo, Yuchen Sun, Xueshan Luo","doi":"10.1145/3585078","DOIUrl":"https://doi.org/10.1145/3585078","url":null,"abstract":"With the widespread use of Internet of Things (IoT) devices and the arrival of the 5G era, edge computing has become an attractive paradigm to serve end-users and provide better QoS. Many efforts have been paid to provision some merging public network services at the network edge. We reveal that it is very common that specific users call for private and isolated edge services to preserve data privacy and enable other security intentions. However, it still remains open to fulfill such kind of mixed requests in edge computing. In this article, we propose a cooperative edge computing framework, i.e., HyEdge, to offer both public and private edge services systematically. To fully exploit the benefits of this novel framework, we define the problem of optimal request scheduling over a given placement solution of hybrid edge servers to minimize the response delay. This problem is further modeled as a mixed integer non-linear programming problem (MINLP), which is typically NP-hard. Accordingly, we propose the partition-based optimization method, which can efficiently solve this NP-hard problem via the problem decomposition and the branch and bound strategies. We finally conduct extensive evaluations with a real-world dataset to measure the performance of our method. The results indicate that the proposed method achieves elegant performance with low computation complexity.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"24 1","pages":"1 - 23"},"PeriodicalIF":2.7,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76548224","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}
Over the past decade, IoT has gained huge momentum in terms of technological exploration, integration, and its various applications even after having a resource-bound architecture. It is challenging to run any high-end security protocol(s) on Edge devices. These devices are highly vulnerable toward numerous cyber-attacks. IoT network nodes need peer-to-peer security, which is possible if there exists proper mutual authentication among network devices. A secure session key needs to be established among source and destination nodes before sending the sensitive data. To generate these session keys, a strong cryptosystem is required to share parameters securely over a wireless network. In this article, we utilize a Rubik's cube puzzle-based cryptosystem to exchange parameters among peers and generate session key(s). Blockchain technology is incorporated in the proposed model to provide anonymity of token transactions, on the basis of which the network devices exchange services. A session key pool randomizer is used to avoid network probabilistic attacks. Our hybrid model is capable of generating secure session keys that can be used for mutual authentication and reliable data transferring tasks. Cyber-attacks resistance and performance results were verified using standard tools, which gave industry level promising results in terms of efficiency, light weightiness, and practical applications.
{"title":"A Rubik's Cube Cryptosystem-based Authentication and Session Key Generation Model Driven in Blockchain Environment for IoT Security","authors":"Ankit Attkan, V. Ranga, Priyanka Ahlawat","doi":"10.1145/3586578","DOIUrl":"https://doi.org/10.1145/3586578","url":null,"abstract":"Over the past decade, IoT has gained huge momentum in terms of technological exploration, integration, and its various applications even after having a resource-bound architecture. It is challenging to run any high-end security protocol(s) on Edge devices. These devices are highly vulnerable toward numerous cyber-attacks. IoT network nodes need peer-to-peer security, which is possible if there exists proper mutual authentication among network devices. A secure session key needs to be established among source and destination nodes before sending the sensitive data. To generate these session keys, a strong cryptosystem is required to share parameters securely over a wireless network. In this article, we utilize a Rubik's cube puzzle-based cryptosystem to exchange parameters among peers and generate session key(s). Blockchain technology is incorporated in the proposed model to provide anonymity of token transactions, on the basis of which the network devices exchange services. A session key pool randomizer is used to avoid network probabilistic attacks. Our hybrid model is capable of generating secure session keys that can be used for mutual authentication and reliable data transferring tasks. Cyber-attacks resistance and performance results were verified using standard tools, which gave industry level promising results in terms of efficiency, light weightiness, and practical applications.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"2014 1","pages":"1 - 39"},"PeriodicalIF":2.7,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73622741","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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