Huadong Ma, Yuan He, Mo Li, Neal Patwari, Stephan Sigg
ACM TIOT launched its first special issue on the theme of wireless sensing for IoT. As an important component of the special issue and a novel practice of the journal, an online virtual workshop will be held, with presentations for each of the accepted articles. Welcome to join us for online discussion! Free registration is required for an attendee of the workshop. The zoom link will be shared to registered attendees before the workshop.
{"title":"Introduction to the Special Issue on Wireless Sensing for IoT","authors":"Huadong Ma, Yuan He, Mo Li, Neal Patwari, Stephan Sigg","doi":"10.1145/3633078","DOIUrl":"https://doi.org/10.1145/3633078","url":null,"abstract":"ACM TIOT launched its first special issue on the theme of wireless sensing for IoT. As an important component of the special issue and a novel practice of the journal, an online virtual workshop will be held, with presentations for each of the accepted articles. Welcome to join us for online discussion! Free registration is required for an attendee of the workshop. The zoom link will be shared to registered attendees before the workshop.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":" 23","pages":"1 - 4"},"PeriodicalIF":2.7,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139197586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Special Issue on Wireless Sensing for IoT: A Word from the Editor-in-Chief","authors":"G. Picco","doi":"10.1145/3633752","DOIUrl":"https://doi.org/10.1145/3633752","url":null,"abstract":"","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"216 ","pages":"1 - 2"},"PeriodicalIF":2.7,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139203625","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 limited resources of Internet of Things (IoT) devices, Symmetric Key Cryptography (SKC) is typically favored over resource-intensive Public Key Cryptography (PKC) to secure communication between IoT devices. To utilize SKC, devices need to execute a key exchange protocol to establish a session key before initiating communication. However, existing SKC-based key exchange protocols assume communication devices have a pre-shared secret or there are trusted intermediaries between them; neither is always realistic in IoT. We introduce a new SKC-based key exchange protocol for IoT devices. While also intermediary-based, our protocol fundamentally departs from existing intermediary-based solutions in that intermediaries between two key exchange devices may be malicious, and moreover, our protocol can detect cheating behaviors and identify malicious intermediaries. We prove our protocol is secure under the universally composable model, and show it can detect malicious intermediaries with probability 1.0. We implemented and evaluated our protocol on different IoT devices. We show our protocol has significant improvements in computation time and energy cost. Compared to the PKC-based protocols ECDH, DH, and RSA, our protocol is 2.3 to 1591 times faster on one of the two key exchange devices and 0.7 to 4.67 times faster on the other.
{"title":"Resilient Intermediary‐Based Key Exchange Protocol for IoT","authors":"Zhangxiang Hu, Jun Li, Christopher Wilson","doi":"10.1145/3632408","DOIUrl":"https://doi.org/10.1145/3632408","url":null,"abstract":"Due to the limited resources of Internet of Things (IoT) devices, Symmetric Key Cryptography (SKC) is typically favored over resource-intensive Public Key Cryptography (PKC) to secure communication between IoT devices. To utilize SKC, devices need to execute a key exchange protocol to establish a session key before initiating communication. However, existing SKC-based key exchange protocols assume communication devices have a pre-shared secret or there are trusted intermediaries between them; neither is always realistic in IoT. We introduce a new SKC-based key exchange protocol for IoT devices. While also intermediary-based, our protocol fundamentally departs from existing intermediary-based solutions in that intermediaries between two key exchange devices may be malicious, and moreover, our protocol can detect cheating behaviors and identify malicious intermediaries. We prove our protocol is secure under the universally composable model, and show it can detect malicious intermediaries with probability 1.0. We implemented and evaluated our protocol on different IoT devices. We show our protocol has significant improvements in computation time and energy cost. Compared to the PKC-based protocols ECDH, DH, and RSA, our protocol is 2.3 to 1591 times faster on one of the two key exchange devices and 0.7 to 4.67 times faster on the other.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"286 6","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139257855","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}
Devkishen Sisodia, Jun Li, Samuel Mergendahl, Hasan Cam
With the growth of the Internet of Things (IoT), the number of cyber attacks on the Internet is on the rise. However, the resource-constrained nature of IoT devices and their networks makes many classical security systems ineffective or inapplicable. We introduce TWINKLE, a two-mode, adaptive security framework that allows an IoT network to be in regular mode for most of the time, which incurs a low resource consumption rate, and to switch to vigilant mode only when suspicious behavior is detected, which potentially incurs a higher overhead. Compared to the early version of this work, this paper presents a more comprehensive design and architecture of TWINKLE, describes challenges and details in implementing TWINKLE, and reports evaluations of TWINKLE based on real-world IoT testbeds with more metrics. We show the efficacy of TWINKLE in two case studies where we examine two existing intrusion detection and prevention systems and transform both into new, improved systems using TWINKLE. Our evaluations show that TWINKLE is not only effective at securing resource-constrained IoT networks, but can also successfully detect and prevent attacks with a significantly lower overhead and detection latency than existing solutions.
{"title":"A Two-Mode, Adaptive Security Framework for Smart Home Security Applications","authors":"Devkishen Sisodia, Jun Li, Samuel Mergendahl, Hasan Cam","doi":"10.1145/3617504","DOIUrl":"https://doi.org/10.1145/3617504","url":null,"abstract":"With the growth of the Internet of Things (IoT), the number of cyber attacks on the Internet is on the rise. However, the resource-constrained nature of IoT devices and their networks makes many classical security systems ineffective or inapplicable. We introduce TWINKLE, a two-mode, adaptive security framework that allows an IoT network to be in regular mode for most of the time, which incurs a low resource consumption rate, and to switch to vigilant mode only when suspicious behavior is detected, which potentially incurs a higher overhead. Compared to the early version of this work, this paper presents a more comprehensive design and architecture of TWINKLE, describes challenges and details in implementing TWINKLE, and reports evaluations of TWINKLE based on real-world IoT testbeds with more metrics. We show the efficacy of TWINKLE in two case studies where we examine two existing intrusion detection and prevention systems and transform both into new, improved systems using TWINKLE. Our evaluations show that TWINKLE is not only effective at securing resource-constrained IoT networks, but can also successfully detect and prevent attacks with a significantly lower overhead and detection latency than existing solutions.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"3 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139263350","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}
Radar collision prediction systems can play a crucial role in safety critical applications, such as autonomous vehicles and smart helmets for contact sports, by predicting impending collision just before it will occur. Collision prediction algorithms use the velocity and range measurements provided by radar to calculate time to collision. However, radar measurements used in such systems contain significant clutter, noise, and inaccuracies which hamper reliability. Existing solutions to reduce clutter are based on static filtering methods. In this paper, we present a deep learning approach using frequency modulated continuous wave (FMCW) radar and inertial sensing that learns the environmental and user-specific conditions that lead to future collisions. We present a process of converting raw radar samples to range-Doppler matrices (RDMs) and then training a deep convolutional neural network that outputs predictions (impending collision vs. none) for any measured RDM. The system is retrained to work in dynamically changing environments and maintain prediction accuracy. We demonstrate the effectiveness of our approach of using the information from radar data to predict impending collisions in real-time via real-world experiments, and show that our method achieves an F1-score of 0.91 and outperforms a traditional approach in accuracy and adaptability.
{"title":"Online learning for dynamic impending collision prediction using FMCW radar","authors":"Aarti Singh, Neal Patwari","doi":"10.1145/3616018","DOIUrl":"https://doi.org/10.1145/3616018","url":null,"abstract":"Radar collision prediction systems can play a crucial role in safety critical applications, such as autonomous vehicles and smart helmets for contact sports, by predicting impending collision just before it will occur. Collision prediction algorithms use the velocity and range measurements provided by radar to calculate time to collision. However, radar measurements used in such systems contain significant clutter, noise, and inaccuracies which hamper reliability. Existing solutions to reduce clutter are based on static filtering methods. In this paper, we present a deep learning approach using frequency modulated continuous wave (FMCW) radar and inertial sensing that learns the environmental and user-specific conditions that lead to future collisions. We present a process of converting raw radar samples to range-Doppler matrices (RDMs) and then training a deep convolutional neural network that outputs predictions (impending collision vs. none) for any measured RDM. The system is retrained to work in dynamically changing environments and maintain prediction accuracy. We demonstrate the effectiveness of our approach of using the information from radar data to predict impending collisions in real-time via real-world experiments, and show that our method achieves an F1-score of 0.91 and outperforms a traditional approach in accuracy and adaptability.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"23 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86369942","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}
Wireless sensing has emerged as a powerful environmental sensing technology that is vulnerable to the impact of all kinds of ambient noises. LoRa is a novel interference-resilient technology of low-power wide-area networks (LPWAN), which has attracted wide attention from scientific and industrial communities. However, LoRa transmission suffers from serious latency in those complex wireless sensing environments requiring transmission reliability. In this paper, we present CH-MAC, the first MAC-layer protocol based on the local corruption nature of packets and the time-varying nature of channels to reduce end-to-end transmission latency in LPWAN with reliable communication requirements. Specifically, CH-MAC employs Luby Transform code to divide and encode the payload into several blocks such that the receiver can retain part of the coded information in the corrupted packets. In addition, CH-MAC utilizes hopping to transmit different blocks of a packet with various channels to avoid sudden noise collision. Moreover, CH-MAC adopts a dynamic packet length adjustment mechanism to mitigate network congestion. Extensive evaluations on a real-world hardware testbed and a simulation platform show that CH-MAC can reduce end-to-end transmission latency by 2.63 × with a communication success rate requirement of > (95% ) compared with state-of-the-art methods.
{"title":"CH-MAC: Achieving Low-latency Reliable Communication via Coding and Hopping in LPWAN","authors":"Junzhou Luo, Zhuqing Xu, Jingkai Lin, Ciyuan Chen, Runqun Xiong","doi":"10.1145/3617505","DOIUrl":"https://doi.org/10.1145/3617505","url":null,"abstract":"Wireless sensing has emerged as a powerful environmental sensing technology that is vulnerable to the impact of all kinds of ambient noises. LoRa is a novel interference-resilient technology of low-power wide-area networks (LPWAN), which has attracted wide attention from scientific and industrial communities. However, LoRa transmission suffers from serious latency in those complex wireless sensing environments requiring transmission reliability. In this paper, we present CH-MAC, the first MAC-layer protocol based on the local corruption nature of packets and the time-varying nature of channels to reduce end-to-end transmission latency in LPWAN with reliable communication requirements. Specifically, CH-MAC employs Luby Transform code to divide and encode the payload into several blocks such that the receiver can retain part of the coded information in the corrupted packets. In addition, CH-MAC utilizes hopping to transmit different blocks of a packet with various channels to avoid sudden noise collision. Moreover, CH-MAC adopts a dynamic packet length adjustment mechanism to mitigate network congestion. Extensive evaluations on a real-world hardware testbed and a simulation platform show that CH-MAC can reduce end-to-end transmission latency by 2.63 × with a communication success rate requirement of > (95% ) compared with state-of-the-art methods.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"192 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84414636","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}
Biaokai Zhu, Zejiao Yang, Yupeng Jia, Shengxin Chen, Jie Song, Sanman Liu, P. Li, Feng Li, Deng-ao Li
Vibration is a normal reaction that occurs during the operation of machinery and is very common in industrial systems. How to turn fine-grained vibration perception into visualization, and further predict mechanical failures and reduce property losses based on visual vibration information, which has aroused our thinking. In this paper, the phase information generated by the tag is processed and analyzed, and MFD is proposed, a real-time vibration monitoring and fault-sensing discrimination system. MFD extracts phase information from the original RF signal and converts it into a markov transition map by introducing White Gaussian Noise and a low-pass filter for denoising. To accurately predict the failure of machinery, a deep and machine learning model is introduced to calculate the accuracy of failure analysis, realizing real-time monitoring and fault judgment. The test results show that the average recognition accuracy of vibration can reach 96.07%, and the average recognition accuracy of forward rotation, reverse rotation, oil spill, and screw loosening of motor equipment during long-term operation can reach 98.53%, 99.44%, 97.87%, and 99.91%, respectively, with high robustness.
{"title":"MFD: Multi-object Frequency Feature Recognition and State Detection Based on RFID-single Tag","authors":"Biaokai Zhu, Zejiao Yang, Yupeng Jia, Shengxin Chen, Jie Song, Sanman Liu, P. Li, Feng Li, Deng-ao Li","doi":"10.1145/3615665","DOIUrl":"https://doi.org/10.1145/3615665","url":null,"abstract":"Vibration is a normal reaction that occurs during the operation of machinery and is very common in industrial systems. How to turn fine-grained vibration perception into visualization, and further predict mechanical failures and reduce property losses based on visual vibration information, which has aroused our thinking. In this paper, the phase information generated by the tag is processed and analyzed, and MFD is proposed, a real-time vibration monitoring and fault-sensing discrimination system. MFD extracts phase information from the original RF signal and converts it into a markov transition map by introducing White Gaussian Noise and a low-pass filter for denoising. To accurately predict the failure of machinery, a deep and machine learning model is introduced to calculate the accuracy of failure analysis, realizing real-time monitoring and fault judgment. The test results show that the average recognition accuracy of vibration can reach 96.07%, and the average recognition accuracy of forward rotation, reverse rotation, oil spill, and screw loosening of motor equipment during long-term operation can reach 98.53%, 99.44%, 97.87%, and 99.91%, respectively, with high robustness.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"21 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75478093","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}
Electronic signatures are widely used in financial business, telecommuting and identity authentication. Offline electronic signatures are vulnerable to copy or replay attacks. Contact-based online electronic signatures are limited by indirect contact such as handwriting pads and may threaten the health of users. Consider combining hand shape features and writing process features to form electronic signatures, the paper proposes an in-air handwritten signature verification system with millimeter-wave(mmWave) radar, namely mmHSV. First, the biometrics of the handwritten signature process are modeled, and phase-dependent biometrics and behavioral features are extracted from the mmWave radar mixture signal. Secondly, a handwritten feature recognition network based on few-sample learning is presented to fuse multi-dimensional features and determine user legitimacy. Finally, mmHSV is implemented and evaluated with commercial mmWave devices in different scenarios and attack mode conditions. Experimental results show that the mmHSV can achieve accurate, efficient, robust and scalable handwritten signature verification. Area Under Curve (AUC) is 98.96 (% ) , False Acceptance Rate (FAR) is 5.1 (% ) at the fixed threshold, AUC is 97.79 (% ) for untrained users.
{"title":"mmHSV: In-Air Handwritten Signature Verification via Millimeter-wave Radar","authors":"Wanqing Li, Tongtong He, Nan Jing, Lin Wang","doi":"10.1145/3614443","DOIUrl":"https://doi.org/10.1145/3614443","url":null,"abstract":"Electronic signatures are widely used in financial business, telecommuting and identity authentication. Offline electronic signatures are vulnerable to copy or replay attacks. Contact-based online electronic signatures are limited by indirect contact such as handwriting pads and may threaten the health of users. Consider combining hand shape features and writing process features to form electronic signatures, the paper proposes an in-air handwritten signature verification system with millimeter-wave(mmWave) radar, namely mmHSV. First, the biometrics of the handwritten signature process are modeled, and phase-dependent biometrics and behavioral features are extracted from the mmWave radar mixture signal. Secondly, a handwritten feature recognition network based on few-sample learning is presented to fuse multi-dimensional features and determine user legitimacy. Finally, mmHSV is implemented and evaluated with commercial mmWave devices in different scenarios and attack mode conditions. Experimental results show that the mmHSV can achieve accurate, efficient, robust and scalable handwritten signature verification. Area Under Curve (AUC) is 98.96 (% ) , False Acceptance Rate (FAR) is 5.1 (% ) at the fixed threshold, AUC is 97.79 (% ) for untrained users.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"28 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82573824","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}
People usually form a social structure (e.g., a leader-follower, companion, or independent group) for better interactions among them and thus share similar perceptions of visible scenes and invisible wireless signals encountered while moving. Many mobility-driven applications have paid much attention to recognizing trajectory relationships among people. This work models visual and wireless data to quantify the trajectory similarity between a pair of users. We design a visual and wireless sensor fusion system, called ViWise, which incorporates the first-person video frames collected by a wearable visual device and the wireless packets broadcast by a personal mobile device for recognizing finer-grained trajectory relationships within a mobility group. When people take similar trajectories, they usually share similar visual scenes. Their wireless packets observed by ambient wireless base stations (called wireless scanners in this work) usually contain similar patterns. We model the visual characteristics of physical objects seen by a user from two perspectives: micro-scale image structure with pixel-wise features and macro-scale semantic context. On the other hand, we model characteristics of wireless packets based on the encountered wireless scanners along the user’s trajectory. Given two users’ trajectories, their trajectory characteristics behind the visible video frames and invisible wireless packets are fused together to compute the visual-wireless data similarity that quantifies the correlation between trajectories taken by them. We exploit modeled visual-wireless data similarity to recognize the social structure within user trajectories. Comprehensive experimental results in indoor and outdoor environments show that the proposed ViWise is robust in trajectory relationship recognition with an accuracy of above 90%.
{"title":"ViWise: Fusing Visual and Wireless Sensing Data for Trajectory Relationship Recognition","authors":"Fang-Jing Wu, Sheng-Wun Lai, Sok-Ian Sou","doi":"10.1145/3614441","DOIUrl":"https://doi.org/10.1145/3614441","url":null,"abstract":"People usually form a social structure (e.g., a leader-follower, companion, or independent group) for better interactions among them and thus share similar perceptions of visible scenes and invisible wireless signals encountered while moving. Many mobility-driven applications have paid much attention to recognizing trajectory relationships among people. This work models visual and wireless data to quantify the trajectory similarity between a pair of users. We design a visual and wireless sensor fusion system, called ViWise, which incorporates the first-person video frames collected by a wearable visual device and the wireless packets broadcast by a personal mobile device for recognizing finer-grained trajectory relationships within a mobility group. When people take similar trajectories, they usually share similar visual scenes. Their wireless packets observed by ambient wireless base stations (called wireless scanners in this work) usually contain similar patterns. We model the visual characteristics of physical objects seen by a user from two perspectives: micro-scale image structure with pixel-wise features and macro-scale semantic context. On the other hand, we model characteristics of wireless packets based on the encountered wireless scanners along the user’s trajectory. Given two users’ trajectories, their trajectory characteristics behind the visible video frames and invisible wireless packets are fused together to compute the visual-wireless data similarity that quantifies the correlation between trajectories taken by them. We exploit modeled visual-wireless data similarity to recognize the social structure within user trajectories. Comprehensive experimental results in indoor and outdoor environments show that the proposed ViWise is robust in trajectory relationship recognition with an accuracy of above 90%.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"76 4 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78465506","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}
Juncen Zhu, Jiannong Cao, Yanni Yang, Wei Ren, Huizi Han
Early detection of fatigue driving is pivotal for safety of drivers and pedestrians. Traditional approaches mainly employ cameras and wearable sensors to detect fatigue features, which are intrusive to drivers. Recent advances in radio frequency (RF) sensing enable non-intrusive fatigue feature detection from the signal reflected by driver’s body. However, existing RF-based solutions only detect partial or coarse-grained fatigue features, which reduces the detection accuracy. To tackle above limitations, we propose a mmWave-based fatigue driving detection system, called mmDrive, which can detect multiple fine-grained fatigue features from different body parts. However, achieving accurate detection of various fatigue features during driving encounters practical challenges. Specifically, normal driving activities and driver’s involuntary facial movements inevitably cause interference to fatigue features. Thus, we exploit unique geometric and behavioral characteristics of fatigue features and design effective signal processing methods to remove noises from fatigue-irrelevant activities. Based on the detected fatigue features, we further develop a fatigue determination algorithm to decide driver’s fatigue state. Extensive experiment results from both simulated and real driving environments show that the average accuracy for detecting nodding and yawning features is about (96% ) , and the average errors for estimating eye blink, respiration, and heartbeat rates are around 2.21bpm, 0.54bpm, and 2.52bpm, respectively. And the accuracy of the fatigue detection algorithm we proposed reached (97.63% ) .
{"title":"mmDrive: Fine-Grained Fatigue Driving Detection Using mmWave Radar","authors":"Juncen Zhu, Jiannong Cao, Yanni Yang, Wei Ren, Huizi Han","doi":"10.1145/3614437","DOIUrl":"https://doi.org/10.1145/3614437","url":null,"abstract":"Early detection of fatigue driving is pivotal for safety of drivers and pedestrians. Traditional approaches mainly employ cameras and wearable sensors to detect fatigue features, which are intrusive to drivers. Recent advances in radio frequency (RF) sensing enable non-intrusive fatigue feature detection from the signal reflected by driver’s body. However, existing RF-based solutions only detect partial or coarse-grained fatigue features, which reduces the detection accuracy. To tackle above limitations, we propose a mmWave-based fatigue driving detection system, called mmDrive, which can detect multiple fine-grained fatigue features from different body parts. However, achieving accurate detection of various fatigue features during driving encounters practical challenges. Specifically, normal driving activities and driver’s involuntary facial movements inevitably cause interference to fatigue features. Thus, we exploit unique geometric and behavioral characteristics of fatigue features and design effective signal processing methods to remove noises from fatigue-irrelevant activities. Based on the detected fatigue features, we further develop a fatigue determination algorithm to decide driver’s fatigue state. Extensive experiment results from both simulated and real driving environments show that the average accuracy for detecting nodding and yawning features is about (96% ) , and the average errors for estimating eye blink, respiration, and heartbeat rates are around 2.21bpm, 0.54bpm, and 2.52bpm, respectively. And the accuracy of the fatigue detection algorithm we proposed reached (97.63% ) .","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"45 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84647069","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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