Reliable communication and synchronization for transiently-powered batteryless sensors are still open challenges. This paper presents a method to synchronize and ensure reliable communication over batteryless sensors with zero energy cost requirements. Our preliminary design combines visible light communication (VLC) and radio-frequency (RF) backscatter into a self-powered autonomous circuit. We enable energy status sharing and communication scheduling services, which provide the fundamental building blocks for future batteryless communication protocols.
{"title":"Autonomous Energy Status Sharing and Synchronization for Batteryless Sensor Networks","authors":"A. Torrisi, K. Yıldırım, D. Brunelli","doi":"10.1145/3485730.3493360","DOIUrl":"https://doi.org/10.1145/3485730.3493360","url":null,"abstract":"Reliable communication and synchronization for transiently-powered batteryless sensors are still open challenges. This paper presents a method to synchronize and ensure reliable communication over batteryless sensors with zero energy cost requirements. Our preliminary design combines visible light communication (VLC) and radio-frequency (RF) backscatter into a self-powered autonomous circuit. We enable energy status sharing and communication scheduling services, which provide the fundamental building blocks for future batteryless communication protocols.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114970803","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}
Dries Van Leemput, Armand Naessens, Robbe Elsas, J. Hoebeke, E. D. Poorter
Wireless Sensor Networks (WSNs) operating in unlicensed frequency bands or employing battery-less devices, require a Duty Cycle (DC) limit to ensure fair spectrum access or limit energy consumption. However, in multi-hop networks, it is up to the network protocol to ensure that all devices comply with such DC restrictions. We therefore developed a distributed DC adherence algorithm that limits the DC of all devices without introducing any additional packet overhead. This paper presents a brief description of the algorithm and evaluates its performance through simulation. Our results show that the algorithm can limit the DC of all devices to ensure no devices must switch off. Our algorithm therefore provides a solution for WSNs where nodes must operate below a DC limit.
{"title":"Algorithm for Distributed Duty Cycle Adherence in Multi-Hop RPL Networks","authors":"Dries Van Leemput, Armand Naessens, Robbe Elsas, J. Hoebeke, E. D. Poorter","doi":"10.1145/3485730.3492874","DOIUrl":"https://doi.org/10.1145/3485730.3492874","url":null,"abstract":"Wireless Sensor Networks (WSNs) operating in unlicensed frequency bands or employing battery-less devices, require a Duty Cycle (DC) limit to ensure fair spectrum access or limit energy consumption. However, in multi-hop networks, it is up to the network protocol to ensure that all devices comply with such DC restrictions. We therefore developed a distributed DC adherence algorithm that limits the DC of all devices without introducing any additional packet overhead. This paper presents a brief description of the algorithm and evaluates its performance through simulation. Our results show that the algorithm can limit the DC of all devices to ensure no devices must switch off. Our algorithm therefore provides a solution for WSNs where nodes must operate below a DC limit.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116643867","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}
Yangfan Zhang, Xiaojing Wang, Chao Feng, Xinyi Li, Yuanming Cai, Yuhui Ren, Fuwei Wang, Ke Li
Human respiration monitoring acts as a crucial role to indicate people's daily health. Compared with traditional respiration monitoring methods, device-free wireless respiration sensing technology is emerging as a promising modality due to the less privacy intrusive and without on-body sensors. However, due to the intrinsic nature of relying on weak reflection signals for sensing, the sensing range is limited. Meanwhile, reliable sensing performance only can be achieved when the environment with little or even no interference. In this work, we propose a WiFi-based respiration system to simultaneously enlarge the sensing range and mitigate the interference. The basic idea is to employ a reconfigurable metasurface to dynamically manipulate electromagnetic waves in the environment to achieve beamforming and beam steering. Our system thus enhances the sensing range and reduces the energy of reflected signals from interferers to ensure reliable performance. Proof-of-concept experiments demonstrate the effectiveness of our scheme.
{"title":"Pushing the Limits of Respiration Sensing with Reconfigurable Metasurface","authors":"Yangfan Zhang, Xiaojing Wang, Chao Feng, Xinyi Li, Yuanming Cai, Yuhui Ren, Fuwei Wang, Ke Li","doi":"10.1145/3485730.3492873","DOIUrl":"https://doi.org/10.1145/3485730.3492873","url":null,"abstract":"Human respiration monitoring acts as a crucial role to indicate people's daily health. Compared with traditional respiration monitoring methods, device-free wireless respiration sensing technology is emerging as a promising modality due to the less privacy intrusive and without on-body sensors. However, due to the intrinsic nature of relying on weak reflection signals for sensing, the sensing range is limited. Meanwhile, reliable sensing performance only can be achieved when the environment with little or even no interference. In this work, we propose a WiFi-based respiration system to simultaneously enlarge the sensing range and mitigate the interference. The basic idea is to employ a reconfigurable metasurface to dynamically manipulate electromagnetic waves in the environment to achieve beamforming and beam steering. Our system thus enhances the sensing range and reduces the energy of reflected signals from interferers to ensure reliable performance. Proof-of-concept experiments demonstrate the effectiveness of our scheme.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117178640","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}
A new type of atomic absorption spectrometer - one that detects trace mercury in host material, based on hyperfine-structure lines in a magnetic field - was developed and tested on various sub-stances. This devl:ce can detect trace mercury to about 0;04 ppm (40 ppb) in about 1 minute. No chemical separation from the host material is necessary ..
{"title":"Mercury","authors":"Xiao Zeng, M. Yan, Mi Zhang","doi":"10.1145/3485730.3485930","DOIUrl":"https://doi.org/10.1145/3485730.3485930","url":null,"abstract":"A new type of atomic absorption spectrometer - one that detects trace mercury in host material, based on hyperfine-structure lines in a magnetic field - was developed and tested on various sub-stances. This devl:ce can detect trace mercury to about 0;04 ppm (40 ppb) in about 1 minute. No chemical separation from the host material is necessary ..","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"61 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120975780","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 Chen, Zhisheng Yan, Hongpeng Guo, Zhe Yang, A. Ali-Eldin, P. Shenoy, K. Nahrstedt
Recent years have witnessed sensors becoming an indispensable part of our life with the camera being one of the most popular and widely deployed sensors. The camera gives rise to numerous vision-based IoT applications that generate high-level understandings of a live video stream by performing analysis on end devices like mobile or embedded devices. Typically, these applications are built with deep learning (DL) models to conduct complex vision tasks, e.g., image classification and object detection. Due to the prohibitive cost of running DL models on end devices close to the camera and with limited computation capabilities, it is widely adopted to offload the computation to a nearby powerful edge server. However, there is a gap between the restricted offloading bandwidth of the end device and the large volume of image data incurred by the live video stream. In this paper, we present Deep Contextualized Compressive Offloading for Images (DCCOI), a lightweight, context-aware, and bandwidth-efficient offloading framework for images. DCCOI consists of the spatial-adaptive encoder, a lightweight neural network, to spatial-adaptively compress the image, and the generative decoder for reconstructing the image from the compressed data. In contrast to existing DL-based encoders, the spatial-adaptive encoder allows an image region to be encoded into different numbers of feature values based on the information in it. This offers a variable-length coding method for image compression, which is a more optimal way for compression than the fix-length coding method took by existing DL-based compression approaches and demonstrates superior accuracy-compression rate trade-offs. We evaluate DCCOI against several baseline compression techniques while serving an object detection-based application. The results show that DCCOI roughly reduces the offloading size of JPEG by a factor of 9 and DeepCOD, the state-of-the-art offloading approach, by 20% with similar accuracy and a compression overhead less than 50ms.
{"title":"Deep Contextualized Compressive Offloading for Images","authors":"Bo Chen, Zhisheng Yan, Hongpeng Guo, Zhe Yang, A. Ali-Eldin, P. Shenoy, K. Nahrstedt","doi":"10.1145/3485730.3493452","DOIUrl":"https://doi.org/10.1145/3485730.3493452","url":null,"abstract":"Recent years have witnessed sensors becoming an indispensable part of our life with the camera being one of the most popular and widely deployed sensors. The camera gives rise to numerous vision-based IoT applications that generate high-level understandings of a live video stream by performing analysis on end devices like mobile or embedded devices. Typically, these applications are built with deep learning (DL) models to conduct complex vision tasks, e.g., image classification and object detection. Due to the prohibitive cost of running DL models on end devices close to the camera and with limited computation capabilities, it is widely adopted to offload the computation to a nearby powerful edge server. However, there is a gap between the restricted offloading bandwidth of the end device and the large volume of image data incurred by the live video stream. In this paper, we present Deep Contextualized Compressive Offloading for Images (DCCOI), a lightweight, context-aware, and bandwidth-efficient offloading framework for images. DCCOI consists of the spatial-adaptive encoder, a lightweight neural network, to spatial-adaptively compress the image, and the generative decoder for reconstructing the image from the compressed data. In contrast to existing DL-based encoders, the spatial-adaptive encoder allows an image region to be encoded into different numbers of feature values based on the information in it. This offers a variable-length coding method for image compression, which is a more optimal way for compression than the fix-length coding method took by existing DL-based compression approaches and demonstrates superior accuracy-compression rate trade-offs. We evaluate DCCOI against several baseline compression techniques while serving an object detection-based application. The results show that DCCOI roughly reduces the offloading size of JPEG by a factor of 9 and DeepCOD, the state-of-the-art offloading approach, by 20% with similar accuracy and a compression overhead less than 50ms.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125352635","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}
D. Wójcik, T. Rymarczyk, M. Oleszek, Lukasz Maciura, P. Bednarczuk
This research aimed to develop a high accuracy machine learning algorithm that can diagnose cardiovascular diseases from the stream of data from multiple body surface potential mapping devices equipped with 102 textile electrodes. The algorithm is based on the 1D convolutional neural network, trained on the comparable real-life data gathered from the FLUKE ECG simulator connected to the resistance-based human phantom. The developed neural network achieved an accuracy of 99.91% on the test data. Additionally, an additional algorithm was developed that can use the neural network to analyse the data streamed from the medical device and notice the medical staff about dangerous heart rhythms detected by the system.
{"title":"Diagnosing Cardiovascular Diseases with Machine Learning on Body Surface Potential Mapping Data","authors":"D. Wójcik, T. Rymarczyk, M. Oleszek, Lukasz Maciura, P. Bednarczuk","doi":"10.1145/3485730.3492883","DOIUrl":"https://doi.org/10.1145/3485730.3492883","url":null,"abstract":"This research aimed to develop a high accuracy machine learning algorithm that can diagnose cardiovascular diseases from the stream of data from multiple body surface potential mapping devices equipped with 102 textile electrodes. The algorithm is based on the 1D convolutional neural network, trained on the comparable real-life data gathered from the FLUKE ECG simulator connected to the resistance-based human phantom. The developed neural network achieved an accuracy of 99.91% on the test data. Additionally, an additional algorithm was developed that can use the neural network to analyse the data streamed from the medical device and notice the medical staff about dangerous heart rhythms detected by the system.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"649 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123346711","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}
Ang Li, Jingwei Sun, Xiao Zeng, Mi Zhang, H. Li, Yiran Chen
Recent advancements in deep neural networks (DNN) enabled various mobile deep learning applications. However, it is technically challenging to locally train a DNN model due to limited data on devices like mobile phones. Federated learning (FL) is a distributed machine learning paradigm which allows for model training on decentralized data residing on devices without breaching data privacy. Hence, FL becomes a natural choice for deploying on-device deep learning applications. However, the data residing across devices is intrinsically statistically heterogeneous (i.e., non-IID data distribution) and mobile devices usually have limited communication bandwidth to transfer local updates. Such statistical heterogeneity and communication bandwidth limit are two major bottlenecks that hinder applying FL in practice. In addition, considering mobile devices usually have limited computational resources, improving computation efficiency of training and running DNNs is critical to developing on-device deep learning applications. In this paper, we present FedMask - a communication and computation efficient FL framework. By applying FedMask, each device can learn a personalized and structured sparse DNN, which can run efficiently on devices. To achieve this, each device learns a sparse binary mask (i.e., 1 bit per network parameter) while keeping the parameters of each local model unchanged; only these binary masks will be communicated between the server and the devices. Instead of learning a shared global model in classic FL, each device obtains a personalized and structured sparse model that is composed by applying the learned binary mask to the fixed parameters of the local model. Our experiments show that compared with status quo approaches, FedMask improves the inference accuracy by 28.47% and reduces the communication cost and the computation cost by 34.48X and 2.44X. FedMask also achieves 1.56X inference speedup and reduces the energy consumption by 1.78X.
{"title":"FedMask","authors":"Ang Li, Jingwei Sun, Xiao Zeng, Mi Zhang, H. Li, Yiran Chen","doi":"10.1145/3485730.3485929","DOIUrl":"https://doi.org/10.1145/3485730.3485929","url":null,"abstract":"Recent advancements in deep neural networks (DNN) enabled various mobile deep learning applications. However, it is technically challenging to locally train a DNN model due to limited data on devices like mobile phones. Federated learning (FL) is a distributed machine learning paradigm which allows for model training on decentralized data residing on devices without breaching data privacy. Hence, FL becomes a natural choice for deploying on-device deep learning applications. However, the data residing across devices is intrinsically statistically heterogeneous (i.e., non-IID data distribution) and mobile devices usually have limited communication bandwidth to transfer local updates. Such statistical heterogeneity and communication bandwidth limit are two major bottlenecks that hinder applying FL in practice. In addition, considering mobile devices usually have limited computational resources, improving computation efficiency of training and running DNNs is critical to developing on-device deep learning applications. In this paper, we present FedMask - a communication and computation efficient FL framework. By applying FedMask, each device can learn a personalized and structured sparse DNN, which can run efficiently on devices. To achieve this, each device learns a sparse binary mask (i.e., 1 bit per network parameter) while keeping the parameters of each local model unchanged; only these binary masks will be communicated between the server and the devices. Instead of learning a shared global model in classic FL, each device obtains a personalized and structured sparse model that is composed by applying the learned binary mask to the fixed parameters of the local model. Our experiments show that compared with status quo approaches, FedMask improves the inference accuracy by 28.47% and reduces the communication cost and the computation cost by 34.48X and 2.44X. FedMask also achieves 1.56X inference speedup and reduces the energy consumption by 1.78X.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114218313","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}
Tiantian Liu, Ming Gao, Feng Lin, Chao Wang, Zhongjie Ba, Jinsong Han, Wenyao Xu, K. Ren
With the advance in automatic speech recognition, voice user interface has gained popularity recently. Since the COVID-19 pandemic, VUI is increasingly preferred in online communication due to its non-contact. Additionally, various ambient noise impedes the public applications of voice user interfaces due to the requirement of audio-only speech recognition methods for a high signal-to-noise ratio. In this paper, we present Wavoice, the first noise-resistant multi-modal speech recognition system that fuses two distinct voice sensing modalities, i.e., millimeter-wave (mmWave) signals and audio signals from a microphone, together. One key contribution is that we model the inherent correlation between mmWave and audio signals. Based on it, Wavoice facilitates the real-time noise-resistant voice activity detection and user targeting from multiple speakers. Furthermore, we elaborate on two novel modules into the neural attention mechanism for multi-modal signals fusion, and result in accurate speech recognition. Extensive experiments verify Wavoice's effectiveness under various conditions with the character recognition error rate below 1% in a range of 7 meters. Wavoice outperforms existing audio-only speech recognition methods with lower character error rate and word error rate. The evaluation in complex scenes validates the robustness of Wavoice.
{"title":"Wavoice: A Noise-resistant Multi-modal Speech Recognition System Fusing mmWave and Audio Signals","authors":"Tiantian Liu, Ming Gao, Feng Lin, Chao Wang, Zhongjie Ba, Jinsong Han, Wenyao Xu, K. Ren","doi":"10.1145/3485730.3485945","DOIUrl":"https://doi.org/10.1145/3485730.3485945","url":null,"abstract":"With the advance in automatic speech recognition, voice user interface has gained popularity recently. Since the COVID-19 pandemic, VUI is increasingly preferred in online communication due to its non-contact. Additionally, various ambient noise impedes the public applications of voice user interfaces due to the requirement of audio-only speech recognition methods for a high signal-to-noise ratio. In this paper, we present Wavoice, the first noise-resistant multi-modal speech recognition system that fuses two distinct voice sensing modalities, i.e., millimeter-wave (mmWave) signals and audio signals from a microphone, together. One key contribution is that we model the inherent correlation between mmWave and audio signals. Based on it, Wavoice facilitates the real-time noise-resistant voice activity detection and user targeting from multiple speakers. Furthermore, we elaborate on two novel modules into the neural attention mechanism for multi-modal signals fusion, and result in accurate speech recognition. Extensive experiments verify Wavoice's effectiveness under various conditions with the character recognition error rate below 1% in a range of 7 meters. Wavoice outperforms existing audio-only speech recognition methods with lower character error rate and word error rate. The evaluation in complex scenes validates the robustness of Wavoice.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"28 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113970497","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}
Yi Zhu, Chenglin Miao, Foad Hajiaghajani, Mengdi Huai, Lu Su, Chunming Qiao
Today, most autonomous vehicles (AVs) rely on LiDAR (Light Detection and Ranging) perception to acquire accurate information about their immediate surroundings. In LiDAR-based perception systems, semantic segmentation plays a critical role as it can divide LiDAR point clouds into meaningful regions according to human perception and provide AVs with semantic understanding of the driving environments. However, an implicit assumption for existing semantic segmentation models is that they are performed in a reliable and secure environment, which may not be true in practice. In this paper, we investigate adversarial attacks against LiDAR semantic segmentation in autonomous driving. Specifically, we propose a novel adversarial attack framework based on which the attacker can easily fool LiDAR semantic segmentation by placing some simple objects (e.g., cardboard and road signs) at some locations in the physical space. We conduct extensive real-world experiments to evaluate the performance of our proposed attack framework. The experimental results show that our attack can achieve more than 90% success rate in real-world driving environments. To the best of our knowledge, this is the first study on physically realizable adversarial attacks against LiDAR point cloud semantic segmentation with real-world evaluations.
{"title":"Adversarial Attacks against LiDAR Semantic Segmentation in Autonomous Driving","authors":"Yi Zhu, Chenglin Miao, Foad Hajiaghajani, Mengdi Huai, Lu Su, Chunming Qiao","doi":"10.1145/3485730.3485935","DOIUrl":"https://doi.org/10.1145/3485730.3485935","url":null,"abstract":"Today, most autonomous vehicles (AVs) rely on LiDAR (Light Detection and Ranging) perception to acquire accurate information about their immediate surroundings. In LiDAR-based perception systems, semantic segmentation plays a critical role as it can divide LiDAR point clouds into meaningful regions according to human perception and provide AVs with semantic understanding of the driving environments. However, an implicit assumption for existing semantic segmentation models is that they are performed in a reliable and secure environment, which may not be true in practice. In this paper, we investigate adversarial attacks against LiDAR semantic segmentation in autonomous driving. Specifically, we propose a novel adversarial attack framework based on which the attacker can easily fool LiDAR semantic segmentation by placing some simple objects (e.g., cardboard and road signs) at some locations in the physical space. We conduct extensive real-world experiments to evaluate the performance of our proposed attack framework. The experimental results show that our attack can achieve more than 90% success rate in real-world driving environments. To the best of our knowledge, this is the first study on physically realizable adversarial attacks against LiDAR point cloud semantic segmentation with real-world evaluations.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127612809","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}
Tiny energy harvesting battery-less devices present a promising alternative to battery-powered devices for a sustainable Internet of Things (IoT) vision. The use of small capacitors as energy storage, along with a dynamic and unpredictable harvesting environment, leads these devices to exhibit intermittent on-off behavior. As the traditional computing models cannot handle this behavior, in this demo we present and demonstrate an energy-aware task scheduler for battery-less IoT devices based on task dependencies and priorities, which can intelligently schedule the application tasks avoiding power failures.
{"title":"Demonstration of an Energy-Aware Task Scheduler for Battery-Less IoT Devices","authors":"Adnan Sabovic, A. Sultania, J. Famaey","doi":"10.1145/3485730.3493358","DOIUrl":"https://doi.org/10.1145/3485730.3493358","url":null,"abstract":"Tiny energy harvesting battery-less devices present a promising alternative to battery-powered devices for a sustainable Internet of Things (IoT) vision. The use of small capacitors as energy storage, along with a dynamic and unpredictable harvesting environment, leads these devices to exhibit intermittent on-off behavior. As the traditional computing models cannot handle this behavior, in this demo we present and demonstrate an energy-aware task scheduler for battery-less IoT devices based on task dependencies and priorities, which can intelligently schedule the application tasks avoiding power failures.","PeriodicalId":356322,"journal":{"name":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127911798","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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