Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems最新文献

Autonomous Energy Status Sharing and Synchronization for Batteryless Sensor Networks 无电池传感器网络的自主能量状态共享与同步

Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2021-11-15 DOI: 10.1145/3485730.3493360

A. Torrisi, K. Yıldırım, D. Brunelli

引用次数: 2

Algorithm for Distributed Duty Cycle Adherence in Multi-Hop RPL Networks 多跳RPL网络中分布式占空比保持算法

Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2021-11-15 DOI: 10.1145/3485730.3492874

Dries Van Leemput, Armand Naessens, Robbe Elsas, J. Hoebeke, E. D. Poorter

引用次数: 0

Pushing the Limits of Respiration Sensing with Reconfigurable Metasurface 用可重构的超表面推动呼吸传感的极限

Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2021-11-15 DOI: 10.1145/3485730.3492873

Yangfan Zhang, Xiaojing Wang, Chao Feng, Xinyi Li, Yuanming Cai, Yuhui Ren, Fuwei Wang, Ke Li

引用次数: 1

Mercury 汞

Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2021-11-15 DOI: 10.1145/3485730.3485930

Xiao Zeng, M. Yan, Mi Zhang

引用次数: 19

Deep Contextualized Compressive Offloading for Images 图像的深度上下文化压缩卸载

Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2021-11-15 DOI: 10.1145/3485730.3493452

Bo Chen, Zhisheng Yan, Hongpeng Guo, Zhe Yang, A. Ali-Eldin, P. Shenoy, K. Nahrstedt

{"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}

引用次数: 2

Diagnosing Cardiovascular Diseases with Machine Learning on Body Surface Potential Mapping Data 基于体表电位映射数据的机器学习诊断心血管疾病

Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2021-11-15 DOI: 10.1145/3485730.3492883

D. Wójcik, T. Rymarczyk, M. Oleszek, Lukasz Maciura, P. Bednarczuk

引用次数: 2

FedMask FedMask

Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2021-11-15 DOI: 10.1145/3485730.3485929

Ang Li, Jingwei Sun, Xiao Zeng, Mi Zhang, H. Li, Yiran Chen

{"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}

引用次数: 68

Wavoice: A Noise-resistant Multi-modal Speech Recognition System Fusing mmWave and Audio Signals 融合毫米波和音频信号的抗噪声多模态语音识别系统

Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2021-11-15 DOI: 10.1145/3485730.3485945

Tiantian Liu, Ming Gao, Feng Lin, Chao Wang, Zhongjie Ba, Jinsong Han, Wenyao Xu, K. Ren

{"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}

引用次数: 32

Adversarial Attacks against LiDAR Semantic Segmentation in Autonomous Driving 针对自动驾驶激光雷达语义分割的对抗性攻击

Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2021-11-15 DOI: 10.1145/3485730.3485935

Yi Zhu, Chenglin Miao, Foad Hajiaghajani, Mengdi Huai, Lu Su, Chunming Qiao

{"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}

引用次数: 21

Demonstration of an Energy-Aware Task Scheduler for Battery-Less IoT Devices 无电池物联网设备的能量感知任务调度程序演示

Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2021-11-15 DOI: 10.1145/3485730.3493358

Adnan Sabovic, A. Sultania, J. Famaey

引用次数: 2