Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems最新文献_第5页

Virtual Keyboard for Wearable Wristbands 可穿戴腕带的虚拟键盘

Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems Pub Date : 2017-11-06 DOI: 10.1145/3131672.3136984

Wenqiang Chen, Y. Lian, Lu Wang, Rukhsana Ruby, Wen Hu, Kaishun Wu

引用次数: 9

The Future of Sensing is Batteryless, Intermittent, and Awesome 传感的未来是无电池的、间歇性的、令人敬畏的

Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems Pub Date : 2017-11-06 DOI: 10.1145/3131672.3131699

Josiah D. Hester, Jacob M. Sorber

{"title":"The Future of Sensing is Batteryless, Intermittent, and Awesome","authors":"Josiah D. Hester, Jacob M. Sorber","doi":"10.1145/3131672.3131699","DOIUrl":"https://doi.org/10.1145/3131672.3131699","url":null,"abstract":"Sensing has been obsessed with delivering on the \"smart dust\" vision outlined decades ago, where trillions of tiny invisible computers support daily life, infrastructure, and humanity in general. Batteries are the single greatest threat to this vision of a sustainable Internet of Things. They are expensive, bulky, hazardous, and wear out after a few years (even rechargeables). Replacing and disposing of billions or trillions of dead batteries per year would be expensive and irresponsible. By leaving the batteries behind and surviving off energy harvested from the environment, tiny intermittently powered computers can monitor objects in hard to reach places maintenance free for decades. The intermittent execution, constrained compute and energy resources, and unreliability of these devices creates new challenges for the sensing and embedded systems community. However, the rewards and potential impact across many fields are worth it, enabling currently impractical applications in health services and patient care, commercial and consumer applications, wildlife conservation, industrial and infrastructure management, even space exploration. This paper highlights major research questions and establishes new directions for the community to embrace and investigate.","PeriodicalId":424262,"journal":{"name":"Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121670795","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: 140

AquaMote

Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems Pub Date : 2017-11-06 DOI: 10.1145/3131672.3136992

Eun Sun Lee, J. Jeyakumar, Bharathan Balaji, R. P. Wilson, Mani Srivastava

引用次数: 3

CamForensics: Understanding Visual Privacy Leaks in the Wild CamForensics:了解野外视觉隐私泄露

Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems Pub Date : 2017-11-06 DOI: 10.1145/3131672.3131683

Animesh Srivastava, Puneet Jain, Soteris Demetriou, Landon P. Cox, Kyu-Han Kim

引用次数: 10

Ultra-Low Power Gaze Tracking for Virtual Reality 虚拟现实的超低功耗注视跟踪

Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems Pub Date : 2017-11-06 DOI: 10.1145/3131672.3136989

Tianxing Li, Emmanuel S. Akosah, Qiang Liu, Xia Zhou

引用次数: 1

Monitoring a Person's Heart Rate and Respiratory Rate on a Shared Bed Using Geophones 使用检波器监测共用床上的人的心率和呼吸频率

Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems Pub Date : 2017-11-06 DOI: 10.1145/3131672.3131679

Zhenhua Jia, Amelie Bonde, Sugang Li, Chenren Xu, Jingxian Wang, Yanyong Zhang, R. Howard, Pei Zhang

{"title":"Monitoring a Person's Heart Rate and Respiratory Rate on a Shared Bed Using Geophones","authors":"Zhenhua Jia, Amelie Bonde, Sugang Li, Chenren Xu, Jingxian Wang, Yanyong Zhang, R. Howard, Pei Zhang","doi":"10.1145/3131672.3131679","DOIUrl":"https://doi.org/10.1145/3131672.3131679","url":null,"abstract":"Using geophones to sense bed vibrations caused by ballistic force has shown great potential in monitoring a person's heart rate during sleep. It does not require a special mattress or sheets, and the user is free to move around and change position during sleep. Earlier work has studied how to process the geophone signal to detect heartbeats when a single subject occupies the entire bed. In this study, we develop a system called VitalMon, aiming to monitor a person's respiratory rate as well as heart rate, even when she is sharing a bed with another person. In such situations, the vibrations from both persons are mixed together. VitalMon first separates the two heartbeat signals, and then distinguishes the respiration signal from the heartbeat signal for each person. Our heartbeat separation algorithm relies on the spatial difference between two signal sources with respect to each vibration sensor, and our respiration extraction algorithm deciphers the breathing rate embedded in amplitude fluctuation of the heartbeat signal. We have developed a prototype bed to evaluate the proposed algorithms. A total of 86 subjects participated in our study, and we collected 5084 geophone samples, totaling 56 hours of data. We show that our technique is accurate -- its breathing rate estimation error for a single person is 0.38 breaths per minute (median error is 0.22 breaths per minute), heart rate estimation error when two persons share a bed is 1.90 beats per minute (median error is 0.72 beats per minute), and breathing rate estimation error when two persons share a bed is 2.62 breaths per minute (median error is 1.95 breaths per minute). By varying sleeping posture and mattress type, we show that our system can work in many different scenarios.","PeriodicalId":424262,"journal":{"name":"Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130671787","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: 65

Enabling En-Route Filtering for End-to-End Encrypted CoAP Messages 启用端到端加密CoAP报文的路由过滤

Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems Pub Date : 2017-11-06 DOI: 10.1145/3131672.3136960

Klara Seitz, Sebastian Serth, Konrad-Felix Krentz, C. Meinel

引用次数: 3

Calibrating Time-variant, Device-specific Phase Noise for COTS WiFi Devices 校正时变，设备特定的相位噪声为COTS WiFi设备

Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems Pub Date : 2017-11-06 DOI: 10.1145/3131672.3131695

Jincao Zhu, Youngbin Im, Shivakant Mishra, Sangtae Ha

{"title":"Calibrating Time-variant, Device-specific Phase Noise for COTS WiFi Devices","authors":"Jincao Zhu, Youngbin Im, Shivakant Mishra, Sangtae Ha","doi":"10.1145/3131672.3131695","DOIUrl":"https://doi.org/10.1145/3131672.3131695","url":null,"abstract":"Current COTS WiFi based work on wireless motion sensing extracts human movements such as keystroking and hand motion mainly from amplitude training to classify different types of motions, as obtaining meaningful phase values is very challenging due to time-varying phase noises occurred with the movement. However, the methods based only on amplitude training are not very practical since their accuracy is not environment and location independent. This paper proposes an effective phase noise calibration technique which can be broadly applicable to COTS WiFi based motion sensing. We leverage the fact that multi-path for indoor environment contains certain static paths, such as reflections from wall or static furniture, as well as dynamic paths due to human hand and arm movements. When a hand moves, the phase value of the signal from the hand rotates as the path length changes and causes the superposition of signals over static and dynamic paths in antenna and frequency domain. To evaluate the effectiveness of the proposed technique, we experiment with a prototype system that can track hand gestures in a non-intrusive manner, i.e. users are not equipped with any device, using COTS WiFi devices. Our evaluation shows that calibrated phase values provide much rich, yet robust information on motion tracking -- 80th percentile angle estimation error up to 14 degrees, 80th percentile tracking error up to 15 cm, and its robustness to the environment and the speed of movement.","PeriodicalId":424262,"journal":{"name":"Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131350596","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: 15

An Empirical Study of WiFi-based Radio Tomographic Imaging 基于wifi的射频层析成像的实证研究

Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems Pub Date : 2017-11-06 DOI: 10.1145/3131672.3136983

D. Piumwardane, C. Suduwella, I. Dharmadasa, A. Sayakkara, Prabhash Kumarasinghe, K. Zoysa, C. Keppitiyagama

引用次数: 1

Towards Self-supervised Face Labeling via Cross-modality Association 跨模态关联的自我监督人脸标记研究

Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems Pub Date : 2017-11-06 DOI: 10.1145/3131672.3136991

Chris Xiaoxuan Lu, Xuan Kan, Stefano Rosa, Bowen Du, Hongkai Wen, A. Markham, A. Trigoni

{"title":"Towards Self-supervised Face Labeling via Cross-modality Association","authors":"Chris Xiaoxuan Lu, Xuan Kan, Stefano Rosa, Bowen Du, Hongkai Wen, A. Markham, A. Trigoni","doi":"10.1145/3131672.3136991","DOIUrl":"https://doi.org/10.1145/3131672.3136991","url":null,"abstract":"Face recognition has become the de facto authentication solution in a broad spectrum of applications, from smart buildings, to industrial monitoring and security services. However, in many of those real-world scenarios, tracking or identifying people with facial recognition is extremely challenging due to the variations in the environment such as lighting conditions, camera viewing angles and subject motion. For most of the state-of-the-art face recognition systems, they need to be trained on a large dataset containing a good variety of labelled face images to work well. However, collecting and manually labelling such datasets is difficult and time consuming, probably more so than developing the algorithms. In this paper, we propose a novel framework to automatically label user identities with their face images in smart spaces, exploiting the fact that the users tend to carry their smart devices while seen by the surveillance cameras. We evaluate our method on 10 users in a smart building setting, and the experimental results show that our method can achieve > 0.9 f1 score on average.","PeriodicalId":424262,"journal":{"name":"Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133272350","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: 1