Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems最新文献_第8页

An efficient CNN model for transportation mode sensing 一种高效的交通方式感知CNN模型

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275160

Ritiz Tambi, Paul Li, Jun Yang

引用次数: 7

Real-Time Emotion Detection via E-See E-See的实时情绪检测

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275213

Weixi Gu, Yue Zhang, Fei Ma, K. Mosalam, Lin Zhang, S. Ni

引用次数: 2

Human Activity Recognition Using Motion Sensors 使用运动传感器识别人类活动

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275198

S. Jeong, T. Kim, Mehmet Rasit Eskicioglu

引用次数: 1

Sleep Position Management System for Enhancing Sleep Quality using Wearable Devices 使用可穿戴设备提高睡眠质量的睡眠姿势管理系统

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275185

Sanghoon Jeon, Anand Paul, S. Son, Y. Eun

引用次数: 0

Automatic Face Recognition Adaptation via Ambient Wireless Identifiers 通过环境无线标识符自动人脸识别适应

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275191

Chris Xiaoxuan Lu, Peijun Zhao, Bowen Du, Hongkai Wen, A. Markham, Stefano Rosa, A. Trigoni

引用次数: 0

SALMA 萨尔玛

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3274844

Bernhard Großwindhager, M. Rath, Josef Kulmer, M. Bakr, Carlo Alberto Boano, K. Witrisal, K. Römer

{"title":"SALMA","authors":"Bernhard Großwindhager, M. Rath, Josef Kulmer, M. Bakr, Carlo Alberto Boano, K. Witrisal, K. Römer","doi":"10.1145/3274783.3274844","DOIUrl":"https://doi.org/10.1145/3274783.3274844","url":null,"abstract":"Setting up indoor localization systems is often excessively time-consuming and labor-intensive, because of the high amount of anchors to be carefully deployed or the burdensome collection of fingerprints. In this paper, we present SALMA, a novel low-cost UWB-based indoor localization system that makes use of only one anchor and that does neither require prior calibration nor training. By using only a crude floor plan and by exploiting multipath reflections, SALMA can accurately determine the position of a mobile tag using a single anchor, hence minimizing the infrastructure costs, as well as the setup time. We implement SALMA on off-the-shelf UWB devices based on the Decawave DW1000 transceiver and show that, by making use of multiple directional antennas, SALMA can also resolve ambiguities due to overlapping multipath components. An experimental evaluation in an office environment with clear line-of-sight has shown that 90% of the position estimates obtained using SALMA exhibit less than 20 cm error, with a median below 8 cm. We further study the performance of SALMA in the presence of obstructed line-of-sight conditions, moving objects and furniture, as well as in highly dynamic environments with several people moving around, showing that the system can sustain decimeter-level accuracy with a worst-case average error below 34 cm.","PeriodicalId":156307,"journal":{"name":"Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems","volume":"102 21","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113945405","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: 58

sKey

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275162

Ke Lin, Siyao Cheng, Jianzhong Li, Yang Zhang, Hong Gao

引用次数: 0

Deep Neural Network-based Telco Outdoor Localization 基于深度神经网络的电信户外定位

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275156

Yige Zhang, Weixiong Rao, Yu Xiao

引用次数: 9

ShieldScatter ShieldScatter

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3274841

Zhiqing Luo, Wei Wang, J. Qu, Tao Jiang, Qian Zhang

{"title":"ShieldScatter","authors":"Zhiqing Luo, Wei Wang, J. Qu, Tao Jiang, Qian Zhang","doi":"10.1145/3274783.3274841","DOIUrl":"https://doi.org/10.1145/3274783.3274841","url":null,"abstract":"The lightweight protocols and low-power radio technologies open up many opportunities to facilitate Internet-of-Things (IoT) into our daily life, while their minimalist design also makes IoT devices vulnerable to many active attacks due to the lack of sophisticated security protocols. Recent advances advocate the use of an antenna array to extract fine-grained physical-layer signatures to mitigate these active attacks. However, it adds burdens in terms of energy consumption and hardware cost that IoT devices cannot afford. To overcome this predicament, we present ShieldScatter, a lightweight system that attaches battery-free backscatter tags to single-antenna devices to shield the system from active attacks. The key insight of ShieldScatter is to intentionally create multi-path propagation signatures with the careful deployment of backscatter tags. These signatures can be used to construct a sensitive profile to identify the location of the signals' arrival, and thus detect the threat. We prototype ShieldScatter with USRPs and ambient backscatter tags to evaluate our system in various environments. The experimental results show that even when the attacker is located only 15 cm away from the legitimate device, ShieldScatter with merely three backscatter tags can mitigate 97% of spoofing attack attempts while at the same time trigger false alarms on just 7% of legitimate traffic.","PeriodicalId":156307,"journal":{"name":"Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122830613","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: 22

CapBand

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3274854

Hoang Truong, Shuo Zhang, Ufuk Muncuk, Phuc Nguyen, Nam Bui, Anh Nguyen, Q. Lv, K. Chowdhury, Thang Dinh, Tam N. Vu

{"title":"CapBand","authors":"Hoang Truong, Shuo Zhang, Ufuk Muncuk, Phuc Nguyen, Nam Bui, Anh Nguyen, Q. Lv, K. Chowdhury, Thang Dinh, Tam N. Vu","doi":"10.1145/3274783.3274854","DOIUrl":"https://doi.org/10.1145/3274783.3274854","url":null,"abstract":"We present CapBand, a battery-free hand gesture recognition wearable in the form of a wristband. The key challenges in creating such a system are (1) to sense useful hand gestures at ultra-low power so that the device can be powered by the limited energy harvestable from the surrounding environment and (2) to make the system work reliably without requiring training every time a user puts on the wristband. We present successive capacitance sensing, an ultra-low power sensing technique, to capture small skin deformations due to muscle and tendon movements on the user's wrist, which corresponds to specific groups of wrist muscles representing the gestures being performed. We build a wrist muscles-to-gesture model, based on which we develop a hand gesture classification method using both motion and static features. To eliminate the need for per-usage training, we propose a kernel-based on-wrist localization technique to detect the CapBand's position on the user's wrist. We prototype CapBand with a custom-designed capacitance sensor array on two flexible circuits driven by a custom-built electronic board, a heterogeneous material-made, deformable silicone band, and a custom-built energy harvesting and management module. Evaluations on 20 subjects show 95.0% accuracy of gesture recognition when recognizing 15 different hand gestures and 95.3% accuracy of on-wrist localization.","PeriodicalId":156307,"journal":{"name":"Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127815621","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: 10