Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems最新文献_第4页

Material Identification with Commodity Wi-Fi Devices 商品Wi-Fi设备的材料识别

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275194

Chao Feng, Xinyi Li, Liqiong Chang, Jie Xiong, Xiaojiang Chen, Dingyi Fang, Baoying Liu, Feng Chen, Zhang Tao

引用次数: 5

Exploiting WiFi Guard Band for Safeguarded ZigBee 利用WiFi保护带实现安全ZigBee

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3274835

Yoon Chae, Shuai Wang, S. Kim

引用次数: 25

Automatic Detection of Minimal Repeated Pattern in Printing Fabric Images 印花织物图像中最小重复图案的自动检测

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275178

Jian Zhang, Heng Zhang, Yanlong Wang, Liling Bo, Jing Sun

引用次数: 1

ECRT ECRT

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275199

Zhihe Zhao, Zhehao Jiang, Neiwen Ling, Xian Shuai, Guoliang Xing

引用次数: 26

HATBED HATBED

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275166

Yi Li, Junyan Ma, Te Zhang

引用次数: 1

Using Deep Learning to Count Garbage Bags 使用深度学习计算垃圾袋

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275167

K. Mikami, Yin Chen, J. Nakazawa

引用次数: 6

EXIMIUS

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3274850

Zhou Qin, Zhihan Fang, Yunhuai Liu, Chang Tan, Wei Chang, Desheng Zhang

{"title":"EXIMIUS","authors":"Zhou Qin, Zhihan Fang, Yunhuai Liu, Chang Tan, Wei Chang, Desheng Zhang","doi":"10.1145/3274783.3274850","DOIUrl":"https://doi.org/10.1145/3274783.3274850","url":null,"abstract":"Urban traffic sensing has been investigated extensively by different real-time sensing approaches due to important applications such as navigation and emergency services. Basically, the existing traffic sensing approaches can be classified into two categories, i.e., explicit and implicit sensing. In this paper, we design a measurement framework called EXIMIUS for a large-scale data-driven study to investigate the strengths and weaknesses of these two sensing approaches by using two particular systems for traffic sensing as concrete examples, i.e., a vehicular system as a crowdsourcing-based explicit sensing and a cellular system as an infrastructure-based implicit sensing. In our investigation, we utilize TB-level data from two systems: (i) vehicle GPS data from 3 thousand private cars and 2 thousand commercial vehicles, (ii) cellular signaling data from 3 million cellphone users, from the Chinese city Hefei. Our study adopts a widely-used concept called crowdedness level to rigorously explore the impacts of various spatiotemporal contexts on real-time traffic conditions including population density, region functions, road categories, rush hours, etc. based on a wide range of context data. We quantify the strengths and weaknesses of these two sensing approaches in different scenarios then we explore the possibility of unifying these two sensing approaches for better performance. Our results provide a few valuable insights for urban sensing based on explicit and implicit data from transportation and telecommunication domains.","PeriodicalId":156307,"journal":{"name":"Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems","volume":"136 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":"123592258","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

IoT for the Power Industry: Recent Advances and Future Directions with Pavatar 电力行业的物联网:Pavatar的最新进展和未来方向

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275179

Yuan He, Junchen Guo, Long Liu, Haozhen Liu, Xinpeng Zhang, Qilong Zhao, Xiaolong Zheng, Meng Jin, Ting Wang, Chunya Liu, Yao Luo, Songzhen Yang, Chengkun Jiang, Xiuzhen Guo, Zihao Yu

引用次数: 4

Mixer 混合机

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3274849

Carsten Herrmann, Fabian Mager, Marco Zimmerling

引用次数: 28

E-Eye: Hidden Electronics Recognition through mmWave Nonlinear Effects 电子眼:毫米波非线性效应下的隐藏电子识别

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3274833

Zhengxiong Li, Zhuolin Yang, Chen Song, Changzhi Li, Zhengyu Peng, Wenyao Xu

{"title":"E-Eye: Hidden Electronics Recognition through mmWave Nonlinear Effects","authors":"Zhengxiong Li, Zhuolin Yang, Chen Song, Changzhi Li, Zhengyu Peng, Wenyao Xu","doi":"10.1145/3274783.3274833","DOIUrl":"https://doi.org/10.1145/3274783.3274833","url":null,"abstract":"While malicious attacks on electronic devices (e-devices) have become commonplace, the use of e-devices themselves for malicious attacks has increased (e.g., explosives and eavesdropping). Modern e-devices (e.g., spy cameras, bugs or concealed weapons) can be sealed in parcels/boxes, hidden under clothing or disguised with cardboard to conceal their identities (named as hidden e-devices hereafter), which brings challenges in security screening. Inspection equipment (e.g., X-ray machines) is bulky and expensive. Moreover, screening reliability still rests on human performance, and the throughput in security screening of passengers and luggages is very limited. To this end, we propose to develop a low-cost and practical hidden e-device recognition technique to enable efficient screenings for threats of hidden electronic devices in daily life. First, we investigate and model the characteristics of nonlinear effects, a special passive response of electronic devices under millimeter-wave (mmWave) sensing. Based on this theory and our preliminary experiments, we design and implement, E-Eye, an end-to-end portable hidden electronics recognition system. E-Eye comprises a low-cost (i.e., under $100), portable (i.e., 11.8cm by 4.5cm by 1.8cm) and light-weight (i.e., 45.5g) 24GHz mmWave probe and a smartphone-based e-device recognizer. To validate the E-Eye performance, we conduct experiments with 46 commodity electronic devices under 39 distinct categories. Results show that E-Eye can recognize hidden electronic devices in parcels/boxes with an accuracy of more than 99% and has an equal error rate (EER) approaching 0.44% under a controlled lab setup. Moreover, we evaluate the reliability, robustness and performance variation of E-Eye under various real-world circumstances, and E-Eye can still achieve accuracy over 97%. Intensive evaluation indicates that E-Eye is a promising solution for hidden electronics recognition in daily life.","PeriodicalId":156307,"journal":{"name":"Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems","volume":"40 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":"132354953","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: 36