Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services最新文献_第2页

Commodity-level BLE backscatter 商品级BLE反向散射

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services Pub Date : 2021-06-24 DOI: 10.1145/3458864.3466865

Ma Zhang, Si Chen, Jia Zhao, Wei Gong

{"title":"Commodity-level BLE backscatter","authors":"Ma Zhang, Si Chen, Jia Zhao, Wei Gong","doi":"10.1145/3458864.3466865","DOIUrl":"https://doi.org/10.1145/3458864.3466865","url":null,"abstract":"The communication reliability of state-of-the-art Bluetooth Low Energy (BLE) backscatter systems is fundamentally limited by their modulation schemes because the Binary Frequency Shift Keying (BFSK) modulation of the tag does not exactly match commodity BLE receivers designed for Gauss Frequency Shift Keying (GFSK) modulated signals with high bandwidth efficiency. Gaussian pulse shaping is a missing piece in state-of-the-art BLE backscatter systems. Inspired by active BLE and applying calculus, we present IBLE, a BLE backscatter communication system that achieves full compatibility with commodity BLE devices. IBLE leverages the fact that phase shift is the integral of frequency over time to build a reliable physical layer for BLE backscatter. IBLE uses instantaneous phase shift (IPS) modulation, GFSK modulation, and optional FEC coding to improve the reliability of BLE backscatter communication to the commodity level. We prototype IBLE using various commodity BLE devices and a customized tag with FPGA. Empirical results demonstrate that IBLE achieves PERs of 0.04% and 0.68% when the uplink distances are 2 m and 14 m respectively, which are 280x and 70x lower than the PERs of the state-of-the-art system RBLE. On the premise of meeting the BER requirements of the BLE specification, the uplink range of IBLE is 20 m. Since BLE devices are everywhere, IBLE is readily deployable in our everyday IoT applications.","PeriodicalId":153361,"journal":{"name":"Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131069218","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

Lost and Found!: associating target persons in camera surveillance footage with smartphone identifiers 失物招领处!:将摄像头监控录像中的目标人物与智能手机识别码相关联

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services Pub Date : 2021-06-24 DOI: 10.1145/3458864.3466904

Hansi Liu, Abrar Alali, Mohamed Ibrahim, Hongyu Li, M. Gruteser, Shubham Jain, Kristin J. Dana, A. Ashok, Bin Cheng, Hongsheng Lu

引用次数: 2

SCOPE 范围

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services Pub Date : 2021-06-24 DOI: 10.1787/1028e588-en

Leonardo Bonati, Salvatore D’oro, S. Basagni, T. Melodia

引用次数: 0

A do-it-yourself computer vision based robotic ball throw trainer 一个自己动手的基于计算机视觉的机器人投球训练器

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services Pub Date : 2021-06-24 DOI: 10.1145/3458864.3466909

Bronson Tharpe, A. Bourgeois, A. Ashok

引用次数: 0

Encrypted cloud photo storage using Google photos 加密云照片存储使用谷歌照片

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services Pub Date : 2021-06-24 DOI: 10.1145/3458864.3468220

John S. Koh, Jason Nieh, S. Bellovin

{"title":"Encrypted cloud photo storage using Google photos","authors":"John S. Koh, Jason Nieh, S. Bellovin","doi":"10.1145/3458864.3468220","DOIUrl":"https://doi.org/10.1145/3458864.3468220","url":null,"abstract":"Cloud photo services are widely used for persistent, convenient, and often free photo storage, which is especially useful for mobile devices. As users store more and more photos in the cloud, significant privacy concerns arise because even a single compromise of a user's credentials give attackers unfettered access to all of the user's photos. We have created Easy Secure Photos (ESP) to enable users to protect their photos on cloud photo services such as Google Photos. ESP introduces a new client-side encryption architecture that includes a novel format-preserving image encryption algorithm, an encrypted thumbnail display mechanism, and a usable key management system. ESP encrypts image data such that the result is still a standard format image like JPEG that is compatible with cloud photo services. ESP efficiently generates and displays encrypted thumbnails for fast and easy browsing of photo galleries from trusted user devices. ESP's key management makes it simple to authorize multiple user devices to view encrypted image content via a process similar to device pairing, but using the cloud photo service as a QR code communication channel. We have implemented ESP in a popular Android photos app for use with Google Photos and demonstrate that it is easy to use and provides encryption functionality transparently to users, maintains good interactive performance and image quality while providing strong privacy guarantees, and retains the sharing and storage benefits of Google Photos without any changes to the cloud service.","PeriodicalId":153361,"journal":{"name":"Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121672952","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: 5

LATTE: online MU-MIMO grouping for video streaming over commodity wifi LATTE:通过商品wifi进行视频流的在线MU-MIMO分组

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services Pub Date : 2021-06-24 DOI: 10.1145/3458864.3466913

H. Pasandi, T. Nadeem

引用次数: 10

nn-Meter: towards accurate latency prediction of deep-learning model inference on diverse edge devices n- meter:在不同边缘设备上实现深度学习模型推理的准确延迟预测

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services Pub Date : 2021-06-24 DOI: 10.1145/3458864.3467882

L. Zhang, S. Han, Jianyu Wei, Ningxin Zheng, Ting Cao, Yuqing Yang, Yunxin Liu

{"title":"nn-Meter: towards accurate latency prediction of deep-learning model inference on diverse edge devices","authors":"L. Zhang, S. Han, Jianyu Wei, Ningxin Zheng, Ting Cao, Yuqing Yang, Yunxin Liu","doi":"10.1145/3458864.3467882","DOIUrl":"https://doi.org/10.1145/3458864.3467882","url":null,"abstract":"With the recent trend of on-device deep learning, inference latency has become a crucial metric in running Deep Neural Network (DNN) models on various mobile and edge devices. To this end, latency prediction of DNN model inference is highly desirable for many tasks where measuring the latency on real devices is infeasible or too costly, such as searching for efficient DNN models with latency constraints from a huge model-design space. Yet it is very challenging and existing approaches fail to achieve a high accuracy of prediction, due to the varying model-inference latency caused by the runtime optimizations on diverse edge devices. In this paper, we propose and develop nn-Meter, a novel and efficient system to accurately predict the inference latency of DNN models on diverse edge devices. The key idea of nn-Meter is dividing a whole model inference into kernels, i.e., the execution units on a device, and conducting kernel-level prediction. nn-Meter builds atop two key techniques: (i) kernel detection to automatically detect the execution unit of model inference via a set of well-designed test cases; and (ii) adaptive sampling to efficiently sample the most beneficial configurations from a large space to build accurate kernel-level latency predictors. Implemented on three popular platforms of edge hardware (mobile CPU, mobile GPU, and Intel VPU) and evaluated using a large dataset of 26,000 models, nn-Meter significantly outperforms the prior state-of-the-art.","PeriodicalId":153361,"journal":{"name":"Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128529444","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: 71

MotionCompass

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services Pub Date : 2021-06-24 DOI: 10.1145/3458864.3467683

Yan He, Qiuye He, Song Fang, Yao Liu

{"title":"MotionCompass","authors":"Yan He, Qiuye He, Song Fang, Yao Liu","doi":"10.1145/3458864.3467683","DOIUrl":"https://doi.org/10.1145/3458864.3467683","url":null,"abstract":"Wireless security cameras are integral components of security systems used by military installations, corporations, and, due to their increased affordability, many private homes. These cameras commonly employ motion sensors to identify that something is occurring in their fields of vision before starting to record and notifying the property owner of the activity. In this paper, we discover that the motion sensing action can disclose the location of the camera through a novel wireless camera localization technique we call MotionCompass. In short, a user who aims to avoid surveillance can find a hidden camera by creating motion stimuli and sniffing wireless traffic for a response to that stimuli. With the motion trajectories within the motion detection zone, the exact location of the camera can be then computed. We develop an Android app to implement MotionCompass. Our extensive experiments using the developed app and 18 popular wireless security cameras demonstrate that for cameras with one motion sensor, MotionCompass can attain a mean localization error of around 5 cm with less than 140 seconds. This localization technique builds upon existing work that detects the existence of hidden cameras, to pinpoint their exact location and area of surveillance.","PeriodicalId":153361,"journal":{"name":"Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116796058","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: 9

Microstructure-guided spatial sensing for low-power IoT 面向低功耗物联网的微结构引导空间传感

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services Pub Date : 2021-06-24 DOI: 10.1145/3458864.3466906

Nakul Garg, Yang Bai, Nirupam Roy

引用次数: 5

Owlet: enabling spatial information in ubiquitous acoustic devices Owlet:在无处不在的声学设备中实现空间信息

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services Pub Date : 2021-06-24 DOI: 10.1145/3458864.3467880

Nakul Garg, Yang Bai, Nirupam Roy

{"title":"Owlet: enabling spatial information in ubiquitous acoustic devices","authors":"Nakul Garg, Yang Bai, Nirupam Roy","doi":"10.1145/3458864.3467880","DOIUrl":"https://doi.org/10.1145/3458864.3467880","url":null,"abstract":"This paper presents a low-power and miniaturized design for acoustic direction-of-arrival (DoA) estimation and source localization, called Owlet. The required aperture, power consumption, and hardware complexity of the traditional array-based spatial sensing techniques make them unsuitable for small and power-constrained IoT devices. Aiming to overcome these fundamental limitations, Owlet explores acoustic microstructures for extracting spatial information. It uses a carefully designed 3D-printed metamaterial structure that covers the microphone. The structure embeds a direction-specific signature in the recorded sounds. Owlet system learns the directional signatures through a one-time in-lab calibration. The system uses an additional microphone as a reference channel and develops techniques that eliminate environmental variation, making the design robust to noises and multipaths in arbitrary locations of operations. Owlet prototype shows 3.6° median error in DoA estimation and 10cm median error in source localization while using a 1.5cm × 1.3cm acoustic structure for sensing. The prototype consumes less than 100th of the energy required by a traditional microphone array to achieve similar DoA estimation accuracy. Owlet opens up possibilities of low-power sensing through 3D-printed passive structures.","PeriodicalId":153361,"journal":{"name":"Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122180482","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: 20