Proceedings of the 27th Annual International Conference on Mobile Computing and Networking最新文献

{"title":"Notification privacy protection via unobtrusive gripping hand verification using media sounds","authors":"Long Huang, Chen Wang","doi":"10.1145/3447993.3483277","DOIUrl":"https://doi.org/10.1145/3447993.3483277","url":null,"abstract":"This work proposes a media sound-based authentication method to protect smartphone notification privacy unobtrusively, which wisely hides or presents sensitive content by verifying who is holding the phone. We show that media sounds, such as the melodies of notification tones (e.g., iPhone message and Samsung whistle) can be directly used to sense and verify the user's gripping hand. Because sounds and vibrations co-exist, we capture two novel responses via the smartphone mic and accelerometer to describe how the individual's contacting palm interferes with the signals in two different domains. Based on the two responses, we develop a convolutional neural network-based algorithm to verify the user. Moreover, because the smartphone sensors are all embedded on the same motherboard, we develop a cross-domain method to validate such hard-to-forge physical relationships among the mic, speaker and accelerometer. They prevent external sounds from cheating the system. Additionally, we consider the notification vibration as a special type of media sound, which also results in two responses, and extend our method to work in the silent mode. Extensive experiments with ten notification tones and four phone models show that our system verifies users with 95% accuracy and prevents replay sounds with 100% accuracy.","PeriodicalId":177431,"journal":{"name":"Proceedings of the 27th Annual International Conference on Mobile Computing and Networking","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129983628","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}

{"title":"Visage","authors":"Sagar Jha, Youjie Li, S. Noghabi, V. Ranganathan, Peeyush Kumar, Andrew Nelson, Michael A. Toelle, Sudipta Sinha, Ranveer Chandra, Anirudh Badam","doi":"10.1145/3447993.3483273","DOIUrl":"https://doi.org/10.1145/3447993.3483273","url":null,"abstract":"Analytics with three-dimensional imagery from drones are driving the next generation of remote monitoring applications. Today, there is an unmet need in providing such analytics in an interactive manner, especially over weak Internet connections, to quickly diagnose and solve problems in the commercial industry space of monitoring assets using drones in remote parts of the world. Existing mechanisms either compromise on the quality of insights by not building 3D images and analyze individual 2D images in isolation, or spend tens of minutes building a 3D image before obtaining and uploading insights. We present Visage, a system that accelerates 3D image analytics by identifying smaller parts of the data that can actually benefit from 3D analytics and prioritizing building, and uploading the localized 3D images for those parts. To achieve this, Visage uses a graph to represent raw 2D images and their relative content overlap, and then identifies the various subgraphs using application knowledge that are good candidates for localized 3D image based insights. We evaluate Visage using data from multiple real deployments and show that it can reduce analytics-latency by up to four orders of magnitude.","PeriodicalId":177431,"journal":{"name":"Proceedings of the 27th Annual International Conference on Mobile Computing and Networking","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130059544","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}

{"title":"Crisp-BP: continuous wrist PPG-based blood pressure measurement","authors":"Yetong Cao, Huijie Chen, Fan Li, Yu Wang","doi":"10.1145/3447993.3483241","DOIUrl":"https://doi.org/10.1145/3447993.3483241","url":null,"abstract":"Arterial blood pressure (ABP) monitoring using wearables has emerged as a promising approach to empower users with self-monitoring for effective diagnosis and control of hypertension. However, existing schemes mainly monitor ABP at discrete time intervals, involve some form of user effort, have insufficient accuracy, and require collecting sufficient training data for model development. To tackle these problems, we propose Crisp-BP, a novel ABP monitoring system leveraging the PPG sensor available in commercial wrist-worn devices (e.g., smartwatches or fitness trackers). It enables continuous, accurate, user-independent ABP monitoring and requires no behavior changes during collecting PPG data. The basic idea is to illuminate a skin/tissue, measure the light absorption, and characterize ABP-related blood volume change in the artery. To obtain accurate measurements and relieve the pain of training data collection, we use an arterial pulse extraction method that removes interference caused by capillary pulses. Moreover, we design a contact pressure estimation method to combat the deficiency of PPG waveform being sensitive to the contact pressure between the sensor and the skin. In addition, we leverage the great power of Bidirectional Long Short Term Memory and design a hybrid neural network model to enable user-independent ABP monitoring, so that users do not have to provide training data for model development. Furthermore, we propose a transfer learning method that first extracts general knowledge from online PPG data, then use it to improve the learning of a new model on our target problem. Extensive experiments with 35 participants demonstrate that Crisp-BP obtains the average estimation error of 0.86 mmHg and 1.67 mmHg and the standard deviation error of 6.55 mmHg and 7.31 mmHg for diastolic pressure and systolic pressure, respectively. These errors are within the acceptable range regulated by the FDA's AAMI protocol, which allows average errors of up to 5 mmHg and a standard deviation of up to 8 mmHg. Our results demonstrate that Crisp-BP is promising for improving the diagnosis and control of hypertension as it provides continuousness, comfort, convenience, and accuracy.","PeriodicalId":177431,"journal":{"name":"Proceedings of the 27th Annual International Conference on Mobile Computing and Networking","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127652136","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}

{"title":"RFID and camera fusion for recognition of human-object interactions","authors":"Xiulong Liu, Dongdong Liu, Jiuwu Zhang, Tao Gu, Keqiu Li","doi":"10.1145/3447993.3483244","DOIUrl":"https://doi.org/10.1145/3447993.3483244","url":null,"abstract":"Recognition of human-object interactions is practically important in various human-centric sensing scenarios such as smart supermarket, factory, and home. This paper proposes an RF-Camera system by fusing RFID and Computer Vision (CV) techniques, which is the first work to recognize the human gestural interactions with physical objects in multi-subject and multi-object scenarios. In RF-Camera, we first propose a dimension reduction method to transform the subject's 3D hand trajectory captured by depth camera to a 2D image, using which the subject's gesture can be recognized. We also propose a method to extract the facial image of target subject from an image that may contain irrelevant subjects, thereby further recognizing his/her identity. Finally, we model the physical movements of the held object's tag and further predict the tag phase data, by comparing which with real phase data of each tag human-object matching can be discovered. When implementing RF-Camera, three technical challenges need to be addressed. (i) To remove noisy data corresponding to irrelevant actions from raw sensing data, we propose a state transition diagram to determine the boundary of effective data. (ii) To predict phase data of the held target tag with unknown hand-tag offset, we quantify target tag trajectory by adding a variable hand-tag vector to captured hand trajectory. (iii) To ensure high reading rates of target tags in tag-dense scenarios, we propose a CV-assisted RFID scheduling method, in which analytics on CV data can help schedule RFID readings. We conduct extensive experiments to evaluate the performance of RF-Camera. Experimental results demonstrate that RF-Camera can recognize the gestural actions, human identity and human-object matching with an average accuracy higher than 90% in most cases.","PeriodicalId":177431,"journal":{"name":"Proceedings of the 27th Annual International Conference on Mobile Computing and Networking","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114377072","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}

{"title":"MIXIQ","authors":"Mohammad Rostami, Xingda Chen, Yuda Feng, K. Sundaresan, Deepak Ganesan","doi":"10.1145/3447993.3483270","DOIUrl":"https://doi.org/10.1145/3447993.3483270","url":null,"abstract":"A long-standing challenge in radios for wearables is to design ultra-low power, yet high performance receivers with good sensitivity and spectral efficiency while being compatible with WiFi. The vanilla envelope detector used in standard UHF RFID is the most popular receivers on backscatter tags since they are passive but suffer from poor sensitivity and cannot decode complex modulations, which makes them a poor choice for directly decoding data from WiFi packets. In this paper, we present the design of an easy-to-prototype ultra-low power WiFi receiver called MIXIQ that operates at μWs of power while providing improved sensitivity and decode-ability of complex high-rate signals. MIXIQ uses the signaling capabilities of the newest standard of WiFi, 802.11ax, to turn a standard WiFi packet into a helper + data signal. The same non-linear RF circuit used in a vanilla envelope detector, when driven by this twin signal, now behaves like a passive mixer i.e. it down-converts the RF carrier data to the sub-MHz range without adding any energy overhead. MIXIQ then uses an ultra low-power largely digital baseband pipeline to (i) significantly boost sensitivity using ultra low power components; (ii) enable the demodulation of complex signals for substantial boost in spectral efficiency. We show that MIXIQ improves upon the vanilla envelope detector by 25dB in sensitivity and 89× in bandwidth efficiency, while consuming 0.3mW for a PCB-based implementation and 40μW for CMOS simulation. We also demonstrate a Hearable system that leverages MIXIQ to improve VOIP reception range by 10× compared to a vanilla envelope detector.","PeriodicalId":177431,"journal":{"name":"Proceedings of the 27th Annual International Conference on Mobile Computing and Networking","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125788150","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}

{"title":"A 2-FA for home voice assistants using inaudible acoustic signal","authors":"Shaohu Zhang, Anupam Das","doi":"10.1145/3447993.3482863","DOIUrl":"https://doi.org/10.1145/3447993.3482863","url":null,"abstract":"Voice assistants have been shown to be vulnerable to replay attacks, impersonation attacks and inaudible voice commands. Existing defenses do not provide a practical solution as they either rely on external hardware or work under very constrained settings. We introduce a hand gesture-based authentication system for smart home voice assistants called HandLock, which uses built-in microphones and speakers to generate and sense inaudible acoustic signals to detect the presence of a known hand gesture. Our proposed approach can act as a second-factor authentication (2-FA) for performing specific sensitive operations like confirming online purchases through voice assistants. The experiments involving 45 participants show that HandLock can achieve on average 96.51% true-positive-rate at the expense of 0.82% false-acceptance-rate.","PeriodicalId":177431,"journal":{"name":"Proceedings of the 27th Annual International Conference on Mobile Computing and Networking","volume":"738 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122982613","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}

{"title":"Distracted driving detection by sensing the hand gripping of the phone","authors":"Ruxin Wang, Long Huang, Chen Wang","doi":"10.1145/3447993.3482861","DOIUrl":"https://doi.org/10.1145/3447993.3482861","url":null,"abstract":"Phone usage while driving is unanimously considered a really dangerous habit due to a strong correlation with road accidents. This paper proposes a phone-use monitoring system that detects the driver's handheld phone use and eliminates the distraction at once. Specifically, the proposed system emits periodic ultrasonic pulses to sense if the phone is being held in hand or placed on support surfaces (e.g., seat and cup holder) by capturing the unique signal interference resulted from the contact object's damping, reflection and refraction. We derive the short-time Fourier transform from the microphone data to describe such impacts and develop a CNN-based binary classifier to discriminate the phone use between the handheld and the handsfree status. Additionally, we design a classification error correction filter to correct the classification errors during the monitoring. The experiments with six people, one phone and one car model show that our system achieves 99% accuracy in recognizing handheld phone-use activities.","PeriodicalId":177431,"journal":{"name":"Proceedings of the 27th Annual International Conference on Mobile Computing and Networking","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121811617","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}

{"title":"SecureSIM","authors":"Jinghao Zhao, Boyan Ding, Yunqi Guo, Zhaowei Tan, Songwu Lu","doi":"10.1145/3447993.3483254","DOIUrl":"https://doi.org/10.1145/3447993.3483254","url":null,"abstract":"The SIM/eSIM card stores critical information for a mobile user to access the 4G/5G network. In this work, we uncover three vulnerabilities of the current SIM practice. We show that the PIN-based access control may expose the in-SIM data to an adversary through both hardware and software. Once exposed, such in-SIM information can be used to reconstruct various keys used for device authentication, data encryption, etc. They thus enable a number of attacks, including traffic eavesdropping, man-in-the-middle attack, impersonation, etc. The fundamental problem is that, the current SIM design does not offer proper authentication and fine-grained access control to hundreds of in-SIM files for various in-card applets and off-card units. We next propose a new solution that offers both authentication and fine-grained access control. Our implementation and evaluation have confirmed the viability of our proposal.","PeriodicalId":177431,"journal":{"name":"Proceedings of the 27th Annual International Conference on Mobile Computing and Networking","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123932765","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}

{"title":"RFlens","authors":"Chao Feng, Xinyi Li, Yangfan Zhang, Xiaojing Wang, Liqiong Chang, Fuwei Wang, Xinyu Zhang, Xiaojiang Chen","doi":"10.1145/3447993.3483238","DOIUrl":"https://doi.org/10.1145/3447993.3483238","url":null,"abstract":"Beamforming can improve the communication and sensing capabilities for a wide range of IoT applications. However, most existing IoT devices cannot perform beamforming due to form factor, energy, and cost constraints. This paper presents RFlens, a reconfigurable metasurface that empowers low-profile IoT devices with beamforming capabilities. The metasurface consists of many unit-cells, each acting as a phase shifter for signals going through it. By encoding the phase shifting values, RFlens can manipulate electromagnetic waves to \"reshape\" and resteer the beam pattern. We prototype RFlens for 5 GHz Wi-Fi signals. Extensive experiments demonstrate that RFlens can achieve a 4.6 dB median signal strength improvement (up to 9.3 dB) even with a relatively small 16 × 16 array of unit-cells. In addition, RFlens can effectively improve the secrecy capacity of IoT links and enable passive NLoS wireless sensing applications.","PeriodicalId":177431,"journal":{"name":"Proceedings of the 27th Annual International Conference on Mobile Computing and Networking","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129906047","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}

{"title":"Loki","authors":"Huanhuan Zhang, Anfu Zhou, Yuhan Hu, Chaoyue Li, Guangping Wang, Xinyu Zhang, Huadong Ma, Leilei Wu, Aiyun Chen, Changhui Wu","doi":"10.1007/springerreference_306664","DOIUrl":"https://doi.org/10.1007/springerreference_306664","url":null,"abstract":"","PeriodicalId":177431,"journal":{"name":"Proceedings of the 27th Annual International Conference on Mobile Computing and Networking","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121989781","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}