The 25th Annual International Conference on Mobile Computing and Networking最新文献

Poster: Edge-cloud Enhancement -- Latency-aware Virtual Cluster Placement for Supporting Cloud Applications in Mobile Edge Networks 海报:边缘云增强——在移动边缘网络中支持云应用的延迟感知虚拟集群布局

The 25th Annual International Conference on Mobile Computing and Networking Pub Date : 2019-10-11 DOI: 10.1145/3300061.3343388

Xuan Liu, B. Cheng, Mei Wang, Junliang Chen

引用次数: 0

Poster 海报

The 25th Annual International Conference on Mobile Computing and Networking Pub Date : 2019-10-11 DOI: 10.1145/3300061.3343410

Royson Lee, Stylianos I. Venieris, Łukasz Dudziak, S. Bhattacharya, Nicholas D. Lane

{"title":"Poster","authors":"Royson Lee, Stylianos I. Venieris, Łukasz Dudziak, S. Bhattacharya, Nicholas D. Lane","doi":"10.1145/3300061.3343410","DOIUrl":"https://doi.org/10.1145/3300061.3343410","url":null,"abstract":"In recent years, convolutional networks have demonstrated unprecedented performance in the image restoration task of super-resolution (SR). SR entails the upscaling of a single low-resolution image in order to meet application-specific image quality demands and plays a key role in mobile devices. To comply with privacy regulations and reduce the overhead of cloud computing, executing SR models locally on-device constitutes a key alternative approach. Nevertheless, the excessive compute and memory requirements of SR workloads pose a challenge in mapping SR networks on resource-constrained mobile platforms. This work presents MobiSR, a novel framework for performing efficient super-resolution on-device. Given a target mobile platform, the proposed framework considers popular model compression techniques and traverses the design space to reach the highest performing trade-off between image quality and processing speed. At run time, a novel scheduler dispatches incoming image patches to the appropriate model-engine pair based on the patch's estimated upscaling difficulty in order to meet the required image quality with minimum processing latency. Quantitative evaluation shows that the proposed framework yields on-device SR designs that achieve an average speedup of 2.13x over highly-optimized parallel difficulty-unaware mappings and 4.79x over highly-optimized single compute engine implementations.","PeriodicalId":223523,"journal":{"name":"The 25th Annual International Conference on Mobile Computing and Networking","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127124693","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: 11

RNN-Based Room Scale Hand Motion Tracking 基于rnn的房间尺度手部运动跟踪

The 25th Annual International Conference on Mobile Computing and Networking Pub Date : 2019-10-11 DOI: 10.1145/3300061.3345439

Wenguang Mao, Mei Wang, Wei Sun, L. Qiu, S. Pradhan, Yi-Chao Chen

{"title":"RNN-Based Room Scale Hand Motion Tracking","authors":"Wenguang Mao, Mei Wang, Wei Sun, L. Qiu, S. Pradhan, Yi-Chao Chen","doi":"10.1145/3300061.3345439","DOIUrl":"https://doi.org/10.1145/3300061.3345439","url":null,"abstract":"Smart speakers allow users to interact with home appliances using voice commands and are becoming increasingly popular. While voice-based interface is intuitive, it is insufficient in many scenarios, such as in noisy or quiet environments, for users with language barriers, or in applications that require continuous motion tracking. Motion-based control is attractive and complementary to existing voice-based control. However, accurate and reliable room-scale motion tracking poses a significant challenge due to low SNR, interference, and varying mobility. To this end, we develop a novel recurrent neural network (RNN) based system that uses speakers and microphones to realize accurate room-scale tracking. Our system jointly estimates the propagation distance and angle-of-arrival (AoA) of signals reflected by the hand, based on AoA-distance profiles generated by 2D MUSIC. We design a series of techniques to significantly enhance the profile quality under low SNR. We feed the profiles in a recent history to our RNN to estimate the distance and AoA. In this way, we can exploit the temporal structure among consecutive profiles to remove the impact of noise, interference and mobility. Using extensive evaluation, we show our system achieves 1.2--3.7~cm error within 4.5~m range, supports tracking multiple users, and is robust against ambient sound. To our knowledge, this is the first acoustic device-free room-scale tracking system.","PeriodicalId":223523,"journal":{"name":"The 25th Annual International Conference on Mobile Computing and Networking","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128225363","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: 59

Keep Others from Peeking at Your Mobile Device Screen! 不要让别人偷看你的手机屏幕!

The 25th Annual International Conference on Mobile Computing and Networking Pub Date : 2019-10-11 DOI: 10.1145/3300061.3343384

Chun-Yu Chen, Bo-Yao Lin, Junding Wang, K. Shin

引用次数: 0

mQRCode mQRCode

The 25th Annual International Conference on Mobile Computing and Networking Pub Date : 2019-10-11 DOI: 10.1145/3300061.3345428

Hao Pan, Yi-Chao Chen, Lanqing Yang, Guangtao Xue, Chuang-Wen You, Xiaoyu Ji

引用次数: 13

Demo 演示

The 25th Annual International Conference on Mobile Computing and Networking Pub Date : 2019-10-11 DOI: 10.1145/3300061.3343377

Jayanth Shenoy, Aditya Tyagi, Meha Halabe, C. Julien

{"title":"Demo","authors":"Jayanth Shenoy, Aditya Tyagi, Meha Halabe, C. Julien","doi":"10.1145/3300061.3343377","DOIUrl":"https://doi.org/10.1145/3300061.3343377","url":null,"abstract":"We present a demonstration of Jive (Joint Integration of VLC and Encryption), a novel encryption key sharing framework utilizing the emerging wireless technology Visible Light Communication (VLC). Based on the idea of transmitting data by modulating light, we are able to (1) share a secret key within a constrained physical space and (2) leverage this shared key to communicate encrypted information among co-located mobile devices. In this demonstration, we showcase our complete implementation of Jive: a VLC transmitter and a VLC receiver. Both endpoints are built using off-the-shelf components. The VLC link is used to distribute a randomly generated secret key that can only be \"observed\" by VLC receivers that are physically in the same space as the transmitter. Each receiver is connected over a serial connection to an Android device; we developed applications for Android that take the key from the VLC receiver and subsequently use the key to encrypt or decrypt application data. Our demo invites participants to create their own encrypted messages in the Android application and interact with the VLC prototype as it transmits encryption keys, thus illustrating our system's ability to bootstrap security among physically co-located devices.","PeriodicalId":223523,"journal":{"name":"The 25th Annual International Conference on Mobile Computing and Networking","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114054969","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: 0

Extracting 3D Maps from Crowdsourced GNSS Skyview Data 从众包GNSS Skyview数据中提取3D地图

The 25th Annual International Conference on Mobile Computing and Networking Pub Date : 2019-10-11 DOI: 10.1145/3300061.3345456

João G. P. Rodrigues, Ana Aguiar

{"title":"Extracting 3D Maps from Crowdsourced GNSS Skyview Data","authors":"João G. P. Rodrigues, Ana Aguiar","doi":"10.1145/3300061.3345456","DOIUrl":"https://doi.org/10.1145/3300061.3345456","url":null,"abstract":"3D maps of urban environments are useful in various fields ranging from cellular network planning to urban planning and climatology. These models are typically constructed using expensive techniques such as manual annotation with 3D modeling tools, extrapolated from satellite or aerial photography, or using specialized hardware with depth sensing devices. In this work, we show that 3D urban maps can be extracted from standard GNSS data, by analyzing the received satellite signals that are attenuated by obstacles, such as buildings. Furthermore, we show that these models can be extracted from low-accuracy GNSS data, crowdsourced opportunistically from standard smartphones during their user's uncontrolled daily commute trips, unleashing the potential of applying the principle to wide areas. Our proposal incorporates position inaccuracies in the calculations, and accommodates different sources of variability of the satellite signals' SNR. The diversity of collection conditions of crowdsourced GNSS positions is used to mitigate bias and noise from the data. A binary classification model is trained and evaluated on multiple urban scenarios using data crowdsourced from over 900 users. Our results show that the generalization accuracy for a Random Forest classifier in typical urban environments lies between 79% and 91% on 4 m wide voxels, demonstrating the potential of the proposed method for building 3D maps for wide urban areas.","PeriodicalId":223523,"journal":{"name":"The 25th Annual International Conference on Mobile Computing and Networking","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120935146","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: 6

Poster 海报

The 25th Annual International Conference on Mobile Computing and Networking Pub Date : 2019-10-11 DOI: 10.1145/3300061.3343386

Zhenggui Xiang

引用次数: 0

Occlumency 大脑封闭术

The 25th Annual International Conference on Mobile Computing and Networking Pub Date : 2019-10-11 DOI: 10.1145/3300061.3345447

Taegyeong Lee, Zhiqi Lin, Saumay Pushp, Caihua Li, Yunxin Liu, Youngki Lee, Fengyuan Xu, Chenren Xu, Lintao Zhang, Junehwa Song

{"title":"Occlumency","authors":"Taegyeong Lee, Zhiqi Lin, Saumay Pushp, Caihua Li, Yunxin Liu, Youngki Lee, Fengyuan Xu, Chenren Xu, Lintao Zhang, Junehwa Song","doi":"10.1145/3300061.3345447","DOIUrl":"https://doi.org/10.1145/3300061.3345447","url":null,"abstract":"Deep-learning (DL) is receiving huge attention as enabling techniques for emerging mobile and IoT applications. It is a common practice to conduct DNN model-based inference using cloud services due to their high computation and memory cost. However, such a cloud-offloaded inference raises serious privacy concerns. Malicious external attackers or untrustworthy internal administrators of clouds may leak highly sensitive and private data such as image, voice and textual data. In this paper, we propose Occlumency, a novel cloud-driven solution designed to protect user privacy without compromising the benefit of using powerful cloud resources. Occlumency leverages secure SGX enclave to preserve the confidentiality and the integrity of user data throughout the entire DL inference process. DL inference in SGX enclave, however, impose a severe performance degradation due to limited physical memory space and inefficient page swapping. We designed a suite of novel techniques to accelerate DL inference inside the enclave with a limited memory size and implemented Occlumency based on Caffe. Our experiment with various DNN models shows that Occlumency improves inference speed by 3.6x compared to the baseline DL inference in SGX and achieves a secure DL inference within 72% of latency overhead compared to inference in the native environment.","PeriodicalId":223523,"journal":{"name":"The 25th Annual International Conference on Mobile Computing and Networking","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115035702","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

Poster 海报

The 25th Annual International Conference on Mobile Computing and Networking Pub Date : 2019-10-11 DOI: 10.1145/3300061.3343396

Namwon An, Hyuk Lim

引用次数: 2