GetMobile-Mobile Computing & Communications Review最新文献_第5页

PPFL

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GetMobile-Mobile Computing & Communications Review Pub Date : 2022-03-30 DOI: 10.1145/3529706.3529715

Fan Mo, H. Haddadi, Kleomenis Katevas, Eduard Marin, Diego Perino, N. Kourtellis

{"title":"PPFL","authors":"Fan Mo, H. Haddadi, Kleomenis Katevas, Eduard Marin, Diego Perino, N. Kourtellis","doi":"10.1145/3529706.3529715","DOIUrl":"https://doi.org/10.1145/3529706.3529715","url":null,"abstract":"Mobile networks and devices provide the users with ubiquitous connectivity, while many of their functionality and business models rely on data analysis and processing. In this context, Machine Learning (ML) plays a key role and has been successfully leveraged by the different actors in the mobile ecosystem (e.g., application and Operating System developers, vendors, network operators, etc.). Traditional ML designs assume (user) data are collected and models are trained in a centralized location. However, this approach has privacy consequences related to data collection and processing. Such concerns have incentivized the scientific community to design and develop Privacy-preserving ML methods, including techniques like Federated Learning (FL) where the ML model is trained or personalized on user devices close to the data; Differential Privacy, where data are manipulated to limit the disclosure of private information; Trusted Execution Environments (TEE), where most of the computation is run under a secure/ private environment; and Multi-Party Computation, a cryptographic technique that allows various parties to run joint computations without revealing their private data to each other.","PeriodicalId":29918,"journal":{"name":"GetMobile-Mobile Computing & Communications Review","volume":"72 1","pages":"35 - 38"},"PeriodicalIF":1.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78192758","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

The Roaming Edge and its Applications 漫游边缘及其应用

IF 1

GetMobile-Mobile Computing & Communications Review Pub Date : 2022-03-30 DOI: 10.1145/3529706.3529708

Suman Banerjee, Remzi H. Arpaci-Dusseau, Shenghong Dai, Kassem Fawaz, Mohit Gupta, Kangwook Lee, S. Venkataraman

{"title":"The Roaming Edge and its Applications","authors":"Suman Banerjee, Remzi H. Arpaci-Dusseau, Shenghong Dai, Kassem Fawaz, Mohit Gupta, Kangwook Lee, S. Venkataraman","doi":"10.1145/3529706.3529708","DOIUrl":"https://doi.org/10.1145/3529706.3529708","url":null,"abstract":"Connected sensors, e.g., cameras, LiDARs, air sensors, installed in a mobile platform (e.g., a terrestrial or aerial vehicle, or special in-person devices) can provide broad views of wide-area environments quickly and efficiently. If many vehicles incorporate such sensing systems, they together can be composed into a unique crowd-sourced platform and can gather fine-grained, diverse, and noisy information at city-scales. However, these sensors can generate large amounts of data and such data is hard to aggregate in a central server. We explore the design of a \"roaming edge\" - the notion that generalpurpose computing be installed in these mobile platforms, which connect over wireless paths to the static infrastructure and to the static edge nodes, to support a broad range of applications. In particular, a roaming edge node allows different sensors and data sources in-range of a mobile platform to connect to it, and supports data processing for necessary local analytics, considering both efficiency and privacy. The roaming edge, of course, does not operate in isolation and we describe a three-tier architecture that integrates it with a static edge and cloudhosted services. This paper also outlines several applications that can leverage opportunities provided by the roaming edge, and focus, briefly, on one - a real-time video query application with public safety implications.","PeriodicalId":29918,"journal":{"name":"GetMobile-Mobile Computing & Communications Review","volume":"51 1","pages":"5 - 11"},"PeriodicalIF":1.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86924869","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

nn-METER nn-METER

IF 1

GetMobile-Mobile Computing & Communications Review Pub Date : 2022-03-30 DOI: 10.1145/3529706.3529712

L. Zhang, Shihao Han, Jianyu Wei, Ningxin Zheng, Ting Cao, Yunxin Liu

{"title":"nn-METER","authors":"L. Zhang, Shihao Han, Jianyu Wei, Ningxin Zheng, Ting Cao, Yunxin Liu","doi":"10.1145/3529706.3529712","DOIUrl":"https://doi.org/10.1145/3529706.3529712","url":null,"abstract":"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 inference is highly desirable for many tasks where measuring the latency on real devices is infeasible or too costly. 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 DNN inference latency 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. nn-Meter achieves significant high prediction accuracy on four types of edge devices.","PeriodicalId":29918,"journal":{"name":"GetMobile-Mobile Computing & Communications Review","volume":"70 1","pages":"19 - 23"},"PeriodicalIF":1.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81563365","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

zTT

IF 1

GetMobile-Mobile Computing & Communications Review Pub Date : 2022-03-30 DOI: 10.1145/3529706.3529714

Seyeon Kim, Kyung Bin, Sangtae Ha, Kyunghan Lee, S. Chong

引用次数: 0

Innovative Human Motion Sensing With Earbuds 创新的人体运动感应耳机

IF 1

GetMobile-Mobile Computing & Communications Review Pub Date : 2022-03-30 DOI: 10.1145/3529706.3529713

Dong Ma, Andrea Ferlini, C. Mascolo

引用次数: 3

OP-VENT OP-VENT

IF 1

GetMobile-Mobile Computing & Communications Review Pub Date : 2022-03-30 DOI: 10.1145/3529706.3529710

W. Dally

引用次数: 1

Efficient Wideband Spectrum Sensing Using Mems Acoustic Resonators 基于Mems声学谐振器的高效宽带频谱传感

IF 1

GetMobile-Mobile Computing & Communications Review Pub Date : 2022-01-07 DOI: 10.1145/3511285.3511293

Junfeng Guan, Jitian Zhang, Ruochen Lu, Hyungjoo Seo, Jin Zhou, S. Gong, Haitham Hassanieh

引用次数: 6

Toward Polymorphic Internet of Things Receivers Through Real-Time Waveform-Level Deep Learning 通过实时波形级深度学习实现多态物联网接收器

IF 1

GetMobile-Mobile Computing & Communications Review Pub Date : 2022-01-07 DOI: 10.1145/3511285.3511294

Francesco Restuccia, T. Melodia

{"title":"Toward Polymorphic Internet of Things Receivers Through Real-Time Waveform-Level Deep Learning","authors":"Francesco Restuccia, T. Melodia","doi":"10.1145/3511285.3511294","DOIUrl":"https://doi.org/10.1145/3511285.3511294","url":null,"abstract":"Wireless systems such as the Internet of Things (IoT) are changing the way we interact with the cyber and the physical world. As IoT systems become more and more pervasive, it is imperative to design wireless protocols that can effectively and efficiently support IoT devices and operations. On the other hand, today's IoT wireless systems are based on inflexible designs, which makes them inefficient and prone to a variety of wireless attacks. In this paper, we introduce the new notion of a deep learning-based polymorphic IoT receiver, able to reconfigure its waveform demodulation strategy itself in real time, based on the inferred waveform parameters. Our key innovation is the introduction of a novel embedded deep learning architecture that enables the solution of waveform inference problems, which is then integrated into a generalized hardware/software architecture with radio components and signal processing. Our polymorphic wireless receiver is prototyped on a custom-made software-defined radio platform. We show through extensive over-the-air experiments that the system achieves throughput within 87% of a perfect-knowledge Oracle system, thus demonstrating for the first time that polymorphic receivers are feasible.","PeriodicalId":29918,"journal":{"name":"GetMobile-Mobile Computing & Communications Review","volume":"6 1","pages":"28 - 33"},"PeriodicalIF":1.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74984299","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

Expanding the Horizons of Wireless Sensing 拓展无线传感的视野

IF 1

GetMobile-Mobile Computing & Communications Review Pub Date : 2022-01-07 DOI: 10.1145/3511285.3511296

Agrim Gupta, C. Girerd, Manideep Dunna, Qiming Zhang, Raghav Subbaraman, Tania. K. Morimoto, Dinesh Bharadia

{"title":"Expanding the Horizons of Wireless Sensing","authors":"Agrim Gupta, C. Girerd, Manideep Dunna, Qiming Zhang, Raghav Subbaraman, Tania. K. Morimoto, Dinesh Bharadia","doi":"10.1145/3511285.3511296","DOIUrl":"https://doi.org/10.1145/3511285.3511296","url":null,"abstract":"All interactions of objects, humans, and machines with the physical world are via contact forces. For instance, objects placed on a table exert their gravitational forces, and the contact interactions via our hands/feet are guided by the sense of contact force felt by our skin. Thus, the ability to sense the contact forces can allow us to measure all these ubiquitous interactions, enabling a myriad of applications. Furthermore, force sensors are a critical requirement for safer surgeries, which require measuring complex contact forces experienced as a surgical instrument interacts with the surrounding tissues during the surgical procedure. However, with currently available discrete point-force sensors, which require a battery to sense the forces and communicate the readings wirelessly, these ubiquitous sensing and surgical sensing applications are not practical. This motivates the development of new force sensors that can sense, and communicate wirelessly without consuming significant power to enable a battery-free design. In this magazine article, we present WiForce, a low-power wireless force sensor utilizing a joint sensing-communication paradigm. That is, instead of having separate sensing and communication blocks, WiForce directly transduces the force measurements onto variations in wireless signals reflecting WiForce from the sensor. This novel trans-duction mechanism also allows WiForce to generalize easily to a length continuum, where we can detect as well as localize forces acting on the continuum. We fabricate and test our sensor prototype in different scenarios, including testing beneath a tissue phantom, and obtain sub-N sensing and sub-mm localizing accuracies (0.34 N and 0.6 mm, respectively).","PeriodicalId":29918,"journal":{"name":"GetMobile-Mobile Computing & Communications Review","volume":"6 1","pages":"38 - 42"},"PeriodicalIF":1.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76407758","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

When Tiny Goes Big 当小人物变大

IF 1

GetMobile-Mobile Computing & Communications Review Pub Date : 2022-01-07 DOI: 10.1145/3511285.3511289

Rakesh Kumar

引用次数: 0