GetMobile Mob. Comput. Commun.最新文献

{"title":"ACTIVITY RECOGNITION ON SMART DEVICES: Dealing with diversity in the wild","authors":"H. Blunck, S. Bhattacharya, Allan Stisen, Thor S. Prentow, M. Kjærgaard, A. Dey, M. Jensen, Tobias Sonne","doi":"10.1145/2972413.2972425","DOIUrl":"https://doi.org/10.1145/2972413.2972425","url":null,"abstract":"Smart consumer devices are omnipresent in our everyday lives. We find novel interesting applications of smart devices, such as mobile phones, tablets, smartwatches and smartglasses, in monitoring personal health, tracking sporting performances, identifying physical activities and obtaining navigation information among many others. These novel applications make use of a large variety of the smart devices' sensors, such as accelerometer, gyroscope and GPS. However, the usefulness of these applications relies often primarily on the abilities of interpreting noisy, and often biased, measurements from said sensors in order to extract high-level context information, such as the activity currently performed by the user.","PeriodicalId":213775,"journal":{"name":"GetMobile Mob. Comput. Commun.","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130359026","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":"Battery-Free Connected Machine Vision with WISPCam","authors":"Saman Naderiparizi, Zerina Kapetanovic, Joshua R. Smith","doi":"10.1145/2972413.2972417","DOIUrl":"https://doi.org/10.1145/2972413.2972417","url":null,"abstract":"Sustained exponential improvements in the energy efficiency of microelectronics has recently enabled us to build battery-free camera systems that are powered entirely by propagating radio waves. This paper describes primitive machine vision applications built using this highly constrained, battery-free camera system. After describing the WISPCam system and its constraints, we show how to use it to capture (relatively) high-resolution images of faces, without ever capturing a full frame at high resolution. This example application illustrates the issues that arise in partitioning a demanding vision application across mobile hardware that is highly constrained in power, storage, computation and communication.","PeriodicalId":213775,"journal":{"name":"GetMobile Mob. Comput. Commun.","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114924889","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":"PowerForecaster: Predicting Power Impact of Mobile Sensing Applications at Pre-Installation Time","authors":"Chulhong Min, Youngki Lee, Chungkuk Yoo, Seungwoo Kang, Inseok Hwang, Junehwa Song","doi":"10.1145/2972413.2972424","DOIUrl":"https://doi.org/10.1145/2972413.2972424","url":null,"abstract":"Excerpted from \"PowerForecaster: Predicting Smartphone Power Impact of Continuous Sensing Applications at Pre-Installation Time\" from Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems with permission. http://dx.doi.org/10.1145/2809695.2809728 © ACM 2015.","PeriodicalId":213775,"journal":{"name":"GetMobile Mob. Comput. Commun.","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122278690","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":"MOBILE SENSING: Retrospectives and Trends","authors":"M. Martonosi","doi":"10.1145/2972413.2972419","DOIUrl":"https://doi.org/10.1145/2972413.2972419","url":null,"abstract":"It is difficult to think back to a time before smartphones existed, with their ubiquitous computing and communication capabilities, and with detailed location sensing easily available from Global Positioning Systems (GPS). In the late 1990s, when my research group began work on mobile sensing, smartphones had not yet been invented. While GPS did exist, GPS receivers were expensive, power-hungry and not widely available. Our first mobile computing project started as a powerefficiency study for a GPS-based interactive campus tour. GPS-based tour applications are familiar now, but were unheard of then, and the physical implementation was a challenge. We used a Palm Pilot PDA (personal digital assistant) connected to an external GPS receiver and an external Wi-Fi card. In those days, PDAs had neither GPS nor any wireless communication capability! Given the bulkiness of the various pieces of our \"app,\" we carried them and their batteries around in a shoebox. Since both the GPS and the radio were quite high power (over 1W), they greatly impacted the system's battery life. Our power-efficiency work explored methods to locally cache maps on the PDA, and to power down modules when not in use.","PeriodicalId":213775,"journal":{"name":"GetMobile Mob. Comput. Commun.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129124684","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 QUARTER CENTURY of User-Centered Design Engineering Project Classes with Multi-Disciplinary Teams","authors":"D. Siewiorek, A. Smailagic","doi":"10.1145/2972413.2972415","DOIUrl":"https://doi.org/10.1145/2972413.2972415","url":null,"abstract":"Carnegie Mellon University has developed and employed a User-Centered Interdisciplinary Concurrent System Design Methodology (UICSM) that takes teams of electrical engineers, mechanical engineers, computer scientists, industrial designers and human computer interaction students that work with an end-user to generate a complete prototype system during a four-month long course. With more than 25 years of use, the last 20 years in a formal class setting, the methodology has proven robust in creating increasingly capable applications leveraging state-of-the-art components. For example, wireless technology bandwidth increased by over four orders of magnitude during the course. UICSM is web-supported and defines intermediary products that document the evolution of the design. These products are posted on the web so that even remote designers and end-users can participate in the design activities. The design methodology proceeds through three phases: conceptual design, detailed design and implementation. End-users critique the design at each phase. In addition, simulated and real application tasks provide further focus for design evaluation. Th e methodology has been used in designing more than three dozen computer systems, with diverse applications ranging from inspection and maintenance of heavy transportation vehicles to augmented reality in manufacturing and plant operations to car/driver interaction. [SSLT 1994; SS 1999; SS 2002a; SS 2002b; SSS 2001; SSMRT 2005].","PeriodicalId":213775,"journal":{"name":"GetMobile Mob. Comput. Commun.","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126242059","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":"MobileHub: No Programmer Effort for Power Efficiency with Sensor Hub","authors":"Haichen Shen, D. Wetherall, A. Balasubramanian, A. LaMarca","doi":"10.1145/2904337.2904342","DOIUrl":"https://doi.org/10.1145/2904337.2904342","url":null,"abstract":"T oday’s smartphones provide a rich sensing platform that developers have used in tens of thousands of mobile applications. Many of these applications require continuous sensing for tasks ranging from simple step counting to more complex fall detection, sleep apnea diagnoses, dangerous driver monitoring and others. Unfortunately, continuous sensing applications are power-hungry. Interestingly, it is neither the sensors nor the computation that make these applications battery drainers. Instead, the main processor needs to be powered on frequently to collect sensor samples, in turn increasing the power consumption [12, 9, 13]. Hardware manufacturers recognize that supporting low-power continuous sensing is crucial. To this end, companies are embedding a low power microcontroller called a sensor hub in their smartphones [11, 10, 2]. Th e sensor hub continuously collects sensor data keeping the higher power main processor idle. In practice, however, sensor hubs fail to deliver on their power-effi ciency promise. Th e problem is in the diffi culty in programming them. For example, to leverage the sensor hub for a fall detection app, the developer not only needs to write the main application, but also needs to program the sensor hub to sense and notify the main application when a fall is detected. Two approaches have been used to make it easier for developers to program a sensor hub: APIs and hardware SDKs. In the API approach [4, 1], a set of important sensor inference functions are exported via high level APIs to the app developers. Th e problem is that the APIs only support a set of predefi ned events or activities such as step counting. Today, a fall detection application cannot use any of the existing APIs to leverage the sensor hub. It is possible that sensor hub APIs will stabilize, but this is unlikely to happen for many years. Consider how location APIs have evolved since the Java Location API (JSR 179) was introduced in 2003. Sensor hubs themselves have regularly been part of phones since 2011, but it is only in 2014 that a small set of sensor APIs are aligning around common functionality. In the meanwhile, ambitious sensing applications such as BeWell [3] cannot leverage the sensor hub for power effi ciency. In the hardware SDK approach, the developer is provided with specialized tools to directly access the sensor hub. For example, TI provides a proprietary TivaWare Sensor Library [5] to allow developers access to functionality not exposed by soft ware APIs. However, MobileHub: No Programmer Effort for Power Efficiency with Sensor Hub Haichen Shen and David Wetherall University of Washington Aruna Balasubramanian Stony Brook University Anthony LaMarca Intel","PeriodicalId":213775,"journal":{"name":"GetMobile Mob. Comput. Commun.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127750500","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 Great Time for Makers","authors":"P. Dutta, Iqbal Mohomed","doi":"10.1145/2904337.2904339","DOIUrl":"https://doi.org/10.1145/2904337.2904339","url":null,"abstract":"LASER CUTTERS First up is laser cutting. Unlike 3D printers, which deposit material one layer at a time (an instance of additive manufacturing), laser cutters use a powerful laser to cut or engrave material (subtractive manufacturing). While laser cutting has been around for some years, your best bet as an independent maker to get access to one was to join a local hackerspace that already had one of these beauties. Not only were these devices heavy and expensive, they required complicated exhaust and ventilation systems. Recently, a successful crowdfunding campaign hatched the Glowforge “3D laser printer”– a laser cutting device targeted for home use by enthusiasts [Figure 1]. The laser isn’t as powerful as some of the larger systems (the basic model comes with a 40W laser while the pro model offers 45W). But this is likely adequate to cut and etch material such as acrylic, wood and leather. What is most exciting is that this is being designed with home use in mind. You still need to deal with fumes – external venting is a default but it also supports a filter attachment that obviates the need for ventilation. With cheap 3D printers, there have been several reported complaints of foul smelling fumes when used in closed spaces (inhaling the odor of plastic burning is likely never a good thing!). Time will tell how well these filtration solutions work in practice.","PeriodicalId":213775,"journal":{"name":"GetMobile Mob. Comput. Commun.","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114862639","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":"SUMMARY REPORT for the NSF workshop on Future Research Infrastructure for the Wireless Edge","authors":"A. Gosain, Suman Banerjee","doi":"10.1145/2904337.2904348","DOIUrl":"https://doi.org/10.1145/2904337.2904348","url":null,"abstract":"Mobile and wireless-based access has transformed the nature of computing, communication and access in the last decade. The number of mobile wireless network-connected devices (i.e. smartphones, wearable computers, Internet of things, etc.) is anticipated to grow exponentially. As these devices become pervasive and the expectations from users continue to grow, researchers and practitioners are experimenting with new mechanisms to deliver at-scale services connecting the distributed cloud directly to end users.","PeriodicalId":213775,"journal":{"name":"GetMobile Mob. Comput. Commun.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123086773","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":"Why and How to Use Phonelab","authors":"Jinghao Shi, Edwin Santos, Geoffrey Challen","doi":"10.1145/2904337.2904350","DOIUrl":"https://doi.org/10.1145/2904337.2904350","url":null,"abstract":"While smartphone app marketplaces have enabled large-scale app-level experimentation, medium-scale experimentation with the platform code implementing the app interface and providing core device services remains difficult for academic researchers. But this is where many of the ideas currently being explored by the mobile systems community must be evaluated---including new networking protocols, security and privacy mechanisms, storage abstractions, and energy management strategies. To enable these experiments, we built and are operating PhoneLab, a 175-smartphone testbed where real users run experimental Android platform builds on their primary devices. We are eager to make PhoneLab useful to the mobile systems community. To aid in this effort, this article discusses why PhoneLab might be useful for your research and provides an overview of how to use the testbed, including examples drawn from our group's current projects.","PeriodicalId":213775,"journal":{"name":"GetMobile Mob. Comput. Commun.","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115618444","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":"Section Editors' Introduction: WHAT IS NEXT FOR MOBILE SENSING?","authors":"R. Kravets, Nic Lane","doi":"10.1145/2904337.2904341","DOIUrl":"https://doi.org/10.1145/2904337.2904341","url":null,"abstract":"8 e now live in a world where forms of mobile sensing – for example, the monitoring of physical activity, sleep habits or commute patterns using a mixture of inertial, location and audio sensors – that until recently were firmly in the domain of research, are now rapidly becoming commonplace application features. They are well known to consumers, present in entry-level watches and phones, and are even available (in some cases) as simple API calls embedded in major mobile OSs. Given such advances in current practice, it is time for us to start thinking of what will be the next generation of key questions that will drive mobile sensing research moving forward. In this issue, we highlight three award-winning papers from ACM UbiComp 2015 – one that received a best paper award and two of which received honorable mentions at the conference. Each of these papers consider emerging topics within mobile sensing and, in this way, contribute to the search for what comes next. We begin with how additional sensory functions can be supported within already tight energy budgets by transparently integrating the use of sensor co-processors. Our next two papers study novel ways in which mobile sensor data may be used, and seek to go beyond increasingly familiar sensing tasks (such as simply counting the number of steps by a user) by automatically monitoring highly complex long-term behavioral outcomes (such as academic success) or quantifying how skilfully an activity (e.g., cooking, painting) is performed, for example. As the application horizons of mobile sensing continue to expand, this evolution will only increase the pressure that exists on the familiar resource bottleneck of mobile battery reserves. In \" MobileHub: No Programmer Effort for Power Efficiency with Sensor Hub \" – one of the winners of Best Paper at UbiComp 2015 – researchers from the University of Washington, Stony Brook University and Intel Research allow applications to benefit from emerging co-processor–based hardware support for low-power sensing. The key innovation is that through a combination of dynamic taint tracking and machine learning, this is achieved transparently to the developer, and requires no modification to existing application code. Although hardware support for sensing promises large leaps in sensor application energy efficiency, conventional approaches to using this hardware require the rewriting of existing sensing algorithms and developers to change the way they interact with sensors within their programs. In \" SmartGPA: How Smartphones can Assess …","PeriodicalId":213775,"journal":{"name":"GetMobile Mob. Comput. Commun.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114303887","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}