关键字 |
| GPS、无线网络、机器学习技术,Android O / S |
介绍 |
| 最近,手机用户的数量越来越多的拥有和使用手机装备由GPS接收器迅速增加,而他们对移动应用程序,使存储、分析和可视化的时空收集信息是明显的。在本文中,我们目前为Android O / S。能源效率的核心位置的是三倍。首先,能源效率的位置收集用户的运动的时空点使用GPS接收器,从而创建和可视化道路用户紧随其后。类似的应用程序相比,收集到的路径,存储在服务器的数据库中,由过滤消毒的地方,躺在用户定义的感兴趣的领域——敏感地区用户不允许被跟踪(例如在家里、医院等)。“手机”为Android O / S的核心是三倍。首先,它收集用户的运动的时空点使用GPS接收器,从而创建和可视化道路用户紧随其后。类似的应用程序相比,收集到的路径,存储在服务器的数据库中,由过滤消毒的地方,躺在用户定义的感兴趣的领域——敏感地区用户不允许被跟踪(例如在家里、医院等)。这种方式,它为最终用户提供了个性化隐私功能。第二个关键功能是手机允许用户注释部分她与标签路径,因此描述当前这样的特性可以被证明是非常有用的自动填写交通科学或执行的调查研究人员活动识别通常局限于使用手动调查处理。第三,它封装了最先进的算法过程以在线的方式接收到的GPS记录和转换成有意义的歌曲,可以存储到数据库的路径进行进一步分析。 Specifically, it includes line simplification methods that compress the incoming stream of time stamped locations (thus reducing the storage cost), which are then partitioned into homogeneous portions according to some spatio-temporal criteria using a state-of the- art segmentation method. According to this segmentation, the path is split into portions (i.e. sub-path, which can be labeled by tags that describe the corresponding spatio-temporal behavior of the user (e.g. STOPPED, when the speed is very low). This is important as it facilitates the user (or some auditing algorithm) to compare her manual annotations with the classified sub-tracks as provided by the segmentation algorithm. The big picture that illustrates these novel features of Easy Tracker is depicted in Fig. 1. |
动机和贡献 |
| 我们通过强调激励当前工作提出系统评估工作。我们将演示能源效率影响因素的基础上位置传感采用手机和总结现有手机使用的限制,防止节能强大。我们数值分析最优传感区间一般手机打开无线网络,描述节能无线传感的关键因素。我们提供WiFi系统考虑的因素识别分析和提供mobility-aware无线传感功能的手机。我们实现WiFi在基于android系统的手机,并评估其性能广泛的室内和室外无线网络测试床。通过真实实验测试床,我们证明了WiFi减少感应频率高达89%,同时保持一个假触发率低。 |
相关工作 |
| 通用类别的目标的人只是喜欢跟踪和可视化的路线,谷歌的Android应用程序最著名的O / S。我们必须注意,这个应用程序是一个相对简单的路径没有高级特性,但是它有超过1000万次的下载。其他类似的应用程序在同一类别更高级的功能,允许用户添加图片或兴趣点和创建向导,可以利用各种各样的地图。另一个范畴是关于应用程序专注于运动/健身。我们发现许多特殊功能,如计算卡路里消耗或心率的测量使用内部或外部传感器。应用程序设计跟踪丢失的设备或装置,属于另一个人(假定许可)。例如,追踪一个移动电话现在是可行的协助下GPS和无线网络。以前收到基站的信号强度变化触发wi - fi扫描。然而,细胞信号强度存在着很大的差别与变化的位置和异构网络环境(例如,发射塔密度)。在使用蓝牙指纹检测APs可用。 However, this requires a training phase to create and maintain the fingerprint database, which could be expensive given the fact that Bluetooth devices are usually highly mobile. In proposed to use a radio to detect Wi-Fi signals in ISM bands. However, this approach requires the use of a second radio, and thus might not be feasible for most mobile phones. In propose to use habitual human mobility to forecast Wi-Fi connectivity. On the other hand, our work focuses more on energy-efficient discovery of Wi-Fi access opportunities. A. Carroll and G. Heiser, “An analysis of power consumption in a mobile phone,” in Proc. USENIX Conf. Annu. Tech. -Mobile consumer-electronics devices, especially phones, are powered from batteries which are limited in size and therefore capacity. This implies that managing energy well is paramount in such devices. Good energy management requires a good understand-ing of where and how the energy is used. To this end we present a detailed analysis of the power consumption of a recent mobile phone, the Openmoko Neo Freerunner. We measure not only overall system power, but the exact breakdown of power consumption by the device’s main hardware components. We present this power breakdown for micro-benchmarks as well as for a number of realistic usage scenarios. These results are validated by over-all power measurements of two other devices: the HTC Dream and Google Nexus One. We develop a power model of the Free runner device and analyses the energy usage and battery lifetime under a number of usage patterns. We discuss the significance of the power drawn by various components, and identify the most promising areas to focus on for further improvements of power management. We also analyze the energy impact of dynamic voltage and frequency scaling of the device’s application processor. In this paper we attempt to answer this question and thus provide a basis for understanding and managing mobile-device energy consumption. Our approach is to measure the power consumption of a modern mobile de-vice, the Openmoko Neo Freerunner mobile phone, bro-ken down to the device’s major subsystems, under a wide range of realistic usage scenarios. |
| 林k ., a . Kansal d Lymberopoulos, f .赵”Energy-accuracy权衡连续移动设备的位置,“在Proc。8日Int。相依移动系统。,达成。,Services, 2010 -Mobile applications often need location data, to update locally relevant information and adapt the device context. While most smart-phones do include a GPS receiver, its frequent use is restricted due to high battery drain. We design and prototype an adaptive location service for mobile devices, a-Loc, that helps reduce this battery drain. Our design is based on the observation that the required location accuracy varies with location, and hence lower energy and lower accuracy localization methods, such as those based on WiFi and cell-tower triangulation, can sometimes be used. Our method automatically determines the dynamic accuracy requirement for mobile search-based applications. As the user moves, both the accuracy requirements and the location sensor errors change. A-Loc continually tunes the energy expenditure to meet the changing accuracy requirements using the available sensors. A Bayesian estimation framework is used to model user location and sensor errors. Experiments are performed with Android G1 and AT&T Tilt phones, on paths that include outdoor and indoor locations, us-ing war-driving data from Google and Microsoft. The experiments show that a-Loc not only provides significant energy savings, but also improves the accuracy achieved, because it uses multiple sensors. Our goal is to develop location as a system service that automatically manages location sensor availability, accuracy, and energy. From an application developer perspective, this simplifies the use of the multiple existing, and potentially forthcoming, location technologies with varying characteristics. From a mobile user experience perspective, this allows the system to optimize battery life by intelligently managing the location energy and accuracy trade-offs based on available sensor capabilities. This is beneficial for mo-bile platforms that allow several third party applications to run on the platform, but at the same time must ensure long battery life for acceptable user experience. |
| z壮族、k金和j·辛格,“提高能效感应手机的位置,“在Proc。8日Int。相依移动系统。,达成。,Services, 2010-Location-based applications have become increasingly popular on smartphones over the past years. The active use of these applications can however cause device battery drain owing to their power-intensive location-sensing operations. This paper presents an adaptive location-sensing framework that significantly improves the energy efficiency of mobile phones running location-based applications. The underlying design principles of the proposed frameworkin-volve substitution, suppression, piggybacking, and adaptation of applications’ location-sensing requests to conserve energy. We implement these design principles on Android-based mobile phones as a middleware. Our evaluation results show that the design principles reduce the usage of the powerintensive GPS (Global Positioning System) by up to 98% and improve battery life by up to 75%. In this paper, we present an energy-efficient location-sensing framework that effectively conserves energy for mobile phones running LBAs. In its core, the proposed framework includes four design principles: Substitution, Suppression, Piggybacking and Adaptation. Briefly, Substitution makes use of alternative location-sensing mechanisms (e.g., network-based location sensing) that consumes lower power than GPS. Suppression uses less power-intensive sensors such as an accelerometer to suppress unnecessary GPS sensing when the user is in static state. Piggy backing synchronizes the location sensing requests from multiple running LBAs. Adaptation aggressively adjusts system-wide sensing parameters such as time and distance, when battery level is low. |
| 和r . j . Paek j . Kim Govindan,“节能rate-adaptive以gps定位手机,”在第八Int Proc。。相依移动系统。,达成。,Services, 2010-Many emerging mobile phone applications require position information to provide location-based or context-aware services. In these applications, GPS is often preferred over its alternatives such as GSM/WiFi based positioning systems because it is known to be more accurate. However, GPS is extremely power hungry. Hence a common approach is to periodically duty-cycle GPS. However, GPS duty-cycling trades-off positioning accuracy for lower energy. A key requirement for such applications, then, is a positioning system that provides accurate position information while spending minimal energy. In this paper, we present RAPS, rateadaptive positioning sys-tem for smartphone applications. It is based on the observation that GPS is generally less accurate in urban areas, so it suffices to turn on GPS only as often as necessary to achieve this accuracy. RAPS uses a collection of techniques to cleverly determine when to turn on GPS. It uses the location-time history of the user to estimate user velocity and adaptively turn on GPS only if the estimated uncertainty in position exceeds the accuracy threshold. It also efficiently estimates user movement using a duty-cycled accelerometer, and utilizes Bluetooth communication to reduce position uncertainty among neighboring devices. Finally, it employs cell tower-RSS blacklisting to detect GPS unavailability (e.g., in-doors) and avoid turning on GPS in these cases. We evaluate RAPS through real-world experiments using a prototype implementation on a modern smartphone and show that it can increase phone life-times by more than a factor of 3.8 over an approach where GPS is always on. |
| z壮族、k金和j·辛格,“提高能效感应手机的位置,“在Proc。8日Int。相依移动系统。,ppl。,Services, 2010-The constrained battery power of mobile devices poses a serious impact on user experience. As an increasingly prevalent type of applications in mobile cloud environments, location-based applications (LBAs) present some inherent limitations concerning energy. For example, the Global Positioning System based positioning mechanism is well-known for its extremely power-hungry attribute. Due t o t he severity of the issue, considerable researches have focused on energy-efficient locating sensing mechanism in the last a few years. In this paper, we provide a comprehensive survey of recent work on low-power design of LBAs. An overview of LBAs and different locating sensing technologies used today are introduced. Methods for energy saving with existing locating technologies are investigated. Reductions of location updating queries and simplifications of trajectory data are also mentioned. Moreover, we discuss cloud-based schemes in detail which try t o develop new energy-efficient locating technologies by leveraging the cloud capabilities of storage, computation and sharing. Finally, we conclude the survey and discuss the future research directions. |
现有的系统 |
| 定位技术主要是基于全球定位系统(GPS),其他技术也从无线网络和GSM获得援助,其中每个可以在能源消耗和定位精度相差很大。众所周知更精确、GPS通常是首选在移动平台上对其替代品,如基于GSM /无线定位系统。用户仍然需要使用网络连接连接GPS活跃用户移动过在不同的位置,并验证GPS信号。没有必要开始跟踪存储的坐标。位置是否存储压缩的决定。 |
提出了系统 |
| 提出了系统的工作原理基于记录路径的几个步骤需要记录一个路径。见图,应用程序首先验证GPS信号。如果它是可用的,追随者开始存储坐标。是否存储位置的决定是由用户启用压缩方法。每次一组坐标存储到数据库中,应用程序,使用TSA(时间&空间算法),如果有必要,路径和计算各种有用的统计信息(路径的总距离、平均速度等)。我们决定将信息存储在一个本地数据库以避免需要互联网连接。为此,我们使用Sqlite数据库嵌入在Android SDK。数据库是一个简单的模式,它包含的主要信息的路径,它来源于的GPS坐标,以及上述统计数据。陪同的地方(POIs)和图片也存储在数据库中。 |
结论和未来的工作 |
| 在本文中,我们提出了一个移动应用程序为Android开发的O / S,使得存储、分析和可视化地图路线的移动用户。与相关的应用程序相比,它提供了新颖的功能在三个层面:(i)它允许用户手动标注的路线与标签描述他们的活动和行为,(2)它封装了一些先进的路径压缩算法的存储成本和质量之间的权衡运动的代表,和(3)它会自动显示段跟踪先进的路径分割算法为了方便自动审计用户的手动注释。第四(初步)的贡献,它使用户能够保护他们的隐私通过定义敏感地区记录是不允许的。这种类型的应用程序可用于多个领域,从路线规划和资源管理(如拼车)娱乐和社交网络(如下一代基于位置的社交网络)。未来工作的挑战包括TSA算法的扩展为了识别每个路径段的活动用户的启用自动验证和审计用户的注释,节能政策以来,我们的应用程序中使用设备有限的能量资源,一种可能性来检测用户的位置在非GPS-available领域(例如,在室内环境中或在地下区域)不失准确性和混合的地方——在云存储模式,让它准备好社交网络应用程序,可以使用基于位置的报警用户想要接触。全面了解互联网的要求对其拓扑结构及其性能的详细信息。鉴于互联网的规模,基于众包的一个方法可能会以一种前所未有的方式适应范围:大量的短程测量的结果,使用目前可用的手机,可以组合在一起,生成一个细粒度的网络地图。除了他们的潜在的巨大数量,使用手机网络监控提供了其他的机会:我)性能是观察到的外围网络,多数最终用户所在地,ii)移动终端允许监测系统收集动力和地理坐标信息。 |
数据乍一看 |
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引用 |
- GPS跟踪。URL: https://play.google.com/store/apps/details?id=com.fsp.android。
- m . Ra j . Paek a . b . Sharma r . Govindan m . h . Krieger m·j·尼利“Energy-delay权衡在智能手机应用程序中,”经验,页。255 - 270年,2010年6月。
- r . j . Paek j . Kim Govindan,“节能rate-adaptive以gps定位的智能手机,”经验,第314 - 299页,2010年6月。
- Android开发者、位置管理器。URL: http://developer.android.com/reference/ android /位置/ LocationManager。html(访问:1 Jun.2012)。
- Android开发者,SQLite数据库。URL: http://developer.android.com/reference/android/database sqlite / SQLiteDatabase。html(访问:1小君。2012)。
- Android开发者,支持多个屏幕。URL: http://developer.android.com/guide/practices screens_support。html(访问:1 Jun.2012)。
- EasyTracker: Android应用程序用于捕获流动行为Alexandros Doulamis Nikos Pelekis YannisTheodoridis。
- SensTrack:节能与智能手机的传感器位置跟踪Lei,江川Liu张洪波江,勇关。
- 还是z。壮族,K.-H。金姆,j·辛格,“提高能源效率的位置传感智能手机上,“在ACM MobiSys Proc. June2010。
- j . y . Wang Lin m . Annavaram问:a .雅各布森j .香港B。Krishnamachar, n . Sadeh表示“一个框架的节能移动Sensingfor用户状态自动识别,“Proc. ACM的经验,2009年6月。
- Ananthanarayanan和斯托伊卡,”Blue-Fi:使用蓝牙信号,提高无线性能”Proc. ACM的经验,2009年6月。
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