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目 录

Energy-Accuracy Trade-off for Continuous Mobile Device Location 3

ABSTRACT 3

1. INTRODUCTION 3

2. RELATED WORK 4

3. SYSTEM OVERVIEW 6

4. SYSTEM DESIGN 7

4.1 Accuracy Models 7

4.2 Energy Models 8

4.3 Selection Algorithm 8

5. EXPERIMENTS AND EVALUATION 8

5.1 Prototype Implementation 9

5.2 Application Accuracy Requirement 9

5.3 System Performance 9

5.4 Discussion 10

6. CONCLUSIONS 10

基于连续移动设备位置的能量精度权衡 12

摘要 12

1. 介绍 12

2. 相关工作 13

3. 系统概述 14

4. 系统设计 14

4.1 精度模型 15

4.2 能源模型 15

4.3 选择算法 16

5. 实验和评价 16

5.1 原型实现 16

5.2 应用精度要求 16

5.3 系统性能 16

5.4 讨论 17

6 结论 17

论文摘自：

Energy-Accuracy Trade-off for Continuous Mobile Device Location

ABSTRACT

Mobile applications often need location data, to update locally relevant information and adapt the device context. While most smartphones 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. ALoc 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 ATamp;T Tilt phones, on paths that include outdoor and indoor locations, using 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.

1. INTRODUCTION

Mobile applications often need location information and a large number of methods for mobile device localization have been developed. With GPS receivers becoming increasingly commonplace in mobile phones and the widespread availability of WiFi and cell-tower signature based location services from Google and other providers, such location information is now becoming a reality. However, mobile applications still cannot assume continuous and ubiquitous location access in their design because of the high energy expense of using the location sensors such as GPS receivers. The variability in accuracy provided by various location sensors and the limits on their coverage areas pose additional challenges for application developers. Using multiple location sensors simultaneously to make up for this variability in accuracy would further increase energy use.

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 mobile 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.

To realize the above goal, we develop an approach based on two observations. First, location applications do not always need the highest available accuracy, such as that provided by GPS in open sky view locations. The accuracy needs vary as the user moves and we can exploit the slack in required accuracy to save energy. Second, a phone has multiple modalities to sense location aside from the GPS: WiFi triangulation, cell-tower triangulation, Bluetooth vicinity, audio-visual sensing, among others. The availability and accuracy of these modalities vary as the user moves, and appropriate modalities can be selected to efficiently meet the location needs at lower energy costs.

2. RELATED WORK

Many location sensing mo

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