NeTS: Small: Collaborative Research: Logical Localization for Mobile Devices through Ambience Sensing

位置的概念很广泛，从物理坐标(纬度/经度)到逻辑位置(如星巴克、沃尔玛、博物馆)。逻辑位置变得越来越重要，因为它们表达了用户的上下文，允许应用程序执行特定于上下文的交互。例如，进入星巴克的人可能会收到购买咖啡的广告。显然，这样的应用程序希望了解用户在一个逻辑空间(如星巴克)的范围内的存在。基于GPS/WiFi/GSM的大量本地化文献本质上主要是物理的，将它们转换到逻辑位置很容易出错。这是因为分隔墙可能会分隔两个逻辑位置，而几米的误差范围可能会将移动设备放置在该墙的不正确一侧。这项提议提出了一种独特的系统，称为SuroundSense，它摆脱了物理定位，并研究了识别逻辑位置的直接方法。SuroundSense的主要思想是利用这样一种观察，即一个逻辑位置在其背景声、光、颜色、RF信号和布局方面具有独特的氛围。通过手机传感器可以感知到环境的属性，并适当地组合起来，形成那个地方的指纹。然后，这个指纹可以用来识别手机的逻辑位置，方法是将其与现有的位置指纹数据库进行比较。例如，星巴克可能会包括咖啡机和微波炉的声音特征，这与餐馆里叉子和勺子发出的叮当声不同。同样，不同商店的装饰中的标志性颜色可能不同，照明风格和商店布局也可能不同，在该位置可以听到的WiFi SSID也不同。所有这些属性的组合，即指纹，可能会显示出合理的多样性，以实现高质量、逻辑的定位。

The notion of location is broad, ranging from physical coordinates (latitude/longitude) to logical locations (like Starbucks, WalMart, museums). Logical locations are gaining importance because they express the context of the user, allowing applications to carry out context-specific interaction. For instance, a person entering Starbucks may receive an advertisement for purchasing coffee. Evidently, such applications expect to learn the existence of the user within the confines of a logical place (like Starbucks). The rich body of localization literature based on GPS/WiFi/GSM is dominantly physical in nature, and translating them to logical locations is error prone. This is because a dividing wall may separate two logical locations and an error margin of few meters may place the mobile device on the incorrect side of this wall. This proposal proposes a unique system called SurroundSense that breaks away from physical localization and investigates direct methods of recognizing logical locations. The main idea in SurroundSense exploits the observation that a logical place has a distinct ambience in terms of its background sound, light, color, RF signals, and layout. The attributes of the ambience can be sensed through mobile phone sensors and suitably combined to form a fingerprint of that place. This fingerprint can then be used to identify the logical location of the phone by comparing it against an existing database of location-fingerprints. As an example, a Starbucks may include sound signatures from coffee machines and microwaves that are different from the clinking of forks and spoons in restaurants. Similarly, signature colors in the decor may vary from store to store, lighting styles and store layouts may be different, and so will the WiFi SSIDs audible at that location. The combination of all these attributes, a fingerprint, is likely to exhibit reasonable diversity for high quality, logical localization.