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）都有。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，一个进入星巴克的人可能会收到购买咖啡的广告。显然，这类应用程序希望在逻辑场所（如星巴克）的范围内了解用户的存在。基于GPS/WiFi/GSM的丰富的定位文献在本质上主要是物理的，将它们翻译成逻辑位置很容易出错。这是因为分隔墙可能会将两个逻辑位置分开，而几米的误差范围可能会将移动设备放置在隔离墙的错误一侧。该提案提出了一个名为SurroundSense的独特系统，该系统脱离了物理定位，并研究了识别逻辑位置的直接方法。SurroundSense的主要思想是利用观察到的逻辑场所在背景声音、光线、颜色、射频信号和布局方面具有独特的氛围。环境的属性可以通过手机传感器来感知，并适当地组合起来，形成该地点的指纹。然后，这个指纹可以通过与现有的位置指纹数据库进行比较来识别手机的逻辑位置。例如，星巴克可能包括来自咖啡机和微波炉的声音特征，这与餐馆里叉子和勺子的叮当声不同。同样，每家店的装饰标志颜色可能不同，照明风格和商店布局可能不同，该地点的WiFi ssid也可能不同。所有这些属性的组合，一个指纹，可能会表现出合理的多样性，以实现高质量，逻辑定位。

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.