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

位置的概念是广泛的，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，这些应用程序希望了解用户在逻辑位置（如星巴克）范围内的存在。基于GPS/WiFi/GSM的丰富的本地化文献本质上主要是物理的，并且将它们转换为逻辑位置容易出错。这是因为分隔墙可以分隔两个逻辑位置，并且几米的误差容限可以将移动终端放置在该墙的不正确的一侧。该提案提出了一种名为SurroundSense的独特系统，该系统摆脱了物理定位，并研究了识别逻辑位置的直接方法。SurroundSense的主要思想利用了这样一种观察，即一个合理的地方在其背景声音、光线、颜色、RF信号和布局方面具有独特的氛围。环境的属性可以通过移动的电话传感器来感测，并且适当地组合以形成该地点的指纹。然后，该指纹可以用于通过将其与现有的位置指纹数据库进行比较来识别电话的逻辑位置。作为示例，星巴克可以包括来自咖啡机和微波炉的声音签名，其不同于餐馆中的叉子和勺子的叮当声。类似地，装饰中的签名颜色可能因商店而异，照明风格和商店布局可能不同，并且在该位置处可听到的WiFi SSID也可能不同。所有这些属性的组合，即指纹，很可能表现出合理的多样性，以实现高质量的逻辑定位。

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.