I am the antenna: accurate outdoor AP location using smartphones

  • Authors:

  • Zengbin Zhang;Xia Zhou;Weile Zhang;Yuanyang Zhang;Gang Wang;Ben Y. Zhao;Haitao Zheng

  • Affiliations:

  • University of California, Santa Barbara, Santa Barbara, CA, USA;University of California, Santa Barbara, Santa Barbara, CA, USA;Xian Jiaotong University, Xi'an, China;University of California, Santa Barbara, Santa Barbara, CA, USA;University of California, Santa Barbara, Santa Barbara, CA, USA;University of California, Santa Barbara, Santa Barbara, CA, USA;University of California, Santa Barbara, Santa Barbara, CA, USA

  • Venue:
  • MobiCom '11 Proceedings of the 17th annual international conference on Mobile computing and networking
  • Year:
  • 2011

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Abstract

Today's WiFi access points (APs) are ubiquitous, and provide critical connectivity for a wide range of mobile networking devices. Many management tasks, e.g. optimizing AP placement and detecting rogue APs, require a user to efficiently determine the location of wireless APs. Unlike prior localization techniques that require either specialized equipment or extensive outdoor measurements, we propose a way to locate APs in real-time using commodity smartphones. Our insight is that by rotating a wireless receiver (smartphone) around a signal-blocking obstacle (the user's body), we can effectively emulate the sensitivity and functionality of a directional antenna. Our measurements show that we can detect these signal strength artifacts on multiple smartphone platforms for a variety of outdoor environments. We develop a model for detecting signal dips caused by blocking obstacles, and use it to produce a directional analysis technique that accurately predicts the direction of the AP, along with an associated confidence value. The result is Borealis, a system that provides accurate directional guidance and leads users to a desired AP after a few measurements. Detailed measurements show that Borealis is significantly more accurate than other real-time localization systems, and is nearly as accurate as offline approaches using extensive wireless measurements.