Mobile applications can benefit from increased awareness of the device's context. Unfortunately, existing solutions for inferring context require special purpose sensors or beacons on the mobile devices or in the physical environment. This requirement significantly limits the deployment of these solutions. In this thesis, I argue that mobile devices can infer a substantial amount of their context by leveraging their existing wireless interfaces to monitor ambient radio sources, such as GSM cell towers or WiFi access points. I focus on two important problems in context-aware computing: localization of mobile devices and detecting proximity between mobile devices for authentication purposes. Specifically, I present an accurate localization system based on fingerprinting of GSM signals. I show that the key to more accurate GSM localization is the use of wide signal strength fingerprints that include readings from a large number of base stations. Next, I present a method that addresses the key drawback of fingerprint-based localization systems - the need to collect extensive measurements to train the system in every target environment. Finally, I show how radio environment sensing can be used to secure the communication of devices that come within close proximity. Removing the need for additional hardware on the mobile devices and in the physical environment renders the approach that I present amenable for widespread deployment.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/17258 |
| Date | 26 February 2009 |
| Creators | Varshavsky, Alexander |
| Contributors | de Lara, Eyal, LaMarca, Anthony |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
| Format | 957190 bytes, application/pdf |
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