Where am I? Characterizing and improving the localization performance of off-the-shelf mobile devices through cooperation
We are increasingly reliant on cellular data services for many types of day-to-day activities, from hailing a cab, to searching for nearby restaurants. Geo-location has become a ubiquitous feature that underpins the functionality of such applications. Network operators can also benefit from accurate...
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| Main Authors: | , , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2016
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/3329 https://ink.library.smu.edu.sg/context/sis_research/article/4331/viewcontent/WhereamICharacterizingAndImproving.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | We are increasingly reliant on cellular data services for many types of day-to-day activities, from hailing a cab, to searching for nearby restaurants. Geo-location has become a ubiquitous feature that underpins the functionality of such applications. Network operators can also benefit from accurate mobile terminal localization in order to quickly detect and identify location-related network performance issues, such as coverage holes and congestion, based on mobile measurements. Current implementations of mobile localization on the wildly-popular Android platform depend on either the Global Positioning System (GPS), Android's Network Location Provider (NLP), or a combination of both. In this paper, we extensively study the performance of such systems, in terms of its localization accuracy. We show through real-world measurements that the performance of GPS+NLP is heavily dependent on the mobility of the user, and its gains on localization performance is minimal, and often even detrimental, especially for network round-trip delays up to 1s. Building upon these findings, we evaluate the efficacy of using Tattle, a cooperative local measurement-exchange system, and propose Delay-Adjusted U-CURE, a clustering algorithm that greatly improves the localization performance of both GPS-only, and GPS+NLP techniques, without keeping expensive system states, nor requiring any location anchors nor additional instrumentation, nor any external knowledge that is not available programmatically to application designers. Our results are promising, demonstrating that median location accuracy improvements of over 30% is achievable with just 3 co-located devices, and close to 60% with just 6 co-located devices. These findings can be used by operators to better manage their networks, or by application designers to improve their location-based services. |
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