Abstract | ||
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The popularity of GPS-enabled smartphones enables a wide variety of new location-based or location-aware services and applications. However, the GPS module in a smartphone produces inaccurate position estimates and incurs high energy consumption, which inhibits the wide use of location-aware applications. To address this, we propose a social-aided cooperative location optimization (Coloc) scheme, which is capable of improving positioning accuracy and achieving low energy consumption. Specifically, our scheme enhances positioning accuracy by fusing the GPS positions of multiple co-located smartphones in a social network, or by neighborhood-based weighted least-squares estimation when relative distances between smartphones are available. The energy efficiency is achieved by sharing location information among co-located users and lower the update rate of the GPS module without sacrificing the accuracy. To validate our proposed approach, we conduct experiments in stationary and moving scenarios. Experimental results show that our proposed cooperative localization scheme can achieve sufficient performance gains in both indoor and outdoor environments. |
Year | DOI | Venue |
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2013 | 10.1109/MASS.2013.78 | MASS |
Keywords | Field | DocType |
gps position,proposed cooperative localization scheme,gps module,improving gps service,co-located user,low energy consumption,incurs high energy consumption,gps-enabled smartphones,positioning accuracy,social collaboration,multiple co-located smartphones,energy efficiency,global positioning system,radionavigation,mobile computing | Mobile computing,Social network,Computer science,Efficient energy use,Global Positioning System,Social computing,Radio navigation,Assisted GPS,Social collaboration,Distributed computing | Conference |
ISSN | Citations | PageRank |
2155-6806 | 2 | 0.37 |
References | Authors | |
16 | 5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Kaikai Liu | 1 | 190 | 20.37 |
Qiuyuan Huang | 2 | 176 | 17.66 |
Jiecong Wang | 3 | 2 | 0.37 |
Xiaolin Li | 4 | 117 | 4.93 |
Dapeng Wu | 5 | 4463 | 325.77 |