Towards Mobile Phone Localization without War-Driving

By Ionut Constandache, Romit Roy Choudhury, Injong Rhee. 2010 Proceedings IEEE INFOCOM, 2010

357 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/0d8d7d713c175fad8094abaf60535d1a6fb255b3

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Citations in the corpus

A Survey on Wireless Indoor Localization from the Device Perspective (2016, methodology): “EnLoc [Constandache et al. 2009] proposes an energy-efficient positioning system by integrating GPS, GSM, and WiFi. They quantify the tradeoff between energy and accuracy that underlies a range of emerging services, and characterize the optimal localization accuracy for a given energy budget. Gao et”
Magicol: Indoor Localization Using Pervasive Magnetic Field and Opportunistic WiFi Sensing (2015, background): “, Dead Reckoning) is a well-studied topic for its infrastructure independency [10, 25, 33–35].”
FM-based indoor localization (2012, background): “Last but not least, energy efficiency is an important consideration when designing localization systems [6, 7, 15].”
Indoor localization without infrastructure using the acoustic background spectrum (2011, background): “use a mobile phone’s accelerometer and compass for localization [9].”
UJIIndoorLoc: A new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems (2014, methodology): “In [8], authors categorised approaches according to the technique used for localization into several paradigms, including calibration-free localization [9], WLAN based techniques [10], Dead-reckoning [11], simultaneous localization and mapping (SLAM) [12] and multi-modal sensing [13].”
Energy-efficient rate-adaptive GPS-based positioning for smartphones (2010, background): “As such, location-aware smartphone applications will have to have application-specific ways to deal with these inaccuracies: techniques such as map-matching [4] and map-snapping are, by now, well-established methods for addressing this problem.”
SensLoc: sensing everyday places and paths using less energy (2010, background): “combine map information and dead reckoning based on low power sensors to reduce GPS samplings [5].”
A Survey of Green Mobile Networks: Opportunities and Challenges (2011, methodology): “Following some innovations of localization methodology, such as the accelerometer-based location estimation [123], and the distributed humanassisted localization in [124], more efforts should be contributed to help LBSs grow green.”
The Internet of Things: A Survey from the Data-Centric Perspective (2013, unknown): “needs of the application [30, 84, 91, 117].”
Mobile Data Offloading: How Much Can WiFi Deliver? (2013, background): “14 examines power consumed for delayed transfers of 10-and 100-MB ﬁ les with various delay deadlines, which is directly translated from their transmission times.”