SurroundSense: mobile phone localization via ambience fingerprinting

By Martin Azizyan, Ionut Constandache, Romit Roy Choudhury. ACM/IEEE International Conference on Mobile Computing and Networking, 2009

841 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Science Applications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/94a84b4038c7c8e18b792d217453e696af81b604

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Citations in the corpus

WILL: Wireless indoor localization without site survey (2012, background): “Recently, SurroundSense [12] performs logical location estimation based on ambience features including sound, light, color, WiFi, and etc.”
Push the limit of WiFi based localization for smartphones (2012, result): “Similar or much larger errors (e.g., > 15m) have been reported in previous studies [3, 27].”
A survey on privacy in mobile participatory sensing applications (2011, background): “Contextual information collected from other embedded sensors (such as points of interest [54], light, and noise) can also be used to identify a person’s location.”
SensLoc: sensing everyday places and paths using less energy (2010, background): “SurroundSense uses ambient sound, light, color, user motion in a place in addition to RF signals [3].”
E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures (2014, methodology): “Another body of related work is indoor localization systems which can be extended to activity recognition [6–9, 11, 14, 17, 21, 22, 30, 34, 37, 38, 41, 43].”
Electronic frog eye: Counting crowd using WiFi (2014, methodology): “RSS-based approaches can be categorized into the Radio Propagation Model [4], [19] and the Fingerprint Method [20], [21], [5].”
Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges (2016, background): “thematic colors along with the lighting styles may provide location-specific signatures which can be fused with the Wi-Fi RSS fingerprints [108].”
SoK: Privacy on Mobile Devices – It’s Complicated (2016, background): “, taps [3, 4], keypresses [5], position [6, 7, 9, 10], current activity [11]).”
Locating in fingerprint space: wireless indoor localization with little human intervention (2012, background): “Global reference points include the last reported GPS location [6], AP’s location [18], similar surrounding sound signature [1], feature-distinct public area, etc.”
Tapprints: your finger taps have fingerprints (2012, background): “These domains range from remote patient monitoring, to localization, to ﬁne grained context-awareness [16, 3, 2, 24].”
SpotFi: Decimeter Level Localization Using WiFi (2015, background): “…idea of these approaches is that one would collect ﬁngerprints such as the vector of RSSIs from a speciﬁc location to all the APs in range, and if in the future a target were at the same location, it would exhibit the same ﬁngerprint, enabling us to localize [14, 15, 16, 17, 18, 19, 20, 21, 22].”
Indoor localization without the pain (2010, background): “SurroundSense [2] builds a map using several features found in typical indoor spaces such as ambient sound, light, color, etc.”
DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications (2017, background): “tem of context-aware apps such as behavior-based authentication [31], [47], [58], [18], [78], [94], fitness monitoring based on activity tracking [5], [76], [57], speech translation [48], [61] and traffic/environmental monitoring [3], [64], [83], [86].”