Outdoor exit detection using combined techniques to increase GPS efficiency

Morillo, Luis Miguel Soria; Ramírez, Juan Antonio Ortega; Álvarez-García, Juan Antonio; Abril, Luis González

doi:10.1016/j.eswa.2012.04.047

Cited by 11 publications

(4 citation statements)

References 10 publications

(10 reference statements)

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“…Although we show that previous work has limitations, we appreciate these studies' contributions to the knowledge base, as they provided first indications in the field and triggered further research with respect to combining location technologies to deliver more accurate results [e.g., 16,22].…”

Section: Discussionmentioning

confidence: 94%

On the (In-)Accuracy of GPS Measures of Smartphones

Bauer

2013

Proceedings of International Conference on Advances in Mobile Computing &Amp; Multimedia - MoMM '13

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Sports tracking applications are increasingly available on the market, and research has recently picked up this topic. Tracking a user's running track and providing feedback on the performance are among the key features of such applications. However, little attention has been paid to the accuracy of the applications' localization measurements. In evaluating the nine currently most popular running applications, we found tremendous differences in the GPS measurements. Besides this finding, our study contributes to the scientific knowledge base by qualifying the findings of previous studies concerning accuracy with smartphones' GPS components.

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Section: Discussionmentioning

confidence: 94%

On the (In-)Accuracy of GPS Measures of Smartphones

Bauer

2013

Proceedings of International Conference on Advances in Mobile Computing &Amp; Multimedia - MoMM '13

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“…To detect the entrance and departure, some research uses both Wi-Fi and GPS signals. Morillo et al [ 17 ] proposed an algorithm that detects an entrance by detecting the loss of GPS signals, and a departure by detecting the Wi-Fi fingerprints measured at the entry of a place. They developed such a hybrid entrance/departure detection algorithm to reduce the energy consumption of GPS on a mobile device.…”

Section: Related Workmentioning

confidence: 99%

VisitSense: Sensing Place Visit Patterns from Ambient Radio on Smartphones for Targeted Mobile Ads in Shopping Malls

Kim

Kang

et al. 2015

Sensors

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In this paper, we introduce a novel smartphone framework called VisitSense that automatically detects and predicts a smartphone user’s place visits from ambient radio to enable behavioral targeting for mobile ads in large shopping malls. VisitSense enables mobile app developers to adopt visit-pattern-aware mobile advertising for shopping mall visitors in their apps. It also benefits mobile users by allowing them to receive highly relevant mobile ads that are aware of their place visit patterns in shopping malls. To achieve the goal, VisitSense employs accurate visit detection and prediction methods. For accurate visit detection, we develop a change-based detection method to take into consideration the stability change of ambient radio and the mobility change of users. It performs well in large shopping malls where ambient radio is quite noisy and causes existing algorithms to easily fail. In addition, we proposed a causality-based visit prediction model to capture the causality in the sequential visit patterns for effective prediction. We have developed a VisitSense prototype system, and a visit-pattern-aware mobile advertising application that is based on it. Furthermore, we deploy the system in the COEX Mall, one of the largest shopping malls in Korea, and conduct diverse experiments to show the effectiveness of VisitSense.

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“…Likewise, it is possible to employ text mining approaches to classify material or immaterial resources into various categories based upon their geospatial features, with the aims to discovering relationships between resources and geographical zones [22]. Such method can detect real-time and geospatial event features for awareness of large-scale events and risk management [23] with pervasive computing for continuous location and resources monitoring [29].…”

Section: Objectivementioning

confidence: 99%