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Sensors 2016, 16(10), 1693; doi:10.3390/s16101693

Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications

Information Technology Laboratory, CINVESTAV-Tamaulipas, Ciudad Victoria C.P. 87130, Tamaulipas, Mexico
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Author to whom correspondence should be addressed.
Academic Editor: Yu Wang
Received: 5 September 2016 / Revised: 24 September 2016 / Accepted: 1 October 2016 / Published: 13 October 2016
View Full-Text   |   Download PDF [2879 KB, uploaded 13 October 2016]   |  

Abstract

The tracking of frequently visited places, also known as stay points, is a critical feature in location-aware mobile applications as a way to adapt the information and services provided to smartphones users according to their moving patterns. Location based applications usually employ the GPS receiver along with Wi-Fi hot-spots and cellular cell tower mechanisms for estimating user location. Typically, fine-grained GPS location data are collected by the smartphone and transferred to dedicated servers for trajectory analysis and stay points detection. Such Mobile Cloud Computing approach has been successfully employed for extending smartphone’s battery lifetime by exchanging computation costs, assuming that on-device stay points detection is prohibitive. In this article, we propose and validate the feasibility of having an alternative event-driven mechanism for stay points detection that is executed fully on-device, and that provides higher energy savings by avoiding communication costs. Our solution is encapsulated in a sensing middleware for Android smartphones, where a stream of GPS location updates is collected in the background, supporting duty cycling schemes, and incrementally analyzed following an event-driven paradigm for stay points detection. To evaluate the performance of the proposed middleware, real world experiments were conducted under different stress levels, validating its power efficiency when compared against a Mobile Cloud Computing oriented solution. View Full-Text
Keywords: stay point; smartphone; Location Based Services (LBS); context-aware; power-aware; event-driven stay point; smartphone; Location Based Services (LBS); context-aware; power-aware; event-driven
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Pérez-Torres, R.; Torres-Huitzil, C.; Galeana-Zapién, H. Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications. Sensors 2016, 16, 1693.

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