Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications
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 receiv...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-10-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/16/10/1693 |
| _version_ | 1811280929761525760 |
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| author | Rafael Pérez-Torres César Torres-Huitzil Hiram Galeana-Zapién |
| author_facet | Rafael Pérez-Torres César Torres-Huitzil Hiram Galeana-Zapién |
| author_sort | Rafael Pérez-Torres |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T01:23:22Z |
| format | Article |
| id | doaj.art-a304803e9737455bafd98a01e03833e1 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T01:23:22Z |
| publishDate | 2016-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a304803e9737455bafd98a01e03833e12022-12-22T03:08:41ZengMDPI AGSensors1424-82202016-10-011610169310.3390/s16101693s16101693Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile ApplicationsRafael Pérez-Torres0César Torres-Huitzil1Hiram Galeana-Zapién2Information Technology Laboratory, CINVESTAV-Tamaulipas, Ciudad Victoria C.P. 87130, Tamaulipas, MexicoInformation Technology Laboratory, CINVESTAV-Tamaulipas, Ciudad Victoria C.P. 87130, Tamaulipas, MexicoInformation Technology Laboratory, CINVESTAV-Tamaulipas, Ciudad Victoria C.P. 87130, Tamaulipas, MexicoThe 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.http://www.mdpi.com/1424-8220/16/10/1693stay pointsmartphoneLocation Based Services (LBS)context-awarepower-awareevent-driven |
| spellingShingle | Rafael Pérez-Torres César Torres-Huitzil Hiram Galeana-Zapién Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications Sensors stay point smartphone Location Based Services (LBS) context-aware power-aware event-driven |
| title | Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications |
| title_full | Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications |
| title_fullStr | Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications |
| title_full_unstemmed | Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications |
| title_short | Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications |
| title_sort | full on device stay points detection in smartphones for location based mobile applications |
| topic | stay point smartphone Location Based Services (LBS) context-aware power-aware event-driven |
| url | http://www.mdpi.com/1424-8220/16/10/1693 |
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