Energy efficient proximity alert on Android

Bulut, Muhammed Fatih; Demirbaş, Murat

doi:10.1109/percomw.2013.6529474

Cited by 9 publications

(3 citation statements)

References 10 publications

(8 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Event detection makes precise activity detection necessary which primarily relies on accelerometer readings and is often supported by additional sensors. Bulut and Demirbas in [2] give a method for mobile localization with varying accuracy levels depending on the devices' type of movement, speed and distance to the points of interest where the most accurate location is needed. They distinguish idle, driving and walking modes.…”

Section: Related Workmentioning

confidence: 99%

Efficient event detection in public transport tracking

Petkovics

Farkas

2014

2014 International Conference on Telecommunications and Multimedia (TEMU)

View full text Add to dashboard Cite

Personal mobile devices are widespread and carried by their users most of the time over the day. Thanks to the integrated sensors they can report about visited places, movement types and speed of the users. However, efficient stopping event detection on public transport vehicles is still a challenge. These events, associated with the coordinates of real stations, can be useful to update public transit timetables according to real-time traffic. In field tests we evaluated the most commonly available suitable sensors' precision and efficiency and developed our Stopping Event Detection Algorithm (SEDA), which utilizes only the accelerometer to find potential stopping times and the Wi-Fi sensor to validate or discard them by a novel localization method. Wi-Fi is used only 6.66% of the time of actual traveling on public vehicles. Our algorithm is shown to recognize properly 82.9-89.47% of public traffic stations while consuming daily only 13% the capacity of an average smartphone's battery.

show abstract

Section: Related Workmentioning

confidence: 99%

Efficient event detection in public transport tracking

Petkovics

Farkas

2014

2014 International Conference on Telecommunications and Multimedia (TEMU)

View full text Add to dashboard Cite

show abstract

“…GPS, WiFi, 3G) available in mobile devices [6,8,10,11] and regulate location update intervals [7] based on factors such as distance to AoIs and transit modes (e.g. idle, walking, driving).…”

Section: Related Workmentioning

confidence: 99%

Extending battery lifetime of mobile devices with geofence capabilities on dynamic-speed urban environments

Loyola

Wong

Pereira

et al. 2013

Proceedings of the Second ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems

View full text Add to dashboard Cite

In this work we present a battery-saving algorithm for Location Based Services (LBS) that exploits the geofence functionalities provided by modern mobile operating systems such as iOS and Android. The algorithm detects the surrounding areas of interest (AoI) by taking advantage of the underlying structure of quadtrees, considerably saving the number of requests to the LBS server made by the application, thus extending its battery lifetime even in dynamicspeed environments. The areas of interest can have any arbitrary shape and are not constrained to circles as in previous work. In our experiments, through empirical and simulation tests, we show that a substantial reduction of battery consumption can be achieved (up to 45%) while keeping a perfect detection accuracy of areas of interest in comparison with periodic polling techniques widely used in current mobile applications, with error rates of up to 55%.

show abstract

“…Loyola et al [4] used an algorithm that reduces the number of requests to the LBS server made by the application thereby saving up to 45% of the battery. Bulut et al [8] presented a method that utilized the user's speed and distance to the POIs to achieve energy efficient proximity alert for Android phones. Kim et al [5] introduced SensLoc that "correctly detects 94% of the place visits, tracks 95% of the total travel distance, and still only consumes 13% of energy than algorithms that periodically collect coordinates to provide the same information".…”

Section: Introductionmentioning

confidence: 99%

Performance Assessment of Geo-triggering in Small Geo-fences: Accuracy, Reliability, and Battery Drain in Different Tracking Profiles and Trigger Directions

Alsaqer

Hilton

Horan

et al. 2015

Procedia Engineering

View full text Add to dashboard Cite

Geo-fencing has been predicted to be a multi-billion dollar market in areas such as retail, ambient intelligence, entertainment, healthcare, etc. Businesses have been adopting geo-fencing technology, and now there are several platform providers such as Google, Qualcomm, Esri, Urban Airship, and others. These tools are continuing to attract application developers; however, best practices for choosing the specific performance options within this technology is still ambiguous. For example, Esri provides a geo-trigger service that allows developers to send targeted messages to users when they enter, exit, or dwell in a geo-fenced area. This service also provides the ability to choose higher levels of accuracy or battery saving by offering different location tracking profiles. This paper investigated two geo-trigger tracking profiles (Fine and Adaptive) to assess their performance in small, outdoor, geo-fenced areas; these two profiles are the most accurate but vary in their battery-use. The results show the Adaptive tracking profile to provide 100% reliability and average accuracy of 68.53 meters in geo-fences between 20-70 meter radii. In addition, the Adaptive tracking profile saved 15.20% battery-life while the user is stationery and 9.23% while the user is moving.

show abstract

Energy efficient proximity alert on Android

Cited by 9 publications

References 10 publications

Efficient event detection in public transport tracking

Efficient event detection in public transport tracking

Extending battery lifetime of mobile devices with geofence capabilities on dynamic-speed urban environments

Performance Assessment of Geo-triggering in Small Geo-fences: Accuracy, Reliability, and Battery Drain in Different Tracking Profiles and Trigger Directions

Contact Info

Product

Resources

About