Computer graphics: principles and practice (2nd ed.)
Computer graphics: principles and practice (2nd ed.)
Quad tree structures for image compression applications
Information Processing and Management: an International Journal - Special issue on data compression for images and texts
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Storing a collection of polygons using quadtrees
ACM Transactions on Graphics (TOG)
Quadtree and R-tree indexes in oracle spatial: a comparison using GIS data
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
EnTracked: energy-efficient robust position tracking for mobile devices
Proceedings of the 7th international conference on Mobile systems, applications, and services
Energy-efficient rate-adaptive GPS-based positioning for smartphones
Proceedings of the 8th international conference on Mobile systems, applications, and services
SensLoc: sensing everyday places and paths using less energy
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Energy-efficient positioning for smartphones using Cell-ID sequence matching
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
Operations on Images Using Quad Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
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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 dynamic-speed 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%.