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

Abstract

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%.

MR*-Tree: Novel Indexing and Retrieving Mechanism for Spatial Objects in Mobile PowerPoint Pages

“…Otherwise, the spatial relationship of nearest is used for determining whether TextPortion and the finger point are in the relation of horizontal (line 8) or vertical (line 15) situations and retrieving TextPortion which is nearest the finger point. Thereafter, TextPortion is expanded as a rectangle of the row and uses areaHrzNearest to find a TextPortion where the cursor should be covered (lines 8-14 and lines [15][16][17][18][19][20][21][22][23][24][25][26]. Note that this is different from nearest for the horizontal and vertical situations.…”

“…This work reminds us that we could deploy our office application on the platform to reduce the computing pressure of single device. Reference [23] presents an algorithm of location-based services (LBS) on the mobile devices to save the battery of these devices. By using the promoted quadtrees, the devices could save the number of requests to the LBS server when detecting the surrounding area of interest.…”

Geofencing 2.0

“…Within the research community, Geofencing is mainly known for two challenges: the impact on the energy consumption of the mobile devices [19,31,27] and on the traffic load within wireless access networks [30]. Additionally, Geofencing raises severe concerns regarding the privacy of the mobile phone user in case her/his location is continuously computed by the network operator infrastructure or is transmitted to a 3rd party service [19].…”

Data-Driven Geofencing Design for Point-Of-Interest Notifiers Utilizing Genetic Algorithm

“…Determining whether a point lies within a geofence has been a significant focus in research, particularly for introducing numerous polygonal low-cost geofences. Novel algorithms based on the quad-tree data structure [14], hybrid hashing scheme [15], trajectory partitioning [16], and the triangle weight characterization with adjacency in three-dimensional environments [17] have been utilized in these studies. In addition, infrastructure and frameworks are crucial for effective management.…”