The popularity of GPS sensors in mobile devices has enabled a new generation of location-based services, in which users record their traveling routes and upload the GPS track points (GTPs) on-the-fly for real-time social sharing, jog journaling, life logging, health care, and map generation. However, directly uploading all GTPs wastes battery energy and network bandwidth on the mobile device, because GTPs are highly redundant. To reduce the redundancies, we propose in this paper the Corner Extraction Technique (CET) to extract the corner points from the GTPs of individual users that can be used to characterize and reconstruct the routes that the user has traveled. Storing corner GTPs requires much less storage space, and transmitting them allows saving in both network bandwidth and energy consumption, leading to optimized GPS-enabled mobile applications. We have conducted both trace-driven simulations and real experiments to demonstrate the merits of the proposed CET approach. The experimental results indicate that: (1) CET results in up to 33 times of compression ratio, (2) CET closely follows the original road segments, and (3) CET saves energy consumption by up to 72%.

Over the past few years, personal sensing applications, such as travel path sharing and location recording, have been more and more popular. These applications use GPS sensors to record track points on smartphones and upload the track points to clouds in real time for information sharing. However, uploading a lot of GPS points may lead to heavy network traffic and much higher power consumption. To address the problem, we present corner extraction by GPS filtering (CEGF) that extracts corner feature GPS points (CFGPs) from GPS track points (GTPs). Applications only need to upload the CFGPs to save the uploading energy on smartphones. CFGPs can be regarded as characteristic points of corners to represent the corresponding roads. To reduce the number of uploaded points, we use CEGF to filter out the CFGPs from a large amount of GTPs.