Degree Granting Department
Computer Science and Engineering
Rafael Perez, Ph.D.
Miguel Labrador, Ph.D.
Hyun Kim, Ph.D.
Thomas Weller, Ph.D.
Dewey Rundus, Ph.D.
Android, global positioning systems, Java Micro Edition, location-based services, mobile phone
This dissertation presents LAISYC, a modular location-aware architecture for intelligent real-time mobile applications that is fully-implementable by third party mobile app developers and supports high-precision and high-accuracy positioning systems such as GPS. LAISYC significantly improves device battery life, provides location data authenticity, ensures security of location data, and significantly reduces the amount of data transferred between the phone and server. The design, implementation, and evaluation of LAISYC using real mobile phones include the following modules: the GPS Auto-Sleep module saves battery energy when using GPS, maintaining acceptable movement tracking (approximately 89% accuracy) with an approximate average doubling of battery life. The Location Data Signing module adds energy-efficient data authenticity to this architecture that is missing in other architectures, with an average approximate battery life decrease of only 7%. The Session Management and Adaptive Location Data Buffering modules also contribute to battery life savings by providing energy-efficient real-time data communication between a mobile phone and server, increasing the average battery life for application data transfer by approximately 28% and reducing the average energy cost for location data transfer by approximately 38%. The Critical Point Algorithm module further reduces battery energy expenditures and the amount of data transferred between the mobile phone and server by eliminating non-essential GPS data (an average 77% reduction), with an average doubling of battery life as the interval of time between location data transmissions is doubled. The Location Data Encryption module ensures the security of the location data being transferred, with only a slight impact on battery life (i.e., a decrease of 4.9%). The LAISYC architecture was validated in two innovative mobile apps that would not be possible without LAISYC due to energy and data transfer constraints. The first mobile app, TRAC-IT, is a multi-modal travel behavior data collection tool that can provide simultaneous real-time location-based services. In TRAC-IT, the GPS Auto-Sleep, Session Management, Adaptive Location Data Buffering, Critical Point algorithm, and the Session Management modules all contribute energy savings that enable the phone's battery to last an entire day during real-time high-resolution GPS tracking. High-resolution real-time GPS tracking is critical to TRAC-IT for reconstructing detailed travel path information, including distance traveled, as well as providing predictive, personalized traffic alerts based on historical and real-time data. The Location Data Signing module allows transportation analysts to trust information that is recorded by the application, while the Location Data Encryption module protects the privacy of users' location information. The Session Management, Adaptive Location Data Buffering, and Critical Point algorithm modules allow TRAC-IT to avoid data overage costs on phones with limited data plans while still supporting real-time location data communication. The Adaptive Location Data Buffering module prevents tracking data from being lost when the user is outside network coverage or is on a voice call for networks that do not support simultaneous voice and data communications. The second mobile app, the Travel Assistance Device (TAD), assists transit riders with intellectual disabilities by prompting them when to exit the bus as well as tracking the rider in real-time and alerting caregivers if they are lost. In the most recent group of TAD field tests in Tampa, Florida, TAD provided the alert in the ideal location to transit riders in 100% (n = 33) of tests. In TAD, the GPS Auto-Sleep, Session Management, Adaptive Location Data Buffering, Critical Point algorithm, and the Session Management modules all contribute energy savings that enable the phone's battery to last an entire day during real-time high-resolution GPS tracking. High-resolution GPS tracking is critical to TAD for providing accurate instructions to the transit rider when to exit the bus as well as tracking an accurate location of the traveler so that caregivers can be alerted if the rider becomes lost. The Location Data Encryption module protects the privacy of the transit rider while they are being tracked. The Session Management, Adaptive Location Data Buffering, and Critical Point algorithm modules allow TAD to avoid data overage costs on phones with limited data plans while still supporting real-time location data communication for the TAD tracking alert features. Adaptive Location Data Buffering module prevents transit rider location data from being lost when the user is outside network coverage or is on a voice call for networks that do not support simultaneous voice and data communications.
Scholar Commons Citation
Barbeau, Sean J., "A Location-Aware Architecture Supporting Intelligent Real-Time Mobile Applications" (2012). USF Tampa Graduate Theses and Dissertations.