>Magister Scientiae - MSc / The development of regional ionospheric Total Electron Content (TEC) models has contributed to understanding the behavior of ionospheric parameters and the coupling of the ionosphere to space weather activities on both local and global scales. In the past several decades, the International Global Navigation Satellite Systems Service (GNSS) networks of dual frequency receiver data have been applied to develop global and regional models of ionospheric TEC. These models were mainly developed in the Northern Hemisphere where there are dense network of ground based GPS receivers for regional data coverage. Such efforts have been historically rare over the African region, and have only recently begun. This thesis reports the investigation of the effect of mid-latitude magnetic storms on TEC over South Africa for portions of Solar Cycles 23 and 24. The MAGIC package was used to estimate TEC over South Africa during Post Solar Maximum, Solar Minimum, and Post Solar Minimum periods. It is found that TEC is largely determined by the diurnal cycle of solar forcing and subsequent relaxation, but effects due to storms can be determined

The term physical accessibility has widely been used by geographers, economists and urban planners and basically reflects the relative ease of access to/from several urban/rural services by considering various travelling costs. Numerous accessibility measures, ranging from simple to sophisticated, can be found in the GIS based accessibility modeling literature. However, whether simple or sophisticated, one of the fundamental shortcomings of the current GIS-based accessibility measures is that they are generally calculated from a fixed catchment area boundary based on constant traveling costs such as Euclidian (bird-flight) distance costs or transportation network-based average speed costs (e.g. 50 km/h for main streets and 30 km/h for local streets, etc.). Although such deterministic approaches are widely used in GIS-based accessibility modeling literature, they are not realistic, especially due to highly variable speeds in road segments and uncertainty in the accuracy and reliability of the accessibility measures. Therefore, this dissertation provides a new stochastic methodology for GIS-based accessibility modeling process by using GPS-based floating car data and Monte Carlo Simulation (MCS) that could handle variations in traveling costs and consider all possible catchment area boundaries, instead of one average or maximum fixed catchment area boundary. The main contribution of the research is that / the proposed physical accessibility modeling could handle uncertainties in transportation costs, create significant improvement on accuracy and reliability of accessibility measures in terms of catchment area boundaries and support decision makers who are supposed to deal with accessibility, location/allocation and service/catchment area related issues. The proposed stochastic methodology is implemented to a case study on medical emergency service accessibility, in Eskisehir, Turkey and the results of the deterministic and stochastic accessibility models are compared. The main focus of the case study is not to evaluate a specific accessibility condition in a detailed manner but to provide a methodological discussion and comparison between the deterministic and stochastic accessibility modeling process. With the implementation to a case study, it is shown that / the results of the proposed methodology are more realistic than the conventional deterministic approaches.

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.

Disputats. Universität Hohenheim, 2005.

Recent accessibility drives and price wars between the major South African (SA) cell phone companies suggest that the landscape for the adoption of mobile learning (m-learning) at the Higher Education Institution (HEI) level may be changing. As such, there is a need to gauge the current mobile readiness of students for m-learning. Mobile technology readiness refers to the extent to which students have access to mobile devices (not only handsets), and can afford data bundles that meet or exceed the requirements of a base set of currently available m-learning applications (Naicker and Van der Merwe 2012). Mobile operational readiness refers to students’ awareness of, attitude towards, support and training that is required for m-learning. This study conducted an assessment of the technology and operational readiness of students at a SA HEI. An in-depth literature survey was undertaken to delineate technology and operational readiness of students for m-learning. For technology readiness, an investigation was conducted on m- learning applications that are currently available and the technology requirements of these mobile applications. This was undertaken to determine the extent that the current student mobile handset profile match these requirements. The literature review also included a search for mobile opeeratratiioonnaall ffaactorctorss ssuuchch aass ssttuuddeennttss’’ aawwaarenerenessss ooff aanndd aattttiittuuddee ttoowwaarrddss mm--lleaearrnininngg as well as m-learning support and training that students require. The philosophical underpinning of this study was based on Activity Theory. The strategy of inquiry employed was a case study approach. Data was collected from students at the Durban University of Technology, a resident based SA HEI. A mixed methods data collection strategy was employed. The researcher used a field survey questionnaire as the primary research instrument to assess mobile technology and operational readiness. Focus group interviews were used as a secondary data gathering tool to triangulate and strengthen the results. The results were presented using descriptive and inferential statistics and were analyzed using the lens of activity theory. In terms of technology readiness, despite a high level of ownership and reasonable compliance with application requirements, data costs remain prohibitive. In assessing operational readiness, despite a positive attitude, the majority of the students require awareness, ongoing support and training. Several recommendations based on the findings are offered. For example, one of the findings showed that mobile connectivity affordability was low amongst students and it is recommended that the HEI work around exorbitant connectivity costs by combining m-learning technologies to form meaningful m-learning approaches at a minimum v cost. Another finding showed low awareness of m-learning at the HEI. A recommendation advanced to combat this finding is for the HEI to encourage and support dialogue among key stakeholders. This study concludes that any m-learning endeavour to implement m-learning at this HEI is bound to fail as only a small percentage of students are aware of m-learning and can afford data bundles to implement m-learning in its true sense. As an implication of this study to other HEI’s, the researcher suggests that regular mobile readiness surveys be conducted. / Science and Technology Education / M. A. (Information Systems)

Recent accessibility drives and price wars between the major South African (SA) cell phone companies suggest that the landscape for the adoption of mobile learning (m-learning) at the Higher Education Institution (HEI) level may be changing. As such, there is a need to gauge the current mobile readiness of students for m-learning. Mobile technology readiness refers to the extent to which students have access to mobile devices (not only handsets), and can afford data bundles that meet or exceed the requirements of a base set of currently available m-learning applications (Naicker and Van der Merwe 2012). Mobile operational readiness refers to students’ awareness of, attitude towards, support and training that is required for m-learning. This study conducted an assessment of the technology and operational readiness of students at a SA HEI. An in-depth literature survey was undertaken to delineate technology and operational readiness of students for m-learning. For technology readiness, an investigation was conducted on m- learning applications that are currently available and the technology requirements of these mobile applications. This was undertaken to determine the extent that the current student mobile handset profile match these requirements. The literature review also included a search for mobile opeeratratiioonnaall ffaactorctorss ssuuchch aass ssttuuddeennttss’’ aawwaarenerenessss ooff aanndd aattttiittuuddee ttoowwaarrddss mm--lleaearrnininngg as well as m-learning support and training that students require. The philosophical underpinning of this study was based on Activity Theory. The strategy of inquiry employed was a case study approach. Data was collected from students at the Durban University of Technology, a resident based SA HEI. A mixed methods data collection strategy was employed. The researcher used a field survey questionnaire as the primary research instrument to assess mobile technology and operational readiness. Focus group interviews were used as a secondary data gathering tool to triangulate and strengthen the results. The results were presented using descriptive and inferential statistics and were analyzed using the lens of activity theory. In terms of technology readiness, despite a high level of ownership and reasonable compliance with application requirements, data costs remain prohibitive. In assessing operational readiness, despite a positive attitude, the majority of the students require awareness, ongoing support and training. Several recommendations based on the findings are offered. For example, one of the findings showed that mobile connectivity affordability was low amongst students and it is recommended that the HEI work around exorbitant connectivity costs by combining m-learning technologies to form meaningful m-learning approaches at a minimum cost. Another finding showed low awareness of m-learning at the HEI. A recommendation advanced to combat this finding is for the HEI to encourage and support dialogue among key stakeholders. This study concludes that any m-learning endeavour to implement m-learning at this HEI is bound to fail as only a small percentage of students are aware of m-learning and can afford data bundles to implement m-learning in its true sense. As an implication of this study to other HEI’s, the researcher suggests that regular mobile readiness surveys be conducted. / Science and Technology Education / M. Sc. (Information Systems)