Navigation Recommender:Real-Time iGNSS QoS Prediction for Navigation Services

Global Navigation Satellite Systems (GNSSs), especially Global Positioning System (GPS), have become commonplace in mobile devices and are the most preferred geo-positioning sensors for many location-based applications. Besides GPS, other GNSSs under development or deployment are GLONASS, Galileo, and Compass. These four GNSSs are planned to be integrated in the near future. It is anticipated that integrated GNSSs (iGNSSs) will improve the overall satellite-based geo-positioning performance. However, one major shortcoming of any GNSS and iGNSSs is Quality of Service (QoS) degradation due to signal blockage and attenuation by the surrounding environments, particularly in obstructed areas. GNSS QoS uncertainty is the root cause of positioning ambiguity, poor localization performance, application freeze, and incorrect guidance in navigation applications. In this research, a methodology, called iGNSS QoS prediction, that can provide GNSS QoS on desired and prospective routes is developed. Six iGNSS QoS parameters suitable for navigation are defined: visibility, availability, accuracy, continuity, reliability, and flexibility. The iGNSS QoS prediction methodology, which includes a set of algorithms, encompasses four modules: segment sampling, point-based iGNSS QoS prediction, tracking-based iGNSS QoS prediction, and iGNSS QoS segmentation. Given that iGNSS QoS prediction is data- and compute-intensive and navigation applications require real-time solutions, an efficient satellite selection algorithm is developed and distributed computing platforms, mainly grids and clouds, for achieving real-time performance are explored. The proposed methodology is unique in several respects: it specifically addresses the iGNSS positioning requirements of navigation systems/services; it provides a new means for route choices and routing in navigation systems/services; it is suitable for different modes of travel such as driving and walking; it takes high-resolution 3D data into account for GNSS positioning; and it is based on efficient algorithms and can utilize high-performance and scalable computing platforms such as grids and clouds to provide real-time solutions. A number of experiments were conducted to evaluate the developed methodology and the algorithms using real field test data (GPS coordinates). The experimental results show that the methodology can predict iGNSS QoS in various areas, especially in problematic areas

Are Clouds Ready for Geoprocessing?

Cloud computing has gained popularity in recent years as a new means to quickly process and share information by using a pool of computing resources. Of existing and new applications that could benefit from cloud computing, geospatial applications, whose operations are based on geospatial data and computation, are of particular interest due to prevalence of large geospatial data layers and to complex geospatial computations. Problems in many compute- and/or data-intensive geospatial applications are even more pronounced when real-time response is needed. While researchers have been resorting to high-performance computing (HPC) platforms for efficient processing such as grids and supercomputers, cloud computing with new and advanced features is potential for geospatial problem solving and application implementation and deployment. In this chapter, we discuss the result of our experiments using Google App Engine (GAE), as a representative of existing cloud computing platforms, for real-time geospatial applications

Uncertainty in personal navigation services

The demand for navigation assistance and advances in several technologies has been paving the way for Personal Navigation Services (PerNavs). As users increasingly rely on PerNavs for navigation assistance, they gain a better understanding of what PerNavs can offer and how they operate. This trend, consequently, will increase the demand for PerNav that can provide high quality solutions. While there have been studies addressing uncertainties associated with selected individual navigation modules, there is a void in the literature addressing the overall uncertainty in PerNavs. In this paper, we discuss uncertainty in PerNavs by analyzing uncertainties associated with each of its modules and how they propagate and impact other modules. A Bayesian network is presented as one possible model to manage (by developers) and communicate (to users) uncertainty in PerNavs. © 2011 The Royal Institute of Navigation