Content uploaded by Eugenio Realini
Author content
All content in this area was uploaded by Eugenio Realini
Content may be subject to copyright.
Implementing grid enabled web services for enhanced positioning using low-cost GPS devices
Abstract
This paper presents web based grid services for enhanced GPS positioning. The adopted positioning engine is goGPS, an open source software package for enhancing the accuracy of low-cost devices. goGPS can be provided as a standardized Web Processing Service (WPS) in order to utilize high-quality location data in a variety of location-related applications. Further, the handling of a large number of users and data volumes is addressed by implementing the services on a grid computing platform for supporting large-scale service developments. Benchmarking tests have been carried out to demonstrate the scalability and interoperability of the system. This research investigates the advantages of utilizing cloud resources for positioning services in order to avoid the direct implementation of a physical grid network and to achieve scalable and fault-tolerant systems. Open source software and standards are extensively utilized in the system development. The outcomes can contribute to widening of market for location-based services (LBS) or location-related businesses by lowering accurate positioning costs and providing standardized and interoperable GPS processing services.
With the rapid development of science and technology, positioning technology has been widely used in people’s daily lives and related scientific research activities. However, the traditional positioning system mostly uses GPS for positioning, and then transmits the positioning information to the remote server through GPRS / GSM, but it is not applicable in remote mountain areas where some base station signals cannot reach. Moreover, the accuracy of single GPS positioning is difficult to be guaranteed. This paper mainly studies the design of Beidou-GPS dual-mode positioning system based on Android platform mobile communication equipment. First, analyse the composition of the satellite positioning system and design the overall architecture of the Beidou-GPS dual-mode positioning system for mobile communication equipment. Then, it analyses the most important star selection algorithm in dual-mode positioning technology, and proposes an improved star selection algorithm based on azimuth. Second, build the overall architecture of the Android platform for mobile communication devices based on dual-mode positioning. Finally, by comparing with the traditional star selection algorithm, the proposed improved positioning algorithm is experimentally verified. Simulation experiment results show that the proposed dual-mode positioning algorithm has high accuracy and can meet the real-time requirements of the system.
Road mapping is traditionally performed by using high-level and high-cost instrumentation such as photogrammetric digital cameras, double frequency GPS receivers, inertial measurement units, etc. However, latest developments in GPS data analysis allow obtaining good results also using low-cost devices supported by software like for example goGPS. This software allows enhancing GPS positioning with low-cost receivers mainly by exploiting the principle of relative positioning with respect to a master station located in the area where the survey is performed. Typically goGPS output is a sequence of estimated points at 1 second sampling rate leading to a huge amount of data and, consequently, arcs when these data are used for road mapping. In this paper we implement an algorithm to reduce the number of arcs by first selecting the nodes using an agglomerative clustering procedure and then fitting the resulting polyline on the original GPS dataset by least-squares adjustment. In this way we aim at automatizing the production of road networks, simplifying the procedure currently used for example in OpenStreetMap. This could be particularly useful for developing countries, where the availability of high cost professional instrumentation is often limited. The proposed method is applied in a test scenario evaluating its performances.
Providing enhanced satellite positioning as a web service can be an effective way to enable low-level GPS receivers to perform surveys with a good accuracy and to reduce hardware cost, by removing computation capability and embedded proprietary software. goGPS is an open source application for achieving sub-meter accuracy with low-cost GPS receivers by exploiting real-time kinematic positioning, Kalman filtering, aid from a digital terrain model, and in general by integrating GPS data with other sources of information. Since goGPS directly processes raw GPS observations, it provides a means to substitute black-box processing components (e.g., GPS chipsets) with open source positioning software. goGPS can work either in real-time or post-processing, by acquiring raw GPS data in input and providing positioning (i.e., coordinates) in output. Though originally developed in MATLAB, goGPS was recently ported to Java in order to have the possibility to provide it as a web service, thus allowing a wider user base to develop and use it. Since real-time GPS positioning heavily relies on fast matrix computation, a careful selection of Java matrix libraries was carried out in order to obtain optimal performances. An Open Geospatial Consortium standard Web Processing Service (WPS) implementation of goGPS by means of ZOO WPS framework was developed and tested in order to let lightweight clients just acquire raw GPS data, send them to a server for processing, and receive back the accurate positioning.
This paper aims to present the ZOO Project, which is a new open source implementation of the Open Geospatial Consortium's (OGC) Web Processing Service (WPS), released under the term of the MIT/X-11 license. Based on a robust server-side C language Kernel (named ZOO Kernel), ZOO Project proposes a new approach to develop, handle and chain standardized GIS-based Web services. A brief review of WPS and existing implementations will be first proposed in order to detail the ZOO Project development background and goals. Then, the ZOO itself will be presented, focussing on its assets and limitations, formost to highlight the new opportunities provided by such a platform. The ZOO Kernel and its architecture will be first examinated, before further explanations on the proposed method for Web services creation. The ZOO JavaScript API that provides an easy way to orchestrate and chain Web services will be then presented through technical ramblings on server-side JavaScript support into ZOO Kernel. Both Kernel and API are illustrated and documented through different Web service code snippets. Some visual examples of client-side interactions are also presented.
Recent advanced performance of low-cost Global Positioning System (GPS) and GPS-enabled cell phones has contributed a great
deal to the development of location-aware services and systems. High-speed broadband technology has promoted collaborative
projects such as OpenStreetMap or other User Generated Contents services. In this research, a Web-based prototype system for
GPS track log and point of interest (POI) management was developed to archive a collaborative framework in field surveys.
The main function of the system can be separated into three parts: data collection, data management, and data quality enhancement.
The system supports real-time data collection for the future ubiquitous environment and also can monitor real-time GPS positions.
This research shows functionalities that can minimize GPS errors using Dilution of Precision filtering and data quality enhancing
techniques using the Douglas-Peucker algorithm and PgRouting. The research introduces a system that provides an interoperable
framework in which to work with other geospatial services through open geospatial standards.
KeywordsPoint of interest (POI)-Location-based service (LBS)-Global Positioning System (GPS)-Quality filtering-Simplification
Digital Elevation Models (DEM) and 3D spatial data plays an important role in typical earth science applications. Numerous
simulations, e.g. flood modeling, and spatial analysis, requires very exact terrain data. During the acquisition of these
data, for an example by means of laserscanning, very large data sets results due to the high measuring point density (up to
four points per square meter). Current classical Geo-Information-System (GIS) software cannot manage the demand of processing
and analyzing these massive raw terrain data. A lack of computing power may appear. There is a need for sophisticated data
processing techniques. For this purpose the use of Grid Computing is a good choice to accomplish high processing performance
and storage capacity. To process these massive raw geodata we develop a range of terrain Web Processing Services (WPS) which
are made available as Grid services.
The use of virtualization, along with an efficient virtual machine management, creates a new virtualization layer that isolates the service workload from the resource management. The integration of the cloud within the virtualization layer, can be used to support on-demand resource provisioning, providing elasticity in modern Internet-based services and applications, and allowing to adapt dynamically the service capacity to variable user demands. Cluster and grid computing environments are two examples of services which can obtain a great benefit from these technologies. Virtualization can be used to transform a distributed physical infrastructure into a flexible and elastic virtual infrastructure, separating resource provisioning from job execution management, and adapting dynamically the cluster or grid size to the users' computational demands. In particular, in this paper we analyze the deployment of a computing cluster on top of a virtualized infrastructure layer, which combines a local virtual machine manager (the OpenNebula engine) and a cloud resource provider (Amazon EC2). The solution is evaluated using the NAS Grid Benchmarks in terms of processing overhead due to virtualization, communication overhead due to the management of nodes across different geographic locations, and elasticity in the cluster processing capacity.