Context-aware GPS Integrity Monitoring for Intelligent Transport Systems (ITS)

The integrity of positioning systems has become an increasingly important requirement for location-based Intelligent Transports Systems (ITS). The navigation systems, such as Global Positioning System (GPS), used in ITS cannot provide the high quality positioning information required by most services, due to the various type of errors from GPS sensor, such as signal outage, and atmospheric effects, all of which are difficult to measure, or from the map matching process. Consequently, an error in the positioning information or map matching process may lead to inaccurate determination of a vehicle’s location. Thus, the integrity is require when measuring both vehicle’s positioning and other related information such as speed, to locate the vehicle in the correct road segment, and avoid errors. The integrity algorithm for the navigation system should include a guarantee that the systems do not produce misleading or faulty information; as this may lead to a significant error arising in the ITS services. Hence, to achieve the integrity requirement a navigation system should have a robust mechanism, to notify the user of any potential errors in the navigation information. The main aim of this research is to develop a robust and reliable mechanism to support the positioning requirement of ITS services. This can be achieved by developing a high integrity GPS monitoring algorithm with the consideration of speed, based on the concept of context-awareness which can be applied with real time ITS services to adapt changes in the integrity status of the navigation system. Context-aware architecture is designed to collect contextual information about the vehicle, including location, speed and heading, reasoning about its integrity and reactions based on the information acquired. In this research, three phases of integrity checks are developed. These are, (i) positioning integrity, (ii) speed integrity, and (iii) map matching integrity. Each phase uses different techniques to examine the consistency of the GPS information. A receiver autonomous integrity monitoring (RAIM) algorithm is used to measure the quality of the GPS positioning data. GPS Doppler information is used to check the integrity of vehicle’s speed, adding a new layer of integrity and improving the performance of the map matching process. The final phase in the integrity algorithm is intended to verify the integrity of the map matching process. In this phase, fuzzy logic is also used to measure the integrity level, which guarantees the validity and integrity of the map matching results. This algorithm is implemented successfully, examined using real field data. In addition, a true reference vehicle is used to determine the reliability and validity of the output. The results show that the new integrity algorithm has the capability to support a various types of location-based ITS services.Saudi Arabia Cultural Burea

Advanced Map Matching Technologies and Techniques for Pedestrian/Wheelchair Navigation

Due to the constantly increasing technical advantages of mobile devices (such as smartphones), pedestrian/wheelchair navigation recently has achieved a high level of interest as one of smartphones’ potential mobile applications. While vehicle navigation systems have already reached a certain level of maturity, pedestrian/wheelchair navigation services are still in their infancy. By comparing vehicle navigation systems, a set of map matching requirements and challenges unique in pedestrian/wheelchair navigation is identified. To provide navigation assistance to pedestrians and wheelchair users, there is a need for the design and development of new map matching techniques. The main goal of this research is to investigate and develop advanced map matching technologies and techniques particular for pedestrian/wheelchair navigation services. As the first step in map matching, an adaptive candidate segment selection algorithm is developed to efficiently find candidate segments. Furthermore, to narrow down the search for the correct segment, advanced mathematical models are applied. GPS-based chain-code map matching, Hidden Markov Model (HMM) map matching, and fuzzy-logic map matching algorithms are developed to estimate real-time location of users in pedestrian/wheelchair navigation systems/services. Nevertheless, GPS signal is not always available in areas with high-rise buildings and even when there is a signal, the accuracy may not be high enough for localization of pedestrians and wheelchair users on sidewalks. To overcome these shortcomings of GPS, multi-sensor integrated map matching algorithms are investigated and developed in this research. These algorithms include a movement pattern recognition algorithm, using accelerometer and compass data, and a vision-based positioning algorithm to fill in signal gaps in GPS positioning. Experiments are conducted to evaluate the developed algorithms using real field test data (GPS coordinates and other sensors data). The experimental results show that the developed algorithms and the integrated sensors, i.e., a monocular visual odometry, a GPS, an accelerometer, and a compass, can provide high-quality and uninterrupted localization services in pedestrian/wheelchair navigation systems/services. The map matching techniques developed in this work can be applied to various pedestrian/wheelchair navigation applications, such as tracking senior citizens and children, or tourist service systems, and can be further utilized in building walking robots and automatic wheelchair navigation systems

Indoor location identification technologies for real-time IoT-based applications: an inclusive survey

YesThe advent of the Internet of Things has witnessed tremendous success in the application of wireless sensor networks and ubiquitous computing for diverse smart-based applications. The developed systems operate under different technologies using different methods to achieve their targeted goals. In this treatise, we carried out an inclusive survey on key indoor technologies and techniques, with to view to explore their various benefits, limitations, and areas for improvement. The mathematical formulation for simple localization problems is also presented. In addition, an empirical evaluation of the performance of these indoor technologies is carried out using a common generic metric of scalability, accuracy, complexity, robustness, energy-efficiency, cost and reliability. An empirical evaluation of performance of different RF-based technologies establishes the viability of Wi-Fi, RFID, UWB, Wi-Fi, Bluetooth, ZigBee, and Light over other indoor technologies for reliable IoT-based applications. Furthermore, the survey advocates hybridization of technologies as an effective approach to achieve reliable IoT-based indoor systems. The findings of the survey could be useful in the selection of appropriate indoor technologies for the development of reliable real-time indoor applications. The study could also be used as a reliable source for literature referencing on the subject of indoor location identification.Supported in part by the Tertiary Education Trust Fund of the Federal Government of Nigeria, and in part by the European Union’s Horizon 2020 Research and Innovation Programme under Grant agreement H2020-MSCA-ITN-2016 SECRET-72242

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