Allison Kealy

Spatial Data Infrastructure Requirements for Mobile Location Based Journey Planning

A growing number of services are now being offered over mobile devices. They typically combine positioning technology, wireless technology and spatial analysis methods applied to detailed geographical, time based data to offer services in support of time critical, spatial, mobile decision making. A collection of research issues need to be addressed in the successful delivery of such services that extend beyond issues of sophisticated network algorithms. Specifically, careful attention needs to be given to: (1) people and user environments; (2) access to networks; (3) policy, privacy and liability; (4) standards and interoperability; and (5) data quality. Spatial Data Infrastructure (SDI) is the collective term for these interconnected issues and has been a traditional area of research associated with geographic information science. In this paper the particular SDI requirements for the successful delivery of Location Based Services (LBS) are explored through the development of a prototype LBS for journey planning. The initial implementation and testing of this prototype has revealed that the SDI context is well suited as a framework within which to examine the related LBS issues. From a more technical perspective, the testing has revealed that data structure and the means by which large data sets are mined (in order to gather information to present to users) is critical to the success of timely information delivery over limited bandwidth media.

Decentralized and coordinate-free computation of critical points and surface networks in a discretized scalar field

This article provides a decentralized and coordinate-free algorithm, called decentralized gradient field (DGraF), to identify critical points (peaks, pits, and passes) and the topological structure of the surface network connecting those critical points. Algorithms that can operate in the network without centralized control and without coordinates are important in emerging resource-constrained spatial computing environments, in particular geosensor networks. Our approach accounts for the discrepancies between finite granularity sensor data and the underlying continuous field, ignored by previous work. Empirical evaluation shows that our DGraF algorithm can improve the accuracy of critical points identification when compared with the current state-of-the-art decentralized algorithm and matches the accuracy of a centralized algorithm for peaks and pits. The DGraF algorithm is efficient, requiring O(n) overall communication complexity, where n is the number of nodes in the geosensor network. Further, empirical investigations of our algorithm across a range of simulations demonstrate improved load balance of DGraF when compared with an existing decentralized algorithm. Our investigation highlights a number of important issues for future research on the detection of holes and the monitoring of dynamic events in a field.

APFiLoc: An Infrastructure-Free Indoor Localization Method Fusing Smartphone Inertial Sensors, Landmarks and Map Information

The utility and adoption of indoor localization applications have been limited due to the complex nature of the physical environment combined with an increasing requirement for more robust localization performance. Existing solutions to this problem are either too expensive or too dependent on infrastructure such as Wi-Fi access points. To address this problem, we propose APFiLoc—a low cost, smartphone-based framework for indoor localization. The key idea behind this framework is to obtain landmarks within the environment and to use the augmented particle filter to fuse them with measurements from smartphone sensors and map information. A clustering method based on distance constraints is developed to detect organic landmarks in an unsupervised way, and the least square support vector machine is used to classify seed landmarks. A series of real-world experiments were conducted in complex environments including multiple floors and the results show APFiLoc can achieve 80% accuracy (phone in the hand) and around 70% accuracy (phone in the pocket) of the error less than 2 m error without the assistance of infrastructure like Wi-Fi access points.

Cooperative Inertial Navigation for GNSS-Challenged Vehicular Environments

Cooperative positioning (CP) is an approach for positioning and/or positioning enhancement among a number of participants, which communicate and fuse their position-related information. Due to the shortcomings of Global Navigation Satellite Systems (GNSSs), modern CP approaches are considered for improving vehicular positioning where the GNSS cannot address the requirements of the specific applications such as collision avoidance or lane-level positioning. An inertial navigation system (INS) has not been considered for CP in the literature. The hybrid INS/GNSS methods used for positioning enhancement in standalone nodes cannot be classified as CP because the position-related data are not communicated between at least two independent entities. In this paper, we present a novel CP technique to improve INS-based positioning in vehicular networks. This cooperative inertial navigation (CIN) method can be used to enhance INS-based positioning in difficult GNSS environments, such as in very dense urban areas and tunnels. In the CIN method that is proposed, vehicles communicate their inertial measurement unit (IMU) and INS-based position data with oncoming vehicles traveling in the opposite direction. Each vehicle fuses the received data with those locally observed and the carrier frequency offset (CFO) of the received packets to improve the accuracy of its position estimates. The proposed method is analyzed using simulations and is also experimentally verified. The experimental results show up to 72% improvement in positioning over the standalone INS-based method.

An Intelligent Navigation Solution for Land Mobile Location-Based Services

In this research, the authors discuss how the integration of spatial information with real-time positioning sensors into a navigation solution can lead to a significant improvement in navigation results as well as prolonging successful navigation in areas were absolute positioning is unavailable. In order to reduce the inaccuracies associated with low-cost inertial sensor over time, the authors suggest integrating the measurements that are provided by navigation instruments with additional spatial information contained within a map database. It is shown that the information contained in a Geographic Information System (GIS) can be extracted and integrated into the navigational solution.

An INS-Aided Tight Integration Approach for Relative Positioning Enhancement in VANETs

Relative positioning among vehicles is a fundamental parameter for advanced applications of intelligent transportation systems such as collision avoidance and road safety. However, the level of positioning accuracy achievable using Global Navigation Satellite Systems does not meet the requirements of these applications. Cooperative positioning (CP) techniques can be used for improving the performance of absolute or relative positioning in a vehicular ad hoc network (VANET). The tight integration of Global Positioning System (GPS) data among communicating vehicles has already been introduced by the authors as a tight CP approach with specific advantages over differential GPS (DGPS) for relative positioning. In this paper, we propose an enhanced tight CP technique adding low-cost inertial navigation sensors and GPS Doppler shifts. Based on analytical and experimental results, the new method outperforms its predecessor and DGPS by 10% and 24%, respectively.

Using Topological Relationships to Inform a Data Integration Process

When spatial datasets are overlaid, corresponding features do not always coincide. This may be a result of the datasets having differing quality characteristics, being captured at different scales or perhaps being in different projections or datums. Data integration methods have been developed to bring such datasets into alignment. Although these methods attempt to maintain topological relationships within each dataset, spatial relationships between features in different datasets are generally not considered. The preservation of inter-dataset topology is a research area of considerable current interest. This research addresses the preservation of topology within a data integration process. It describes the functional models established to represent a number of spatial relationships as observation equations. These are used to provide additional information concerning the relative positions of features. Since many topological relationships are best modelled as inequalities, an algorithm is developed to accommodate such relationships. The method, based on least squares with inequalities (LSI), is tested on simulated and real datasets. Results are presented to illustrate the optimal positioning solutions determined using all of the available information. In addition, updated quality parameters are provided at the level of the individual coordinate, enabling communication of local variation in the resultant quality of the integrated datasets.

Intelligent Location Models for Next Generation Location-Based Services

To become truly ubiquitous, next generation location-based services (LBS) will have to rely on mobile platforms upon which multiple sensors and measurement systems have been integrated to provide continuous, three-dimensional positioning and orientation. Such technologies are explored today for example in mobile mapping systems, vehicle navigation systems and mobile robot navigation. Next-generation LBS also need theoretically sound methods to translate position into location information. The article addresses this problem: the transformation of position into meaningful and reliable location, and the transformation of location knowledge into positioning constraints. It suggests by this way an intelligent location model that integrates sensor fusion with spatial knowledge fusion via a feedback cycle. It is shown that this feedback cycle consists of three layers: spatial constraints, temporal constraints and spatiotemporal constraints.

User-Independent Motion State Recognition Using Smartphone Sensors

The recognition of locomotion activities (e.g., walking, running, still) is important for a wide range of applications like indoor positioning, navigation, location-based services, and health monitoring. Recently, there has been a growing interest in activity recognition using accelerometer data. However, when utilizing only acceleration-based features, it is difficult to differentiate varying vertical motion states from horizontal motion states especially when conducting user-independent classification. In this paper, we also make use of the newly emerging barometer built in modern smartphones, and propose a novel feature called pressure derivative from the barometer readings for user motion state recognition, which is proven to be effective for distinguishing vertical motion states and does not depend on specific users’ data. Seven types of motion states are defined and six commonly-used classifiers are compared. In addition, we utilize the motion state history and the characteristics of people’s motion to improve the classification accuracies of those classifiers. Experimental results show that by using the historical information and human’s motion characteristics, we can achieve user-independent motion state classification with an accuracy of up to 90.7%. In addition, we analyze the influence of the window size and smartphone pose on the accuracy.

Evaluating the performance of low cost MEMS inertial sensors for seamless indoor/outdoor navigation

For all mobile, location based applications, location availability (either on demand or continuously) is the primary performance requirement of the positioning technologies used. In most cases, this requirement outweighs that of meeting a specified accuracy, as the granularity of information provided to the user can be scaled around the computed positioning accuracy. What is therefore important is being able to generate a position solution and its accuracy at a specified level of confidence. For these applications, meeting the requirement of 100% availability is a significant challenge for individual positioning technologies, even more so when navigating between indoor and outdoor environments. Whilst operating under ideal operating conditions, GPS provides excellent positioning coverage. In indoor environments, position solutions can be generated using infrastructure based technologies such as RFiD and WiFi or augmentation sensors such as inertial navigation systems. Micro- Electromechanical Sensor (MEMS) inertial sensors are a popular option as they offer an autonomous capability that can potentially augment performance seamlessly across indoor and outdoor environments with marginal cost implications. This paper presents the results of a practical test undertaken to evaluate the performance of commercially available MEMS inertial sensors. In particular, results obtained that characterize the performance of these sensors against GPS in the transition zone between indoor and outdoor environments will be presented.