RFID-Based Indoor Spatial Query Evaluation with Bayesian Filtering Techniques

People spend a significant amount of time in indoor spaces (e.g., office buildings, subway systems, etc.) in their daily lives. Therefore, it is important to develop efficient indoor spatial query algorithms for supporting various location-based applications. However, indoor spaces differ from outdoor spaces because users have to follow the indoor floor plan for their movements. In addition, positioning in indoor environments is mainly based on sensing devices (e.g., RFID readers) rather than GPS devices. Consequently, we cannot apply existing spatial query evaluation techniques devised for outdoor environments for this new challenge. Because Bayesian filtering techniques can be employed to estimate the state of a system that changes over time using a sequence of noisy measurements made on the system, in this research, we propose the Bayesian filtering-based location inference methods as the basis for evaluating indoor spatial queries with noisy RFID raw data. Furthermore, two novel models, indoor walking graph model and anchor point indexing model, are created for tracking object locations in indoor environments. Based on the inference method and tracking models, we develop innovative indoor range and k nearest neighbor (kNN) query algorithms. We validate our solution through use of both synthetic data and real-world data. Our experimental results show that the proposed algorithms can evaluate indoor spatial queries effectively and efficiently. We open-source the code, data, and floor plan at https://github.com/DataScienceLab18/IndoorToolKit

Towards Mobility Data Science (Vision Paper)

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from the metadata. PDF has not been change

Appearance-based indoor localization: a comparison of patch descriptor performance

Vision is one of the most important of the senses, and humans use it extensively during navigation. We evaluated different types of image and video frame descriptors that could be used to determine distinctive visual landmarks for localizing a person based on what is seen by a camera that they carry. To do this, we created a database containing over 3 km of video-sequences with ground-truth in the form of distance travelled along different corridors. Using this database, the accuracy of localization - both in terms of knowing which route a user is on - and in terms of position along a certain route, can be evaluated. For each type of descriptor, we also tested different techniques to encode visual structure and to search between journeys to estimate a user's position. The techniques include single-frame descriptors, those using sequences of frames, and both colour and achromatic descriptors. We found that single-frame indexing worked better within this particular dataset. This might be because the motion of the person holding the camera makes the video too dependent on individual steps and motions of one particular journey. Our results suggest that appearance-based information could be an additional source of navigational data indoors, augmenting that provided by, say, radio signal strength indicators (RSSIs). Such visual information could be collected by crowdsourcing low-resolution video feeds, allowing journeys made by different users to be associated with each other, and location to be inferred without requiring explicit mapping. This offers a complementary approach to methods based on simultaneous localization and mapping (SLAM) algorithms.Comment: Accepted for publication on Pattern Recognition Letter

Iconic Indexing for Video Search

Submitted for the degree of Doctor of Philosophy, Queen Mary, University of London

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

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity