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Adaptive, reliable, and accurate positioning model for location-based services
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis presents a new strategy in achieving highly reliable and accurate position solutions fulfilling the requirements of Location-Based Services (LBS) pedestriansā applications. The new strategy is divided into two main parts. The first part integrates the available positioning technology within the surrounding LBS application context by introducing an adaptive LBS framework. The context can be described as a group of factors affecting the application behaviour; this includes environmental states, available resources and user preferences. The proposed adaptive framework consists of several stages, such as defining the contextual factors that have a direct effect on the positioning performance, identifying preliminary positioning performance requirements associated with different LBS application groups, and introducing an intelligent positioning services selection function. The second part of this work involves the design and development of a novel positioning model that is responsible for delivering highly reliable, accurate and precise position solutions to LBS users. This new model is based on the single frequency GPS Standard Positioning Service (SPS). Additionally, it is incorporated within the adaptive LBS framework while providing the position solutions, in which all identified contextual factors and application requirements are accounted. The positioning model operates over a client-server architecture including two main components, described as the Localisation Server (LS) and the Mobile Unit (MU). Hybrid functional approaches were developed at both components consisting of several processing procedures allowing the positioning model to operate in two position determination modes. Stand-alone mode is used if enough navigation information was available at the MU using its local positioning device (GPS/EGNOS receiver). Otherwise, server-based mode is utilised, in which the LS intervenes and starts providing the required position solutions. At the LS, multiple sources of GPS augmentation services were received using the Internet as the sole augmentation data transportation medium. The augmentation data was then processed and integrated for the purpose of guaranteeing the availability of valid and reliable information required for the provision of accurate and precise position solutions. Two main advanced position computation methods were developed at the LS, described as coordinate domain and raw domain.
The positioning model was experimentally evaluated. According to the reported results, the LS through the developed position computation methods, was able to provide position samples with an accuracy of less than 2 meters, with high precision at 95% confidence level; this was achieved in urban, rural, and open space (clear satellite view) navigation environments. Additionally, the integrity of the position solutions was guaranteed in such environments during more than 90% of the navigation time, taking into consideration the identified integrity thresholds (Horizontal Alert Limits (HAL)=11 m). This positioning performance has outperformed the existing GPS/EGNOS service which was implemented at the MU in all scenarios and environments. In addition, utilising a simulation evaluation facility the developed positioning model performance was quantified with reference to a hybrid positioning service that will be offered by future Galileo Open Service (OS) along with GPS/EGNOS. Using the statistical t-test, it was concluded that there is no significant difference in terms of the position samplesā accuracy achieved from the developed positioning model and the hybrid system at a particular navigation environment described as rural area. The p-value was 0.08 and the level of significance used was 0.05. However, a significant difference in terms of the service integrity for the advantage of the hybrid system was experienced in all remaining scenarios and environments more especially the urban areas due to surrounding obstacles and conditions
Trust-based algorithms for fusing crowdsourced estimates of continuous quantities
Crowdsourcing has provided a viable way of gathering information at unprecedented volumes and speed by engaging individuals to perform simple microātasks. In particular, the crowdsourcing paradigm has been successfully applied to participatory sensing, in which the users perform sensing tasks and provide data using their mobile devices. In this way, people can help solve complex environmental sensing tasks, such as weather monitoring, nuclear radiation monitoring and cell tower mapping, in a highly decentralised and parallelised fashion. Traditionally, crowdsourcing technologies were primarily used for gathering data for classifications and image labelling tasks. In contrast, such crowdābased participatory sensing poses new challenges that relate to (i) dealing with humanāreported sensor data that are available in the form of continuous estimates of an observed quantity such as a location, a temperature or a sound reading, (ii) dealing with possible spatial and temporal correlations within the data and (ii) issues of data trustworthiness due to the unknown capabilities and incentives of the participants and their devices. Solutions to these challenges need to be able to combine the data provided by multiple users to ensure the accuracy and the validity of the aggregated results. With this in mind, our goal is to provide methods to better aid the aggregation process of crowdāreported sensor estimates of continuous quantities when data are provided by individuals of varying trustworthiness. To achieve this, we develop a trustābased in- formation fusion framework that incorporates latent trustworthiness traits of the users within the data fusion process. Through this framework, we develop a set of four novel algorithms (MaxTrust, BACE, TrustGP and TrustLGCP) to compute reliable aggregations of the usersā reports in both the settings of observing a stationary quantity (Max- Trust and BACE) and a spatially distributed phenomenon (TrustGP and TrustLGCP). The key feature of all these algorithm is the ability of (i) learning the trustworthiness of each individual who provide the data and (ii) exploit this latent userās trustworthiness information to compute a more accurate fused estimate. In particular, this is achieved by using a probabilistic framework that allows our methods to simultaneously learn the fused estimate and the usersā trustworthiness from the crowd reports. We validate our algorithms in four key application areas (cell tower mapping, WiFi networks mapping, nuclear radiation monitoring and disaster response) that demonstrate the practical impact of our framework to achieve substantially more accurate and informative predictions compared to the existing fusion methods. We expect that results of this thesis will allow to build more reliable data fusion algorithms for the broad class of humanācentred information systems (e.g., recommendation systems, peer reviewing systems, student grading tools) that are based on making decisions upon subjective opinions provided by their users
Mining human mobility patterns from pervasive spatial and temporal data
Recent advances in communication, sensors and processors have made pervasive systems more computationally powerful and increasingly popular. These systems are deployed everywhere all the time while remaining transparent. Take smartphones as an example; they have become an integral part of human life and people carry them wherever they go. Coupled with the popularity of pervasive systems and user tracking, this has opened up excellent opportunities to analyse human mobility. This can be applied to a broad range of location-based services such as smart navigation and recommendation systems. Data from pervasive systems has temporal, spatial and spatio-temporal aspects that can be leveraged for mining human mobility patterns. Temporal data such as time series from embedded sensors on smartphones does not usually have any information about locations, while time stamps are discarded in spatial data. The list of significant locations visited by the user is an example of spatial data. The third group of data is spatio-temporal data that has both temporal and spatial aspects such as users' trajectories. In this dissertation, we analyse human mobility by mining these three kinds of data. In each chapter, we look at a specific aspect to infer key information about usersā mobility including transition time detection, movement graph summarisation, and trajectory prediction. We analyse temporal information from time series data to extract transition times in daily activities. The transition times denote when user activities change such as when the user goes to work or when the user goes shopping. In addition to applications in location-based services, extracting the transition times helps us to understand human mobility patterns across the whole day. We tackle scalability to enable processing to take place on resource-constrained devices. We introduce Shrink as a new summarisation method to compress large scale graphs. Trajectories and movements of the user can be transformed into a graph in which each node represents stay points and each edge represents distance. Since this graph is very large, Shrink is used to reduce the size of the movement graph while preserving distances between nodes. The property that is preserved in the compressed graph, also known as the coarse graph, is the distance between the nodes. Shrink is a query friendly compression, which means the compressed graph can be queried without decompression. As the complexity of distance-based queries such as shortest path queries is highly dependent on the size of the graph, Shrink improves performance in terms of time and storage. We also investigate the effect of compression on the human mobility mining algorithms and show that the summarisation provides a trade-off between efficiency and granularity. We also analyse spatial-temporal data by predicting user trajectory based on historical data. Specifically, given the historical data and the userās trajectory in the first part of the current day (e.g. trajectory in the morning), we predict how users will complete their trajectory in that particular day (e.g. predicting the trajectory for the rest of the day or the afternoon). The granularity of the predicted trajectory is the same as the granularity of the given trajectories. We emphasize that the predicted trajectory includes the sequence of future locations, the stay times, and the departure times. This enhances the user experience because by having the detailed trajectory in advance, location-based services can notify users about the consequence of the movement. In summary, this thesis contains efficient algorithms that can be applied to diverse aspects of pervasive signals for mining human mobility. The new algorithms are aimed at problems in transition time detection, summarisation, and prediction. The solutions address the scalability issues and can work in big pervasive temporal and spatial data effectively and accurately
Digital traces of human mobility and interaction: models and applications
In the last decade digital devices and services have permeated many aspects of everyday life. They generate massive amounts of data that provide insightful information about how people move across geographic areas and how they interact with others. By analysing this detailed information, it is possible to investigate aspects of human mobility and interaction. Therefore, the thesis of this dissertation is that the analysis of mobility and interaction traces generated by digital devices and services, at different timescales and spatial granularity, can be used to gain a better understanding of human behaviour, build new applications and improve existing services. In order to substantiate this statement I develop analytical models and applications supported by three sources of mobility and interaction data: online social networks, mobile phone networks and GPS traces.
First, I present three applications related to data gathered from online social networks, namely the analysis of a global rumour spreading in Twitter, the definition of spatial dissemination measures in a social graph and the analysis of collaboration between developers in GitHub. Then I describe two applications of the analysis of country-wide data of cellular phone networks: the modelling of epidemic containment strategies, with the goal of assessing their efficacy in curbing infectious diseases; the definition of a mobility-based measure of individual risk, which can be used to identify who needs targeted treatment. Finally, I present two applications based on GPS traces: the estimation of trajectories from spatially-coarse temporally-sparse location traces and the analysis of routing behaviour in urban settings
Cooperative Radio Communications for Green Smart Environments
The demand for mobile connectivity is continuously increasing, and by 2020 Mobile and Wireless Communications will serve not only very dense populations of mobile phones and nomadic computers, but also the expected multiplicity of devices and sensors located in machines, vehicles, health systems and city infrastructures. Future Mobile Networks are then faced with many new scenarios and use cases, which will load the networks with different data traffic patterns, in new or shared spectrum bands, creating new specific requirements. This book addresses both the techniques to model, analyse and optimise the radio links and transmission systems in such scenarios, together with the most advanced radio access, resource management and mobile networking technologies. This text summarises the work performed by more than 500 researchers from more than 120 institutions in Europe, America and Asia, from both academia and industries, within the framework of the COST IC1004 Action on "Cooperative Radio Communications for Green and Smart Environments". The book will have appeal to graduates and researchers in the Radio Communications area, and also to engineers working in the Wireless industry. Topics discussed in this book include: ā¢ Radio waves propagation phenomena in diverse urban, indoor, vehicular and body environmentsā¢ Measurements, characterization, and modelling of radio channels beyond 4G networksā¢ Key issues in Vehicle (V2X) communicationā¢ Wireless Body Area Networks, including specific Radio Channel Models for WBANsā¢ Energy efficiency and resource management enhancements in Radio Access Networksā¢ Definitions and models for the virtualised and cloud RAN architecturesā¢ Advances on feasible indoor localization and tracking techniquesā¢ Recent findings and innovations in antenna systems for communicationsā¢ Physical Layer Network Coding for next generation wireless systemsā¢ Methods and techniques for MIMO Over the Air (OTA) testin
Learning cognitive maps: Finding useful structure in an uncertain world
In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. Ā© 2007 Springer-Verlag Berlin Heidelberg
Proceedings of the Second International Mobile Satellite Conference (IMSC 1990)
Presented here are the proceedings of the Second International Mobile Satellite Conference (IMSC), held June 17-20, 1990 in Ottawa, Canada. Topics covered include future mobile satellite communications concepts, aeronautical applications, modulation and coding, propagation and experimental systems, mobile terminal equipment, network architecture and control, regulatory and policy considerations, vehicle antennas, and speech compression
Cooperative Radio Communications for Green Smart Environments
The demand for mobile connectivity is continuously increasing, and by 2020 Mobile and Wireless Communications will serve not only very dense populations of mobile phones and nomadic computers, but also the expected multiplicity of devices and sensors located in machines, vehicles, health systems and city infrastructures. Future Mobile Networks are then faced with many new scenarios and use cases, which will load the networks with different data traffic patterns, in new or shared spectrum bands, creating new specific requirements. This book addresses both the techniques to model, analyse and optimise the radio links and transmission systems in such scenarios, together with the most advanced radio access, resource management and mobile networking technologies. This text summarises the work performed by more than 500 researchers from more than 120 institutions in Europe, America and Asia, from both academia and industries, within the framework of the COST IC1004 Action on "Cooperative Radio Communications for Green and Smart Environments". The book will have appeal to graduates and researchers in the Radio Communications area, and also to engineers working in the Wireless industry. Topics discussed in this book include: ā¢ Radio waves propagation phenomena in diverse urban, indoor, vehicular and body environmentsā¢ Measurements, characterization, and modelling of radio channels beyond 4G networksā¢ Key issues in Vehicle (V2X) communicationā¢ Wireless Body Area Networks, including specific Radio Channel Models for WBANsā¢ Energy efficiency and resource management enhancements in Radio Access Networksā¢ Definitions and models for the virtualised and cloud RAN architecturesā¢ Advances on feasible indoor localization and tracking techniquesā¢ Recent findings and innovations in antenna systems for communicationsā¢ Physical Layer Network Coding for next generation wireless systemsā¢ Methods and techniques for MIMO Over the Air (OTA) testin
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