There have been a number of methods proposed for increasing the precision of mobile positioning systems. One of the latest methods is Assisted GPS, A-GPS, on the Secure User Plane for Location, SUPL, platform, which seems to be a very interesting alternative from TeliaSoneras perspective, thanks to minimal infrastructural investment costs. According to theory and lab testing A-GPS has the potential of providing a very good customer value in relation to the investment needed. There is, however, a great need to see the performance when used in real user environments and with real user equipment. This is the basis for the choice of field testing as the method used in this thesis. The result from the field tests conducted in this thesis shows that the performance of A-GPS is very good in outdoor environments, but when used in indoor environments, poor signal strength in combination with multipath and fading becomes a problem with low accuracy and long response times as a result. Using a hosted mapping service, in combination with A-GPS, provides the possibilities of launching location based services even outside the home network. TeliaSonera had already found such a hosted mapping service that matched their compatibility, and reliability, requirements. This thesis investigates this hosted mapping service further and finds that the quality of the cartographic presentation of the map information is very poor. The conclusion is that A-GPS performance, today, is limited by the hardware and algorithms used. If these are further adapted to indoor conditions, A-GPS has the potential of providing the customer value promised by the theoretical performance. For a successful launch of A-GPS services there is a great need of better cartographic presentation of map information, than what is currently is provided by the investigated hosted mapping service.

Spatial subscriptions have been used to specify locations of interest in Distributed Event-based Systems (DEBSs). However, current DEBSs representations to support spatial subscriptions are not expressive enough to describe some forms of subscriptions in mobile settings. For instance, users are not allowed to specify a spatial subscription that refers to other more well-known locations, in case they are not familiar with the names of their current locations. In addition, the middleware in existing DEBSs does not support changes at runtime, and modification to these middleware systems to support spatial subscriptions are highly coupled with specific DEBS infrastructures. In this thesis, I argue that by enhancing the expressiveness of spatial subscriptions, a new model of mobile spatial subscriptions for location-aware services can be defined and a reusable plug-in implementation approach that supports existing DEBSs can be developed. This thesis first summarizes the essential abstractions to specify mobile spatial subscriptions, and analyze the expressiveness of existing DEBSs to support these abstractions. Second, it proposes a three-level mobile spatial subscription model, which supports the essential abstractions used to specify spatial subscriptions. The first level of the model handles subscriptions consisting of geometric coordinates; the second level supports subscriptions with location labels; the third level interprets subscriptions which specify locations by stating their dynamic properties. Next, a plug-in implementation approach is introduced, and hence, the three-level model can be integrated with different DEBSs with minimal modification to the middleware. The subscription model is implemented as a subscriber/publisher component, instead of directly modifying the existing DEBS. Finally, I develop a prototype system, Dynamic Mobile Subscription System (DMSS), and illustrate the usefulness and applicability of the three-level model and the plug-in implementation approach.

The vision of ubiquitous computing is to seamlessly integrate information processing into everyday objects and activities. Part of this integration is an awareness on the part of a system of its user's context. Context can be composed of several variables --- such as a user's current activity, goals, or state of mind --- but location (both past and present) is almost always a key component. Determining location outdoors has become quite simple and pervasive with today's low-cost handheld Global Positioning System (GPS) receivers. Technologies enabling the location of people and objects to be determined while indoors, however, have lagged due to their extensive infrastructure requirements and associated cost. Just as GPS receivers utilize radio signals from satellites to triangulate their position, an indoor real-time locating system (RTLS) must also make use of some feature of the environment to determine the location of mobile units. Since the signal from GPS satellites is not sufficiently strong to penetrate the structure of a building, indoor RTLS systems must either use some existing feature of the environment or generate a new one. This typically requires a large amount of infrastructure (e.g. specialized RF receivers, additional 802.11 access points, RFID readers, etc.) to be deployed, making indoor RTLSs impractical for the home. While numerous techniques have been proposed for locating people and objects within a building, none of these has yet proven to be a viable option in terms of cost, complexity of installation, and accuracy for home users. This dissertation builds on work by Patel et al. in which the home power lines are used to radiate a low-frequency wireless RF signal that mobile tags use for location fingerprinting. Leveraging the existing power line permits this system to operate on far less additional infrastructure than existing solutions such as cellular (GSM and CDMA), 802.11b/g, and FM radio based systems. The contributions of this research to indoor power line-based RTLS are threefold. First, I examine the temporal stability of a power line based RTLS system's output. Fingerprinting-based RTLS relies upon some feature of the environment, such as the amplitude of an RF signal, to be stable over time at a particular location (temporal stability), but to change in space (spatial differentiability). I show that a power line-based RTLS can be made much more resistant to temporal instability in individual fingerprint components by utilizing a wide-band RF fingerprint. Next, I directly compare the temporal stability of the raw features used by various fingerprinting based indoor RTLSs, such as cellular, 802.11b/g, and FM radio. In doing so, I show that a power line based indoor RTLS has an inherent advantage in temporal stability over these other methods. Finally, I characterize the power line as a receiving antenna for low-powered wireless devices within the home, thus allowing the power line to not only transmit the RF signals used for fingerprinting, but also to receive the sensed features reported by location tags. Here, I show that the powerline is a viable receiver for these devices and that the globally available 27.12 MHz ISM band is a good choice of frequency for communications.

Advances in sensing and positioning technology, fueled by wide deployment of wireless networks, have made many devices location-aware. These emerging technologies have enabled a new class of applications, known as Location-Based Services (LBS), offering both new business opportunities and a wide array of new quality of life enhancing services. One example of such services is spatial alarms, an enabling technology for location-based advertisement, location-based alerts or reminders and a host of other applications. On the other hand, the ability to locate mobile users accurately also opens door for new threats - the intrusion of location privacy. The time series of location data can be linked to personal identity, which leads to unauthorized information exposure about the individual's medical conditions, alternative lifestyles, unpopular political views or location-based spam and stalking. Thus, there are two important challenges for location-based service provisioning. How do we scale LBSs in the presence of client mobility and location dependent constraints for the multitude of new, upcoming location-based applications under a common framework? How do we provide anonymous location- based services with acceptable performance and quantifiable privacy protection in the next generation of mobile networks, systems and applications? This dissertation delivers technical solutions to address these important challenges. First, we introduce spatial alarms as the basic primitive to represent a class of locationbased services that require location-based trigger capability. Similar to time-based alarms, spatial alarms serve as spatial event reminders that enable us to express different location-based information needs supported by a variety of applications ranging from location-based advertisements, location-based personal assistants, to friend locator services like Google Latitude. We develop a generalized framework and a suite of optimization techniques for server-centric scalable processing of spatial alarms. Our architecture and algorithm development provide significant performance enhancement in terms of system scalability compared to naive spatial alarm processing techniques, while maintaining high accuracy for spatial alarm processing on the server side and reduced communication costs and energy consumption on the client side. Concretely, we develop safe period optimizations for alarm processing and introduce spatial alarm grouping techniques to further reduce the unnecessary safe period computation costs. In addition, we introduce a distributed alarm processing architecture that advocates the partitioning of the alarm processing load among the server and the relevant mobile clients to reduce the server load and minimize the client-to-server communication cost through intelligent distribution and parallelization. We also explore a variety of optimization opportunities such as incorporating non-spatial constraints into the location-based information monitoring problem and utilizing efficient indexing methods such as bitmap indexing to further enhance the performance and scalability of spatial alarm processing in the presence of mobility hotspots and skewed spatial alarm distributions. Second, we develop the PrivacyGrid framework for privacy-enhanced location service provisioning, focusing on providing customizable and personalized location privacy solutions while scaling the mobile systems and services to a large number of mobile users and a large number of service requests. The PrivacyGrid approach has three unique characteristics. First, we develop a three-tier architecture for scaling anonymous information delivery in a mobile environment while preserving customizable location privacy. Second, we develop a suite of fast, dynamic location cloaking algorithms. It is known that incorporation of privacy protection measures may lead to an inherent conflict between the level of privacy and the quality of services (QoS) provided by the location-based services. Our location cloaking algorithms can scale to higher levels of location anonymity while achieving a good balance between location privacy and QoS. Last but not the least; we develop two types of location anonymization models under the PrivacyGrid architecture, one provides the random way point mobility model based location cloaking solution, and the other provides a road network-based location privacy model powered by both location k-anonymity and segment s-anonymity. A set of graph-based location cloaking algorithms are developed, under the MobiCloak approach, to provide desired levels of privacy protection for users traveling on a road network through scalable processing of anonymous location services. This dissertation, to the best of our knowledge, is the first one that presents a systematic approach to the design and development of the spatial alarm processing framework and various optimization techniques. The concept of spatial alarms and the scaling techniques developed in this dissertation can serve as building blocks for many existing and emerging location-based and presence based information and computing services and applications. The second unique contribution made in this dissertation is its development of the PrivacyGrid architecture for scaling anonymous location based services under the random waypoint mobility model and its extension of the PrivacyGrid architecture through introducing the MobiCloak road-network based location cloaking algorithms with reciprocity support for spatially constrained network mobility model. Another unique feature of the PrivacyGrid and MobiCloak development is its ability to protect location privacy of mobile users while maintaining the end-to-end QoS for location-based service provisioning in the presence of dynamic and personalized privacy constraints.

<p>There have been a number of methods proposed for increasing the precision of mobile positioning systems. One of the latest methods is Assisted GPS, A-GPS, on the Secure User Plane for Location, SUPL, platform, which seems to be a very interesting alternative from TeliaSoneras perspective, thanks to minimal infrastructural investment costs.</p><p>According to theory and lab testing A-GPS has the potential of providing a very good customer value in relation to the investment needed.</p><p>There is, however, a great need to see the performance when used in real user environments and with real user equipment. This is the basis for the choice of field testing as the method used in this thesis.</p><p>The result from the field tests conducted in this thesis shows that the performance of A-GPS is very good in outdoor environments, but when used in indoor environments, poor signal strength in combination with multipath and fading becomes a problem with low accuracy and long response times as a result.</p><p>Using a hosted mapping service, in combination with A-GPS, provides the possibilities of launching location based services even outside the home network. TeliaSonera had already found such a hosted mapping service that matched their compatibility, and reliability, requirements. This thesis investigates this hosted mapping service further and finds that the quality of the cartographic presentation of the map information is very poor.</p><p>The conclusion is that A-GPS performance, today, is limited by the hardware and algorithms used. If these are further adapted to indoor conditions, A-GPS has the potential of providing the customer value promised by the theoretical performance. For a successful launch of A-GPS services there is a great need of better cartographic presentation of map information, than what is currently is provided by the investigated hosted mapping service.</p>

Spatial subscriptions have been used to specify locations of interest in Distributed Event-based Systems (DEBSs). However, current DEBSs representations to support spatial subscriptions are not expressive enough to describe some forms of subscriptions in mobile settings. For instance, users are not allowed to specify a spatial subscription that refers to other more well-known locations, in case they are not familiar with the names of their current locations. In addition, the middleware in existing DEBSs does not support changes at runtime, and modification to these middleware systems to support spatial subscriptions are highly coupled with specific DEBS infrastructures. In this thesis, I argue that by enhancing the expressiveness of spatial subscriptions, a new model of mobile spatial subscriptions for location-aware services can be defined and a reusable plug-in implementation approach that supports existing DEBSs can be developed. This thesis first summarizes the essential abstractions to specify mobile spatial subscriptions, and analyze the expressiveness of existing DEBSs to support these abstractions. Second, it proposes a three-level mobile spatial subscription model, which supports the essential abstractions used to specify spatial subscriptions. The first level of the model handles subscriptions consisting of geometric coordinates; the second level supports subscriptions with location labels; the third level interprets subscriptions which specify locations by stating their dynamic properties. Next, a plug-in implementation approach is introduced, and hence, the three-level model can be integrated with different DEBSs with minimal modification to the middleware. The subscription model is implemented as a subscriber/publisher component, instead of directly modifying the existing DEBS. Finally, I develop a prototype system, Dynamic Mobile Subscription System (DMSS), and illustrate the usefulness and applicability of the three-level model and the plug-in implementation approach.

Conventional Wi-Fi location estimation techniques using radio fingerprinting typically require a lengthy initial site survey. It is suggested that the lengthy site survey is a barrier to adoption of the radio fingerprinting technique. This research investigated two methods for reducing or eliminating the site survey and instead build the radio map on-the-fly. The first approach utilized a deterministic algorithm to predict the user's location near each access point and subsequently construct a radio map of the entire area. This deterministic algorithm performed only fairly and only under limited conditions, rendering it unsuitable for most typical real-world deployments. Subsequently, a probabilistic algorithm was developed, derived from a robotic mapping technique called simultaneous localization and mapping. The standard robotic algorithm was augmented with a modified particle filter, modified motion and sensor models, and techniques for hardware-agnostic radio measurements (utilizing radio gradients and ranked radio maps). This algorithm performed favorably when compared to a standard implementation of the radio fingerprinting technique, but without needing an initial site survey. The algorithm was also reasonably robust even when the number of available access points were decreased.

Nowadays, with the rapid development of GPS-equipped mobile devices, location-based social networks have been emerging to bridge the gap between the physical world and online social networking services. Various types of data, such as personal locations, check-ins, microblogs and social relations, have been available in location-based social networks. Efficiently managing and analyzing such data to meet users' daily query requirements become a challenging task. Among all the existing works in location-based social networks, group query is one of the most important research topics. In this thesis, we investigate query techniques for location-based services in social networking applications. Specifically, considering a location-based social network, we study spatial-aware interest group queries, geo-social {dollar}k{dollar}-cover group queries, and social-aware ridesharing group queries. Firstly, we study the spatial-aware interest group queries in location-based social networks. Recently, most of the location-based social networks release check-in services that allow users to share their visiting locations with their friends. These locations, considered as spatial objects, are usually associated with a few tags that describe the features of those locations. Utilizing such information, we propose a new type of \emph{Spatial-aware Interest Group} (SIG) query that retrieves a user group of size {dollar}k{dollar} where each user is interested in the query keywords and the users are close to each other in the Euclidean space. We prove this query problem is NP-complete, and develop two efficient algorithms IOAIR and DOAIR based on the IR-tree for the processing of SIG queries. We also validate the performance efficiency of the proposed query processing algorithms by empirical evaluation. Secondly, we study the problem of geo-social {dollar}k{dollar}-cover group queries for collaborative spatial computing. In this problem, we propose a novel type of geo-social queries, called \emph{Geo-Social K-Cover Group} (GSKCG) query, which is based on spatial containment and a new modeling of social relationships. Intuitively, given a set of spatial query points and an underlying social network, a GSKCG query finds a minimum user group in which the members satisfy certain social relationship and their associated regions can jointly cover all the query points. Albeit its practical usefulness, the GSKCG query problem is NP-complete. We consequently explore a set of effective pruning strategies to derive an efficient algorithm for finding the optimal solution. Moreover, we design a novel index structure tailored to our problem to further accelerate query processing. Extensive experiments demonstrate that our algorithm achieves desirable performance on real-life datasets. Thirdly, we study the problem of social-aware ridesharing group queries. With the deep penetration of smartphones and geo-locating devices, ridesharing is envisioned as a promising solution to transportation-related problems such as congestion and air pollution for metropolitan cities. Despite the potential to provide significant societal and environmental benefits, ridesharing has not so far been as popular as expected. Notable barriers include the social discomfort and safety concerns when traveling with strangers. To overcome these barriers, in this thesis, we propose a new type of \emph{Social-aware Ridesharing Group} (SaRG) query which retrieves a group of riders by taking into account their social connections besides traditional spatial proximities. Because the SaRG query problem is NP-hard, we design an efficient algorithm with a set of powerful pruning techniques to tackle this problem. We also present several incremental strategies to accelerate the search speed by reducing the repeated computations. Moreover, we propose a novel index tailored to the proposed problem to further speed up the query processing. Experimental results on real datasets show that our proposed algorithms achieve desirable performance. The works of this thesis show that the group query processing techniques are effective, which would facilitate the wider deployment of such query services in real applications

Information privacy is a major concern for consumers adopting emerging technologies dependent on location-based services. This study sought to determine whether a relationship exists among factors of personalization, locatability, perceived playfulness, privacy concern and behavioral intention to disclose personal information for individuals using location-based, geosocial networking applications. Questionnaire responses from undergraduate students at a 4-year university provide insight into these relationships. Multiple regression results indicated that there was a statistically significant relationship between the four significant predictor variables and the dependent variable. Analysis of beta weights, structure coefficients, and commonality analysis shed light on the variance attributable to the predictor variables of the study. Findings provide understanding of the specific factors examined in the study and have implications for consumers, businesses, application designers, and policymakers. The results from this study contribute to an understanding of technology acceptance theory and offer insight into competing beliefs that may affect an individual’s behavioral intention to disclose personal information. Knowledge gained form the study may be useful for overcoming challenges related to consumer adoption of location-based services that require disclosure of personal information.

Ericsson is developing a new technology for accurate indoor positioning, named the HIP-solution. The HIP-solution is a joint product with areas of use in both the public safety market for emergency positioning and as a commercial platform for location based services. The driving force behind the development of Ericsson's HIP solution is E911, a legal requirement imposed in theU.S.where operators are required to position an emergency call within a radius of 50-300 meter, depending on available positioning technology. Location Based Services (LBS) are the other important market for the HIP-solution where the technology can be used to offer customized services for mobile users based on their location, such as guidance to the nearest banking cash machine, or whereabouts of friends or family.   This master thesis is conducted at Ericsson, with the purpose of evaluate the HIP-solutions U.S market potential. The theoretical framework used in this paper is the Real Option Pricing Theory, with emphasis on the Expand Option. The theory provides a context for evaluating the HIP-solutions market potential based not only on its physical characteristics, but also of on future expand options and how well Ericsson manages to capture the cloud of externalities the HIP-solution as a joint product.   Keywords Ericsson, HIP-High Precision In-building Positioning, U.S Emergency positioning legislation, Location Based Serviced, App, Real Option, Expand Option.