Location Information Taxonomy for Location Based Services

Transcription

1 6 th International Symposium on Mobile Mapping Technology, Presidente Prudente, São Paulo, Brazil, July 21-24, 2009 Location Information Taxonomy for Location Based Services A. Kealy a, *, M. Duckham a, G. Retscher b, G. Roberts c, S. Winter a a Department of Geomatics, The University of Melbourne, Victoria, 3010, Australia - akealy@unimelb.edu.au b Institute of Geodesy and Geophysics, Vienna University of Technology, Gusshausstrasse 27-29, 1040, Vienna, Austria - gretsch@pop.tuwien.ac.at c IESSG, University of Nottingham, University Park, Nottingham, NG7 2RD, UK - gethin.roberts@nottingham.ac.uk KEY WORDS: Positioning, Location Based Services, Integrated Systems, Mobile Positioning, Location ABSTRACT: The proliferation of location based services (LBS) in development or use across various sectors of society has drawn increased attention to the performance capabilities of the technologies and processing techniques used to provide position or location information. Where positioning performance has traditionally centred and been evaluated around the accuracy requirements of individual applications, other performance characteristics such as availability and usability are emerging as equally if not more important. This paper presents the results of a study conducted to review the relationships between the performance capabilities of indoor positioning technologies, the algorithms used to integrate measurements from these technologies and the spatial cognition tools used to improve the relevance of the position information provided to the user. In this study, an innovative taxonomy that classifies location responses from typical LBS into characteristics associated with the location information and those associated with the generation of this information has been developed. Relationships within the taxonomy are verified through the results of a practical study. 1. INTRODUCTION Society s increasing use and development of services where the content is tailored to the current location and context of a mobile user, reflects the utility being created by the convergence of mobile and human computing, wireless communications and positioning technologies. To describe the performance requirements of these location based services (LBS), standard measures of positioning quality, i.e., accuracy, precision and reliability need to be redefined to characterize not only the output of a measurement sensor or integration process but also the models and computations used to generate application and user specific location information. Whilst the positioning accuracy of a specific technology has traditionally been the primary criteria defining its use in LBS, the requirement for ubiquitous positioning and the concept of context awareness has established higher priorities for other criteria such as availability and usability. This paper presents the results of a study conducted to identify relationships between the performance capabilities of a range of indoor positioning technologies (including MEMS INS, RFID, WiFi and High Sensitivity GNSS), the algorithms used to integrate measurements from these technologies (including Kalman filter, particle filter, etc.) and the spatial cognition tools used to improve the usability of the position information provided to the user. This paper introduces an innovative taxonomy that classifies the characteristics of location information against the attributes of the location information itself as well as the attributes of the information generation process. The terms used in the taxonomy are defined and results of a practical study are presented as a means of verifying relationships that exist across the taxonomy. 2. CLASSIFICATION OF LOCATION BASED There are many examples of taxonomies generated for LBS available in the literature. Giaglis et al. (2002) present a taxonomy based around a classification of LBS, their accuracy needs and application environment. Hightower and Borriello (2001) present a taxonomy of location sensing technologies based around characteristics of the technologies themselves. The aim of their study was to aid LBS developers in selecting an appropriate location-sensing technology. Zeimpekis et al. (2003) present a taxonomy that combines the LBS requirements characteristics of Giaglis et al. (2002) with some of the technology attributes of Hightower and Borriello (2001). Dobson (2004) develops a taxonomy of answers that an LBS might want to receive. This taxonomy considers the multiplicity of possible ways to conceptualise location and the complexity associated with generating appropriate computer representations and programming interfaces. To build LBS that are responsive to individual users perceptions of location, it is necessary to understand the relationships that exist between the characteristics of the location expressions generated or required by LBS and those of the technologies and tools used to generate them. This study aims to develop a taxonomy that facilitates a comprehensive analysis of these relationships. In Giaglis et al. (2002) five classes of services are identified. Table 1 shows these classes as well as some sample location queries that can be made for each class of service. In summary, all of these responses attempt to provide an answer to where questions. Table 2 shows a listing of sample responses (location expressions) that users or LBS can expect to receive from these queries. What is significant about all of

2 the location expressions in Table 2 is that they refer to the same location. EMERGENCY NAVIGATION INFORMATION MARKETING TRACKING BILLING EXAMPLE LOCATION QUERIES Where is the incident? Where are the responders/vehicles? Where am I? Where you going? Where are the restaurants within walking distance? Where is the closest tram stop that goes to the city? Who are potential customers nearby? Are the children where they are allowed to be? Where are my assets? You were caught speeding here? Table 1. Taxonomy of LBS and related location queries LOCATION EXPRESSIONS 20 Grattan Street, Parkville, Vic, 3010 Near the Royal Women s Hospital In my office In the Engineering building In Melbourne E S ~ 500m from the Melbourne shopping precinct North of the CBD Entering a parking restricted area In an allowed area Table 2. LBS location expressions The location expressions in Table 2 can be classified according to the characteristics of the expression itself and those of the mechanisms used to generate that information. Figure 1 shows the taxonomy developed and used in this study which is based around this classification. Whilst it is accepted in this study that there are obvious relationships that exist between attributes of the location information and the characteristics of how it was generated (e.g., the accuracy of the location sensor directly affects the accuracy of the location expression), this study aims to determine whether any other relationships can be established across the taxonomy. 2.1 Characteristics of Location Information: Definitions Location information refers to the expressions themselves that are used to answer questions relating to where something or someone is. The consistent characteristics of these expressions are; 1. Spatial referencing. Kolodziej et al. (2006) defines an absolute spatial reference as one in which objects have specific coordinates, e.g., x,y,z or are positioned as a metric offset from a fixed reference system. Absolute reference systems may be local or global and typically describe the unique location of an object. Relative location can be described as the position of an object relative to an arbitrary location mark using orientation, distance or topological relationships. For LBS, this location mark may be a land mark ( in front of the church ) or the mobile users themselves ( after three hundred meters turn left ). For example, the location expression E S is absolute, however the same point expressed as in the Engineering building is relative. 2. Granularity. Granularity refers to the spatial scale of a location expression. Montello (1993) provides definitions of four classes of location granularity based on the projective size of the space relative to the user. Figural space is projectively smaller than the body; its properties may be directly perceived from one place without appreciable locomotion. Vista space is projectively as large or larger than the body but can be visually apprehended from a single place without appreciable locomotion. Environmental space is projectively larger than the body and surrounds it. It is in fact too large and otherwise obscured to apprehend directly without considerable locomotion. Geographical space is projectively much larger than the body and cannot be apprehended directly through locomotion; rather, it must be learned via symbolic representations such as maps or models that essentially reduce the geographical space to figural space. For example, location expressions such as in my office can be classed as vista and in Melbourne can be classed as environmental. 3. Accuracy. Accuracy is defined as a measure of how close the location expression is to the true location of the object. This may be a quantitative value determined from a numerical analysis or it may be qualitative based on it fitness for use in the application or on the qualitative aspects of non-spatial data sets used in generating the location expression. For example, the accuracy required for an in-car navigation system can be described as ±5m representing the quantitative accuracy of a GPS position, this can also be described as low representing the qualitative accuracy requirements for in-car navigation systems. 4. Useability. Hunter et al. (2003) present a definition of useability as The capability of the software product to be understood, learned, used and attractive to the user, when used under specified conditions. Three classes of usability are described that can apply to LBS: Understandability. The capability of LBS to enable the user to understand how it can be used for particular tasks and conditions of use. Learnability. The capability of LBS to enable the user to learn its application. Operability. The capability of LBS to enable the user to operate and control it. 2.2 Characteristics of Location Information Generation: Definitions Table 2 present examples of location expressions that respond to requests made or questions asked of LBS. The generation of the information underpinning these expressions can be classified according to: Availability. Availability can be defined both spatially and temporally. Temporally it is defined as the percentage of time that a position solution can be computed by the positioning sensor or technology. Depending on the application, availability is also a function of the positioning accuracy and can be defined as the percentage of time that a positioning solution can

3 be computed to the specified accuracy required for the application. Spatially it refers to the coverage provided in terms of point locations or regions. For example, a GPS receiver can provide continuous positioning at a specified accuracy across a region when sufficient satellites are available over a region. GPS positioning becomes unavailable when operating in an indoor environment. Activation. Two activation modes exist for LBS and are typically based on the level of user interaction. An explicit activation requires the user to provide some input to retrieve information, e.g., requesting route directions to a specific location from an in-car navigation system. An implicit activation is one in which the user provides no input but information specific to their location is provided, e.g., you were caught speeding here. Source. Source refers to the methods for capturing or deriving location information. The information can be sensed directly, e.g., a user provides address details to emergency services or a GPS receiver measures the user s position. Alternatively, position information can be derived from a fusion of measurements or technologies including other sensors, user knowledge, logical constraints or existing data. For example LBS that require high availability of location information could integrate GPS/WiFi/Cell ID to position seamlessly in indoor/outdoor environments.

4 3. SOURCE OF INFORMATION GENERATION Within this study the relationship between the source of information generation and the attributes of the location information is of particular interest and a number of practical experiments were conducted to study this relationship. 3.1 Directly Sensed Data Locations that are directly sensed can either be user defined or measured by some sensor system. User defined information can either be crisp where the user is able to provide information that uniquely describes their location, e.g., x,y,z and 20 Grattan Street, Parkville, Victoria or vague where the user is only able to provide relative information, and which is based on the user s perception of space around him, e.g., near the Royal Women s Hospital. Location directly sensed by a sensor system can be determined explicitly by a spatial sensor, e.g., a GPS receiver or derived from a non spatial sensor that implies a location, e.g., using a swipe card to enter a building. For this study we will focus on spatial sensors. Hightower and Borriello (2001) present a comprehensive summary of location sensing technologies and their associated attributes of accuracy, scale, costs and limitations. For this study we will focus only on positioning technologies that represent state of the art for operating in GPS difficult environments, e.g., high sensitivity (HS) GPS, WiFi, RFID and UWB. Figure 2 shows a plot of position solutions computed from two commercially available HS single frequency GPS receivers, the SiRFstarIII TM (red dots) and ublox Antaris4 TM (green dots). Figure 2 also shows solutions computed from a dual frequency Leica 1200 GNSS receiver (blue dots). This solution was post processed to generate ambiguity fixed solutions where possible. All data was collected simultaneously on 1 st May 2008 at 10:30am for a time period of around 30mins and covering a distance of approximately 5km around the central business district (CBD) in Melbourne. The receivers were mounted on a vehicle and the epoch interval for all receivers was 1 second. Figure 2 also shows the solutions obtained along an enlarged region on Collins Street, the primary business street in the Melbourne CBD comprising multi storey office buildings and car parks. It can be seen that the Leica GNSS receiver was unable to provide precise ambiguity fixed solutions for the majority of time on this street. Even with a combined GPS/GLONASS constellation, outages of up to 2mins were experienced with only 27% of the time the receiver able to compute an ambiguity fixed solution. Based on the performance of HS GPS receivers it could be argued that there is no need to augment GPS performance in difficult environments with any alternative positioning technology. However, as shown in Figure 2 the effects of positioning using weak signals can still result in the deterioration of the solution accuracy with drifts and incorrect paths evident in the solution. Figure 3 shows the results obtained from the commercially available Skyhook wireless positioning system (WPS). The Figure 2. High sensitivity GPS positioning Skyhook WPS TM is one component of Skyhook s hybrid positioning system (XPS) which switches between GPS, Cell Tower triangulation and WiFi to deliver a position solution. By driving streets in cities around the world with a sensitive antenna coupled with mapping software, Skyhook has generated an extensive database containing the locations of established access points globally. When the Skyhook antenna finds a WiFi signal, it tracks its fingerprint the area it covers. It also grabs that transmitter s MAC as a specific and unique numeric identifier. A Skyhook client software running on a WiFi enabled mobile device checks to see how many Wi-Fi access points

5 or base stations are within range. No matter how many access point signals are found nearby around, the device grabs their MAC numbers and compares them with the Skyhook database. Using the previously collected maps that are associated with those MAC numbers gathered when Skyhook mapped the area the software can triangulate an accurate location. The Skyhook software is designed to work with as few as two mapped signals or upwards of 20 different signals. Navigating the same trajectory in Figure 2, a client application developed for the HTC Dream TM Android operating system was used to capture the Skyhook WiFi positioning solution. These were then compared to those of the ublox high sensitivity GPS receiver. The plot shows (blue line) that the Skyhook errors have a mean of approximately 130m and a standard deviation of around 100m. This is in contrast to the errors sent by the skyhook server (green line) which has a mean of around 50m and a standard deviation of around 30m. Although some of this error could be attributed to errors in the GPS solution, what is interesting is the correlation between the two sets of errors with the Skyhook values consistently lower. significant is the link between the accuracy and availability of the sensed data and the attributes associated with the location expression communicated to the user. 3.2 Usability Considerations The accuracy and granularity of location information are correlated with its usability, which will be studied now. Usability is a complex parameter when describing a location expression in that it is context-dependent, in particular, dependent on the individual user abilities and preferences, and also their task at hand. Even within a particular LBS application usability can require that accuracy and granularity are not necessarily a constant (Tenbrink and Winter 2009). However, for the purpose of this paper we confine usability with respect to an application, and exclude aspects of personalization, which are covered elsewhere (e.g., Poslad et al. 2001; Fink and Kobsa 2002). Location-based services are dealing with human orientation ( you are here ) or navigation ( how to find your way ), answering where questions in human understandable terms (understandability in the taxonomy of Figure 1 is the most relevant for location information). Only relative location expressions fulfil this requirement. Even the traditional point on a map is not useful as an absolute location within a reference frame, but only by its relative location to other mapped features. With respect to a task, usability becomes a matter of granularity, or level of abstraction. A spatial reference can be too coarse providing insufficient information to support orientation or navigation or too fine providing unnecessary or even distracting detail. Ideally the spatial reference is of a granularity that just enables the user to solve an orientation or navigation task, i.e., a granularity that resolves just all ambiguities in the environment. We call information of maximum usability (in a particular decision situation) if it resolves all ambiguities in the orientation or navigation task in an environment and is the shortest possible information that does so (Frank 2003 discusses this idea as pragmatic information content). The granularity of a verbal or graphical spatial reference is a deliberate choice of the location-based service, based on assumptions of usability, and limited by the sensed location information, including its accuracy, availability and reliability. A mapping function from position to location was proposed by Kealy et al (2007) purely based on the physical structure of an environment, such that the structure constraints positioning, and by that way improves the positioning accuracy. Another approach of linking a position of a certain accuracy with a location boils down to choosing the spatial reference that forms the minimum bounding polygon of the position s error ellipse. Both approaches are data driven; but the current argument is that usability must be purpose driven. Figure 3. WiFi positioning errors from Skyhook WPS TM These two examples demonstrate that individually each technology is capable of providing some level of positioning accuracy and solution availability. What is Assume a hierarchical organization of spatial references, each hierarchy level corresponding to a level of spatial granularity of the environmental features referred to. Then a measure of usability, U, can be defined as the distance between the reached level of granularity, P, and the level of granularity of the information of maximum usability, M:

6 U = P M (1) Table 3. Comparison of Kalman filter and particle filter (Sundvall, 2004) Defined this way, the usability of the ideal reference is 0, and the larger the measure becomes the less usable is the reference chosen with the minimum bounding polygon of the position s error ellipse. However, if P M is negative, i.e., the granularity of the positioning finer than the reference of maximum usability, P can be lifted to M to select the maximum usable one, and only if P M is positive the most usable reference cannot be provided. All what it needs is a method to determine M. This method must be based on relevance, a concept from communication theory (Grice 1975; Sperber and Wilson 1986). Obviously, relevance is independent from positioning qualities. Hierarchies can be built from different ways of abstracting an environment. Three convincingly demonstrated ways are by means of (a) physical structure, (b) functional structure, and (c) social structure (Gaiser 2008; Richter et al. 2009). For example, an indoor environment can be structured by grouping rooms connected with each other (a physical structure), by grouping rooms serving the same function, e.g., offices (b functional structure), or by grouping rooms belonging to the same social entity, e.g., a company or department (c social structure). All of these types of hierarchies can be used in orientation or navigation tasks: Orientation ( you are here ): You are on the second floor (a) You are in a computer lab (b) You are in the Department of Geomatics (c) Navigation ( how to find your way ): That s on the second floor (a) That s in the student computer lab (b) That s in the Department of Geomatics (c) Orientation is related to place descriptions (Shanon 1979; Plumert et al. 1995), and navigation to destination descriptions (Tomko and Winter 2009). An interesting property of both is their typical appearance as hierarchic descriptions (e.g., You are on the second floor, Block B ), which can also mix between different hierarchizations (e.g., That s in the student computer lab, second floor ). Tomko and Winter have provided selection rules of location references based on relevance, i.e., providing location references at level M. The ultimate goal of LBS would be to flexibly respond to the available accuracy of the sensed information and to provide detail that reflects this accuracy and more significantly in a format that recognises the user s preference for receiving location information. Interestingly, there exists another relationship that can aid in improving the accuracy of the sensed information. The coverage and activation of the sensed information provides useful constraints that can be integrated with the measured data. Consequently, the best solution would be based around the fusion of all available signals and constraints. The success of this approach is however highly dependent on the integration approach adopted. 3.3 Integrated Data Commonly adopted integration architectures for positioning aim to combine measurements from several sensors within an optimal estimation process typically a Kalman filter. Figure 4 shows the results obtained from integrating RFID positions with those computed by a low cost MEMS inertial sensor (INS). Figure 4. Kalman filter solution for integrated INS and RFID (Retscher and Fu 2009) The red line represents the solution generated without any filtering, while the green line represents the result of the positions filtered by the Kalman Filter. It can be seen that without filtering the error in position increased over the duration of the test with a maximum error in y-direction of around 2.50m and a maximum error in x-direction of around 5.00m. This error was significantly reduced using the Kalman Filter to integrate both sets of data with a maximum error in x-direction is 0.41m, and maximum error in y-direction is 1.04m. The maximum error in position is 1.05m. Other estimation algorithms such as particle filters can be used to generate similar solutions with the particle filter giving an approximate solution to an exact model, rather than the optimal solution to an approximate model. Table 3 presents a comparison of Kalman filtering and particle filtering algorithms. The use of low cost, noisy sensors, e.g., MEMS INS as well as the complexities associated with modelling signals that are highly sensitive to their environment, e.g., WiFi and RFID challenges the use of these integration algorithms

7 particularly for real-time applications which comprise the majority of LBS. Figure 5 shows the results obtained through the further integration of constraint measurements within a Kalman filter solution. A practical study was undertaken to represent a typical mobile user travelling in a vehicle from a location identified as work to a destination location identified as home. This experiment used a low-cost GPS receiver typical of current in-car navigation systems. To evaluate the performance improvements brought about by integrating multiple sensors within a Kalman filter, the observations from the GPS receiver were combined with directions as observed by a low-cost on-board MEMS INS and distance measurements from the vehicle odometer. Figure 5 shows a two minute snap shot of the data collected, during which time the vehicle travelled approximately 0.5km. Over the period shown, GPS observations (white dots) were unavailable for approximately 30sec. Although this is a relatively short time period, the vehicle made a sharp turn onto a new road (Brunswick Road). Whilst the Kalman filter solution is still able to use the additional sensors to maintain the trajectory travelled (triangles), it is obvious that errors in the processing result in the filter being unable to compute an accurate trajectory as the vehicle turns a corner. 4. CONCLUSIONS A taxonomy for location expressions used or generated by LBS has been developed in this study. It has been shown that the accuracy of the information source is inextricably linked to the accuracy and granularity of the resulting location expression and consequently on its usability. Integration algorithms used to generate location information typically do not allow for the integration of context or event information, both of which can constrain and improve the solution generated. In developing LBS an important consideration is the adaptability of the information provided to the user and the user s context. By understanding the relationship between measured or sensed quantities and a user s interpretation of what this information represents can contribute significantly to creating greater utility for LBS. 5. REFERENCES Dobson, S., 2004: A taxonomy for thinking about location in pervasive computing. Technical report TCD-CS Department of Computer Science, Trinity College Dublin. Duckham, M.; Bennett, R.; Bishop, I.; Fraser, C.; Kealy, A.; Leach, J.; Ogleby, C.; Rajabifard, A.; Williamson, I.; Winter, S., 2009: Ambient Spatial Intelligence for Sustainable Cities, 11th International Conference on Computers in Urban Planning and Urban Management (CUPUM2009), Hong Kong. Fink, J.; Kobsa, A., 2002: User Modeling in Personalized City Tours. Artificial Intelligence Review, 18 (1): Frank, A.U., 2003: Pragmatic Information Content - How to Measure the Information in a Route Description. In: Duckham, M.; Goodchild, M.F.; Worboys, M. (Eds.), Foundations in Geographic Information Science, Taylor & Francis, London, pp Gaiser, V.A., 2008: Improving Spatial Awareness in an Indoor Environment with Wireless Positioning Technology. Master thesis, Department of Information Systems and Department of Geomatics, The University of Melbourne, Melbourne, Australia. Figure 5. Integrating user knowledge into the Kalman filter solution By integrating knowledge of the user s planned trajectory to travel between work and home the Kalman filter algorithm can be constrained to follow Lygon Street and constrain the errors in the inertial sensor to constrain the heading measurement to the direction of Lygon Street. This approach works well and results obtained as shown in Figure 5 demonstrate the ability of the navigation system to maintain the trajectory travelled by constraining the errors inherent in the positioning technologies (white line). Giaglis, G.M.; Pateli, A.; Fouskas, K.; Kourouthanassis P.; Tsamakos A., 2002: On the Potential Use of Mobile Positioning Technologies in Indoor Environments. 15 th Electronic Commerce Conference -e-reality: Constructing the e-economy, Bled, Slovenia. Grice, P., 1975: Logic and Conversation. Syntax and Semantics, 3: Hightower, J.; Borriello, G., 2001: Location Systems for Ubiquitous Computing. IEEE Computer, 34(8): Hunter, G.J.; Wachowicz, M.; Bregt, A.K., 2003: Understanding Spatial Data Usability. Data Science Journal, 2:

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