Sharing of Distributed Geospatial Data through Grid Technology national climate data center (Ramapriyan et al., 2006) The data volume will increase significantly if similar models of finer spatial resolutions, such as km, are used The models are being changed and refined from time to time and new geospatial data, the NASA EOS data and NOAA climate data, are being collected by satellites continuously A fixed computing environment that contains only static data sources will not fulfill such kind of geospatial applications Consequently, a capability of seamless and dynamic accessing to large quantities of distributed geospatial data is the key to the success of today’s and tomorrow’s geospatial applications Although much progress in high performance computing has been made in recent years, there still lacks a mechanism to enable global-scale integration and sharing of large quantities of data, such as geospatial data, from large-scale, heterogeneous, and distributed storage systems Fortunately, the emerging Grid technology might be able to solve this problem Grid technology is a form of distributed computational technology that involves the coordination and sharing of computing, application, data, storage, and network resources across dynamic and geographically dispersed organizations (Foster et al., 2001) Resource sharing in a Grid is highly controlled Resource providers and consumers define clearly and carefully what is shared, who is allowed to share, and the conditions under which the sharing occurs Individuals and/or institutions agreeing to follow such sharing rules form a virtual organization (VO) The resource sharing across multiple VOs is enabled by the Grid technology The intrinsic advantages of the Grid technology fit the problems of the sharing of distributed geospatial data very well (Di, 2005) The Globus Toolkit, currently at version 4, is an open source toolkit for building Grids provided by the Globus Alliance It provides many useful components and services that make the use of Grid technology easier 224 SH AR ING OF GEOSP AT IAL D AT A THROUGH GR ID TECHNO LOGY To enable the sharing of distributed geospatial data, a large-scale infrastructure that can integrate the currently dispersed data together and enable the efficient sharing of those huge amounts of geospatial data in a secure and controllable manner is crucial But because geospatial data are huge in quantity and geographically distributed across heterogeneous environments, there are still a lot of problems need to be faced with and solved in order to create such an infrastructure Those major problems and how they can be addressed by Grid technology are discussed in the following section System heterogeneity There are hundreds of large geospatial data centers and countless small or personal data centers around the world Platforms and systems used to store and manage the geospatial data in each center may vary greatly There are many types of high performance storage systems used, such as the Distributed Parallel Storage System (DPSS), the High Performance Storage System (HPSS), and the Storage Resource Broker (SRB) Unfortunately, these storage systems typically use incompatible protocols for data access (Allcock et al., 2002) Also, the diversity of platforms and systems on which geospatial applications are running greatly increase the data sharing difficulty Thus, geospatial applications should be presented with a uniform view of data and uniform mechanisms for accessing the data independent from the platforms and systems used Grid technology addresses this problem by providing storage system abstraction and uniform API for data accessing Several components and tools have been provided in the Globus Toolkit, including GridFTP and OGSA-DAI, to integrate heterogeneous systems and make the geospatial data accessible throughout the Internet Uniform mechanism to publish and discover geospatial data Usually geospatial data are pub- Sharing of Distributed Geospatial Data through Grid Technology lished by extracting their attributes – geospatial metadata, storing and managing them within catalogues, and making the metadata queryable Heterogeneity exists in this process because, currently, different models are used to describe geospatial metadata and different methods are used to query geospatial metadata For example, Earth Observation System (EOS) ClearingHOuse (ECHO) and EOS Data Gateway (EDG) both provide the capabilities to publish and discover NASA EOS data, each with a different model to describe NASA EOS metadata and a different approach for users to search NASA EOS data To solve this problem, two issues need to be addressed One issue is the need for a widely accepted domain metadata schema to eliminate semantic heterogeneity of different metadata models There are domain standards for geospatial metadata schemas available to address this issue, such as ISO 19115 – Geographic Information Metadata (ISO, 2003a) and ISO 19115 part – extensions for imagery and gridded data (ISO, 2003b) The other issue is the need for uniform interfaces for publishing and discovering geospatial data from different metadata catalogues An example of such uniform interfaces is the Catalogue Service – Web Profile (CSW) developed by the Open Geospatial Consortium (OGC) (Nebert and Whiteside, 2005; Wei et al., 2005) The intrinsic Service Oriented Architecture (SOA) characteristic of the Grid technology enables the cooperation of different catalogues With Grid technology, legacy catalogues can be wrapped and exposed as Web services which provide uniform publishing and discovering interfaces, while leaving the internal mechanisms of the catalogues untouched Grid technology also provides a mechanism for creating federations of distributed catalogue services Queries to any single accessing point of such a federation can be delivered to all the catalogue services throughout the federation Thus the discovery of geospatial data can be much more efficient Performance Geospatial data are not only large in quantity but also huge in size Although the computing capability and network bandwidth are increasing rapidly, accessing and transferring large amounts of geospatial data are still huge burdens Grid technology provides several mechanisms that can improve availability and accessing performance of geospatial data, one of which is an important component within a dataintensive Grid environment – Data Replication System (DRS) provided by Globus Toolkit A data replica is a full or partial copy of the original data (Chervenak et al., 2001) With the help of DRS, multiple replicas of the original geospatial data can be created, distributed, and managed across different storage systems and data centers DRS monitors the storage systems, computing platforms, and networks within a Grid environment in real time If a user wants to access a specific geospatial data, DRS will choose one replica which provides the best accessing performance for the user DRS can even choose more than one replica for the user and provide the user with a stripped-style data accessing mechanism which enables the user to retrieve different parts of the original geospatial data from different replicas simultaneously and combine those different parts into a complete data after retrieving Multiple replicas are created to increase the availability of geospatial data; otherwise a single failure will make those geospatial data unavailable The accessing performance is also improved by choosing optimized replicas Other mechanisms are also provided by Grid technology to improve the accessing performance and reliability for geospatial data, like GridFTP, which provides much more improved data transfer performance than the traditional FTP protocol Security Security is a critical issue associated with the sharing of geospatial data Many of the geospatial data are sensitive and restricted to be accessed by only some special persons or organizations Some of the geospatial data are to be shared for commercial purposes and are 225 Sharing of Distributed Geospatial Data through Grid Technology associated with an accessing fee Currently different organizations and communities are using diverse mechanisms to handle security related issues, such as authentication, authorization, and access control Consequently, there is a need for a uniform security mechanism to coordinate the sharing of geospatial data across those naturally untrusted organizations and user communities while keeping the diverse local security mechanism intact The Grid Security Infrastructure (GSI) provided by the Grid technology can be used to address this problem Based on GSI, each geospatial organization or user community can form a VO Each individual user, machine, storage system, application, or a VO will have one or more certificates as its identity Certain trust relationships can be set up among different VOs (Welch et al 2003) As a consequence, a larger VO is formed Thus, fine-grain access control policies on geospatial data can be issued to any individual user, application, or VO that has one or more certificates through Community Authorization Service (CAS) provided by the Globus Toolkit Currently, the X.509 certificates based on Public Key Infrastructure (PKI) are used by Grid technology and to provide high-level authentication, authorization, and single sign-on functionality (Welch 2005) Today, efforts have been taken by some geoscience communities to leverage Grid technology for the sharing of geospatial data For example, Earth System Grid (ESG) is a research project sponsored by the U.S Department of Energy (DOE) Office of Science to address the formidable challenges associated with enabling analysis of and knowledge development from global earth system models The goal of ESG is to provide a seamless and powerful environment that enables next generation climate research by integrating distributed federations of supercomputers and large-scale data & analysis servers through a combination of Grid technology and emerging community technologies The Center for Spatial Information Science and System (CSISS) in George Mason 226 University also developed a prototype system for efficient sharing, customization, and acquisition of distributed NASA EOS data by integrating the Grid technology and Open Geospatial Consortium (OGC) Web Services technologies This prototype system involves three partners distributed across the United States: George Mason University, NASA Ames Research Center, and Lawrence Livermore National Lab Each partner forms a VO and trust relationships are set up among those three VOs to create an integrated Grid environment About 20TB of remote sensing and climate simulation data are shared among this prototype Grid-enabled Catalogue Service for Web (CSW) was implemented to provide uniform mechanism for data publication and discovery Data Replication System and Resource Selection components were also implemented to improve the performance of data sharing The customization of data was achieved by leveraging OGC Web Services, such as Web Coverage Service (WCS) and Web Map Service (WMS), to provide more options for geospatial data accessing FUTURE TRENDS The goal of the Grid technology is to create a computing and data management infrastructure that will provide the electronic underpinning for a global society in business, government, research, science, and entertainment (Berman et al., 2003) As an essential information source for scientific research and even people’s everyday life, distributed geospatial data all over the world are also doomed to be integrated to form a global-scale warehouse to promote the sharing of geospatial data Grid technology is still young and there are many open issues to be addressed and missing functionalities to be developed New computing and network technologies are also emerging and advancing, such as the wireless and mobile computing technologies, which greatly extend the boundary for the sharing of geospatial informa- Sharing of Distributed Geospatial Data through Grid Technology tion With the maturation of Grid technology and the advancement of computing and network technologies, this will not only be a dream: wherever the geospatial data are, they can be shared and accessed from almost anywhere at anytime CONC LUS ION With the rapid accumulation of geospatial data and the advancement of geoscience, there is a critical requirement for an infrastructure that can integrate large-scale, heterogeneous, and distributed storage systems for the sharing of geospatial data within multiple user communities The emerging Grid technology can address the problems associated with the sharing of distributed geospatial data, including the heterogeneity of computing platforms and storage systems, uniform mechanism to publish and discover geospatial data, performance issues, and security and access control issues Some efforts within the geospatial society have been taken to leverage the Grid technology for the sharing of distributed data With the maturation of Grid technology, the integration and sharing of distributed geospatial data will be easier and more efficient REFERENCES Allcock, B., Bester, J., Bresnahan, J., Chervenak, L A., Foster, I., Kesselman, C., Meder, S., Nefedova, V., Quesnel, D., & Tuecke, S (2002, May) Data Management and Transfer in High Performance Computational Grid Environments Parallel Computing Journal, 28(5), 749-771 Berman, F., Fox, G., & Hey, T., (2003) The Grid: past, present, future In Berman, F., Fox, G., and Hey, A eds, Grid Computing: Making the Global Infrastructure a Reality, 9-50 Wiley, New York, NY, USA Chervenak, A., Foster, I., Kesselman, C., Salisbury, C., & Tuecke, S (2001) The Data Grid: Towards an Architecture for the Distributed Management and Analysis of Large Scientific Datasets Journal of Network and Computer Applications, 23, 187-200 Di, L (2005) The Geospatial Grid In Rana, S and Sharma, J (eds.), Frontiers of Geographic Information Technology Springer-Verlag Foster, I., Kesselman, C., & Tuecke, S., (2001) The Anatomy of the Grid: Enabling Scalable Virtual Organizations International Journal Supercomputer Applications, 15(3) ISO (2003a) Geographic Information – Metadata, ISO 19115:2003 May 08, 2003, 140pp ISO (2003b) Geographic Information – Metadata – Part 2: Extensions for imagery and gridded data, ISO/WD 19115-2.2 Oct 13, 2003, 41pp Karimi, A H & Peachavanish, R., (2005) Interoperability in Geospatial Information Systems In Khosrow-Pour, M (eds.), Encyclopedia of Information Science and Technology Hershey, PA: Idea Group Reference Lamberti, F., & Beco, S., (2002) SpaceGRID An international programme to ease access and dissemination of Earth Observation data/products: How new technologies can support Earth Observation Users Community 22nd EARSeL Symposium & General Assembly, Prague, Czech Republic, June 4-6, 2002 Lo, C P., & Yeung, A K W., (2002) Concepts and techniques of geographic information systems Upper Saddle River, NJ: Prentice Hall Nebert, D., & Whiteside, A., 2005 OGCTM Catalogue Services Specification (Version 2.0.0) OGC Document Number: 04-021r3, 187pp Ramapriyan, H., Isaac, D., Yang, W., Bonnlander, B., & Danks, D., (2006) An Intelligent Archive Testbed Incorporating Data Mining – Lessons and 227 Sharing of Distributed Geospatial Data through Grid Technology Observations IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2006 July 3- August 4, 2006, Denver, Colorado Wei, Y., Di, L., Zhao, B., Liao, G., Chen, A., Bai, Y., & Liu, Y (2005) The design and implementation of a Grid-enabled catalogue service IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2005 on July 25-29, 2005, Seoul, Korea Welch, V (2005) Globus Toolkit Version Grid Security Infrastructure: A Standards Perspective Welch, V., Siebenlist, F., Foster, I., Bresnahan, J., Czajkowski, K., Gawor, J., Kesselman, C Meder, S., Pearlman, L., & Tuecke, S (2003) Security for Grid Services Twelfth International Symposium on High Performance Distributed Computing (HPDC-12), IEEE Press key TER MS Certificate: A public key and information about the certificate owner bound together by the digital signature of a CA In the case of a CA certificate the certificate is self signed, i.e., it was signed using its own private key Data Replica: A complete or partial copy of original data DPSS: The Distributed-Parallel Storage System (DPSS) is a scalable, high-performance, distributed-parallel data storage system orginally developed as part of the DARPA -funded MAGIC Testbed, with additional support from the U.S Dept of Energy, Energy Research Division, Mathematical, Information, and Computational Sciences Office 228 Grid Technology: Grid technology is an emerging computing model that provides the ability to perform higher throughput computing by taking advantage of many networked computers to model a virtual computer architecture that is able to distribute process execution across a parallel infrastructure GridFTP: Extension of traditional FTP protocol It is a uniform, secure, high-performance interface to file-based storage systems on the Grid HPSS: High Performance Storage System (HPSS) is hierarchical storage system software that manages and accesses terabytes to petabytes of data on disk and robotic tape libraries OGSA-DAI: Open Grid Services Architecture – Data Accessing Interface It is a middleware product which supports the exposure of data resources, such as relational or XML databases, on to Grids SRB: The Storage Resource Broker (SRB) is a Data Grid Management System (DGMS) or simply a logical distributed file system based on a client-server architecture which presents the user with a single global logical namespace or file hierarchy Virtual Organization: A Virtual Organization is a group of individuals or institutions who share the computing resources of a “Grid” for a common goal X.509: In cryptography, X.509 is an ITU-T standard for public key infrastructure (PKI) X.509 specifies, amongst other things, standard formats for public key certificates and a certification path validation algorithm Section VI Location-Based Services 230 Chapter XXIX Cognitively Ergonomic Route Directions Alexander Klippel University of Melbourne, Australia Kai-Florian Richter Universität Bremen, Germany Stefan Hansen Spatial/Information Systems Ltd./LISAsoft, Australia Abstr act This contribution provides an overview of elements of cognitively ergonomic route directions Cognitive ergonomics, in general, seeks to identify characteristics of cognitive information processing and to formalize these characteristics such that they can be used to improve information systems For route directions, an increasing number of behavioral studies have, for example, pointed to the following characteristics: the use of landmarks, changing levels of granularity, the qualitative description of spatial relations The authors detail these aspects and additionally introduce formal approaches that incorporate them to automatically provide route directions that adhere to principles of cognitive ergonomics C ogn it ive Aspects Di rect ions of R oute Route directions fascinate researchers in several fields Since the 70s linguists and cognitive scientists have used verbal route directions as a window to cognition to learn about cognitive processes that reflect structuring principles of environmental knowledge (e.g., Klein, 1978) Over the last decade, the number of publications on various aspects of route directions has increased Next to the general aspects of how to provide route direc- Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited Cognitively Ergonomic Route Directions tions and how to identify principles that allow us to define what makes route directions cognitively ergonomic, technical aspects of navigation support systems have become an additional focus The question required from the latter perspective is part of a broader approach that aims to formally characterize the meaning (semantics) of spatial relations In other words, if we want to bridge the gap between information systems and behavioral analysis we have to answer how we perform the transition from data to knowledge Several key elements can be identified based on psychological and linguistic literature on route directions that are pertinent for cognitively ergonomic route directions (Denis, 1997; Lovelace, Hegarty, & Montello, 1999; Tversky & Lee, 1999) These comprise the conceptualization of directions at decision points, the spatial chunking of route direction elements to obtain hierarchies and to change the level of granularity, the role of landmarks, the communication in different modalities, the traveling in different modes, and aspects of personalization (see Table 1) Most research on routes and route directions deals with navigation in urban structures such as street networks The results discussed in this article focus on this domain Appro aches t o R epresent R oute K now ledge ing Behavioral studies have substantiated key elements of cognitively ergonomic route directions To implement these aspects in information systems detailed formal characterizations of route knowledge are required The approaches discussed below are a representative vocabulary that allows for the characterization of mental conceptualization processes reflecting the results from behavioral studies (see Table 1) In this sense we can refer to them as Ontologies of Route Knowledge (Chandrasekaran, Josephson, & Benjamins, 1999; Gruber, 1993) In Guarino’s terminology these approaches would most likely be called domain ontologies (Guarino, 1998) One of the earliest approaches is the TOUR model by Kuipers (Kuipers, 1978) that later developed into the Spatial Semantic Hierarchy (SSH) (Kuipers, 2000) Kuipers and his collaborators developed this approach to add the qualitativeness that can be found in the organization of a cognitive agent’s spatial knowledge to approaches in robotics The latter classically relied more on quantitative spatial descrip­tions The SSH allows for modeling cognitive rep­resentations of space as well as for building a framework for robot navigation, i.e qualitative and quantita- Table Cognitive ergonomics of route directions Cognitively ergonomic route directions • are qualitative, not quantitative, • allow for different levels of granularity and organize spatial knowledge hierarchically, • reflect cognitive conceptualizations of directions at decision points, • chunk route direction elements into larger units to reduce cognitive load, • use landmarks to: ° disambiguate spatial situations, ° anchor turning actions, ° and to confirm that the right actions have been taken, • present information in multimodal communication systems allowing for an interplay of language and graphics, but respecting for the underlying conceptual structure, • allow for an adaptation to the user’s familiarity with an environment, as well as personal styles and different languages 231 Cognitively Ergonomic Route Directions tive aspects are combined The SSH especially reflects the aspect of hierarchical organization of spatial knowledge by providing different levels of information representation: the sensory, control, causal, topological, and metrical level Ontological characterizations are developed for each level to match human cognitive processes The Route Graph model (Werner, KriegBrückner, & Herrmann, 2000) describes key elements for route based navigation Similar to the SSH, it allows representing knowledge on different levels of granularity However, it is much more abstract and does not provide any processes for acquiring this knowledge It is intended to provide a formalism expressing key notions of route knowledge independent of a particular implementation, agent, or domain Its focus is on a sound formal specification of basic elements and operations, like the transition from route knowledge to survey knowledge by merging routes into a graph-like structure A linguistically grounded approach with the aim to generate verbal route directions is the CORAL project by Dale and coworkers (e.g., Dale, Geldof, & Prost, 2005) One of the central aspects of their approach is the organization of parts of a route into meaningful units, a process they call segmentation Instead of providing turn-by-turn directions, this approach allows for a small number of instructions that capture the most important aspects of a route The employed modeling language is called Route Planning Markup Language (RPML) Formalisms that model route knowledge on the conceptual level can be found in the theory of wayfinding choremes (Klippel, Tappe, Kulik, & Lee, 2005) and context-specific route directions (Richter & Klippel, 2005) These approaches model route knowledge modality-independent on the conceptual level The wayfinding choreme theory employs conceptual primitives—as the result of conceptualization processes of a cognitive agent incorporating functional as well as geometrical environmental aspects—to define 232 basic as well as super-ordinate valid expressions on different levels of granularity The approach to context-specific route directions builds on this theory A systematics of route direction elements determines which, and how, entities may be referred to in route directions Accordingly, abstract relational specifications are inferred by optimization processes that adapt route directions to environmental characteristics and inherent route properties Human wayfinding, however, may not be restricted to a single mode of transportation A typical example is public transport, where travelers frequently switch between pedestrian movement and passive transportation (trains, buses, etc.) Timpf (2002) analyzed route directions for multi-modal wayfinding and developed two different ontologies of route knowledge: one representing knowledge from the perspective of the traveler and one taking the perspective of the transportation system The former focuses on movement along a single route, i.e., actions to perform to reach the destination, while the latter provides concepts referring to the complete transportation network An industry approach for formalizing route knowledge can be found in Part 6: Navigation Service of the OpenLS specification The OpenGIS Location Services (OpenLS) Implementation Specification (Mabrouk, 2005) describes an open platform for location-based application services, the so called GeoMobility Server (GMS) proposed by the Open Geospatial Consortium (OGC) It offers a framework for the interoperable use of mobile devices, services and location-related data The Navigation Service described in Part of the OpenLS specification provides the accessing client, amongst other services, with preprocessed data that is required for the generation of route directions Based on XML specifications, it defines a data structure that allows clients to generate their own route directions which may accord more to a user’s preferences The used data model structures the route in maneuvers Cognitively Ergonomic Route Directions (descriptions combining a turn at a decision point and proceeding on the following route segment) and enhances them with additional information about route elements C ore Aspects of C ogn it ive ly E rgono mic R oute Di rect ions In the following, three aspects that are at the core of cognitively ergonomic route directions will be discussed in greater detail: cognitively adequate direction concepts, the use of landmarks, and spatial chunking to obtain hierarchies and change the level of granularity Conceptualization of Directions at D ecision Points The specification of direction changes is the most pertinent information in route directions While current route information systems heavily rely on street names to identify the proper direction to take, behavioral research (Tom & Denis, 2003) has shown that from a cognitive perspective, street names are not the preferred means to reorient oneself People rather rely on landmarks (as discussed in the next section) and appropriate direction concepts On the most basic level we have to specify the correspondence between a direction change (in terms of the angle) and a direction concept For example, which sector is applicable to a concept like “turn right”? On a more elaborate level, we have to specify alternative direction concepts and detail their scope of application Figure shows some examples of how the same direction change can result in different direction concepts (and corresponding verbalizations) depending, among other things, on the spatial structure in which the change occurs We need this level of specificity for two reasons First, a qualitative but precise direction model allows for verbally instantiating a situation model (Zwaan & Radvansky, 1998) of the encountered intersections Second, intersections can function as landmarks Just like classical examples of landmarks, such as the Eiffel Tower, in the context of a specific route, a salient intersection can be Figure A change of a direction is associated with different conceptualizations according to the intersection at which it takes place The ‘pure’ change may be linguistically characterized as take the second exit at the roundabout (a) At intersection (b) it might change to the second right; at intersection (c) it may change to fork right, and at (d) it becomes veer right 233 Location-Based Performance Tuning in Mobile Sensor Networks strained devices with wireless communication capability, processing power, and environment sensing equipment Sensor nodes can be attached to mobile devices such as mobile robots forming a Mobile Sensor Network (MSN) There has been considerable research on designing mobile platforms to transport wireless sensors The Millibot project at Carnegie Mellon University (Bererton et al., 2000) focused on constructing heterogeneous distributed robots that combine mobile platforms with different sensor devices such as cameras, temperature sensors, movement sensors, and so forth Robomote (Sibley et al., 2002), designed in the Robotic Embebbed Systems Laboratory at the University of Southern California is a robot platform that functions as a single mobile node in a mobile sensor network Figure shows actual pictures of the millibot and robomote platforms Another example includes efforts on using commercial-off-the-shelf components to build inexpensive and modular robots (Bergbreiter and Pister, 2003) Mobile Sensor Network consists of mobile platform (e.g., mobile robots) carrying wireless sensor devices that can be deployed in conjunction with stationary sensor nodes to acquire and process data for surveillance and tracking, environmental monitoring for highly sensitive areas, or execute search and rescue operations Resource constraints of MSNs make it difficult to utilize them for advanced environmental monitoring that requires data intensive collaboration between the robots (e.g., exchange of multimedia data streams) (Scerri et al., 2003; Scerri, Xu et al., 2004) To meet the application requirements, the data exchange must be performed over a wireless link Meanwhile, even high rate wireless networks (e.g., 802.11 networks) use a best-effort service that has limitations of data intensive multimedia applications since it can lead to packet loss, delay and jitter (Kurose and Ross, 2005) The problem aggravates in low rate wireless sensor networks, (e.g., 802.15.4 networks) (Zheng and Lee, 2004) In this chapter, we consider location-based approach for performance tuning in MSNs This approach assumes that each node in MSN is aware of its geographic location Note, that using Global Positioning System (GPS) is not always possible in such systems because of severe energy and location precision constraints Commonly MSNs utilize ad-hoc localization methods based on nodes calculating their coordinates using special beacon nodes whose positions are known Further consideration of this subject is beyond the scope of this chapter PERFOR MANCE TUN ING IN Mob ile SENSOR NETWORKS Applications using MSNs have stringent requirements for efficient mechanisms of querying sensor data and delivering the query result Minimizing Figure Millibot (left) and robomote (right) 261 Location-Based Performance Tuning in Mobile Sensor Networks sensor query response time becomes crucial in mobile sensor networks At the same time, minimizing energy consumption per query is equally crucial for these battery-powered devices In general, the time/energy trade-offs involve energy and time gain/loss associated with specific layouts of the nodes Proper positioning (relocation) of mobile sensors combined with changing the transmission ranges of sensors have a considerable impact on the time/energy trade-off Specifically, both factors impact the following characteristics of a Mobile Sensor Network: • • 262 Collision-free concurrency Packet collisions is one of the major sources of energy and time waste in MSN Once any two or more nodes in the same Collision Domain (CD) transmit packets at the same time, a collision occurs, and packets are corrupted and discarded Packet collisions can be avoided by minimizing the number of intersecting CDs and by synchronizing data transmissions among nodes within the same CD Both relocation and changing the transmission range of sensor nodes could result in changing the number of potential collision-free concurrent transmissions Filtering factor Relocation and changing the transmission range of sensors can result in changing the number of hops and intermediate transmission nodes involved in query execution This, however, brings both benefits and penalties If the filtering factor of the intermediate node is low (i.e., it just retransmits the data) then it can introduce some time and energy loss due to extra hop From the other side, the intermediate node does reduce the data transmission ranges, which results in energy conservation If the intermediate node does a lot of filtering, the benefits include spending less energy in order to transmit less data A mobile sensor query is characterized by large data streams among participating nodes with possible in-node data filtering/aggregation, which can be described as a tree-like data delivery pattern (query routing tree) Mobile sensors are moved into target positions according to the selected query routing tree and mobile sensor deployment plans taking into consideration the current topology of stationary sensor nodes, the applications’ coverage requirements, and the collision domains of the sensor nodes Figure elaborates on the concept of collision domains in a typical wireless network such as IEEE 802.15.4 (Zheng and Lee, 2004) and illustrates how collisions are handled in such a network Consider two nodes n1 and n2 that wish to communicate Assuming that all sensor nodes use the same frequency band for transmission, two transmissions that overlap will get corrupted (collide) if the sensor nodes involved in transmission or reception are in the same collision domain CD(n1,n2) defined as the union of the transmission ranges of n1 and n2 In Figure 2, nodes n1, n2, n3, and n4, n5 and n6 are in the same collision domain This implies that when n1 and n2 are communicating, n3, n4, n5 and n6 cannot participate in any communications A typical wireless network handles collisions using carrier sense multiple-access with collision avoidance (CSCMA-CA) (Zheng and Lee, Figure Collision domain of two communicating sensors n5 n1 n6 n3 Rtx Ctx n2 n4 Location-Based Performance Tuning in Mobile Sensor Networks 2004) In general, before starting a transmission, nodes must sense the channel for a predetermined amount of time (waiting time) If the channel is busy, the nodes wait for the predetermined amount of time after the channel becomes free In addition, nodes backoff for a random time to avoid the possibility that two or more nodes transmit at the same time after the waiting period For this entire period, the node must sense the channel and this consumes energy Each packet also needs to be acknowledged by the receiver since wireless channels are unreliable In general, mobile sensors should position themselves and adjust their transmission power so as to minimize overlap of CDs in the query tree In some cases, however, this general strategy may result in time and/or energy loss In order to capture the associated tradeoff, we introduce a concept of collision-free concurrency (cfc) of a query tree We say that query tree T1 has higher cfc than an equivalent query tree T2, if T1 enables more concurrent transmissions without risk of collisions than T2 A query tree has cfc=1, if it allows for all potentially concurrent transmission pairs to occur Two elementary transmissions et1 and et2 are potentially concurrent in a query tree T if they not share the same destination and there is no strict order between et1 and et2 For example, consider the query tree in Figure 3a which was generated for some query Q The query tree in Figure 3a is associated with cfc=1/3, since it allows for one transmission pair (n4~n2, n5~n3) to occur without risk of collisions Note that the query tree includes three potentially concurrent transmission pairs: (n4~n2, n5~n3), (n4~n2, n3~n1), and (n5~n3, n2~n1) Here ni~nj denotes an elementary one-hop transmission from a sensor node ni to node nj On the other hand, Figure 3b illustrates a query tree with cfc=0 since none of the three potentially concurrent transmission pairs can be performed without risk of collisions An optimization technique should be applied to teamwork planning in the MSN to take into account the time/energy trade-offs In general, the trade-offs involve energy and time gain/loss associated with specific layouts of the wireless sensors The mobile robots should position themselves and adjust their transmission power so as to minimize overlap of CDs in the query tree The optimizer is responsible for the choice of the best query routing tree The mobile robots should move into target positions according to the selected query tree Some of the mobile robots can facilitate data delivery acting as intermediate nodes rather than data acquisition nodes Such mobile facilitators can introduce extra hops in order to reduce transmission ranges of the data acquisition nodes In addition, the facilitators can also act as filters aggregating/compressing the input data and so decreases the amount of data transmitted from the remote nodes to the root node Figure Explanation of collision-free concurrency (b) (a) n1 n1 n4 n2 n3 n3 n2 n5 n4 n5 263 Location-Based Performance Tuning in Mobile Sensor Networks Figure Re-positioning of mobile facilitator (a) (b) s1 s1 s0 s1 m s2 s3 s0 m s2 s3 Figure shows a query tree topology with four previously positioned nodes s0, s1, s2, s3 and three different positions of a mobile facilitator m The facilitators consume extra energy and introduce some extra processing delay However, by reducing the transmission range and data stream sizes, they are also capable of reducing the overall query time and energy consumption To sum up, given a query, the coverage requirements, and the initial position of the mobile robots, the query optimizer shifts through possible mobile robots positions in order to generate the candidate trees with acceptable response time and energy consumption ALGEBR AIC OPT IMIZAT ION In this section we consider an algebraic query optimization technique based on a Data Transmission Algebra (DTA) (Zadorozhny et al., 2005) that allows a query optimizer to generate query routing trees to maximize collision-free concurrent data transmissions taking into account intermediate hops and filtering factors of facilitators The DTA consists of a set of operations that take transmissions between wireless sensor nodes as input and produce a schedule of transmissions as their result A one-hop elementary transmission from sensor node ni to node nj is denoted as ni~nj Each transmission ni~nj is associated 264 (c) s0 m s2 s3 with a collision domain CD(ni, nj) as defined in the previous section A transmission schedule is either an elementary transmission, or a composition of elementary transmissions using one of the operations of the DTA The basic DTA includes three operations that combine two transmission schedules A and B: o(A,B) This is a strict order operation, that is, A must be executed before B a(A,B) This is an overlap operation, that is, A and B can be executed concurrently c(A,B) This is a non-strict order operation, that is, either A executes before B, or vice versa For an example of the DTA operations consider the query tree in Figure It shows some DTA specifications that reflect basic constraints of the query tree For instance, operation o(n4~n2, n2~n1) specifies that transmission n2~n1 occurs after n4~n2 is completed This constraint reflects a part of the query tree topology Operation c(n2~n1, n3~n1) specifies that there is an order between transmissions n2~n1 and n3~n1 since they share the same destination However this order is not strict Operation a(n4~n2, n5~n3) specifies that n4~n2 can be executed concurrently with n5~n3, since neither n3 nor n5 belongs to CD(n4,n2), and neither n4 nor n2 are in CD(n5,n3) Location-Based Performance Tuning in Mobile Sensor Networks Figure Example of DTA specifications Initial S pecification: n1 n4 n2 n3 n5 n4~ n n2~ n n5~ n n3~ n o(n ~ n2,n2~ n1 ) o(n ~ n3, n3~n1) c(n2 ~ n1, n3~n1) E le me ntary T ransm is s ion: S trictly O rdered T ransm is s ions: N on-stric tly O rdere d T ransm is s ions: C oncurren t T ransm is s ions: C om plete S chedule: n4 ~ n o (n4 ~ n2, n2~n 1) c (n2 ~ n1, n 3~ n1 ) a (n4 ~ n 2, n 5~ n ) a(n ~ n2, n5~ n3 ) a(n ~ n2, n3~n1) a(n ~ n3, n2~n1) o ( a (n4 ~ n2, n5~ n3 ), c(n2 ~ n1, n3~n 1) ) Each operation of the DTA specification defines a simple transmission schedule that consists of two elementary transmissions The DTA introduces a set of transformation rules (Zadorozhny et al., 2004) that can be used to generate more complex schedules Figure shows an example of a complete schedule that includes all elementary transmissions of the query tree Figure also shows the initial DTA specification reflecting basic constraints of the query tree The initial specification consists of a set of elementary transmissions reflecting the tree topology imposed by the query semantics, as well as order and overlap operations over the elementary transmissions Figure shows query routing trees from Figure with four previously positioned nodes s0, s1, s2, s3 and three different positions of a mobile facilitator m Note how the re-positioning of the facilitator is reflected in the initial DTA specifications is1, is2 and is3 Out of the many possible query routing trees and transmission schedules the optimizer should select an option with an acceptable query response time and overall energy consumption This is a multi-objective optimization (MOP) problem (Miettinen, 1999) In general, the MOP aims at minimizing values of several objective functions f1,…fn under a given set of constraints To choose between different vectors of the optimization objectives, the optimizer utilizes the concept of Pareto optimality (Miettinen, 1999) Informally, an objective vector is said to be Pareto optimal (also called Pareto front) if all other feasible vectors in the objective space have a higher value for at least one of the objective functions, or else have the same value for all objectives Among all Pareto optimal solutions, the optimizer should chose one using an application-dependent utility function The optimizer should evaluate time and energy gains/losses and make a preference considering the relative importance of time and energy in the context of a specific query Figure reports on Pareto fronts for a simple two-hop query tree of nodes with some data aggregation/filtering at intermediate nodes For example, filtering factor 0.2 means that 20% of data delivered to an intermediate node will be forwarded to the base station A major observation here is an increase of variability in both time and energy consumption with a decrease of the facilitator filtering factor This means that in general, the optimizer can benefit from higher filtering factors (the lower filtering factor reduces more input data) However, there is a considerable risk for the optimizer to behave as badly as in the case of a high filtering factor The 265 Location-Based Performance Tuning in Mobile Sensor Networks Figure Impact of mobility on DTA specification (a) s1 s0 s3 is1: c(s1~s0,s2~s0) c(s2~s0,s3~s0) (b) s1 m s2 s0 m s2 s3 optimizer explores related time/energy tradeoffs maximizing benefits and avoiding risks of selecting bad schedules The assumption is that the energy source used for moving the platform is separate from the sensor batteries Considering both energy and time to move is an interesting issue that should be explored separately and more work in this area is required Note that the algebraic optimization may be expensive due to its combinatorial nature The number of alternative query trees and schedules grow at least exponentially with the number of sensor nodes and elementary transmissions participating in the query In order to handle the optimization complexity, the optimizer should use scalable techniques, such as utilize heuristic-based pruning that eliminates suboptimal alternatives and randomized algorithms (Ioannidis and Kang, 1990) Figure Actual Pareto fronts explored by optimizer 266 is2: c(s1~s0,m~s0) c(m~s0,s3~s0) o(s2~m,m~s0) a(s2~m,s1~s0) (c) s1 s0 is3: c(s1~s0, m~s0) o(s2~m, m~s0) o(s3~m, m~s0) a(s2~m, m~s0) a(s3~m, s1~s0) m s2 s3 C onc lus ion Mobile Sensor Networks include a large collection of collaborating mobile sensor devices The mobile nodes can be deployed in conjunction with stationary sensor nodes to perform mission critical surveillance and monitoring tasks Such applications require efficient collaboration between mobile sensors, which imply intensive data exchange over wireless links Meanwhile, resource constraints of MSNs make it difficult to utilize them for advanced data-intensive monitoring tasks In this article we considered location-based approach for performance tuning that significantly facilitates this challenge Other approaches can also be adopted to optimize data exchange over a wireless link in mobile environments For example, Particle sys- Location-Based Performance Tuning in Mobile Sensor Networks tems utilize nondestructive bit wise arbitration for channel access that considerably reduces the collisions rates (Decker et al., 2005) Several distributed time slot scheduling algorithms for collision-free communications were implemented as an extension of basic Time Division Multiple Access (TDMA) protocols Ammar and Stevens (1991) proposed a distributed TDMA protocol with a goal to permit mobile sensors to move and then reallocate themselves a time slot without involving the entire network Ali et al (2002) proposed a distributed and adaptive TDMA algorithm for multi-hop mobile networks One concern with this design is that dynamic topology changes may lead to frequent exchanges of control packets that could consume bandwidth and energy resources In general, the distributed TDMA scheduling schemes have considerable control message overhead for building data delivery schedules R eferences Ali, F., Appani, P., Hammond, J., Mehta, V., Noneaker, D., & Russell, H (2002) Distributed and Adaptive TDMA Algorithms for MultipleHop Mobile Networks, Proceedings of MILCOM, 546-551 Ammar M., &Stevens, D (1991) A Distributed TDMA Rescheduling Procedures for Mobile Packet Radio Networks, Proceedings of IEEE International Conference on Communications (ICC), 1609-1613 Bererton, C., Navarro-Serment L., Grabowski, R., Paredis, C., & Khosla, P (2000) Millibots: Small distributed robots for surveillance and mapping Proceedings of Government Microcircuit Applications Conference, 162-167 Bergbreiter, S., & Pister K.S (2003) CotsBots: an Off-the-Shelf Platform for Distributed Robotics Proceedings of in the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, 1632-1637 Decker, C., Krohn, A., Beigl, M., & Zimmer, T (2005) The Particle Computer System Proceedings of the ACM/IEEE Fourth International Conference on Information Processing in Sensor Networks (IPSN05), 443-448 Ioannidis, Y., & Kang, Y (1990) Randomized algorithms for optimizing large join queries Proceedings of ACM SIGMOD, 312-321.   Kurose, J F., & Ross, K W (2005) Computer Networking: A Top-down Approach AddisonWesley 712 p Miettinen, K (1999) Nonlinear Multiobjective Optimization Kluwer Academic Publisher, 298 Scerri, P., Pynadath, D., Johnson, L., Rosenbloom, P., Si, M., Schurr, N., & Tambe, M (2003) A Prototype Infrastructure for Distributed Robot Agent Person Teams Proceedings of 2nd Int Joint Conference on Autonomous Agents and Multiagent Systems, 433-440.   Scerri, P., Xu, Y., Liao, E., Lai, J., & Sycara, K (2004) Scaling Teamwork to Very Large Teams Proceedings of AAMAS, 888-895 Sibley, G., Rahimi, M and Sukhatme, G (2002) Robomote: A Tiny Mobile Robot Platform for Large-Scale Ad Hoc Sensor Networks Proceedings of the Intl Conference on Robotics and Automation, 1143-1148 Zadorozhny, V., Sharma, D., Krishnamurthy, P., & Labrinidis, A (2005) Tuning query performance in sensor databases Proceedings of MDM 247-251.   Zadorozhny, V., Chrysanthis, P., & Krishnamurthy, P (2004) A Framework for Extending the Synergy between MAC Layer and Query Optimization in Sensor Networks Proceedings of DMSN VLDB Workshop, 68-77 Zheng, J., & Lee, M (2004) Will IEEE 802.15.4 Make Ubiquitous Networking a Reality? A Discussion on a Potential Low Power, Low Bit Rate 267 Location-Based Performance Tuning in Mobile Sensor Networks Standard IEEE Communications Magazine, 42(6), 140- 146 Mobile Facilitator: A mobile sensor device that facilitates data delivery in a mobile sensor network acting as an intermediate node rather than a data acquisition wireless sensor key T er ms Mobile Sensor Device: A mobile platform that functions as a single mobile node carrying a wireless sensor in a mobile sensor network Collision Domain: The union of the transmission ranges of two communicating wireless sensors Collision-Free Concurrency: A quality measure of a query routing tree in a mobile sensor network reflecting the number of concurrent transmission pairs that can be performed within the query routing tree without risking packet collisions Data Transmission Algebra (DTA): An algebraic formalism consisting of a set of operations that take transmissions between wireless sensors as input and produce a schedule of transmissions as their result MSN query optimizer utilizes DTA to select a query routing tree and transmission schedules with an acceptable query response time and overall energy consumption 268 Mobile Sensor Network (MSN): A collection of collaborating mobile sensor devices deployed in conjunction with stationary sensor nodes to perform surveillance and monitoring tasks Packet Collision: A phenomena that occurs once any two or more nodes in the same collision domain transmit packets at the same time When collision occurs packets are typically corrupted and discarded Query Routing Tree: A tree-like data delivery pattern generated by a query in mobile sensor networks Wireless Sensor: A small resource-constrained device with wireless communication capability, processing power and environment sensing equipment 269 Chapter XXXIV Location-Based Services: A Taxonomy on Theory and Practice Henrik Hanke University of Duisburg-Essen, Germany Alf Neumann University of Cologne, Germany Abstr act The provisioning of Location-Based Services (LBS) follows the chain of determination of a position, mapping this information onto a natural language-based description of this position and performing the service itself The evolution of technologies regarding applications and infrastructure, standards and contents has brought up various streams that have influenced the development of this chain over the past years (Zeimpekis et al., 2003) On the one hand, emerging theoretical concepts have been showing the way for many commercial and non-commercial services On the other hand, the conceptual evolution has been accompanied by significant investments of mobile technology companies and service providers to the further development of practical solutions (Gessler and Jesse, 2001) Introduct ion A wide field for technological innovation, the conceptual discussion of LBS has widely remained a technology issue dominated by the development of positioning techniques, infrastructure and data transmission concepts This chapter re-emphasizes the term service, including information and functionality, which is offered by LBS applications and consumed by customers It sheds light on the Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited Location-Based Services ubiquitous information management approach as important foundation for advanced mobile data services (Acharya et al., 2004) Furthermore, the chapter provides an overview of the essential service concepts and relevant implications, challenges, and opportunities that can be derived from the application context of LBS Finally, a taxonomy on theory and practice is presented that draws the link from the technology to the service U b iqu it ous Infor mat ion Man age ment Along with the sophistication and increasing performance of communication devices, such as Personal Digital Assistants (PDAs), mobile phones as well as wireless communication networks, the environment and the world increasingly adopts a mobile character In this respect, a very important driver is constituted by a ubiquitous information management concept, which is free from temporal and, in general, also from spatial constraints In such mobile computing, ubiquitous computing or pervasive computing environments, mobile databases and the data dissemination infrastructure are two integral components especially in the context of LBS Data dissemination can follow push-based or pull-based information flows depending on where location and application data is processed This can be done either on the server side or on the device, i.e., client side (Acharya et al., 2004) In push-based systems, data is pushed into a wireless channel and a user tunes in and downloads the required data This approach can process read-only transactions and may include popular data like the stock quotes, news, weather information, traffic information On the other side, in pull-based wireless services, a user induces the server to process specific transactions and send the result to the user through a back chan- 270 nel These transactions can be simple queries or update transactions The two-tier concept of information management to disseminate, process and further store data can be collectively termed information layer Loc at ion-Ad apted S er v ices LBS provide users of mobile devices personalized services tailored to their current location These central information services fall into three broad categories that also emphasize the added value for consumers: positioning and location identification, contextual and environmental location information as well as navigation between different locations There exists a vast body of literature on positioning technologies reaching from the early Active Badge indoor locating solutions to the Global Positioning System (GPS) and the more recent Wireless Local Area Network (WLAN) and Bluetooth concepts (King et al., 2006) The diversity of the underlying technological basis as well as the opportunities and limitations among these approaches in design and characteristic means of data networks correspond to the increasing need for adapted LBS infrastructures These are tailored to the specific requirements of different types of locations, different modes of mobility and distance (Zeimpekis et al., 2003) The precision of location information and the distance of a mobile device to a Location Service Provider (LSP) are two factors that play a key role in this context They present the overall framework in which the service layer of key features of applications and infrastructure technology is embedded Physical and Symbolic Location A LSP can provide two kinds of location information: physical and symbolic Physical positions are Location-Based Services determined by longitude, latitude and altitude In contrast, symbolic location encompasses abstract ideas of where something is: in a certain room, in a certain town or next to a certain object (Gessler and Jesse, 2001) A system providing a physical position can usually be augmented to provide the corresponding symbolic location information with additional information, infrastructure, or both For example, a PDA equipped with a GPS receiver may access a separate database that contains the positions and geometric service regions of other objects to provide applications with symbolic information (Brumitt et al., 2000) Linking real-time train positions to the reservation and ticketing database can help locate a passenger on a train Applications can use the physical position to determine an entire range of symbolic information (Hightower & Borriello 2001) The distinction between physical position and symbolic location is more pronounced with some technologies than others GPS is clearly a physical positioning technology Bluetooth Indoor Positioning Service (BIPS) systems are symbolic location technologies mostly based on proximity to known objects However, some systems such as Cricket can be used in either mode, depending on their specific configuration (King et al., 2006) Location Distance and Mobility Tilson et al (2004) identify three types of mobility: micro, local and remote Micro mobility is related to moveable artifacts in very confined spaces Relative location information is more useful than absolute position The need for very short-range positioning favors particular technologies, for example, Ultra Wideband (UWB) Local mobility, for example indoors, is related to the mobility of people or objects at a particular location and the need for local awareness This normally implies stringent positioning requirements Applications in this context require location information to be determined down to a few meters and translated for the appropriate context Remote mobility is related to movements between different distant locations Following the interdependencies between different types of mobility and the design of LBS, a compatible concept of distance may be developed from short to long range with similar implications on infrastructure requirements, techniques and precision of positioning systems, context and navigation services Table provides a taxonomy of LBS functions within the service and information layer framework and summarizes the implications on the key features of LBS data dissemination, infrastructure and applications Loc at ion-Ad apted T echno logy As mentioned above, LBS exploit knowledge of the location of a mobile device to provide services to it and utilize the location information in some way or the other Hence, tracking a mobile device and the delivery of location-adapted services prerequisites the process of gaining concrete position information, that can be carried out in different ways, with different location contextrelated technological implications, opportunities and challenges LBS management can be classified into two dimensions: collection of position-specific data in order to realize the service and the implementation and operation of an application in terms of the service itself (Weinberg, 2004) Each dimension itself can be subdivided in relation to the organization of the infrastructure On the one hand, a dimension might be network-based It would consist of centralized and aggregated information, which are dependent on core network features and resources, such as mobile phone antennas On the other hand, distributed network-centric features can be classified as organizational edge of the infrastructure 271 Location-Based Services Table LBS functions – a service and information taxonomy Service layer Navigation Push global positioning, indoor positioning mobile advertisement traffic alerts data available from readonly channel Pull emergency response, proximity information service mobile yellow pages, location-aware billing routing, direction finding data processed and updated on-demand precision for local and micro mobility Information layer Context location description, contextual alerts support of micro, local and remote mobility Key features of data dissemination Positioning Key features of applications and infrastructure technology In order for LBS to add value at all, minimum precision requirements concerning tracking exist While network coverage and hence tracking and positioning precision of a device is high in densely populated areas, it is rather low outside of cities, as the distances between reference cells grow While within cities a coverage using traditional technology up to 100m is possible, it can reduce down to 10 km in rural areas (Steinfield, 2004) Hence, coverage is negatively correlated to population density Location T racking and Location Awareness Traditionally, a mobile device is traceable by using the service infrastructure it communicates with or is tied to As a handset involved in the process of offering LBS has no built-in position-finder or location-determination technology, the process is referred to as location tracking (Seydim et al., 2001) The infrastructure is made up of several 272 antennas, called towers or cells The tower transmitting the signal to devices is referred to as Cell Of Origin (COO) On a basis of the knowledge of the COO, the Time Of Arrival (TOA) of signals between the handset and the tower is measured in order to determine the device’s position Although this method provides distinct location information, it lacks precision In order to get more precise location information, the structure of the cell needs to be revived A cell usually consists of three or more antennas, which raises the opportunity to aim at the difference at which devices signals reach theses three or more towers This process is called Time Difference Of Arrival (TDOA) As these need no further handset, client-side, technology other than a simple antenna, supplementary technology is needed in order to generate high-level tracking precision By measuring the time at which signals from the towers reach the handset, precision levels up to 30 m are possible Hence, the TDOA functionality is being reversed The Enhanced Observed Time Differ- Location-Based Services ence (EOTD) thus needs the handset to be much more involved in position determination Encountering the core mechanism, location awareness is referred to as the situation in which the handset does not need any further server-side technologies beneath the retrieval of locationinitiated data, such as geographic position, in order to provide LBS (Steinfield, 2004) Hence, the position-originating and position-finding technologies are integrated within the handset In this respect, the handset gains ability to generate knowledge of position by itself Requiring a highly equipped handset, accuracy of the locating-process can achieve a maximum level, enabling a location-range of less than 10 m (Agarwal et al., 2002) Therefore, if satellite communication can be ensured, handsets might be traced anywhere, anytime Enhancing GPS capabilities, it is possible to make a combined use of network technologies and equipment By implementing such hybrid systems, a major weakness of GPS is addressed Hence, the provision of tracking-capabilities in aggravating circumstances (Zeimpekis et al., 2003), such as erratic satellite communication, can be ensured in use of assisted GPS (A-GPS) Therefore, A-GPS can provide and speed up tracking with further robustness against up-link and down-link data transmission failures and uncertainty server with the prerequisiting location information The server-side application analysis, prepares and outputs the data in order for the handset to draw the requested information This might be accomplished by using the simplest handset software, such as the Wireless Application Protocol (WAP) browser to which the data is downloaded and displayed in according to the LBS In this sense, many sources of uncertainty and instability can arise and conflict with the LBS primary goal of robustness, for example, data communication issues, server-side processing errors or even downtimes Providing faster services in terms of application processing, flexibility and mobility can be ensured by the translocation of the services operation Hence, the computing task regarding the processing of the application itself might be executed on the client-side Provided the handset has appropriate software implemented, it can therefore perform the LBS self-sufficiently: once the location is determined, the application runs locally with the user, hence being independent from core network resources The built-in software processes the information gained through communication by itself, for example, placing the user on a map stored in the memory of the handset Location D ata Interpretation C onc lus ion Following the collection of adequate and crucial position information, these data need to be interpreted and prepared in the next step according to the LBS task This process can again be structured into core and edge technology based systems (Weinberg, 2004) Using network resources for the provisioning of LBS in terms of prepared data, the handset does not process any intelligent software by itself It is rather relying on server-side application outputs Active uplink data transmission provides the While it is technically possible to precisely locate a device within a few meters or less, the device needs to meet specific requirements These might reduce the proposed usefulness of LBS, as they possibly conflict with other non-LBS provisioning goals, such as small and handy terminals Higher precision in localization demands higher costs in infrastructure as well as in handset setup (Patterson et al., 2003) Implementing GPS competency into a handset raises the need for further built-in hardware-components, as well as 273 Location-Based Services higher conductible battery and processing power, which is resulting in higher expenses, as well as distorted devices The interaction between the above mentioned edge-core and collection-operation relation delimit not only the implementation of a LBS concerning the underlying infrastructural opportunities in terms of the alignment of a service, but also LBS goals such us precision or processing power (Weinberg, 2004) For example, while it is possible to use core technologies within a data collection process and provide a precise location information, the further generation of a valueadded services might not be: routing information within a navigation application requires continuous communication in order to compute the correct route Continuous communication itself induces higher network traffic and hence network affliction and requires stronger battery-power On the other hand, in order to use this service with a GPS-based technology, stronger processing power and other handset hardware is required; yet, network affliction would be reduced The challenges and opportunities of LBS in terms of services adapted to the specificities of different locations and context-related information needs of LBS users fall into two broad categories: mobility and distance requirements, accuracy and precision Therefore, it is primarily the diversity that stands out regarding the currently state-of-the-art of LBS devices, applications and the technology infrastructure at the micro, local and remote level The different location contextrelated prerequisites make it hard to integrate the existing variety of systems into one scalable, capable and cost efficient solution (Hightower and Borriello, 2001) Table provides a final overview on the location- and positioning-related technology issues within the dichotomy of edge and core properties and makes clear reference to the precision challenge 274 R eferences Acharya, D., Kumar, V., & Dunham, M H (2004) Infospace: Hybrid and adaptive public data dissemination system for ubiquitous computing Journal for Wireless Communications and Mobile Computing Agarwal, N., Basch, J., Beckmann, P., Bharti, P., Bloebaum, S., Casadei, C., Chou, A., Enge, P., Fong, W., Hathi, W., Mann, w., Stone, J., Tsitsiklis, J., & Van Roy, B (2002) Algorithms for GPS operation indoors and downtown GPS Solutions, 6, 149-160 Brumitt, B., Krumm, J., Meyers, B., & Shafer, S (2000) Ubiquitous computing and the role of geometry IEEE Personal Communications (Special Issue on Smart Spaces and Environments), 7-5, 41-43 Gessler, S., & Jesse, K (2001) Advanced location modeling to enable sophisticated LBS provisioning in 3G networks In M Beigl, P Gray, & D Salber (Eds.) Proceedings of the Workshop on Location Modeling for Ubiquitous Computing Atlanta, USA Hightower, J., & Borriello, G (2001) A survey and taxonomy of location systems for ubiquitous computing In Computer Science and Engineering (Ed.) Technical Report (document number UWCSE 01-08-03) Washington, DC: University of Washington King, T., Haenselmann, T., Kopf, S., & Effelsberg, W (2006) Positionierung mit Wireless-LAN und Bluetooth Praxis der Informationsverarbeitung und Kommunikation, 29(1), 9-17 Patterson, C.A., Muntz, R.R., & Pancake, C.M (2003) Challenges in location-aware computing In IEEE CS & IEEE ComSoc Seydim, A.Y., Dunham, M.H & Kumar, V (2001) Location dependent query processing Proceedings of the 2nd ACM international workshop on Location-Based Services Table LBS technology – a taxonomy on edge and core properties Device ↔ Edge [+] GPS Infrastructure ↔ Core Edge NavigationService via PDA NET Core Edge Asset Tracking in retail chain List Nearby Shops on Mobile Core BT Operation Accuracy & Precision Edge Edge Tracking through mobile Phone [-] Core COO / EOTD Core Collection data engineering for wireless and mobile access, pp.47-53, Santa Barbara,CA, USA Steinfield, C (2004) The development of location based services in mobile commerce In: B Priessl, H Bouwman, & C Steinfield (Eds.), E-Life after the dot.com bust Berlin, Germany: Springer Verlag Weinberg, G (2004) Location based services case study MIT Center for E-Business Boston Zeimpekis, V., Giaglis, G M., & Lekakos, G (2003) A taxonomy of indoor and outdoor positioning techniques for mobile location services SIGecom Exchange, (4), 19-27 key T er ms Bluetooth Indoor Positioning Service (BIPS): A symbolic location technology based on proximity to known objects Cell Of Origin (COO): In a traditional communication environment a COO is referred to as the cell or tower, a handset device is tied to Enhanced Observed Time Difference (EODT): Reversing the TDOA-principle, EOTD measures the transferring-time of signals from the towers to the device Location Service Provider (LSP): A provider of a value-adding service, which is dependent on location information 275 ... 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