Handbook of Research on Geoinformatics - Hassan A. Karimi Part 6 pot

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Sharing of Distributed Geospatial Data through Grid Technology
national climate data center (Ramapriyan et al.,
2006). The data volume will increase signicantly
if similar models of ner spatial resolutions, such
as 1 km, are used. The models are being changed
and rened from time to time and new geospatial
data, the NASA EOS data and NOAA climate
data, are being collected by satellites continu-
ously. A xed computing environment that con-
tains only static data sources will not fulll 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 dis-
persed organizations (Foster et al., 2001). Resource
sharing in a Grid is highly controlled. Resource
providers and consumers dene clearly and care-

fully 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 t 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.
sh Ar Ing of geosp At IAL dAt 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 efcient sharing of those huge amounts of
geospatial data in a secure and controllable man-
ner 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 ad-
dressed 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. Plat-
forms and systems used to store and manage the
geospatial data in each center may vary greatly.
There are many types of high performance stor-
age 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 sys-
tems 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 difculty. 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-
225
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 ad-
dressed. 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 2 – 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
Prole (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 differ-
ent 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 creat-
ing 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
efcient.
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 transfer-
ring 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 data-
intensive 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 plat-
forms, and networks within a Grid environment
in real time. If a user wants to access a specic
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 associ-
ated 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
226
Sharing of Distributed Geospatial Data through Grid Technology
associated with an accessing fee. Currently dif-
ferent 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 mecha-
nism 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, stor-
age system, application, or a VO will have one
or more certicates 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, ne-grain access control
policies on geospatial data can be issued to any
individual user, application, or VO that has one
or more certicates through Community Autho-
rization Service (CAS) provided by the Globus
Toolkit. Currently, the X.509 certicates based
on Public Key Infrastructure (PKI) are used by
Grid technology and to provide high-level au-
thentication, authorization, and single sign-on
functionality (Welch 2005).
Today, efforts have been taken by some geosci-
ence 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) Ofce of
Science to address the formidable challenges as-

sociated 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 genera-
tion 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
University also developed a prototype system for
efcient 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 scientic
research and even people’s everyday life, distrib-
uted 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-
227
Sharing of Distributed Geospatial Data through Grid Technology
tion. With the maturation of Grid technology and
the advancement of computing and network tech-
nologies, 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 distrib-
uted 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 efcient.
references
Allcock, B., Bester, J., Bresnahan, J., Cherve-
nak, 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., Salis-

bury, C., & Tuecke, S. (2001). The Data Grid:
Towards an Architecture for the Distributed
Management and Analysis of Large Scientic
Datasets. Journal of Network and Computer Ap-
plications, 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). In-
teroperability 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/prod-
ucts: 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. OGC
TM
Cata-
logue Services Specication (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
228
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 implementa-
tion 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 4 Grid
Security Infrastructure: A Standards Perspec-
tive.
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
Certicate: A public key and information
about the certicate owner bound together by
the digital signature of a CA. In the case of a CA
certicate the certicate 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 Ofce.
Grid Technology: Grid technology is an
emerging computing model that provides the
ability to perform higher throughput computing
by taking advantage of many networked comput-
ers to model a virtual computer architecture that
is able to distribute process execution across a
parallel infrastructure.
GridFTP: Extension of traditional FTP pro-
tocol. It is a uniform, secure, high-performance
interface to le-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 le system based on
a client-server architecture which presents the
user with a single global logical namespace or
le hierarchy.
Virtual Organization: A Virtual Organiza-
tion 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 species, amongst other things, standard
formats for public key certicates and a certica-
tion 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
Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.
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 formal-
ize 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.
cogn It Ive Aspects of r oute
dIrect Ions
Route directions fascinate researchers in several
elds. Since the 70s linguists and cognitive scien-
tists have used verbal route directions as a window
to cognition to learn about cognitive processes
that reect structuring principles of environmen-
tal 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-
231
Cognitively Ergonomic Route Directions
tions and how to identify principles that allow us
to dene 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 identied based
on psychological and linguistic literature on route
directions that are pertinent for cognitively ergo-
nomic 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 Ing
r oute know Ledge
Behavioral studies have substantiated key ele-
ments of cognitively ergonomic route directions.
To implement these aspects in information systems
detailed formal characterizations of route knowl-
edge are required. The approaches discussed
below are a representative vocabulary that allows

for the characterization of mental conceptualiza-
tion processes reecting the results from behav-
ioral 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 devel-
oped into the Spatial Semantic Hierarchy (SSH)
(Kuipers, 2000). Kuipers and his collaborators
developed this approach to add the qualitative-
ness that can be found in the organization of a
cognitive agent’s spatial knowledge to approaches
in robotics. The latter classically relied more on
q
uan
titative spatial descriptions. The SSH al-
lows
for modeling cognitive representations of
space as well as for building a framework for
robot navigation, i.e. qualitative and quantita-
Table 1. Cognitive ergonomics of route directions
Cognitively ergonomic route directions
• are qualitative, not quantitative,
• allow for different levels of granularity and organize spatial knowledge hierarchically,
• reect 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 conrm 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.
232
Cognitively Ergonomic Route Directions
tive aspects are combined. The SSH especially
reects 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, Krieg-
Brückner, & 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 specication 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
waynding choremes (Klippel, Tappe, Kulik, &
Lee, 2005) and context-specic route directions
(Richter & Klippel, 2005). These approaches
model route knowledge modality-independent
on the conceptual level. The waynding choreme
theory employs conceptual primitives—as the
result of conceptualization processes of a cogni-
tive agent incorporating functional as well as
geometrical environmental aspects—to dene
basic as well as super-ordinate valid expressions
on different levels of granularity. The approach
to context-specic route directions builds on
this theory. A systematics of route direction ele-
ments determines which, and how, entities may
be referred to in route directions. Accordingly,
abstract relational specications are inferred by

optimization processes that adapt route directions
to environmental characteristics and inherent
route properties.
Human waynding, 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 direc-
tions for multi-modal waynding 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 specication. The Open-
GIS Location Services (OpenLS) Implementation
Specication (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 6
of the OpenLS specication provides the access-

ing client, amongst other services, with prepro-
cessed data that is required for the generation of
route directions. Based on XML specications,
it denes 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
233
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.
core Aspects of cogn It Ive Ly
ergono MIc r oute dIrect 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
decision points
The specication 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 re-

orient 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 alterna-
tive direction concepts and detail their scope
of application. Figure 1 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 specicity 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 land-
marks, such as the Eiffel Tower, in the context
of a specic route, a salient intersection can be
Figure 1. A change of a direction is associated with different conceptualizations according to the inter-
section 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.
234
Cognitively Ergonomic Route Directions
used to organize spatial knowledge. This aspect
has not yet gained much attention.

Enriching Route Directions with
Landmarks
Analyzing human route directions shows how
prominently landmarks are used to structure
the respective spatial knowledge, to give the
instructed the possibility to assure that they are
still following the correct route, and to anchor
required turning actions. Since landmarks seem
to be such an important part of human-generated
route directions their integration is pertinent for
automatically generating cognitively ergonomic
instructions.
Several classications of landmarks and their
characteristics have been discussed in the litera-
ture. One of the rst assessments is presented by
Lynch (1960) who distinguishes Landmarks as one
of ve elements that structure urban knowledge:
path, edges, districts, nodes, and landmarks. It
is commonly agreed that the landmark account
should comprise all ve elements, as according
to Presson and Montello (1988) everything that
stands out of the background may serve as a
landmark. That is, given the right spatial context
different features of an environment may serve as
landmarks. Sorrows and Hirtle (1999) distinguish
three characteristics important for making an ob-
ject a landmark: its visual, semantic, and structural
characteristics. Additionally, landmarks can be
categorized according to their cognitive function
within route directions, their geometry, and their

spatial relation to the route. Humans conceptualize
landmarks either as point-like, linear, or area-like
entities. However, these conceptualizations do not
necessarily correspond to the geometric charac-
teristics of objects but reect the schematization
processes cognitive agents apply (Herskovits,
1986). A detailed description of the different
roles of landmarks is necessary to allow for their
integration in an automatic generation process.
For example, a simple, yet as of today unexplored
way to enrich route directions with landmarks
is to include references to salient intersections,
like T-intersections or roundabouts, which are
easy to identify automatically. This also reects
the direction concepts humans employ with such
structures (see also Figure 1).
Spatial Chunking: Hierarchies
and Levels of Granularity
The hierarchical organization of spatial infor-
mation and exibly changing between levels
of granularity are omnipresent in the cognitive
organization of spatial knowledge (Hobbs, 1985;
Kuipers, 2000). Chunking elementary waynding
actions (such as turns at intersections) in order
to impose a hierarchical structure and to change
the level of granularity reects not only cogni-
tive conceptualization processes but organizes
route knowledge in a cognitively ergonomic
way. Especially users who are familiar with an
environment can prot from such an approach.

In general, providing a user with too much detail
violates ndings of cognitive science, as for ex-
ample formulated in Clark’s 007 Principle: “In
general, evolved creatures will neither store nor
process information in costly ways when they can
use the structure of the environment and their
operations upon it as a convenient stand-in for
the information-processing operations concerned.
That is, know only as much as you need to know
to get the job done.” (Clark, 1989, p. 64)
Structuring route descriptions by subsuming
instructions gives users a coarse overview over a
route, which is easier to perceive and quite often
sufcient for successful waynding, especially
if the user is familiar with the environment. Of
course, the subsumed information still has to be
accessible in case the user needs it (or, as discus-
sions on positioning technologies in this volume
show, the user may simply re-query a new route
from his new position). This may either be pos-
sible by zoom-in operations, i.e., by accessing the
next, more detailed level of the hierarchy, or by
235
Cognitively Ergonomic Route Directions
(mental) inference processes. Such inferences, for
example, extract from an instruction like “turn left
at the dead end” information on which action to
perform at all intersections before the dead end,
namely to continue straight (e.g., Duckham &
Kulik, 2003). The following cognitive strategies

for spatial chunking are discussed in the litera-
ture (Dale et al., 2005; Klippel, Tappe, & Habel,
2003): numerical chunking, structure chunking,
landmark chunking, and chunking using the street
level hierarchy.
t he MuLt IMod AL present At Ion
of r oute k now Ledge
The multimodal communication of spatial in-
formation is a core aspect of human cognition:
linguistic expressions, graphical representations
such as sketch maps, and gestures are channels
along which humans naturally communicate (Ovi-
att, 2003). Each representational medium—each
channel—has advantages in specic contexts but
may fail in other situations (Kray, Laakso, Elting,
& Coors, 2003). For example, natural language
expressions are inherently underspecied: a term
like turn right is applicable to a range of different
turning angles at an intersection and therefore
may be sufcient in many situations. Figrue 2,
however, shows a situation that requires a complex
explanation if a description is provided in linguis-
tic terms. In this case, a graphic representation
is more suitable to communicate the situation at
hand. Communication channels also differ with
respect to their suitability in the identication
of landmarks. A salient object at an intersection
might be visually easily identiable and recog-
nisable, but hard to describe linguistically. An
expression like follow the road to the dead end

on the other hand, may chunk a large part within
a route linguistically and therefore, communicate
the spatial situation more efciently if the dead
end is a long way away and hard to depict on a
small screen.
The communication of route information,
whether visually, linguistically, or in any other
modality, has to follow the same guidelines as
established for the structuring of route knowledge.
Cluttering any communication process has shown
to violate cognitive ergonomics and to slow
down information processing. This connement
to sparseness has been shown for visual route
directions, for example, by Agrawala and
S
tol
te (2000), who based their route direction tool
on results obtained from sketch maps (Tversky
& Lee, 1999).
suMMAr y
In the last decades, research on route directions in
linguistics and cognitive science revealed many
underlying principles and processes of human
route direction production and comprehension,
and, thus, provides us with an understanding
of what constitutes cognitively ergonomic route
directions. However, this understanding has to
be formally specied to be implemented in in-
formation systems for waynding assistance, like
internet route-planners. In essence, three cognitive

principles need to be implemented in waynd-
ing assistance systems to generate cognitively
ergonomic route directions: adequate direction
concepts, the enrichment of route directions with
Figure 2. Complex intersection
236
Cognitively Ergonomic Route Directions
landmarks, and spatial chunking which allows for
a hierarchical structuring of route knowledge and
representations on different levels of granularity.
To this end, we need a thorough understanding of
which direction concept humans apply in which
situation, a detailed ontology of the different kinds
of landmarks and the role they may take in route
directions, as well as formal characterizations
that model hierarchical structures and guide the
changes of granularity.
r eferences
Agrawala, M., & Stolte, C. (2000). A design and
implementation for effective computer-gener-
ated route maps. In AAAI Symposium on Smart
Graphics, March 2000. Stanford.
Chandrasekaran, B., Josephson, J. R., & Ben-
jamins, V. R. (1999). What are ontologies, and
why do we need them? IEEE Intelligent Systems
and Their Applications, 14(1), 20-26.
Clark, A. (1989). Microcognition: Philosophy,
cognitive science, and parallel distributed pro-
cessing. Cambridge, MA: MIT Press.
Dale, R., Geldof, S., & Prost, J P. (2005). Using

natural language generation in automatic route
description. Journal of Research and practice in
Information Technology, 37(1), 89-105.
Denis, M. (1997). The description of routes: A
cognitive approach to the production of spatial
discourse. Cahiers de Psychologie Cognitive,
16, 409-458.
Duckham, M., & Kulik, L. (2003). “Simples”
paths: Automated route selection for navigation.
In W. Kuhn, M. Worboys, & S. Timpf (Eds.),
Spatial Information Theory: Foundations of
Geographic Information Science. Conference on
Spatial Information Theory (COSIT) 2003. (pp.
182-199). Berlin: Springer.
Gruber, T. R. (1993). A translation approach to
portable ontologies. Knowledge Acquisition, 5(2),
199-220.
Guarino, N. (1998). Formal ontology and infor-
mation systems. In N. Guarino (Ed.), Formal
Ontology in Information Systems. Proceedings of
FOIS’98, Trento, Italy, 6-8 June 1998. (pp. 3-15).
Amsterdam: IOS Press.
Herskovits, A. (1986). Language and Spatial
Cognition: An Interdisciplinary Study of the
Representation of the Prepositions in English.
Cambridge, UK: Cambridge University Press.
Hobbs, J. R. (1985). Granularity. In A. Joshi (Ed.),
Proceedings of the 9th International Joint Con-
ference on Articial Intelligence. Los Angeles,
CA (pp. 432-435). San Francisco, CA: Morgan

Kaufmann.
Klein, W. (1978). Wegauskuenfte. Zeitschrift für
Literaturwissenschaft und Linguistik, 33, 9-57.
Klippel, A., Tappe, T., & Habel, C. (2003). Pic-
torial representations of routes: Chunking route
segments during comprehension. In C. Freksa, W.
Brauer, C. Habel & K. F. Wender (Eds.), Spatial
Cognition III. Routes and Navigation, Human
Memory and Learning, Spatial Representa-
tion and Spatial Learning. (pp. 11-33). Berlin:
Springer.
Klippel, A., Tappe, T., Kulik, L., & Lee, P. U.
(2005). Waynding choremes - A language for
modeling conceptual route knowledge. Journal
of Visual Languages and Computing, 16(4),
311-329.
Kray, C., Laakso, K., Elting, C., & Coors, V.
(2003). Presenting route instructions on mobile
devices. Paper presented at the IUI’03, January
12-15, 2003, miami, Florida, USA.
Kuipers, B. (1978). Modelling spatial knowledge.
Cognitive Science, 2(2), 129-154.
237
Cognitively Ergonomic Route Directions
Kuipers, B. (2000). The spatial semantic hierarchy.
Articial Intelligence, 119, 191-233.
Lovelace, K., Hegarty, M., & Montello, D. R.
(1999). Elements of good route directions in
familiar and unfamiliar environments. In C.
Freksa & D. M. Mark (Eds.), Spatial information

theory. Cognitive and computational foundations
of geographic information science (pp. 65-82).
Belin: Springer.
Lynch, K. (1960). The image of the city. Cam-
bridge, MA: MIT Press.
Mabrouk, M. (2005). OpenGis Location Services
(OpenLS): Core Services. OGC Implementation
Specication 05-016 Version 1.1 Open Gis Con-
sortium Inc.
Oviatt, S. L. (2003). Multimodal interfaces. In J.
Jacko & A. Sears (Eds.), The Human-Computer
Interaction Handbook: Fundamentals, Evolving
Technologies and Emerging Applications. (pp.
286-304). Mahwah, NJ: Lawrence Erlbaum.
Presson, C. C., & Montello, D. R. (1988). Points
of reference in spatial cognition: Stalking the elu-
sive landmark. British Journal of Developmental
Psychology, 6, 378-381.
Richter, K F., & Klippel, A. (2005). A model for
context-specic route directions. In C. Freksa,
M. Knauff & B. Krieg-Brueckner (Eds.), Spatial
Cognition IV. Reasoning, Action, and Interac-
tion: International Conference Spatial Cogni-
tion 2004, Frauenchiemsee, Germany, October
11-13, 2004, Revised Selected Papers (pp. 58-78).
Berlin: Springer.
Sorrows, M., & Hirtle, S. C. (1999). The nature
of landmarks for real and electronic spaces. In C.
Freksa & D. M. Mark (Eds.), Spatial information
theory. Cognitive and computational foundations

of geographic information science (pp. 37-50).
Berlin: Springer.
Timpf, S. (2002). Ontologies of waynding: A
traveler’s perspective. Networks and Spatial
Environments, 2, 3-33.
Tom, A., & Denis, M. (2003). Referring to land-
mark or street iniformation in route directions:
What difference does it make? In W. Kuhn, M.
Worboys & S. Timpf (Eds.), Spatial information
theory. Foundations of geogrpahic information
science. International conference, COSIT 2003,
Kartause Ittingen, Switzerland, September 2003.
(pp. 362-374). Berlin: Springer.
Tversky, B., & Lee, P. U. (1999). Pictorial and
verbal tools for conveying routes. In C. Freksa &
D. M. Mark (Eds.), Spatial information theory.
Cognitive and computational foundations of
geographic information science (pp. 51-64).
Berlin: Springer.
Werner, S., Krieg-Brückner, B., & Herrmann,
T. (2000). Modeling navigational knowledge by
route graphs. In C. Freksa, W. Brauer, C. Habel
& K. F. Wender (Eds.), Spatial cognition II.
Integrating abstract theories, empirical studies,
formal methods, and practical applications. (pp.
295-316). Berlin: Springer.
Zwaan, R. A., & Radvansky, G. A. (1998). Situation
models in language comprehension and memory.
Psychological Bulletin, 123(2), 162-185.
key t er Ms

Cognitive Ergonomics: The design of infor-
mation systems that places a strong emphasis on
cognitive aspects. In the case of route directions
the design aims for a lower cognitive load and
enhanced location awareness at the same time.
Granularity: Here, it refers to the detail in
route directions; from coarse levels for general
planning to ner levels to provide context-specic
information, for example at decision points.
Landmark: Any entity in the environment
that sticks out from the background.
238
Cognitively Ergonomic Route Directions
OpenLS: Specication of an open platform
for location-based services dening their core
functionality (directory service, gateway service,
location utility service, presentation service, route
service).
Personalization: Adaptation of information
presentation and interaction with a device / soft-
ware to the needs and preferences of a specic,
individual user.
Route Directions: A set of instructions that
allow a waynder in known or unknown envi-
ronments to follow a route from a start point to
a destination.
Spatial Semantic Hierarchy (SSH): A com-
putational model dening acquisition and repre-
sentation of spatial knowledge on different levels
of abstraction ranging from sensory information

to topological knowledge.
Waynding: The cognitive conceptual activ-
ity of planning and nding ones way.
Waynding Choremes: Mental conceptu-
alizations of functional waynding and route
direction elements.
239
Chapter XXX
Multicast Over Location-Based
Services
Péter Hegedüs
Budapest University of Technology and Economics, Hungary
Mihály Orosz
Budapest University of Technology and Economics, Hungary
Gábor Hosszú
Budapest University of Technology and Economics, Hungary
Ferenc Kovács
Budapest University of Technology and Economics, Hungary
Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.
Abstr Act
This chapter details the potential found in combining to different technologies. The two basically dif-
ferent technologies, LBSs in mobile communication and the well-elaborated multicast technology are
merged in the multicast via LBS solutions. As this chapter demonstrates, this emerging new area has a
lot of possibilities, which have not been completely utilized.
Introduct Ion
Currently, an important area of mobile commu-
nication is ad-hoc computer networks, where
mobile devices need base stations however they
form an overlay without any Internet-related
infrastructure, which is a virtual computer net-

work among them. In this case, the selective,
240
Multicast Over Location-Based Services
bAckground
The positioning technologies in the LBS solutions
are based on the various distances of the commu-
nication mobile from the different base stations.
With advances in automatic position sensing and
wireless connectivity, the application range of
mobile LBSs is rapidly developing, particularly
in the area of geographic, tourist and local travel
information systems (Ibach et al., 2005). Such
systems can offer maps and other area-related
information. The LBS solutions give the capability
to deliver location-aware content to subscribers on
the basis of the positioning capability of the wire-
less infrastructure. The LBS solutions can push
location-dependent data to mobile users according
to their interest or the user can pull the required
information by sending a request to a server that
provides location-dependent information.
LBSs process information with respect to the
location of one or several persons, also referred to
as targets before presenting it to the user. In recent
years, LBSs have become increasingly important
and have helped accelerate the development to-
wards ubiquitous computing environments. Tradi-
tional LBSs map targets to locations (e.g., Where
is person X located?), i.e., they nd the position
of a specic person or group of people. This type

of LBS is denoted as Tracking Services.
There are a lot of location positioning meth-
ods and technologies, such as the satellite-based
Global Positioning System (GPS) that is widely
applied (Hofmann-Wellenhof et al., 1997). The
location determination methods that do not use
the GPS can be classied into three categories:
Proximity, Triangulation (lateration), and Scene
analysis or pattern recognition (Hightower &
Borriello, 2001). Signal strength is frequently
applied to determine proximity. As a proximity
measurement, if a signal is received at several
known locations, it is possible to intersect the
coverage areas of that signal to calculate a location
area. If one knows the angle of bearing (relative
to a sphere) and distance from a known point to
location-related communication has not been
solved completely.
Traditional Location-Based Services (LBSs)
determine the current location of a given person
or a given group of people in order to process
location-dependent information. This use does
not cover the full range that is conceivable for
these services. This article introduces so-called
Zone Services as a new sub-category of LBSs. In
contrast to traditional LBSs, Zone Services col-
lect information about persons currently located
in a given geographic area. For these services,
new considerations regarding data collection,
privacy, and efciency have to be made. Hence, it

has to be determined what techniques or mecha-
nisms common in traditional LBSs or in other
areas like databases or mobile communication
systems can be reused and what concepts have
to be developed.
One of the various communication models
among software entities is the one-to-many data
dissemination, called multicast. The multicast
communication over mobile ad-hoc networks
has increasing importance (Hosszú, 2005). The
article described the fundamental concepts and
solutions on the area of LBSs and the possible
multicasting over the LBS systems. This kind
of communication is in fact a special case of the
multicast communication model, called geocast,
where the sender disseminates data to a subset
of the multicast group members that are in a
specic geographical area. This chapter shows
that this special kind of multicast utilizes the
advantages of LBSs, since multicast is based on
location-aware information that is available in
location-based solutions.
The two basically different technologies, LBSs
in mobile communication and the well-elaborated
multicast technology are merged in the multicast
via LBS solutions. As the chapter demonstrates,
this emerging new area has a lot of possibilities,
which has not been completely utilized.
241
Multicast Over Location-Based Services

the target device, then the target location can be
accurately calculated. Similarly, if somebody
knows the range from three known positions
to a target, then the location of the target object
can be determined. A GPS receiver uses range
measurements to multiple satellites to calculate
its position. The location determination methods
can be server-based or client-based according
to the place of computation (Hightower & Bor-
riello, 2001).
LBSs utilize their ability of location-aware-
ness to simplify user interactions. With advances
in wireless connectivity, the application range of
mobile LBSs is rapidly developing, particularly in
the eld of tourist information systems - telematic,
geographic, and logistic information systems.
However, current LBS solutions are incompat-
ible with each other since manufacturer-specic
protocols and interfaces are applied to aggregate
various components for positioning, networking,
or payment services. In many cases, these com-
ponents form a rigid system. If such a system has
to be adapted to another technology, e.g., moving
from GPS based positioning to in-house IEEE
802.11a-based Wireless Local-Area Network
(WLAN) or Bluetooth based positioning, it has
to be completely redesigned (Haartsen, 1998).
As such, the ability of interoperation of differ-
ent resources under changeable interconnection
conditions becomes crucial for the end-to-end

availability of the services in mobile environments
(Ibach, & Horbank, 2004).
Chen et al. (2004) introduces an enabling in-
frastructure, which is a middleware, in order to
support LBSs. This solution is based on a Location
Operating Reference Model (LORE) that solves
many problems of constructing LBSs, including
location modeling, positioning, tracking, location-
dependent query processing and smart location-
based message notication. Another interesting
solution is the mobile yellow page service.
An interesting development is the Compose
project, which aims to overcome the drawbacks
of the current solutions by pursuing a service
integrated approach that encompasses pre-trip
and on-trip services where on-trip services
could be split into in-car and last-mile services
(Bocci, 2005). The pre-trip service means the
3D navigation of the users in a city environ-
ment, and the on-trip service means the in-car
and the last-mile services together. The in-car
service is a composition of an LBS and a satellite
broadcasting/multicasting method. In this case,
the user has wireless-link access by Personal
Digital Assistant (PDA) to broadcast or multicast.
The last-mile service helps the mobile user with
PDAs to receive guidance during the nal part
of the journey.
The article focuses on the multicast solutions
over the current LBS solutions. This kind of com-

munication is in fact a special case of the multicast
communication model, called geocast, where the
sender disseminates the data to a subset of the
multicast group members that are in a specic
geographical area.
MuLt IcAst Ing
The models of multicast communication differ in
the realization of the multiplication function in
the intermediate nodes. In the case of Datalink-
Level the intermediate nodes are switches, on
the Network-Level they are routers and on the
Application-Level the fork points are applica-
tions on hosts.
The Datalink-Level based multicast is not ex-
ible enough for new applications, which is why it
has no practical importance. The Network-Level
Multicast (NLM), known as IP-multicast, is well
elaborated and sophisticated routing protocols
are developed for it. However, it has not yet been
widely deployed since routing among the Autono-
mous Systems (AS) has not been solved perfectly.
The application level solution gives less efciency
compared to the IP-multicast, however, its deploy-
ment depends on the application itself and it has
no inuence on the operation of the routers. That
242
Multicast Over Location-Based Services
is why the Application-Layer Multicast (ALM)
is currently increasing in importance.
There are a lot of various protocols and imple-

mentations of the ALM, some of which are suit-
able for communication over wireless networks,
which enhance the importance of the ALM. The
reason for this is that in the case of mobile devices
the importance of ad-hoc networks is increasing.
Ad-hoc is a network that does not need any infra-
structure. Such networks are Bluetooth (Haartsen,
1998) and Mobile Ad Hoc NETwork (MANET),
which comprise a set of wireless devices that can
move around freely and communicate in relaying
packets on behalf of one another (Mohapatra et
al., 2004).
In computer networking, there is a weaker
denition of this ad-hoc network. Ad-hoc is a
computer network that does not need a routing
infrastructure. It means that the mobile devices
that use base stations can create ad-hoc computer
networks. In such situations, the usage of Applica-
tion-Level Networking (ALN) technology is more
practical than IP-Multicast. In order to support this
group communication, various multicast routing
protocols are developed for the mobile environ-
ment. The multicast routing protocols for ad-hoc
networks differ in terms of state maintenance,
route topology and other attributes.
The simplest ad-hoc multicast routing methods
are . ooding and tree-based routing. Flooding
is very simple, which offers the lowest control
overhead at the expense of generating high data
trafc. This situation is similar to the traditional

IP-Multicast routing. However, in a wireless
ad-hoc environment, the tree-based routing
fundamentally differs from a wired IP-Multicast
situation, where tree-based multicast routing
algorithms are obviously the most efcient ones,
such as in the Multicast Open Shortest Path First
(MOSPF) routing protocol (Moy, 1994). Though
tree-based routing generates optimally small
data trafc on the overlay in the wireless ad-hoc
network, the tree maintenance and updates need
a lot of control trafc. That is why the simplest
methods are not scalable for large groups.
A more sophisticated ad-hoc multicast rout-
ing protocol is the Core-Assisted Mesh Proto-
col (CAMP), which belongs to the mesh-based
multicast routing protocols (Garcia-Luna-Aceves
& Madruga, 1999). It uses a shared mesh to
support multicast routing in a dynamic ad-hoc
environment. This method uses cores to limit the
control trafc needed to create multicast meshes.
Unlike the core-based multicast routing protocol
as the traditional Protocol Independent Multi-
cast-Sparse Mode (PIM-SM) multicast routing
protocol (Deering et al., 1996), CAMP does not
require that all trafc ow through the core nodes.
CAMP uses a receiver-initiated method for routers
to join a multicast group. If a node wishes to join
the group, it uses a standard procedure to announce
its membership. When none of its neighbors are
mesh members, the node either sends a join request

toward a core or attempt to reach a group member
using an expanding-ring search process. Any mesh
member can respond to the join request with a
join Acknowledgement (ACK) that propagates
back to the request originator.
Compared to the mesh-based routing protocols,
which exploit variable topology, the so-called
gossip-based multicast routing protocols exploit
randomness in communication and mobility.
Such multicast routing protocols apply gossip
as a form of randomly controlled ooding to
solve the problems of network news dissemina-
tion. This method involves member nodes to
talk periodically to a random subset of other
members. After each round of talk, the gossipers
can recover their missed multicast packets from
each other (Mohapatra et al., 2004). Compared
to the deterministic approaches, this probabilistic
method will better work in a highly dynamic ad
hoc network because it operates independently of
network topology and its random nature ts the
typical characteristics of the network.
243
Multicast Over Location-Based Services
t he Loc At Ion-A wAre
MuLt IcAst
The geocasting can be combined with ooding.
Such methods are called forwarding zone meth-
ods, which constrain the ooding region. The
forwarding zone is a geographic area that extends

from the source node to cover the geocast zone.
The source node denes a forwarding zone in the
header of the geocast data packet. Upon receiving
a geocast packet, other machines will forward
it only if their location is inside the forwarding
zone. The Location-Based Multicast (LBM) is
an example for such geocasting-limited ooding
(Ko & Vaidya, 2002).
An interesting type of ad-hoc multicasting is
the geocasting. The host that wishes to deliver
packets to every node in a certain geographical
area can use such a method. In this case, the po-
sition of each node with regard to the specied
geocast region implicitly denes group member-
ship. Every node is required to know its own
geographical location. For this purpose they can
use the GPS. The geocasting routing method does
not require any explicit join or leave actions. The
members of the group tend to be clustered both
geographically and topologically. The geocast-
ing method of routing exploits the knowledge
of location.
LbM geoc Ast Ing And Ip
MuLt IcAst Ing
Using LBM in a network where routers are in
xed locations and their directly connected
hosts are within a short distance, the location of
these hosts can be approximated with the loca-
tion of their router. These requirements are met
by most of the GSM (Global System for Mobile

Communications), UMTS (Universal Mobile
Telecommunication System), WIFI (Wi-Fi Cer-
tied), WIMAX (Worldwide Interoperability
for Microwave Access) and Ethernet networks,
therefore a novel IP layer routing mechanism can
be introduced.
This new method is a simple kind of the geo-
casting-limited ooding, extending the normal
Multicast Routing Information Base (MRIB)
with the geological location of the neighbor rout-
ers. Every router should know its own location,
and a routing protocol should be used to spread
location information between routers. The new IP
protocol is similar to the User Datagram Protocol
(UDP) protocol, but it extends it with a source
location and a radius parameter. The source loca-
tion parameter is automatically assigned by the
rst router. When a router receives a packet with
empty source location, it assigns its own location
to it. The radius parameter is assigned by the ap-
plication itself, or it can be an administratively
dened parameter
This method requires changes in routing
operational systems, but offers an easy way to
start geocasting services on an existing IP in-
frastructure without using additional positioning
devices (e.g., GPS receiver) on every sender and
receiver. The real advantages of the method are
that geocasting services can be offered for all
existing mobile phones without any additional

device or infrastructure.
f uture t rends
The multicast communication over mobile ad-
hoc networks has increasing importance. The
article has described the fundamental concepts
and solutions. It especially focused on the area of
LBSs and the possible multicasting over them. It
was shown that a special kind of the multicast,
called geocast communication model utilizes
the advantages of LBSs, since it is based on the
location-aware information made available in the
location-based solutions.
There are two known issues of this IP level
geocasting. The rst problem is the scalability, the
ooding type of message transfer is less robust as
244
Multicast Over Location-Based Services
compared to multicast tree based protocols, but
this method is more efcient in a smaller envi-
ronment than using tree allocation overhead of
multicast protocols. The second issue is that the
source must be connected directly to the router
that is physically in the center position in order to
become source of a session. The proposed geocast-
ing-limited ooding protocol should be extended
to handle those situations where the source of a
session and the target geological location are in
different places.
conc Lus Ion
The two basically different technologies, the

Location-Based Services in the mobile commu-
nication world and the well-elaborated multicast
communication technology of the computer
networking are jointed in the multicast over LBS
solutions. As it was described, this emerging new
area has a lot of possibilities, which have not been
completely utilized.
As a conclusion it can be stated that despite
the earlier predicted slower development rate of
the LBS solutions, nowadays the technical possi-
bilities and the consumers’ demands have already
met. The geospatial property of LBSs provides
technical conditions to apply a specialized type
of the multicast technology, called geocasting,
which gives an efcient and user group targeted
solution for one-to-many communication.
r eferences
Bocci, L. (2005). Compose Project Web Site,
Retrieved from application.
it/compose
Chen, Y., Chen, Y. Y., Rao, F. Y., Yu, X. L., Li,
Y., & Liu, D. (2004). LORE: An Infrastructure to
Support Location-aware services. IBM Journal
of Res. & Dev., 48(5/6), 601-615.
Deering, S. E., Estrin, D., Farinacci, D., Jacobson,
V., Liu, C-G., & Wei, L. (1996). The PIM architec-
ture for wide-area multicast routing. IEEE/ACM
Trans. on Networking, 4(2), 153-162.
Garcia-Luna-Aceves, J. J., & Madruga, E. L. (1999,
August). The Core-Assisted Mesh Protocol. IEEE

Journal of Selected Areas in Communications,
1380-1394.
Haartsen, J. (1998). The universal radio interface
for ad hoc, wireless connectivity. Ericsson Re-
view,3. Retrieved 2004 from csson.
com/review
Hightower, J., & Borriello, G. (2001 Aug.). Loca-
tion Systems for Ubiquitous Computing. IEEE
Computer, 57-65.
Hofmann-Wellenhof, B., Lichtenegger, H., &
Collins, J. (1997). Global Positioning System:
Theory and Practice. Fourth Edition, Springer-
Verlag Wien, New York, NY.
Hosszú, G. (2005). Reliability Issues of the Mul-
ticast-based Mediacommunication. In Pagani, M.
(Ed.), Encyclopedia of Multimedia Technology
and Networking (pp. 875-881). Hershey, PA: Idea
Group Reference.
Ibach, P., Horbank, M. (2004). Highly-Available
Location-based Services in Mobile Environments.
Paper presented at the International Service Avail-
ability Symposium 2004, Munich, Germany,
May 13-14.
Ibach, P., Tamm, G., & Horbank, M. (2005). Dy-
namic Value Webs in Mobile Environments Using
Adaptive Location-Based Services. In Proceed-
ings of the 38th Hawaii International Conference
on System Sciences (9 pages). IEEE.
Ko, Y-B., & Vaidya, N. H. (2002). Flooding-Based
Geocasting Protocols for Mobile Ad Hoc Net-

works. Proceeding of the Mobile Networks and
Applications. Kluwer Academic, 7(6), 471-480.
245
Multicast Over Location-Based Services
LocatioNet, (2006). LocatioNet and Ericsson En-
ter Into Global Distribution Agreement. Retrieved
from
Mohapatra, P., Gui, C., & Li, J. (2004). Group
Communications in Mobile Ad Hoc Networks.
Computer, 37(2), 52-59.
Moy, J. (March 1994). Multicast extensions to
OSPF. Network Working Group RFC 1584.
Uppuluri, P., Jabisetti, N., Joshi, U., & Lee, Y.
(2005). P2P Grid: Service Oriented Framework for
Distributed Resource Management. In Proceed-
ings of the IEEE International Conference on Web
Services, July 11-15, Orlando, FL, USA.
Weiss, D. (2006). Zone Services — A New Ap-
proach of Location-based Services, Retrieved
from />key t er Ms
Ad-hoc Computer Network: Mobile devices
that require base stations can create the ad-hoc
computer network if they do not need routing
infrastructure.
Application-Layer Multicast (ALM): A
novel multicast technology, which does not require
any additional protocol in the network routers,
since it uses the traditional unicast (one-to-one) IP
transmission. Its other name: Application-Level
Multicast (ALM).

Application-Level Network (ALN): The
applications, which are running in the hosts, can
create a virtual network from their logical con-
nections. This is also called overlay network. The
operations of such software entities are not able
to understand without knowing their logical rela-
tions. In most cases these ALN software entities
use the P2P model (see below), not the client/
server (see below) for the communication.
Autonomous System (AS): A network with
common administration; it is a basic building
element of the Internet. Each AS is independent
from the others.
Client/Server Model: It is a communicating
model, where one hardware or software entity
(server) has more functionalities than the other
entity (the client), whereas the client is responsible
to initiate and close the communication session
towards the server. Usually the server provides
services that the client can request from the server.
Its alternative is the P2P model (see below).
Geocast: One-to-many communications
among communicating entities, where an entity
in the root of the multicast distribution tree sends
data to that certain subset of the entities in the
multicast dissemination tree, which are in a spe-
cic geographical area.
IP-Multicast: Network-level multicast tech-
nology, which uses the special class-D IP-address
range. It requires multicast routing protocols in the

network routers. Its other name: Network-Level
Multicast (NLM).
Multicast Routing Protocol: In order to
forward the multicast packets, the routers have
to create multicast routing tables using multicast
routing protocols.
Multicast Tree: A virtual graph, which
gives the paths of sending multicast data from
the source (the root of the tree) to the nodes of
the tree. Its other name: Dissemination tree or
Distribution Tree.
Peer-to-Peer (P2P): It is a communication
model where each node has the same author-
ity and communication capability. They create
a virtual network, overlaid on the Internet. Its
members organize themselves into a topology
for data transmission.
246
Chapter XXXI
Routing
Kevin M. Curtin
George Mason University, USA
Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.
Abstr Act
Routing is the act of selecting a course of travel. Routing problems are one of the most prominent and
persistent problems in geoinformatics. This large research area has a strong theoretical foundation
with ties to operations research and management science. There are a wide variety of routing models
to t many different application areas, including shortest path problems, vehicle routing problems, and
the traveling salesman problem, among many others. There are also a range of optimal and heuristic
solution procedures for solving instances of those problems. Research is ongoing to expand the types of

routing problems that can be solved, and the environments within which they can be applied.
Introduct Ion
Routing is the act of selecting a course of travel.
This process is undertaken by nearly every active
person every day. The route from home to school
or work is chosen by commuters. The selection
of stops one will make for shopping and other
commercial activities and the paths between
those stops is a routing activity. Package delivery
services plan routes for their trucks in such a way
that packages are delivered within specied time
windows. School buses are assigned routes that
will pick up and deliver children in an efcient
manner. Less tangible objects such as telephone
calls or data packets are routed across informa-
tion networks. Routing is the most fundamental
logistical operation for virtually all transportation
and communications applications.
As in the examples above, routing is frequently
seen as a practical effort to accomplish some goal.
247
Routing
Its importance to geoinformatics, however, lies
in the nature of routing as a general problem.
Transportation, communications, or utility sys-
tems can all be modeled as networks—connected
sets of edges and vertices—and the properties of
networks can be examined in the context of the
mathematical discipline of graph theory. Routing
procedures can be performed on any network

dataset, regardless of the intended application.
This chapter will discuss the formulation of rout-
ing problems including shortest path problems,
and will review in detail general vehicle routing
problems and the traveling salesman problem.
Solution procedures for routing problems are
discussed and future trends in routing research
are outlined.
bAckground
Generally, a routing procedure is based on an
objective—or goal—for the route, and a set of
constraints regarding the route’s properties. By
far the most common objective for routing prob-
lems is to minimize cost. Cost can be measured
in many different ways, but is frequently dened
as some function of distance, time, or difculty
in traversing the network. Thus the problem of
locating the least cost or shortest path between
two points across a network is the most common
routing problem. It is also a problem for which
there are several extremely efcient algorithms
that can determine the optimal solution. The most
widely cited algorithm that solves the least cost
path problem on directed graphs with non-nega-
tive weights was developed by Edsgar Dijkstra
(1959), and an even more efcient version of
this algorithm—the two-tree algorithm—exists
(Dantzig, 1960). Alternative algorithms have been
presented that will solve this problem where nega-
tive weights may exist (Bellman, 1958), where

all the shortest paths from each node to every
other node are determined (Dantzig, 1966; Floyd,
1962), and where not only the shortest path but
also the 2
nd
, 3
rd
, 4
th
, or k
th
shortest path must be
found (Evans & Minieka, 1992).
network des Ign prob LeMs
The shortest path problem is just one of a class
of related routing problems that can be described
as network design problems. Network design
problems require that some combination of the
elements of a network (edges and vertices) be cho-
sen in order to provide a route (or routes) through
the network. This group includes the minimal
spanning tree problem, the Steiner tree problem,
the Traveling Salesman Problem, and the vehicle
routing problem, among many others (Magnanti
& Wong, 1984). The modeling of these problems
frequently takes the form of integer programming
models. Such models dene an objective and a set
of constraints. Solution procedures are applied that
require decisions to be made that generate a route
that optimizes the objective while respecting the

constraints. Given the limited space in this forum,
the following sections will focus on the modeling
of two signicant routing problems in an effort
to demonstrate the characteristics of the general
class. Vehicle Routing Problems are presented in
order to discuss the range of possible objectives
for routing problems, and the Traveling Salesman
Problem is presented to demonstrate the formula-
tion of the objectives and constraints.
Vehicle Routing Problems
Vehicle Routing Problems (VRPs) are those that
seek to nd a route or routes across a network
for the delivery of goods or for the provision of
transport services. From their earliest incarnations
VRPs have been formulated as distance or cost
minimization problems (Clarke & Wright, 1964;
Dantzig & Ramser, 1959). This overwhelming
bias persists to this day. Nine out of ten research
articles regarding route design in the context of
transit routing written between 1967 and 1998 and
248
Routing
reviewed by Chien and Yang (2000) employed
a total cost minimization objective. When the
route is intended as a physical transport route,
the cost objective is nearly always formulated
as a generalized measure of operator costs (List,
1990), user costs (Dubois et al., 1979; Silman et
al., 1974), or both operator and user costs (Ceder,
2001; Chien et al., 2001; Lampkin & Saalmans,

1967; Newell, 1979; Wang & Po, 2001).
The few exceptions include a model that maxi-
mizes consumer surplus (Hasselström, 1981), a
model that seeks to maximize the number of public
transport passengers (van Nes et al., 1988), a model
that seeks equity among users (Bowerman et al.,
1995), a model that seeks to minimize transfers
while encouraging route directness and demand
coverage (Zhao & Gan, 2003), and a model that
seeks to maximize the service provided to the
population with access to the route (Curtin &
Biba, 2006). VRPs for transport services can
be designed to either determine single optimal
routes, or a system of routes (Ceder & Wilson,
1986; Chakroborty & Dwivedi, 2002; List, 1990;
Silman et al., 1974)
A substantial subset of the literature posits
that routing problems are not captured well by
any single optimization objective, but rather
multiple objectives should be considered (Current
& Marsh, 1993). Among the proposed multi-ob-
jective models are those that tradeoff maximal
covering of demand against minimizing cost
(Current & Pirkul, 1994; Current et al., 1984,
1985; Current & Schilling, 1989), those that seek
to both minimize cost and maximize accessibil-
ity in terms of distance traveled (Current et al.,
1987; Current & Schilling, 1994), and those that
tradeoff access with service efciency (Murray,
2003; Murray & Wu, 2003).

Regardless of the objective that is deemed ap-
propriate for a routing application, the problem
will frequently be posited in the form of a struc-
tured mathematical model. In the next section
the Traveling Salesman Problem is presented to
demonstrate how such models are formulated.
t he t raveling salesman problem
The Traveling Salesman Problem (TSP) is argu-
ably the most prominent problem in combinatorial
optimization. The simple way in which the prob-
lem is dened in combination with its notorious
difculty has stimulated many efforts to nd an
efcient solution procedure. The TSP is a classic
routing problem in which a hypothetical salesman
must nd the most efcient sequence of destina-
tions in his territory, stopping only once at each,
and ending up at the initial starting location. The
TSP has its origins in the Knight’s Tour problem
rst formally identied by L. Euler and A. T.
Vandermonde in the mid-1700s. In the 1800s, the
problem was identied as an element of graph
theory and was studied by the Irish mathemati-
cian, Sir William Rowan Hamilton. The problem
was named the Hamiltonian cycle problem in his
honor (Hoffman A. J. & Wolfe P., 1985).
The rst known mention of the TSP under that
name appeared in a German manual published
in 1832, and this was followed by four applied
appearances of the problem in the late 1800s and
early 20

th
century (Cook, 2001). The mathemati-
cian and economist Karl Menger publicized the
TSP in the 1920s in Vienna (Applegate D., 1998),
then introduced it in the United States at Harvard
University as a visiting lecturer, where the prob-
lem was discussed with Hassler Whitney who at
that time was doing his Ph.D. research in graph
theory. In 1932, the problem was introduced at
Princeton University by Whitney, where A. W.
Tucker and Merrill Flood discussed the problem
in the context of Flood’s school-bus routing study
in New Jersey (Schrijver, 2004). Flood went on to
popularize the TSP at the RAND Corporation in
Santa Monica, California in late 1940s. In 1956
Flood mentioned a number of connections of the
TSP with the Hamiltonian paths and cycles in
graphs (Flood, 1956). Since that time the TSP
has been considered one of the classic models in
combinatorial optimization, and is used as a test
case for virtually all advancements in solution
procedures.