domain work shop upon location based service ,time upe deya par marks nahi mile :(

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Domain Seminar Report on
LOCATION BASED SERVICES
Submitted by:-
B.Tech (CSE/IT) II Semester
Under the Guidance of
Amity school of Engineering and Technology
AMITY UNIVERSITY RAJASTHAN
Declaration

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I hereby declare that the report entitled ______________ submitted for the partial fulfilment of B.Tech
degree is my original work and the report has not formed the basis for the awardof any degree,associate
ship, fellowship or any other similar titles.
Signature of the student:
Counter Signature of the Guide:
Name of the Guide : __________________
Designation:________________________
Date:

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Acknowledgements
First of all, I would like to sincerely thank my supervisor, ___________, for his/her persistent
support, guidance, help, and encouragement during the whole process of my study.
Moreover, I would like to thank our Director- ASET and Dr. Tarun Kumar Sharma, HOD, CSE
who were always there whenever we needed any support.
I would also like to thank my parents for their well wishes to complete this work. Finally thanks
to all friends for their support.
(Name and Signature of the student)

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Contents
Abstract Pg.No.
List ofFigures and Tables Pg.No.
1 Introduction ( Times NewRoman 12,Bold)
1.1 Now we should understand why location is useful? Pg.No.
2 History
3 Ongoing evolution ofLBS Research ( Times NewRoman 12,Bold)
3.1 More Diverse Applications Pg.No.
3.1.1 LBSN Pg.No.
3.1.2 LBG Pg.No.
3.1.3 Location based fitness monitoring and healthcare
3.1.4
3.
Transport LBS Pg.No.
3.2 Usability, privacy, and social aspects of LBS Pg.No.
4 Scientific Research Agenda( Times NewRoman 12,Bold)
4.1 Ubiquitous Positioning ( Times New Roman , 12) Pg.No.
5 Towards Location Based sciences( Times NewRoman 12,Bold)
5.1 How location tracking works? ( Times New Roman , 12) Pg.No.
5.2 How does GPS Receivers works? ( Times New Roman , 12) Pg.No.
6 Chapter Name( Times NewRoman 12,Bold)
6.1 First Heading ( Times New Roman , 12) Pg.No.
6.2 First Heading ( Times New Roman , 12) Pg.No.
Conclusion Pg.No.
References Pg.No.

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Abstract
We are now living in a mobile information era, which is fundamentally changing science
and society. Location Based Services (LBS), which deliver information depending on the
location of the (mobile) device and user, play a key role in this mobile information era.
This article first reviews the ongoing evolution and research trends of the scientific field
of LBS in the past years. To motivate further LBS research and stimulate collective
efforts, this article then presents a series of key research challenges that are essential to
advance the development of LBS, setting a research agenda for LBS to ‘positively’ shape
the future of our mobile information society. These research challenges cover issues
related to the core of LBS development (e.g. positioning, modelling, and
communication), evaluation, and analysis of LBS-generated data, as well as social,
ethical, and behavioural issues that rise as LBS enter into people’s daily lives.

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List of Figures and Tables
Figure 1 Example application domains of LBS
Figure 2 Different types of context factors that might be relevant to LBS
Figure 3 Emerging interface technologies in LBS.
Figure 4 The ‘key research challenges’

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Chapter -1
INTRODUCTION
Location-based services (LBS), also known as location services, mobile location-based service,
wireless location services, is an innovative technology that provides information or making
information available based on the geographical location of the user. This new technology is
giving a great impact to how we live and do businesses. Knowing the physical position of a user
at any given time can be a huge potential to application service providers. This service allows
mobile users (MUs) use services based on their position or geographic location.
LBS is an information service and has a number of uses in social networking today as
information, in entertainment or security, which is accessible with mobile devices through
the mobile network and which uses information on the geographical position of the mobile
device.
LBS can be used in a variety of contexts, such as health, indoor object search, entertainment, work, personal
life, etc.
LBS include services to identify the location of a person or object, such as discovering the
nearest cash machine (ATM) or the whereabouts of a friend or employee. LBS include parcel
tracking and vehicle tracking services. LBS can include mobile commerce when taking the form
of coupons or advertising directed at customers based on their current location. They include
personalized weather services and even location-based games.
1.1 Now we should understand why locationis useful?
LBS is a service trying to find out the answer to “WHERE”, e.g. “Where am I?”; “Where is the
restaurant?”; “Where are the target customers of our company”; “Where is my friend John?”, etc.
Location is so important to human lives that is essential to how people organize their
surroundings. It is of essential value that we can readily exploit to model reality. With today’s
advanced telecommunication and information technology, people in different market sectors,
such as business, consumer and government, can handle their surroundings at any time and any
place.
1.2 Uses of location-based services
Companies have found several ways to use a device’s location:
 Store locators. Using location-based intelligence, retail customers can quickly find the
nearest store location.

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 Proximity-based marketing. Local companies can push ads only to individuals within the
same geographic location. Location Based Mobile networking delivers ads to potential
customers within that city who might actually act on the information.
 Travel information. An LBS can deliver real-time information, such as traffic updates or
weather reports, to the smartphone so the user can plan accordingly.
 Roadside assistance. In the event of a blown tire or accident, many roadside assistance
companies provide an app that allows them to track your exact location without the need for
giving directions.
 Mobile workforce management. For logistics-dependent companies that employ
individuals out in the field or at multiple locations, an LBS allows employees to check in at a
location using their mobile device.
 Fraud prevention. An LBS creates another level of security by matching a customer’s
location through the smartphone to a credit card transaction. Tying the smartphone’s location
to a credit card allows you to flag transactions made across several geographic locations over
a short time.
Chapter -2

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HISTORY
NOW WE SHALL GO DEEP INTO THE HISTORY OF LOCATION BASED SERVICES
(LBS).
Location-based services (LBS) are a part of virtually all controland policy systems which work in computers
today. They have evolved from simple synchronization based service models to authenticated and complex
tools for implementing virtually any location based service model or facility.
The vision for this was created by Todd Glasey and others in the mid-1990s working inside the American Bar
Associations Information Security Committee. Glassey designed basic control policies, which were filed for
US patent US6370629 by Glassey's patent agents in 1998 finally.
In 1990 International Teletrac Systems (later PacTelTeletrac), founded in Los Angeles CA, introduced the
world's first dynamic real-time stolen vehicle recovery services. As an adjacency to this they began
developing location based services that could transmit information about location-based goods and services to
custom-programmed alphanumeric Motorola pagers. In 1996 the US federal communication
commission (FCC) issued rules requiring all US mobile operators to locate emergency callers. This rule was a
compromise resulting from US mobile operators seeking the support of the emergency community in order to
obtain the same protection from lawsuits relating to emergency calls as fixed-line operators already had.
As a result of these efforts in 1999 the first Digital Location Based Service Patent was filed in the US and
ultimately issued after nine office actions in March 2002. The patent has controls which when applied to
today's networking models provide key value in all systems.
In May 2002, go2 and AT&T MOBILITY launched the first (US) mobile LBS local search application that
used Automatic Location Identification (ALI) technologies mandated by the FCC. go2 users were able to use
AT&T's ALI to determine their location and search near that location to obtain a list of requested locations
(stores, restaurants, etc.) ranked by proximity to the ALI provide by the AT&T wireless network. The ALI
determined location was also used as a starting point for turn by turn directions.
The main advantage is that mobile users do not have to manually specify ZIP codes or other location
identifiers to use LBS, when they roam into a different location. GPS Tracking is a major enabling ingredient,
utilizing access to mobile web.
NOW WE WILL SEE ABOUT EVOLUTION IN LOCATION BASED SERVICES (LBS).
Ongoing evolution of LBS research
In the first two issues of the Journal of Location Based Services provided a critical
review of the research field of LBS. Recent years have witnessed rapid advances in LBS
with the continuous evolvement of mobile devices and communication technologies.
Research on LBS has been approached from different disciplines and different
perspectives. In the following, we review the key trends in the LBS domain over the last

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10 years, mainly on the aspects of application domains, application environments
(outdoor and indoor), context-awareness, user interfaces, evaluation, and analysis of LBS
data.
MORE DIVERSE APPLICATIONS
In 2007, provided a comprehensive review on the application fields of LBS. They
showed that mobile guides and navigation systems (e.g. car navigation systems and
pedestrian navigation systems) were the largest groups of LBS applications. Mobile
guides can be considered as ‘portable, location-sensitive and information-rich digital
guides to the user’s surroundings’. They often provide functionalities, such as mobile
search, ‘you-are-here’ maps, and tour guides for tourism and recreational purposes.
Navigation systems (e.g. for car drivers or pedestrians) are designed to assist in people’s
wayfinding tasks in unfamiliar environments. While mobile guides and navigation
systems continue to be some of the main LBS applications and are still being improved
(e.g. landmark-based navigation and inclusion of real-time traffic information) more
diverse LBS applications have been emerging and becoming more mature in recent
years.
Figure 1. Example application domains of LBS.
In the following we introduce some of the main ones:

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 Location based social networks (LBSN). The increasing availability of location-
aware technology (e.g. GPS) enables people to add a location dimension to existing
online social networks in various ways, e.g. uploading geotagged contents (e.g.
photos, videos, and recorded routes), sharing the present location (e.g. ‘check-in’ at
Foursquare), commenting on an event at the place where it is happening (e.g. via
Twitter), or leaving ratings/tips/reviews for a location (e.g. a restaurant) Examples of
mobile LBSN applications include Foursquare, Facebook, Google+, Twitter, and
Instagram. These applications allow end users to share information with friends,
connect with friends and get alerted when they are nearby, explore places and events
in the real world, and receive location based advertisements. While these
applications are mostly developed by companies, many research studies utilize the
user-generated contents of these applications for making (location)
recommendations), modelling individual and crowd mobility, understanding the
dynamics and semantics of cities and detecting and managing real-time events (e.g.
disasters)
 Location based gaming (LBG). Typically, LBG maps real world environments into
a virtual world, where players must move themselves in real life to explore the
virtual world and accomplish tasks related to the game itself .The maps, tasks, and
other contents presented on the mobile devices are adapted to the location of the
player. According to the game objectives, players may collect virtual objects, such as
weapons, ammunition, or treasures. These objects can be used to achieve benefits in
the virtual world, or even in the real world. Examples of popular LBG include
Geocaching, Ingress, and Pokémon Go.
 Location based fitness monitoring and healthcare. Another rapidly growing group
of LBS applications focuses on healthcare, particularly on outdoor exercise and
fitness monitoring (e.g. running app, Moves, and Run keeper), remote health
monitoring supporting dementia patients and their care givers in wandering events,
and emergency situation detecting and reporting. For the first type of application (i.e.
fitness monitoring), gamification techniques and social networking aspects are often
used to motivate and promote physical activities and healthy living styles. In
addition to location-tracking technologies, these applications often make use of other
sensors available on smartphones, such as accelerometers and pedometers. In recent
years, there has been a trend on combining LBS with other wearable sensors (e.g. for
sensing heart rate, blood pressure, EEG signal, and body temperature) for health
monitoring, and providing personalized healthcare information and services
 Transport LBS. By tradition, transport has been one of the main application fields
of LBS. Applications include those for driver assistance, passenger information, and
vehicle management. Car navigation systems are probably the most popular LBS
applications, which provide wayfinding assistance for drivers, and are still being
improved with new features, such as real-time traffic information. For example,

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Waze (https://www.waze.com/) crowdsources traffic and road information to
provide drivers with real-time navigation supports. LBS and tracking techniques
have now been extensively used for vehicle management and logistic tracking. In
recent years, applications beyond car navigation and vehicle management have been
emerging. For example, for driver assistance and passenger guidance, applications
for finding available on-street parking spaces (e.g. Park
bob http://www.parkbob.com/), safety warning, and multimodal routing have
appeared. There are also studies using LBS to promote more healthy, greener (lower
CO2 emission), and more active mobility behaviors.
 Location based assistive technology. LBS are also being used as assistive
technology to enable visually impaired people, and disabled and elderly people to
perform their daily living activities independently and to experience an improved
quality of life. These assistive systems provide assisted-living functions, such as
personalized navigation and wayfinding, obstacle detection, space perception, and
independent shopping. With the increasingly aging population, one can expect that
more and more location based assistive systems will be developed and employed in
the future.
Recent years have also seen the application domains of LBS being expanded into disaster
and emergency, supporting citizens’ involvements in society (e.g. for crime mapping,
reporting urban problems), education (learning in the field), entertainment (e.g. music,
insurance, billing, and supporting production processes in factories While most LBS
applications are developed primarily for supporting individual users, some researchers
have started to develop LBS applications to support groups of users for collaborative task
solving, such as wayfinding and museum visiting.
From outdoor to indoor and mixed outdoor/indoor environments
In the early 2000s, research on LBS had been mainly focused on outdoor environments,
also due to the lack of reliable indoor positioning methods, as well as the lack of indoor
GIS data. In recent years, LBS have become more and more popular not only in outdoor
environments, but also in shopping malls, museums, airports, many other indoor
environments, and even mixed outdoor/indoor environments. This is mainly due to the
advancements in indoor positioning and indoor spatial data modelling and the increasing
availability of indoor GIS data in the last decade.
Different location sensor technologies have been developed and tested for estimating the
location of a smartphone in indoor, such as WiFi, RFID Bluetooth, NFC, and UWB.
Positioning methods, such as proximity sensing, lateration, dead reckoning, and pattern
matching (e.g. fingerprinting) were employed. Among them, WiFi-based fingerprinting

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has attracted a lot of research interest, and likely represents the “state-of-the-art” in
indoor positioning. Bluetooth Low Energy (BLE) beacons, such as Apple’s iBeacon,
Google’s URIBeacon and Eddystone, and Radius Networks’ AltBeacon, are also often
employed for indoor positioning, using their ‘proximity’ mode. To improve position
accuracy, map-matching using building floor plans, as well as signal fusion with other
sensors.
As mentioned before, recent years have also seen rapid advances in indoor spatial data
modelling, as well as the increasing availability of indoor GIS data. A number of indoor
space models have been developed, from 3D building models, such as cityGML and
Building Information Modelling BIM, over polygonal approaches to network solutions
like Geometric Network Model (GNM), corridor derivation, cell-decomposed networks,
and visibility-based models. Recently, the Open Geospatial Consortium (OGC) approved
a new standard IndoorGML for the representation and exchange of geoinformation for
indoor applications, with GNM as the underlying network. OpenStreetMap also
introduced an indoor tagging schema for indoor mapping
(https://wiki.openstreetmap.org/wiki/Proposed_features/IndoorOSM), which can be used
to model different floor levels, different indoor elements (e.g. rooms, areas, walls, and
corridors), and connections between different elements. In recent years, research has also
paid attention to connecting outdoor and indoor environments
These rapid advances in indoor positioning and indoor data modelling have triggered
many innovative indoor LBS applications, such as indoor navigation/wayfinding guides
at museums/exhibitions, emergency response, shopping guides, and location based
advertisement. Typical venues of indoor LBS applications include universities/schools,
museums, complex transport hubs (e.g. airports and train/subway stations), and shopping
malls.
Towards context-awareness
As the term suggests, location plays an essential role in LBS as it determines the services
and information the user might expect. However, there is more to context than location.
Relying only on the location sometimes may lead to irrelevant results, as two persons
using an LBS at the same location, or even the same person at the same location but
within a different context (e.g. time of the day, seasons, with whom), might expect
different answers. For effectively supporting the user, LBS should provide information
and services adapted not only to the user’s location, but also her/his other context
information. In the past years, a lot of research advances have been made to enabling
context-awareness in LBS, particularly on the aspects of context acquisition, context
representation, and context adaptation.
Various studies have attempted to identify the different kinds of context factors that are
potentially relevant to an LBS user, and proposed some classifications of context factors.

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An example (see FIGURE 2) of a classification of context factors consists of users (e.g.
their user profiles, preferences, and emotion status), location, time (e.g. time of the day
and seasons), orientation/heading, navigation history, purpose of use, social aspects,
physical surrounding (e.g. weather, light, and noise level), and technical aspects (e.g.
device and network connectivity). This kind of classifications provides some structure for
the potential list of context factors, while which factors to be considered and modelled
depends on the specific LBS applications. Different methods have been proposed to help
LBS developers identify relevant context factors. The acquisition of context information
can be done using various sources, such as sensors that are available on smartphones (e.g.
GPS, accelerometer, and gyroscope), wearable sensors (e.g. physiological sensors, such
as the ones for sensing emotion and heart rate), or sensors present in the environment
(static sensors), as well as monitoring or querying Web applications and services or
obtained from users’ explicit inputs when using the application. Several studies focus on
deriving high-level context information (e.g. ‘driving’) from low-level raw data,
especially numeric sensor outputs.
Figure 2. Different types of context factors that might be relevant to LBS
The collectedcontextual data need to be represented through a context model to allow
efficient structuring and retrieval of these data. Bettini et al. identify five context model
categories: key-value, markup-based, object-role-based, spatial, and ontology based.
Grifoni, D’Ulizia, and Ferri surveyed many LBS applications and found that the key-
value model and the ontology-based model are the most used ones. They further proposed
a set of requirements that ensure good performance of the context model: mobility,
heterogeneity, relationships and dependencies, timeliness, reasoning, scalability, privacy,
and human collaboration.

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Several studies have investigated how context data can be utilised to adapt services for
the user. Grifoni, D’Ulizia, and Ferri categorised techniques used in context-aware
adaptation into four types: similarity-based reasoning, collaborative filtering, machine
learning, and rule-based reasoning. These different techniques vary in the flexibility they
allow, as well as the adaptation quality (e.g. precision and recall) they offer. In terms of
the automation of the adaptation process, Schou differentiates between self-adaptation
(the system adapts without any interaction with the user) and controlledadaptation (the
user needs to initiate the process). Huang and Gao argue that adaptive services (self-
adaptation) rather than adaptable ones (controlled adaptation) should be introduced for
LBS applications, considering the fact that LBS users are often involved in many tasks
and activities while using mobile devices. Additionally, the small screen sizes restrict
interaction functionalities on these mobile devices.
These rapid advances in context acquisition, representation and adaptation have triggered
many context-aware LBS, such as for navigation and wayfinding, location
recommendations and mobile guides, mobile learning, healthcare, and entertainment.
Towards non-intrusive user interfaces
Early LBS applications mainly communicated information to the users via visual (e.g.
maps) and auditory (e.g. verbal instructions) interfaces on smartphones. Recent years
have seen rapid advances towards more ‘natural’ and non-intrusive user interfaces for
LBS, particularly on the aspects of representation forms (e.g. visual, auditory, and
tactile), interface technologies and devices (e.g. smartphones and smartwatches),
interaction modality (e.g. touch, gesture, and gaze based), and context awareness.
Visual interfaces, particularly mobile maps, are still the main communication form in
LBS. However, new types of visual interfaces, such as 3D, virtual reality (VR), and AR,
are being developed. Tactile interfaces, such as vibration on smartphones and wearable
sensors, are also used in LBS. There is also a trend of combining multiple presentation
forms (e.g. maps and voices) to achieve better user experience in LBS. Research attention
has also been drawn to compare the effectiveness of different interfaces in LBS. In terms
of interface technologies and devices, smartphones are not the only mobile client in LBS.
More and more mobile and wearable devices are introduced for LBS applications (Figure
3), such as smartwatches, digital glasses, mobile projectors (e.g. smartphones with in-
built projector), head-mounted displays (HMDs), haptic devices, as well as public
displays. In addition to conventional interaction methods, such as touch, voice-based,
gesture-based, and gaze-based methods (i.e. use eye movement to control interaction)
have been introduced for LBS applications as well.

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Figure 3. Emerging interface technologies in LBS.
More and more people are carrying different mobile devices (e.g. smartphones,
smartwatches, digital glasses, and other wearable sensors) at the same time. Therefore,
several studies have started to explore cross-device interaction in LBS. There are studies
combining smartphones with other devices in the environment, for example, with digital
signage and public displays.
Another rapid advance can be observed in context-aware user interfaces in LBS Context
information has been used to adapt the contents to be presented, as well as the ways how
these contents are presented. For example, Tiina and Nivala adapt mobile tourist maps to
the current season, as well as to the users and usage situations. Raubal and Panov use
location, time (e.g. daytime vs. night-time), velocity, and direction for adapting mobile
maps for pedestrian navigation.
2.5. Usability, privacy, and social aspects of LBS
In the early 2000s, the LBS research community had mainly focused on the technical
challenges, and relatively little attention was paid to non-technical issues. Recent years
have seen an increasing interest in adopting interdisciplinary research in LBS. Usability
evaluation with intended users has become a ‘default action’ when developing LBS.
There is also a body of research focusing on understanding motivations for and
acceptance of LBS applications. Beyond usability and user motivation, many studies
have started to investigate the social, ethical, and legal aspects brought by LBS. For
example, Abbas, Michael, and Michael provide a state-of-the-art review, and argue that
the two prominent ethical dilemmas in LBS are ‘the risk of privacy breaches’ and ‘the
possibility of increased monitoring leading to unwarranted surveillance by institutions
and individuals’. Different technical solutions, as well as regulatory
considerations/actions have been proposed to tackle these ethical challenges. For
example, different so-called privacy enhancing methods have been developed to address

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users’ privacy concerns in LBS, such as privacy-by-design, and anonymisation (e.g.
spatial cloaking, space transformation, and geomaskingwhich however does not seem to
be widely adopted by companies yet. In terms of regulatory actions, recently a new
European Union (EU) law ‘General Data ProtectionRegulation (GDPR)’ has come into
force, which focuses on data protection and privacy of all individuals within the EU. In
addition to these social, ethical, and legal issues, researchers have also explored the
business issues in LBS.
Research interests have also been drawn to study the impact of LBS on both individuals
and society in the mobile information era . For example, several studies have focused on
the impact of LBS on users’ spatial awareness, spatial memory, and sense of place, in the
context of navigation systems, and location based games. Researchers have also
investigated how LBS influence consumers’ choice behaviour, and the changes to
healthier mobility behaviour.
As more and more LBS are entering into the general public’s daily lives, we expect that
research on these aspects will become more prominent, which will enable us to better
understand the impact of LBS on our mobile lives and vice versa.
2.6. Mining big spatial data to better understand our society
The increasing use of LBS, as well as the growing ubiquity of location/activity sensing
technologies have led to an increasing availability of location based tracking data (e.g.
floating car data and georeferenced mobile phone data), social media data (e.g. Twitter
data), and crowdsourced geographic information . These data have created unprecedented
opportunities for researchers from various disciplines. Mining these data (e.g. via data
mining or visual analytics) enables people to better understand and model human
mobility , geosocial networks , city dynamics, and semantics , as well as to monitor and
optimise traffic , detect real-time events, and manage disasters . This research is part of
an emerging trans-disciplinary field named ‘urban informatics’, which investigates the
‘acquisition, integration, and analysis of big and heterogeneous data generated by diverse
sources in urban spaces, such as sensors, devices, vehicles, buildings, and humans, to
tackle the major issues that cities face (e.g. air pollution, increased energy consumption,
and traffic congestion)’ , and thus improve the quality of life.
2.7. Summary and discussion
several key trends can be observed from the research and industrial development in the
LBS domain over the last 10 years: from mobile guide and navigation systems to more
diverse applications, from outdoor environments to indoor and mixed outdoor/indoor
environments, from location-awareness to context-awareness, from smartphone-based
mobile maps and audio only to more diverse and ‘natural’ user devices and interfaces,

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from technology-driven to more holistic research considering both technical and non-
technical issues (e.g. social and ethical aspects), as well as the rise of analysis of (big)
LBS-generated data. It is important to note that while rapid advances have been made on
these aspects, many open issues still exist, especially concerning ubiquitous positioning,
context modelling and context-aware LBS, social, ethical and behavioural implications.
As mobile devices and communication technologies seem to continuously be improved at
a very fast speed, as well as the increasing smartness of our environments and cities (e.g.
with different kinds of sensors), we expect the demand of LBS in different aspects of our
daily life will continue to be very strong, which will push LBS towards the 4A vision
(anytime, anywhere, for anyone and anything). This will open up a lot of additional
research questions, both basic and applied, to the LBS research community in the coming
years.
3. Scientific research agenda
To motivate further LBS research and stimulate collective efforts in our rapidly evolving mobile
information society, we believe that it is important to develop a cross-cutting research agenda,
identifying the key research questions and challenges that are essential to meet the increasing
societal demands of LBS. The development of this research agenda was designed as a joint
activity of the LBS research community, which mainly consists of experts from GIScience,
cartography, geomatics, surveying, computer science, and social sciences. Based on the 31 one-
paragraph proposals, and the feedback and results of the workshop and conference session, we
identified a list of ‘key research challenges’ that should be addressed to bring LBS to a higher
level to better benefit our human society and environment.
These research challenges can be classified into seven broad areas (Figure 4): Positioning,
Modelling, Communication, Evaluation, Applications, Analysis of LBS-generated data, and
Social and Behavioural Implications of LBS. The first three areas (the inner groups) represent
the core of LBS (‘How to make it work’), as every LBS application needs to handle the main
tasks of positioning, data modelling, and information communication. ‘Evaluation’ is important
to ensure that a developed LBS meets users’ needs. Sufficiently addressing these four aspects
would prepare LBS to be ready for different kinds of applications, such as navigation, mobile
guides, transportation, healthcare, and entertainment. These LBS applications not only help to
facilitate people’s daily activities and decision-making in space, but also generate a lot of data
about how people use, travel, and interact with each other in the environment. ‘Analysis of LBS-
generated data’ (e.g. location based tracking data, social media data, and crowdsourced
geographic information) helps to better understand people’s behaviours in different
environments, which enables various innovative applications (e.g. transport, urban planning, and
smart city), as well as provides insights to further improve these LBS applications. Beyond these,
‘social and behavioural implications of LBS’ raise as LBS enter into people’s daily lives.

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Figure 4. The ‘key research challenges’ organised into seven broad areas: positioning,
modelling, communication, evaluation, applications, analysis of LBS-generated data,
social and behavioural implications of LBS.
Figure 4. The ‘key research challenges’ organised into seven broad areas: positioning,
modelling, communication, evaluation, applications, analysis of LBS-generated data,
social and behavioural implications of LBS.
In the following, we introduce each of these identified research challenges, mainly
focusing on their importance, extent, open questions, existing research efforts (if any),
and potential opportunities. We don’t particularly focus on specific types of LBS

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‘Applications’, as sufficiently addressing the other identified challenges would prepare
LBS to be ready for different kinds of applications.
3.1. Ubiquitouspositioning
To provide services and content relevant to the location, LBS need to determine where
the user is. Therefore, positioning or location determination is a crucial technology for
LBS . As LBS become increasingly important and pervasive in our daily life, ubiquitous
positioning is needed to provide an accurate and timely estimate of a user’s or an object’s
location at all times and in all environments . While GPS (as well as some other similar
systems, such as GLONASS, Galileo, and BeiDou) is available in outdoor environments,
its positioning accuracy varies, and often gets worse in dense urban environments (due to
urban canyon effects). For indoor environments, while other positioning methods and
technologies start to appear, such as WiFi-based fingerprinting and Bluetooth beacons,
achieving accurate and reliable positioning is still a long way to go. In this section, we
present challenges that refer to these issues in the context of LBS.
4. Towards location based science (LBscience)?
While the early development of LBS seems to be mainly driven by applications and
technology, researchers in the LBS community actually have been trying to focus on the
foundational research issues that are independent of applications and technology. As an
example, the above research agenda aims to contribute an updated perspective on the
future of LBS research. To motivate further research, it might be necessary to recognise
and develop the role of science in LBS.
In general, the scientific domain that many LBS researchers have been working on, as
well as the above research agenda aims to contribute to can be defined as a field that
studies computational techniques, and social and ethical issues of deriving, modelling,
communicating, and analysing of location based information, i.e. information relevant to
users’ or intelligent agents’ locations. It concerns scientific knowledge on which LBS
applications are based. Firstly, it considers both computational techniques that enable the
implementation of LBS applications, and social and ethical issues that rise as LBS
applications enter into everyday life. Secondly, it deals with deriving, modelling,
communicating, and analysing of location based information, in which ‘deriving’ is based
on ubiquitous positioning and context acquisition, ‘modelling’ concerns computationally
representing location based information, ‘communicating’ is about conveying location-
based information to a user or intelligent agent, ‘analysing’ focuses on the analysis of
LBS-generated data. All above issues are inline with the research agenda outlined
in Section 3.

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This raises a series of interesting and important questions: Can we call this scientific
domain Location Based Science (LBScience)? In other words, is there a LBScience for
LBS, like Geographic Information Science (GIScience) for geographic information
systems (GIS)? If yes, what is its study subject? What are the generic (rather than specific
to particular fields of applications or particular technologies) questions that make up
LBScience? What are the boundaries of LBScience and its cognate scientific disciplines,
such as GIScience?
This paper does not aim to answer these questions, but rather encourages the LBS
community to think about them. We believe that by thinking about science rather than
software systems, and by identifying the key scientific questions of the field, we can
better ensure a long-term prosperous future of the LBS domain.
5. Conclusions
In this article, we first reviewed the state-of-the-art in LBS research, and identified
several research trends in recent years. These range from mobile guides and navigation
systems to more diverse applications, from outdoor to indoor and mixed outdoor/indoor
environments, from location based to context-aware, from maps and audio to more
diverse and ‘natural’ interfaces, from technology-oriented to interdisciplinary research,
and analysis of big spatial data. We then presented a series of key research challenges
that are essential for further development of LBS (Table 1), setting a research agenda for
LBS to ‘positively’ shape the future of our mobile information society. These research
challenges cover issues related to the core of LBS development (e.g. positioning,
modelling, and communication), evaluation, and analysis of LBS-generated data, as well
as social, ethical and behavioural issues that rise as LBS enter into people’s daily lives.
How Location Tracking
Works
Mobile phones are becoming more than just a way to call a friend, they are now allowing
us to organize our lives, connect to the Internet, shop and take photos. Soon, new lo-
cation-based services will be offered as new location-aware technology is rolled out.
These location-based services will offer personalized services that are connected the
specific location.
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Currently, the most recognized location-based service is the navigation systems found in
many new cars. As these technologies advance, it will be easier to find the services you
are looking for. For example, if you are looking for an ATM, you just ask for it and the
system gives you the location and directions. Other services include traffic advisories and
roadside assistance.
On a smaller scale, wireless LANs will be set up in malls and other areas of commerce to
locate wireless devices equipped to receive messages. Here is where retailers can send
coupons or other offers to your cell phone as you walk through their stores. Shoppers will
likely have the choice to opt out of these services.
The success or failure of location-based services largely depends on the roll out of E911
Phase 2 deployment, which is requiring wireless service providers to more accurately
locate cell phones in case of emergencies.
In America, we learn from an early age to call 911 when there's an emergency. When we
dial 911, the call is automatically forwarded to a public-safety answering point (PSAP),
also called a 911 call center. When the call is answered, the 911 operator is provided with
automatic location information (ALI), pinpointing the exact position of the call. Today,
many areas also have Enhanced 911 (E-911), which allows a PSAP to determine the
general location from where the call originated, but cannot yet pinpoint the location.
According to the Cellular Telephone Industry Association (CTIA), 150,000 emergency
wireless calls are made in the United States each day. The government has stepped in to
ensure that E-911 capabilities are improved. New technologies being developed by
wireless service providers at the demand of the Federal Communications Commission are
expected to enhance the location-finding ability of E-911 to locate the exact position of a
wireless emergency call.
The FCC is rolling out E-911 in phases:
 Phase 0 - This is the basic 911 process. Wireless calls are sent to a PSAP. Service
providers must direct a call to a PSAP even if the caller is not a subscriber to their
service.

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 Phase I - The FCC's rule requires that a phone number display with each wireless
911 call, allowing the PSAP operator to call back if there is a disconnection.
 Phase II - The final phase requires carriers to place GPS receivers in phones in
order to deliver more specific latitude and longitude location information.
Location information must be accurate within 164 to 984 feet (50-300 meters).
Without Phase II, a caller's location can only be narrowed down to the cell from which
the call originated. When Phase II is implemented, a cell-phone user's phone number, or
Automatic Number Identification (ANI), and the address and location of the receiving-
antenna site will be sent to the E-911 Tandem, the switch that routes 911 calls to the
appropriate PSAP based on the ANI-defined geographic location. Once the caller's voice
and ANI are transferred to the PSAP, the PSAP operator will be able to view a graphic
display that shows the longitude and latitude of the person as accessed through GPS
satellites. The operator's computer will link to the ALI database, which stores address
data and other information.
The implementation of Phase II technology introduces new commercial opportunities. As
mentioned in the previous section, location-based services will leverage the infrastructure
of E-911 technology to deliver commercial services to phones, including advertising.
These new technologies also create concerns over privacy, which we will examine in the
next section.
On February 16, 1968, Alabama Senator Rankin Fite made the first 911 call in the United
States in Haleyville, Alabama. The Alabama Telephone Company carried the call. A

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week later, Nome, Alaska, implemented a 911 system. In 1973, the White House's Office
of Telecommunication issued a national statement supporting the use of 911 and pushed
for the establishment of a Federal Information Center to assist government agencies in
implementing the system.
WHY 911?
Have you ever wondered why 911 was chosen as the universal emergency code in the
United States? Prior to the 1960s, there was no universal number to call for emergency
help. In 1967, the Federal Communications Commission met with AT&T to establish
such a number, according to the National Emergency Number Association (NENA). But
why did they choose 911? Why not 422 or 111?
There are several reasons why 911 was chosen. It's a short, easy to remember number,
but more importantly, 911 was a unique number -- it had never been designated for an
office code, area code or service code.
How GPS Receivers Work

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Our ancestors had to go to pretty extreme measures to keep from getting lost. They
erectedmonumental landmarks, laboriously drafted detailed maps and learned to read
the stars in the night sky.
Things are much, much easier today. For less than $100, you can get a pocket-sized
gadget that will tell you exactly where you are on Earth at any moment. As long as you
have a GPS receiver and a clear view of the sky, you'll never be lost again.
In this article, we'll find out how these handy guides pull off this amazing trick. As we'll
see, the Global Positioning System is vast, expensive and involves a lot of technical
ingenuity, but the fundamental concepts at work are quite simple and intuitive.
When people talk about "a GPS," they usually mean a GPS receiver. The Global
Positioning System(GPS) is actually a constellation of 27 Earth-orbiting satellites (24 in
operation and three extras in case one fails). The U.S. military developed and
implemented this satellite network as a military navigation system, but soon opened it up
to everybody else.
Each of these 3,000- to 4,000-pound solar-powered satellites circles the globe at about
12,000 miles (19,300 km), making two complete rotations every day. The orbits are
arranged so that at any time, anywhere on Earth, there are at least four satellites "visible"
in the sky.
A GPS receiver's job is to locate four or more of these satellites, figure out the distance to
each, and use this information to deduce its own location. This operation is based on a
simple mathematical principle called trilateration. Trilateration in three-dimensional
space can be a little tricky, so we'll start with an explanation of simple two-dimensional
trilateration.
2-D Trilateration
PREV NEXT

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Imagine you are somewhere in the United States and you are TOTALLY lost -- for
whatever reason, you have absolutely no clue where you are. You find a friendly local
and ask, "Where am I?" He says, "You are 625 miles from Boise, Idaho."
This is a nice, hard fact, but it is not particularly useful by itself. You could be anywhere
on a circle around Boise that has a radius of 625 miles, like this:
You ask somebody else where you are, and she says, "You are 690 miles from
Minneapolis, Minnesota." Now you're getting somewhere. If you combine this
information with the Boise information, you have two circles that intersect. You now
know that you must be at one of these two intersectionpoints, if you are 625 miles from
Boise and 690 miles from Minneapolis.
If a third person tells you that you are 615 miles from Tucson, Arizona, you can eliminate
one of the possibilities, because the third circle will only intersect with one of these
points. You now know exactly where you are -- Denver, Colorado.

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This same concept works in three-dimensional space, as well, but you're dealing
with spheres instead of circles. In the next section, we'll look at this type of trilateration.

The Earth itself can act as a fourth sphere -- only one of the two possible points will
actually be on the surface of the planet, so you can eliminate the one in space. Receivers
generally look to four or more satellites, however, to improve accuracy and provide
precise altitude information.
In order to make this simple calculation, then, the GPS receiver has to know two things:
 The location of at least three satellites above you
 The distance between you and each of those satellites
The GPS receiver figures both of these things out by analyzing high-frequency, low-
power radio signals from the GPS satellites. Better units have multiple receivers, so they
can pick up signals from several satellites simultaneously.
Radio waves are electromagnetic energy, which means they travel at the speed of light
(about 186,000 miles per second, 300,000 km per second in a vacuum). The receiver can
figure out how far the signal has traveled by timing how long it took the signal to arrive.
In the next section, we'll see how the receiver and satellite work together to make this
measurement.

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GPS Calculations
On the previous page, we saw that a GPS receiver calculates the distance to GPS
satellites by timing a signal's journey from satellite to receiver. As it turns out, this is a
fairly elaborate process.
At a particular time (let's say midnight), the satellite begins transmitting a long, digital
pattern called a pseudo-random code. The receiver begins running the same digital
pattern also exactly at midnight. When the satellite's signal reaches the receiver, its
transmission of the pattern will lag a bit behind the receiver's playing of the pattern.
The length of the delay is equal to the signal's travel time. The receiver multiplies this
time by the speed of light to determine how far the signal traveled. Assuming the signal
traveled in a straight line, this is the distance from receiver to satellite.
In order to make this measurement, the receiver and satellite both need clocks that can be
synchronized down to the nanosecond. To make a satellite positioning system using only
synchronized clocks, you would need to have atomic clocks not only on all the satellites,
but also in the receiver itself. But atomic clocks cost somewhere between $50,000 and
$100,000, which makes them a just a bit too expensive for everyday consumer use.
The Global Positioning System has a clever, effective solution to this problem. Every
satellite contains an expensive atomic clock, but the receiver itself uses an
ordinary quartz clock, which it constantly resets. In a nutshell, the receiver looks at
incoming signals from four or more satellites and gauges its own inaccuracy. In other
words, there is only one value for the "current time" that the receiver can use. The correct
time value will cause all of the signals that the receiver is receiving to align at a single
point in space. That time value is the time value held by the atomic clocks in all of the
satellites. So the receiver sets its clock to that time value, and it then has the same time
value that all the atomic clocks in all of the satellites have. The GPS receiver gets atomic
clock accuracy "for free."
When you measure the distance to four located satellites, you can draw four spheres that
all intersect at one point. Three spheres will intersect even if your numbers are way off,
but four spheres will not intersect at one point if you've measured incorrectly. Since the

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receiver makes all its distance measurements using its own built-in clock, the distances
will all be proportionallyincorrect.
The receiver can easily calculate the necessary adjustment that will cause the four spheres
to intersect at one point. Based on this, it resets its clock to be in sync with the satellite's
atomic clock. The receiver does this constantly whenever it's on, which means it is nearly
as accurate as the expensive atomic clocks in the satellites.
In order for the distance information to be of any use, the receiver also has to know where
the satellites actually are. This isn't particularly difficult because the satellites travel in
very high and predictable orbits. The GPS receiver simply stores an almanac that tells it
where every satellite should be at any given time. Things like the pull of the moon and
the sun do change the satellites' orbits very slightly, but the Department of Defense
constantly monitors their exact positions and transmits any adjustments to all GPS
receivers as part of the satellites' signals.
In the next section, we'll look at errors that may occur and see how the GPS receiver
corrects them.
Differential GPS
So far, we've learned how a GPS receiver calculates its position on earth based on the
information it receives from four located satellites. This system works pretty well, but
inaccuracies do pop up. For one thing, this method assumes the radio signals will make
their way through the atmosphere at a consistent speed (the speed of light). In fact, the
Earth's atmosphere slows the electromagnetic energy down somewhat, particularly as it
goes through the ionosphere and troposphere. The delay varies depending on where you
are on Earth, which means it's difficult to accurately factor this into the distance
calculations. Problems can also occur when radio signals bounce off large objects, such
as skyscrapers, giving a receiver the impression that a satellite is farther away than it
actually is. On top of all that, satellites sometimes just send out bad almanac data,
misreporting their own position.
Differential GPS (DGPS) helps correct these errors. The basic idea is to gauge GPS
inaccuracy at a stationary receiver station with a known location. Since the DGPS

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hardware at the station already knows its own position, it can easily calculate its
receiver's inaccuracy. The station then broadcasts a radio signal to all DGPS-equipped
receivers in the area, providing signal correctioninformation for that area. In general,
access to this correctioninformation makes DGPS receivers much more accurate than
ordinary receivers.
The most essential function of a GPS receiver is to pick up the transmissions of at least
four satellites and combine the information in those transmissions with information in an
electronic almanac, all in order to figure out the receiver's position on Earth.
Once the receiver makes this calculation, it can tell you the latitude, longitude and
altitude (or some similar measurement) of its current position. To make the navigation
more user-friendly, most receivers plug this raw data into map files stored in memory.
You can use maps stored in the receiver's memory, connect the receiver to
a computer that can hold more detailed maps in its memory, or simply buy a detailed map
of your area and find your way using the receiver's latitude and longitude readouts. Some
receivers let you download detailed maps into memory or supply detailed maps with
plug-in map cartridges.
A standard GPS receiver will not only place you on a map at any particular location, but
will also trace your path across a map as you move. If you leave your receiver on, it can
stay in constant communication with GPS satellites to see how your location is changing.
With this information and its built-in clock, the receiver can give you several pieces of
valuable information:
 How far you've traveled (odometer)
 How long you've been traveling
 Your current speed (speedometer)
 Your average speed
 A "bread crumb" trail showing you exactly where you have traveled on the map
 The estimated time of arrival at your destination if you maintain your current
speed

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For lots more information on GPS receivers and related topics, check out the links below.
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