Indoor location based services challenges, requirements and usability of current solutions

Abstract

Indoor Location Based Services (LBS), such as indoor navigation and tracking, still have to deal with both technical and non-technical challenges. For this reason, they have not yet found a prominent position in people’s everyday lives. Reliability and availability of indoor positioning technologies, the availability of up-to-date indoor maps, and privacy concerns associated with location data are some of the biggest challenges to their development. If these challenges were solved, or at least minimized, there would be more penetration into the user market. This paper studies the requirements of LBS applications, through a survey conducted by the authors, identifies the current challenges of indoor LBS, and reviews the available solutions that address the most important challenge, that of providing seamless indoor/outdoor positioning. The paper also looks at the potential of emerging solutions and the technologies that may help to handle this challenge.

Full text

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Indoor Location Based Services Challenges, Requirements and Usability of
Current Solutions
Anahid Basiri* (a), Elena Simona Lohan (b), Terry Moore (c), Adam Winstanley (d), Pekka Petolta (c), Chris Hill (c), Pouria
Amirian (e), Pedro Silva (b)
A.basiri@southampton.ac.uk; elena-simona.lohan@tut.fi terry.moore@nottingham.ac.uk;
Adam.winstanley@nuim.ie; pekka.peltola1@nottingham.ac.uk; Chris.Hill@nottingham.ac.uk; pouria.amirian@os.uk;
pedro.silva@tut.fi
(a) Department of Geography and Environment, The University of Southampton, Southampton, So17 1BJ, United Kingdom.
(b) Laboratory of Electronics and Communications Engineering, Tampere University of Technology, Korkeakoulunkatu 1,
33720 Tampere, Finland.
(c) Nottingham Geospatial Institute, The University of Nottingham, Innovation Park, Triumph Road, Nottingham, NG7 2TU,
United Kingdom.
(d) Department of Computer Science, Maynooth University, Maynooth, Co Kildare W23 F2H6, Ireland.
(e) Ordnance Survey GB, Explorer House, Adanac Drive, Southampton. SO16 0AS, United Kingdom.

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Indoor Location Based Services Challenges, Requirements and Usability of
Current Solutions
Abstract—Indoor Location Based Services (LBS), such as indoor navigation and tracking, still have to deal with both technical
and non-technical challenges. For this reason, they have not yet found a prominent position in people’s everyday lives. Reliability
and availability of indoor positioning technologies, the availability of up-to-date indoor maps, and privacy concerns associated with
location data are some of the biggest challenges to their development. If these challenges were solved, or at least minimized, there
would be more penetration into the user market. This paper studies the requirements of LBS applications, through a survey
conducted by the authors, identifies the current challenges of indoor LBS, and reviews the available solutions that address the most
important challenge, that of providing seamless indoor/outdoor positioning. The paper also looks at the potential of emerging
solutions and the technologies that may help to handle this challenge.
Key Words: Indoor Positioning, Location-Based Services, Location Privacy
I. I
NTRODUCTION
Location Based Services (LBS), such as navigation, Location Based Social Networking (LBSN), asset finding and tracking,
are used by many people widely around the world (Bao at al. 2015), (Bent-ley et al. 2015). About three quarters (74%) of
smartphone device owners are active users of LBS (Pew Research 2013). However, when used indoors, applications have
difficultly providing the same level of positioning accuracy, continuity and reliability as outdoors (Maghdid et al. 2016).
Global Navigation Satellite Systems (GNSS) are the most widely used positioning technology for outdoor use (GSA, 2015).
However their signals can be easily blocked, attenuated or reflected (Kjærgaard at al. 2010). This makes them unreliable
indoors, making it impossible to seamlessly use them for positioning across outdoor and indoor environments. Many life-
saving services, such as for emergencies and security, could be improved hugely if indoor LBS could address this challenge.
In addition, although people spend most of their time inside, indoor LBS generates less than 25% of total revenue (ABI
research 2015). If LBS could overcome these challenges, its market will develop and more users will be attracted. This paper
identifies these challenges using a survey of the latest research and the results of a survey conducted by the authors. The
paper also evaluates current solutions and uses this analysis to identify the most suitable solution among those currently
available.
Research into the challenges presented by LBS is on-going (Maghdid et al. 2016), (Niu et al., 2015), (Tyagi and Sreenath
2015), (Wang et al. 2016). This paper considers their findings, in addition to a comprehensive survey targeting ordinary LBS
users, application developers, component providers and companies, market analysts and content providers. This synthesizes
both the technical and non-technical challenges in one study. The most important challenge identified by this paper is
providing Quality of Positioning Services (QoPS) – the functional and non-functional parameters that include accuracy,
availability, and cost (both to the user and for infrastructure deployment) including the availability, continuity, and accuracy
of positioning services for indoor use. Other major challenges are identified as concerns over privacy associated with location
data and the overall cost of services.
Some of these challenges, including accuracy and reliability, are directly linked to the effectiveness of positioning
technologies while others, such as cost and privacy, are closely related to them. However, there are some issues that are
independent, such as the business model used and the social acceptability of an application. The latter have been reviewed
elsewhere (Basiri et al., 2016a).
This paper reviews the technologies which are currently being used as solutions to these challenges. Also, based on the
results of a survey, a literature review and analysis on the available systems, this paper compiles the requirements of current
LBS applications. By comparing the technological requirements of LBS applications and the available solutions, the paper
assesses the usability of the current technologies for five application categories.
In addition, an analytical tool is described to evaluate the usability and fitness-to-purpose of each positioning technology for
specific applications. The application requirements might differ slightly from the general category it falls into. This tool uses
the Analytic Hierarchy Process (AHP) (Saaty, 1980) to select the most appropriate technology among those currently
available according to the positional requirements for the application. AHP is a powerful tool for systematic multi-criteria
decision-making. The developed tool is sufficiently flexible that it can assess new LBS applications, which are currently
emerging very frequently.

3
In section two, the structure of the survey and the process of the identification of LBS challenges and requirements are
explained. Section three studies the current solutions to the identified challenges and a usability analysis tool is introduced
and used.
II. I
DENTIFICATION OF
I
NDOOR
LBS
R
EQUIREMENTS AND
C
HALLENGES
Although some of the challenges in the development of LBS are shared by a wide range of applications, their impact can
vary from one application to another. For example, the availability and the accuracy of indoor positioning services is one of
the major obstacles for indoor applications. The main positioning technology, Global Navigation Satellite Systems (GNSS)
such as GPS, is not usually available. A lack of accurate positioning is a major issue for tracking and navigation services.
However, in advertising and social networking applications, a hundred-meter locational error might be satisfactory.
Therefore, if we separate LBS applications into categories, we can identify the shared issues within each. This section
describes the process of identifying each application’s requirements, its categorization based on this, and the implementation
challenges. This is based on a literature review and the results of a survey.
A. Survey Structure and Participants
The web-based survey, conducted in May 2015 for three months, had 245 participants (212 valid responses), aged between
18 and 73 years, with 164 male and 48 female respondents. The distribution of 212 participants and their level of expertise in
LBS are shown in table 1.
Participants Group Percentage
LBS
ordinary
users
(use LBS applications,
devices and/or services in daily life) 54.72%
LBS application developers
(design, develop, or
deploy LBS applications and services) 9.43%
LBS content providers
(provide content and/or
information, such as map, points of interest and
advertisements, to be delivered through LBS
applications and/or services) and components
companies (produce LBS components, such as
antennas, receivers and transmitters)
1.89%
LBS researcher and LBS market analyst
(study LBS and related technologies, applications
and markets)
26.42%
Other 7.55%
T
ABLE
1.T
HE CATEGORIES OF THE PARTICIPANTS IN THE SURVEY
The frequency of using LBS applications and the number of devices owned with positioning capabilities varied among the
different participant groups. However, across all a minimum of 52.63% of the users have three or four devices with
positioning capabilities, such as mobile phones, vehicle satellite navigation, fitness devices, iWatch, iPod, iPad), and a
minimum of 44.44% on average use their location-based devices at least twice a day. The frequency of using LBS
applications by the largest participant group (LBS ordinary users) is shown in figure 1.

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Fig. 1. The frequency of use of the location-enabled devices (left) and applications (right) by ordinary users of LBS.
B. LBS application segmentation
The participants were asked about the frequency of use of several applications, including navigation, tracking, emergency
and safety, local news, location-based social networking, travel guidance, elderly assisted living, and pet/asset finding. The
participants were asked about the important features of these that they would consider when buying, downloading or in use.
For each application, the participants were asked to rank the features by importance to them, including the cost of first
purchase, update fees, battery consumption, user-friendliness of the interface, size and weight (of the device), location
accuracy, continuity of service (seamlessly indoor/outdoor), delay in providing service, and privacy features. The participants
were also asked about their minimum (and maximum) requirements for each of these features that would provide an
“acceptable” quality of service.
The Random Forest method (Grömping, 2009) was used to cluster applications based on the answers from the various
groups and identify the requirements of each category (table 2). Random Forest method classifies (or provide with a
regression trees) each node (input data). Each node is split using the best split among all variables/parameters, here such as
privacy, power consumption, etc. In a random forest, each node is split using the best among a subset of predictors randomly
chosen at that node. Random Forest is very user friendly in the sense that it has only two parameters (the number of variables
in the random subset at each node and the number of trees in the forest), and is usually not very sensitive to their values.
Based on this method, the five application categories of indoor LBS were classified as:
• Indoor navigation and tracking (such as pedestrian navigation, indoor tracking),
• Marketing (shopping advertisements, proximity-based voucher sharing),
• Entertainment (location-based social networking and fun sharing, location-based gaming),
• Location-based information retrieval (such as in-gallery tours, underground real-time information),
• Emergency and security applications (such as ambient assisted living, E112 response).
These results were within two STD when measured for significance and compatibility in responses. This satisfies the
required Quality of Service (QoS) identified by other studies (Ghai and Agarwal 2013), (Harle 2013), (Abbas 2015), (Torres
et al 2014), (Wirola et al. 2010). They mainly identify positional accuracy and availability, privacy, cost, power consumption,
reliability and continuity of service, plus the response time.
LBS Category Applications Examples Quality of Service Requirement
Navigation and
Tracking
•
Pedestrian Navigation
• Path Finding And Routing
• Tracking
• Asset Finding
- Response in near-real-time
- Accuracy within a few meters
- Seamless availability (indoors and outdoors)
- Good reliability and continuity of service
- Low-medium power consumption
- Reasonable or cheap price
- Strong privacy preservation
Marketing
•
LB (Social) Marketing
• Advertisement
• Proximity-Based Voucher/
Offers/ Rewards
• LB Social Reward Sharing
- Medium to low availability
- Response in few minutes
- Accuracy in the order of hundreds of meters
- Medium reliability and continuity
- Very low power consumption

5
•
Location Based Dealing
- Free or very inexpensive
- Medium to strong privacy preservation
Entertainment
•
LB Social Networking
• LB Gaming
• LB Fun Sharing
• Find Your Friend
• LB Chatting
• LB Dating
- Medium to high availability (seamless indoors and
outdoors)
- Response in real-time or a few seconds
- Accuracy in the order of tens of meters
- High reliability and continuity
- Low power consumption
- Reasonable or cheap price
- Medium privacy preservation
Location-Based
Information
Retrieval
•
Location-Based Q&A
(Query)
• Proximity Searching
• Tourist Guide
• Transportation Info.
- Medium availability
- Response in real-time or a few seconds
- Accuracy from a few meters (e.g. for tourist guide
and proximity search) to hundreds of meters
- High reliability and continuity
- Low power consumption
- Reasonable or cheap price
- Medium privacy preservation (depending on the
application)
Safety and Security
•
Emergency Services
• Emergency Alert Services
• Ambient Assisted Living
• Security Surveillance
-Very high availability (seamless indoors and
outdoors)
- Response in real-time or few seconds
- Accuracy of tens of meters or lower
-Very high reliability and continuity
- Low power consumption
- Reasonable or cheap price
- Medium or low privacy preservation
T
ABLE
2.
LBS
APPLICATION SEGMENTS AND THE IDENTIFIED REQUIRED FEA TURES USING THE
R
ANDOM
F
OREST METHOD
In addition to having a better understanding of the requirements of each application category, the results give the pairwise
comparison ratio for the AHP analysis to find the best positioning technology, among those currently available.
C. Identification of current LBS challenges
The answers to these questions also indicate one of the most important challenges of the development of LBS markets – a
lack of mutual understanding among the value chain. One of the best examples of this is the underestimation of the users’
concerns regarding privacy by developers (Basiri et al., 2016a). Ordinary users prioritized privacy as one the most important
features, except in emergency, safety and security-related services, while developers believe that privacy is less important
than cost and a well-designed user interface. There is also a need for technological development to bridge the gap between
what developers need and what content and technology providers can deliver.
In another question, participants were asked to name and rank the important criteria for LBS applications to become
successful. Predictably, the answers to this question vary between different participant groups. For example, availability of an
API for developers was voted as one of the most important features (figure 2) while it was not even mentioned by ordinary
users or technology providers.

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Fig. 2. The ranking of the features contributing to the success of an LBS application from the developers' perspective.
Based on this analysis, weighted by the number and the role of participants, and clustered using the Random Forest
method, the top three biggest challenges for LBS applications were identified as (1) Quality of positioning service, (2)
Privacy concerns, (3) Availability of the content.
Privacy concerns refer to the (perception of) issues concerning the mis/re-use and/or inference of positional data by the
service provider or a third party. Availability of content refers to the possibility of having access to the data, services and
information essentially required to provide the service. This includes up-to-date maps, APIs, contextual data, and so on.
These three challenges to the development of LBS have been identified in market reports and literature reviews. Knowing
these requirements, the current solutions can be explored and evaluated to see if they are being addressed and, if not, where
are the deficiencies and how they can be bridged.
III. I
NDOOR
LBS
CHALLENGES AND THE
P
OTENTIAL
S
OLUTIONS
A. Positioning Requirements and Solutions
Reliable, inexpensive indoor positioning is needed for many LBS applications. It needs to be able to localize users
accurately and work seamlessly with outdoor positioning technologies (Mautz 2012). In this subsection we review
positioning technologies from a quality-of-service point of view to give a clearer picture of what is the biggest challenge to
achieving this.
In general, localization technologies can be categorized into three main groups: Beacon-based positioning technologies,
Dead-Reckoning (DR), and Device Free. Some technologies blend more than one of these, so can be classified into a fourth
group Multisensory positioning. Each will now be described.
1) Beacon-based positioning systems
GNSS, the most widely used outdoor positioning technology, uses Radio-Frequency (RF) signals. However, the signals
can be easily attenuated, reflected and/or blocked by buildings, walls and roofs (Kjærgaard at al. 2010). There have been
attempts to use GNSS signals inside buildings using ground-based PseudoLites (PL) (Kuusniemi et al. 2012) mimicking
satellite signals or high-sensitivity GNSS (HSGNSS) receivers. However, despite being technologically possible, neither
could become a ubiquitous solution for “indoor GNSS” due to the high costs involved.
PL requires installation of many stations, thus it is not a low-cost solution and must be carefully planned so as not to
interfere with GNSS. Effective HSGNSS receivers can be expensive, up to hundred euros depending on the features the
module offers (Pinchin et al. 2013). Moreover, the signals indoors are so weak that it is very difficult to acquire a dynamic
position easily. Television broadcast and cellular signals penetrate buildings better than GNSS (Torres-Solis et al. 2013). The
positioning accuracy that can be achieved with these signals is not accurate, often greater than 50m (Deng et al. 2013),
(Samama 2012), (Bonenberg at al. 2014), (Bonenberg et al. 2013), (Bonenberg at al. 2012).
In addition to these technologies, there are some other methods that can be applied for GNSS-based positioning in partially

7
denied areas. These include shadow matching (Groves, 2015). Digital Video Broadcasting — Terrestrial (DVB-T) relies on
orthogonal frequency-division multiplexing (OFDM), which can provide fine information regarding the channel state.
Besides that, the emitters' locations are usually known, which also offers a great advantage over the other technologies.
However, one of the main challenges is the low number of emitters. In addiiton, the receiver has to identify and match the
incoming signal to a specific emitter. This poses a question on how accurate and reliable this can be done, increasing the risk
of errors in the position estimation (Huang et al. 2013).
Wireless Local Area Networks (WLAN) technologies are certainly one the most popular positioning technologies provided
based on the RF-based technologies, which had not been developed initially for positioning purpose. IEEE 802.11 is one of
the most popular standards for WLAN. This protocol has made its way to almost every electronic device. Since most recent
IEEE 802.11 protocols rely on OFDM signals, these signals pose a new opportunity for positioning. Due to its ubiquitous
availability in urban environments, residential and commercial, it can be used for indoor positioning with an acceptable
availability. For positioning these networks have been used mostly under fingerprinting solutions, offering a relatively good
performance, 5 to 10 meters, in densely covered areas (Shrestha at al. 2013), (Nurminen et al. 2013).
These signals report on the channel state, which can be exploited in a positioning context to obtain range measurements.
This metric is more reliable than the Received Signal Strength Indicator (RSSI) but it also requires accurate environment
models. However, these models are difficult to build, since most channel effects are difficult to model or understand how to
properly model them. Therefore a training phase could also be necessary (Xiao et al. 2013).
There are many existing Wi-Fi access points. Signal strength and flight time are usually the wanted attributes. 802.11v
consists also of positioning protocol. (Ciurana et al. 2011) assesses the 802.11v standard for Time of Arrival (ToA)
positioning. Furthermore (Sendra et al. 2011) compares the coverage and interference of the different protocols in the 802.11
families. In (Hao 2013) Wi-Fi access point signal strengths were collected for fingerprinting. The strength was represented
according to the Wi-Fi Access Point MAC addresses. (Hejc et al. 2014) used Wi-Fi with GNSS receiver and IMU. Moving
from indoor to outdoor environment is challenging because the GNSS requires time to achieve the first fix. Thus it is
necessary to identify these transition region characteristics between the technologies used. There is also work going on with
the next-generation 802.11az amendment, which is designed for new positioning applications designed to run on wireless
networks.
Ultra-wideband (UWB) characteristics offer advantages for coping with multipath. Particularly its impulse radio short
pulses make it easier to detect the multipath components. Repeatability is a strong advantage for the ultra-wideband
approach. This means that the positioning result stays consistent over a time period (Meng et al. 2012). UWB tag was placed
on shoe and helmet in (Zampella et al. 2012). The tag measurements on the shoe had much more outliers due to non-line-of-
sight conditions. Although high time resolution of UWB signals makes it easier to distinguish between original and multipath
signals, the non-line-of-sight condition is still a challenge.
Bluetooth is another wireless technology standard for exchanging data over short distances (Hossain et al. 2007), which
has increasingly become popular since the release of the standard Bluetooth 4.0 protocol. Bluetooth low energy (BLE) is a
version of Bluetooth meant for low power applications, which allows some of applications to operate in a continuous manner
for extended periods of several months. Due to its power efficiency and low cost, BLE can be deployed in several tags or
beacons throughout the environment, in order to offer a more accurate indoor positioning solution (Silva et al., 2015). A
shorter operation range allows for the proximity based positioning, providing a better performance regarding the estimated
position error. The specification does not set an upper limit for the BLE range of operation, but experiments show that over
20 meters the RSS become very low, making the positioning practically impossible.
RFID system consists of RFID readers and transceivers or tags. In the active approach, the user carries the reader and scans
the tags in the environment. In the passive approach, the user carries the tag and the environment has readers set up for
positioning. The passive RFID detection range is very short (2m) and in practice, a stand-alone passive system would be
costly to set up. Privacy is of concern especially in passive RFID tag systems where the computation capability of the tag
cannot support necessary cryptographic data protection. RFID is implemented generally as a proximity positioning system
(Fujimoto et al. 2011), (Seco at al. 2010), (Pateriya et al. 2011), (Hasani at al. 2015).
Cameras can also be used for positioning in several ways. The user can carry the camera and the images can be matched
against available geo-referenced photos (Basiri et al., 2016b). Basiri et al. (2014) used markers/codes placed at landmarks and
a mobile phone camera was used to identify unique markers and look up the corresponding position in a database. Kivimaki
et al. (2014) lists infrared sensor technologies. However, micro-bolometer and Golay cell-based infrared cameras are very
expensive and may not be applicable for many indoor LBS applications. Thermopiles and pyroelectric sensors, although less
accurate, are very affordable. These can be effective in low lighting conditions where conventional image processing is
impossible.
Compressible media, such as sound and ultrasonic signals travel through a medium like air and the received strength or the

8
time of travel can help to calculate the position of the receivers. Signal strength, form recognition and travel time are the
common methods used to derive the location. Hoflinger et al. (2014) used signal amplitude envelope detection on received
chirp-form signals. Rishabh et al. (2012) used time of arrival (ToA) to calculate the position. The timing was based on
detecting specific sound signals by comparing them with the reference signals at base stations. The recorded signal detection
was carried out by cross-correlation with the reference signals. The sound source can be carried by the user or multiple sound
sources can be located within the environment as base stations. Multipath, echoes and ambient noise in the environment make
sound-based localisation system design challenging.
2) Dead-Reckoning (DR) positioning systems
Dead-reckoning positioning systems can be classified into two groups; plain Inertial Navigation Systems (pINS) and Step
and Heading Systems (SHS). With arrival of Microelectro Mechanical System (MEMS) INS found wide use. Smartphones
with inertial sensors, such as accelerometers and gyroscopes, allow us to use them as input devices for Pedestrian Dead
Reckoning (PDR). The increased interest in the MEMS sensor utilization is related to their small size (in cm order) and low
cost due to the silicon fabrication process. In the most common configurations, MEMS inertial units comprise accelerometers
that provide the user position by double integrating the specific force along its sensitive axis; MEMS gyroscopes, measuring
the body rotational motion across each sensitive axis, with respect to the body sensor frame and 2- or 3-axes accelerometers
and gyroscopes along with the magnetometers measuring the heading of the vehicle. In many cases only horizontal
positioning is of great interest, a standalone position from the dead-reckoning MEMS sensor can be provided from the use of
two gyroscopes and one accelerometer. (Racko et al., 2016) used smartphone sensors, including low-cost Inertial
Measurement Unit (IMU), for PDR and compared with more precise and expensive Xsens IMU. The accuracy of inertial
sensors has increased in the past few years, but they still cannot alone provide proper accuracy because of many negative
effects, such as heading drift due to gyroscope bias (Racko et al., 2016). Among the pINSs, the tactical grade IMU have a
drift of a few meters in a minute (Boll at al., 2011), but they are quite expensive and bulky for many LBS applications. On
the other hand, the low-cost MEMS inertial measurement units require additional external features, such as zero velocity
updates, map matching or external sensor aid, to achieve similar accuracy (Harle 2013), (Hide et al. 2010), (Pinchin et al.
2014), (Hide et al. 2012). Skog et al (2010) evaluated zero-velocity detectors for foot-mounted INS. |Gait style, step size
estimation and attitude determination are the key parameters in Step and Heading Systems. Map matching techniques aided
inertial navigation (Pinchin et al. 2013), bring the low-cost MEMS INS accuracy closer to that required for indoor LBS. Also,
cold atom interferometry and chip-scale atomic clocks are still under development (Groves 2014). Dead reckoning systems
are not generally considered as stand-alone positioning systems as they have to rely on the calibration of external positioning
technologies such as GNSS and Wi-Fi due to their drift. Drift of position is the challenge in inertial dead reckoning, and the
double integration of acceleration data into positional information is hard to stabilize. Another challenge is the initialization
of the IMU parameters. If the starting position and heading are slightly wrong these errors will accumulate over time. Pinchin
et al. (2012) uses the cardinal directions of the built environments as a map-matching technique to adjust the user track and
position. A comprehensive literature review on inertial positioning systems has been published by Harle (2013). Step and
Heading Systems (SHS) use estimates of step length and heading. Peak-detection, zero crossing, template matching and
spectral frequency analysis are some of the approaches to detect steps. Skog et al (2010) compared four step detection
algorithms: acceleration moving variance, magnitude, angular energy rate detection and a likelihood method that combined
all three. Slippery ground, shuffling and use of elevators are all challenges for estimating the next step position. These make
it difficult to detect zero velocity thresholds or zero angular velocity. Alternative and even more complex ways for getting the
inertial navigation solution are for example by using learning methods like statistical model comparisons of learnt IMU
records, artificial neural networks and regression forests (Nguyen et al, 2010). In summary, the inertial systems as dead
reckoning systems are not sufficiently accurate for indoor positioning by themselves.
3) Device-free positioning
Tactile sensors, such as piezoelectric, capacitive touch surfaces, levers and buttons can recognize the presence of a user at
a certain location. Tactile localization is based on the deployment of sensors or probes being in direct physical contact with a
surface or an obstruction. Similarly, an odometer is direct and continuous (Kivimaki et al. 2014, Middleton et al. 2009).
Localization using tactile sensors is relatively straightforward and accurate. However, identification in public environments
may need additional information, such as a camera image, to identify and deliver the correct location for the targeted user.
Identity for odometry, on the other hand, is easier to implement but it requires the user to carry the sensor.
Cameras, such as CCTVs, also can be used for positioning; the user (feature or marker) can be detected by a camera
network covering the environment (Torres-Solis et al. 2010). Using visual odometry, location can be tracked using image
flow by comparing patterns in sequential images. A stereovision setup can also be applied for more accurate camera
movement estimation or three-dimensional positioning.

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Barometers are relatively easy to use for measuring air pressure, particularly indoors, and this makes it feasible to use it for
detecting changes in height or altitude. Floor level was successfully distinguished by Bai et al. (2013). As weather conditions
can change, affecting the reference pressure, measured pressure and the temperature, calculating the correct height is
challenging in a real time application.
As mentioned before, magnetic-based positioning technologies determine location based on the magnetic field value
assigned to each point. However, the existences of the metallic objects or radio devices often make this very difficult with
magnetometers. Zampella et al. (2012) measured the stable magnetic field while stationary. If there was any angular rate
detected during the stance this was used to correct the yaw drift and gyroscope bias. Fuzzy Inference System (FIZ) (Afzal et
al. 2011) uses four magnetic field parameters to detect whether the magnetic field was disturbed inside a building (Hao at al.
2010). As practical experiments and requirements analysis have shown, a single positioning technology cannot be the answer
to the requirements of many applications of indoor LBS. Multi-sensor positioning can solve some problems for some
applications. Improvements in the sensitivity and accuracy of current sensors, upcoming technologies such as BLE, Galileo
with its higher signal penetration, a change in policy and legislation regarding the use of some technologies such as
pseudolites can help to improve the quality of indoor positioning services.
Table 3 summarizes the important characteristics of surveillance positioning systems. They include the possibility of being
used stand-alone, the achievable accuracy, cost of the sensor and components on the user’s device, cost of implementations
and the deployment of the infrastructure for a citywide application, privacy (system security measures against location
information hacking categorised into three categories of (a) high (the positioning signal is broadcasted from the terminal and
device receive and calculated location with a minimum communication over network, e.g. GNSS is highly privacy
preserving), (b) medium (device can receive and calculate the location but it needs communications over network and the
device is potentially identifiable by the transmitter, e.g. Wi-Fi based positioning), and (c) low (where the location are not
calculated on the device and a third party can only send back the location to the user, e.g. positioning using CCTV cameras),
power consumption (on the user device), coverage of the positional signals, and required data rate.
Positioning
technology Stand-
aloneness Data
(output)
rate
Accuracy Coverage
(range of the
positioning
signals)
Cost for users Cost of the
Infrastructure Computational
load/Battery
consumption
Privacy
GNSS Stand-alone ~1Hz 4m – 7m Generally
available
outdoors
£1-£100 (e.g. u-
blox LEA5H
~£50)
Billions of Pounds (but
already existing) 150mW- 1.5W High
Pseudolite Stand-alone ~1Hz 3m-7m ~50km Locata receiver
~£5000/ IFEN
NavX
~£100000 per transmitter
~1W transmit power High
Mobile
networks Stand-alone 1Hz-a
few Hz 1m-a few
hundreds of
meters
~ A few km >£10(OMAP) Millions of Pounds (but
already existing) ~1W(TI OMAP) Medium
WiFi RSS Stand-alone 0.25Hz,
3Hz,
0.2Hz
2m – 4m 10cm-50m HP Ipaq £77 20£-(more than £50) per
Access Point >1W, 700mW (for
WSN802GX),
>500mW for transmit
and 200mW for
receivers
Medium
WiFi
ToF/AoA Stand-alone 1-10Hz 1.7m– 10m ~25m >£5 >£50 (AP Prices) >1W/ 100mW Medium
UWB ToF Stand-alone ~25Hz,
>10Hz 15cm- 1m
1.5m-3m
(for UWB
RSS
Proximity/
Scene
Analysis
~5m-175m £60 (for
ubisense tag
IP63 slim)-
£1000
(laboratory
equipment)
Expensive laboratory
equipment >1W/ (500mW
transceiver)/ ~300mW
receiver and 600mw
transmitter)
Medium
RFID active Stand-alone 0.5Hz,
0.2Hz 1m-3m/ 30 – 100m ~£300 (I-Card
III interrogator),
>£500 M220
reader
>£10 per tag ~250mW Medium
RFID passive Stand-alone 20Hz,
80Hz 15cm-
50cm ~2m >£10 per tag ~£200 >£1000 per reader <50mW for tag and
300mW for reader Low

10
T
ABLE
3.
POSITIONING
TECHNOLOGIES
SPECIFICATIONS
AND
FEATURES
This paper applies a usability analysis to select the most suitable positioning technology, among those already available,
for each LBS application segment. To do so, AHP methodology (Saaty, 1980) is used to make the comparisons of objectives
and alternatives in a pairwise manner. Analytic Hierarchy Process (AHP) is one of the Multi-Criteria Decision Making
(MCDM) processes, which derives ratio scales from paired comparisons between criteria and factors (Saaty, 1980). AHP can
systematically help decision makers to select between choices based on criteria and factors, which can represent priorities and
preferences. One of the most valuable aspects of AHP is the flexibility to consider both quantitative and qualitative
parameters and factors to prioritise the choices (Saaty, 1980). This enables decision makers to include almost any kind of
criterion, from wide range of natures, allowing AHP to be practically applied in many real-world decision-making problems.
In addition, AHP can accept human inconsistencies in judgments. AHP is based on pairwise comparisons, ideally done by
experts.
The AHP has been applied to a wide range of problem situations, however, one of the most widely used applications of AHP
is selecting among competing alternatives in a multi-objective environment. It is based on the well-defined mathematical
structure of consistent matrices and their associated right-Eigen vector's ability to generate true or approximate weights
Bluetooth RSS Stand-alone 0.2Hz,
2Hz,
1Hz,
30Hz
2m-5m Modifiable (1-
25m, 150m in
open fields)
~£5 receiver £5-£30 per tag 25mW- 50mW High
Barometer Assistive ~2Hz 33cm-0.2m Ubiquitously ~£10 Not applicable ~5mW High
Sound Stand-alone 1Hz-tens
of Hz 1cm-1m ~3m-10m/ £10-~£300 £10-£100 per node 20mW-100mW Medium
Infrared (IR)
marker or
reflective
element
Stand-alone ~50Hz 10cm-
6m(for
active
Badges
~6m (depends
on tag
placement)
~£1 (marker)-
~£10(camera) £1 (marker)-£10
(camera) <50mW (for markers)-
165mW (for camera)+
processing
consumption
Low (for
environm
ent)/ high
(for user
with the
camera)
Infrared (IR)
Light
Image feature
matching
Stand-alone ~20Hz 0.2 – 0.8m ~6m- 10m ~£1
(thermopile) ~£1 per thermopile-
€8000 microbolometer
camera
<50mW (thermopile) Low (for
environm
ent)/ high
(for user
with the
camera)
Magnetometer Stand-alone
(needs
magnetic
maps)
5Hz-
75Hz 1mm for
permanent
magnet-
20cm for
fingerprinti
ng
1m magnetic
fingerprint map £2-£10 >£2*n <50mW High for
sensor
but low
for user if
carrying
a magnet
Electromagnet
ic system Stand-alone 1Hz 1% of the
range ~ 5m- 20m >£1000 ~£16 per mm^2 >1W Low
Light Image
marker Stand-alone
and
Assistive
(for
snapshots or
odometry)
5Hz-
30Hz 1mm-30cm ~6m (resolution
dependent) ~£10- £500 >£10 for marker amount 200mW- ~2W High (if
user
carries
the
camera)
Light
Image feature
matching
Stand-alone 5Hz-
30Hz ~10cm (1%
drift for
odometer)
~6m (resolution
dependent) ~£1 for
odometer- £100
for camera
modules
~£10-£100 per camera 50mW for odometer
and up to 1W for
cameras
High
(odomete
ry and
user
carrying)
Tactile
On user
device
Assistive 50-
500Hz Ubiquitously Very low High
Tactile
Environment Stand-alone 22Hz-
60Hz 4cm-40cm Ubiquitously Low ~£100 (per 3x2m^2 area)
Low
Tactile
Odometer Assistive 4 pulse
per
rotation
Ubiquitously Low ~150mW High

11
(Saaty, 1980). To do so, AHP methodology includes comparisons of objectives and alternatives in a pairwise manner. The
AHP converts individual preferences into ratio-scale weights that are combined into linear additive weights for the associated
alternatives. These resultant weights are used to rank the alternatives and, thus, assist the decision maker (DM) in making a
choice or forecasting an outcome. In order to select the most suitable positioning technology, the selection criteria are first
set. As discussed in section 2.2, the participants of the survey gave a score to each feature of LBS applications. These scores
are used for the pair-wisely comparison of features, that is finding the ratio/value showing which feature has priority over the
others (Basiri et al., 2015). For example, for the group covering navigation and tracking, according to the criteria pairwise
comparison matrix (with consistency ratio of 1.5% and eigenvalue of 5.067) the weight of quality features of sorted as
follow: coverage/range (38.3%), cost to the user (20.1%), power consumption (15.8%), accuracy (14.5%) privacy (5.9%),
and cost of the infrastructure (5.4%).
As a second level comparison, the pair-wise comparison from the criteria point of view, the results of the experiments and
literature review summarized in tables 3 and 4, are used. This means, for example, regarding accuracy, the priority of GNSS
over WLAN is determined based on the ratio of the accuracy of GNSS positioning (4m-7m) with respect to the WLAN's
(2m-4m). For qualitative parameters some values are assigned to the scores. For example, for privacy, technologies are
weighted as GNSS (and HSGNSS, Pseudolite, barometer+GNSS, INS+GNSS) (33.8%), UWB (12.5%), BLE (12.5%),
Ultrasound (11.2%), WLAN (11.3%), RFID active (8.4%), tactile floor (5.1%) and RFID passive (4.2%), and camera (1.1%).
The results have a consistency ratio of 1.5% and principal eigenvalue of 8.142.
At this stage, the positioning technologies, which cannot be used as a stand-alone technology, such as a barometer, are
either excluded or the combination of them with another technology is considered as one single alternative. Based on the
calculated priority and weights of positioning technologies and also quality features of each LBS application group, it is
possible to prioritize each technology for each application.
Priority of each technology = summation of (importance of each quality feature * priority of the technology
from quality feature perspective)
For example for the application group of information retrieval, the GNSS and WLAN are the most suitable positioning
technologies with values of 16.2% and 16.5%, respectively. This can be done for all the application groups and the most
suitable positioning technology for each application group is shown in table 4.
Indoor LBS
Category The Top3 Most Suitable Positioning
Technology already available
Indoor Navigation
and Tracking
1.
Bluetooth Low Energy (BLE)
-
17.27%
2. Wireless Local Area Networks (WLAN)-
13.75%
3. (GNSS+INS)-13.3%
Marketing
1.
Wireless Local Area Networks (WLAN)
-
12.65%
2. Bluetooth Low Energy (BLE)-10.25%
3. Mobile Network-8.47%
Entertainment
1.
Wireless Local Area Networks (WLAN)
-
17.45%
2. Camera-16.98%
3. Mobile Network -10.43%
Location-Based
Information Retrieval
1.
RFID
-
10.43%
2. Bluetooth Low Energy (BLE)-9.67%
3. Wireless Local Area Networks (WLAN)-
9.65%
Safety and Security
1.
(GNSS+INS)
-
10.43%
2. Wireless Local Area Networks (WLAN)-
8.74%
3. The rest are almost equally unsuitable
(suitability less than 5%)
T
ABLE
4.
P
OSITIONING TECHNOLOGIES SUITABILITY FOR EACH
LBS
APPLICATION CATEGORY

12
B. Privacy concerns
Personalization is one of the key features of intelligent, context-aware, adaptive LBS. However, this requires the storage of
personal preferences, activity history, current location and previous movements (Toch et al., 2012). The threats associated
with the violation of location privacy can dramatically limit the development, adoption and growth of LBS applications. LBS
require the user to disclose their location to enable personalization. Service providers can potentially store, use (or misuse,
reuse), and sell location data. Such potential threats can discourage users (Chin et al., 2012). Unrestricted access to
information about an individual’s location could potentially lead to harmful encounters.
In addition, an individual’s location history can potentially disclose activities, preferences, health, background and history
and other (even more) private aspects of life. In particular, if the locations are accompanied by temporal information, the
trajectory of movement, then more can be revealed (Chen et al., 2013). De Montjoye et al. (2013) understood that only four
anonymous spatio-temporal points are enough to uniquely identify 95% of the individuals within the crowd.
In addition to these potential threats, lack of awareness regarding issues of location privacy among ordinary users may
introduce an even big threat to LBS markets: the public may overestimate the threat (Shokri, 2015), (Chin et al., 2012). This
might be partially due to the fact that the necessary guards to protect location privacy do not need to be the same for all
applications and services. The level of accuracy, the potential of unauthorized access and/or inference of higher-level private
information, and the impact of any privacy violation in each application can be different (Puttaswamy 2014). The level of
privacy for each application category identified within the survey is illustrated in table 1.
In order to access location-based services, mobile users have to disclose their location to the service providers. However,
such information can be simply reused by the same or other sectors without the user’s permission. In order to protect the
privacy of the LBS users, there are several approaches and mechanisms which we can categorize into four groups; regulatory,
privacy policies, anonymity, and obfuscation.
Regulatory approaches to privacy develop and define rules to manage the privacy of individuals and the public. Although
these are being developed by governments and legislative sectors and are, in general, strictly enforceable, they have faced
several challenges. In addition, due to the time-consuming and complicated process involved, the number of privacy
regulations is still relatively small for this fast-growing technology and they are far behind the needs and demands.
While regulatory approaches target global or group-based safeguards, privacy policies provide more flexible and adaptive
protection mechanisms for individuals (Myles et al., 2003), (Gorlach, 2004). Location privacy policies, such as the Internet
Engineering Task Force (IETF) GeoPrive, the World Wide Web Consortium’s privacy preferences project (P3P) and
Personal Digital Rights Management (PDRM) are current protection approaches. The nature of LBS applications introduces a
big challenge to these privacy policies. The rapidly changing, highly innovative and fast growing ecosystem of LBS makes it
difficult to update, issue or adapt the policies to protect emerging applications and technologies.
Anonymity-based approaches, such as K-Anonymity (Sweeney, 2002), disassociate location information from the user’s
identity and minimizes the possibility of inference and traceability the other information. Although they are technically easy
to implement, they can be a barrier to the personalization of LBS, which are becoming more common and for many
applications essential (Xu et al., 2011). A possible solution for this can be pseudonym-based approaches as they allow
partially some levels of personalization by keeping the individual anonymous while giving a persistent identity (an alias or
pseudonym). The pseudonym can be linked to their actual identity when using higher safeguards. However, location patterns
may lead to identification if this data is combined with other data as well. Sweeney (2002) shows that 87% of people can be
uniquely identified by combining otherwise anonymous attributes, such as their postcode, age and gender.
Obfuscation lowers the positional quality of the recorded user location to protect it from misuse by degrading the quality of
locational information through the addition of inaccuracy, imprecision and vagueness (Duckham, 2006). As it mainly deals
with the quality of positional data, table 2 summarizes aspects of quality-of-service provided by the common LBS positioning
technologies.
It can be the case that for many scenarios more than one privacy protection approach is required. Table 5 summarizes the
challenges and disadvantages of each four categories identified. Despite the need for these multiple approaches to protect
user privacy, in many situations (location) data does not need protection. Due to their spatial and/or temporal inaccuracy,
there are some datasets that may not be worth attacking and therefore (extra) protection may no longer be required. However,
one application's public data can be considered private for another, and vice versa. Also, social trends and public perception
of the concept of privacy is fluid.
Privacy Protection Category
Disadvantages And Challenges

13
Regulatory
•
The possibility of having different interpretations and implementations of the very same
rule and regulation.
• The small number of rules and regulations due to the time-consuming and complicated
process of their development, particularly for fast-growing, innovative and rapidly
changing technologies and applications.
• The regulations, on their own, cannot guarantee or even prevent the invasion of privacy
and they only act after the privacy violation has happened.
Policy
•
The rapidly changing, highly innovative and fast growing ecosystem of LBS makes it
difficult to update, issue or adapt privacy policies
• The privacy policies need to rely on the available regulation to be practically applicable
and the liability relies on supporting regulations and rules.
Anonymity
•
Anonymity can be viewed as a barrier to the personalisation features of LBS, which are
becoming more and more popular and, for many applications, essential.
• The pattern of anonymised data may lead to identification of the individual if combined
with other data.
Obfuscation
•
Obfuscation can compromise the quality of LBS responses that depend on the quality of
positional data.
• It needs user authentication.
• Obfuscation assumes that users are able to choose what information to reveal to a
service provider, which may not always be the case.
T
ABLE
5.
PRIVACY PROTECTION APPROACHES
C. Availability of Content
LBS is supposed to provide tailored information to users with satisfy their requests, needs, situations and preferences. This
requires the availability of relevant information to be filtered based on the query and contextual information. Among all the
relevant data sources, maps and other spatial datasets are essential for the functionality of many LBS applications. These
include transport networks for routing and navigation and locational maps of points-of-interest. However this content,
particularly for indoors, raises issues of privacy and legal concerns. In addition, the often limited access makes it is difficult
to assure the quality of indoor data such as its reliability and its spatial, temporal and thematic accuracy (Basiri et al., 2016d).
Google is one of the major providers of indoor LBS. Their product tells customers what floor they are on in a building.
Google’s indoor mapping concentrates mainly on important well-frequented buildings such as major airports. Detailed floor
plans automatically appear when the user is viewing the map and the map is zoomed to buildings where indoor map data is
available. But even for this newest release, many indoor areas are not available and, even when present, does not provide full
navigational instructions. For example, stairs between floors are not included. Overall, indoor map coverage and resolution is
not comparable with that for outdoors.
The poor coverage of indoor maps is not mainly a technical issue (Lorenz et al., 2013). It is more due to the privacy issues
associated with privately-owned properties and also the lack of suitable policies and technical standards for privacy
protection this data.
One of the solutions, which has already shown its practicality and growing popularity, is crowd-sourcing and volunteer-
based mapping (Sui et al., 2012). Collaborative mapping through crowd-sourcing is one method of generating spatial content.
It involves contributions from a large, disparate group of individuals. These methods, part of Web 2.0, use applications that
allow people to upload information easily and allow many others to view and react to this information (Basiri et al., 2016c).
There are several tools available which allow users to create and edit web content, including tagging tools, wiki software
and web-based spatial data editors. This method of data collection and generation uses citizens in large-scale data collection,
sometimes also with the participation of companies and is referred to as volunteered geographic information (VGI). This
approach could be very suitable for indoor mapping. The popularity of VGI is growing. Table 6 shows that the number of
contributors in 2016 has been six times that in 2011 and more than 3.5 billion nodes and 450 million ways (links) have been
stored, a three-times increase.
These approaches can be partially used by mapping agencies and data gathering institutions. Despite the popularity and the
involvement of citizens with the collection of geospatial data, there is still only poor mapping coverage for indoor spaces.
VGI projects, such as OpenStreetMap (OSM), are contributing to the increasing interest in indoor mapping but there is still a
long way to go. Standardization of data formats, scale, metadata and privacy policies are still needed. Global coverage of
indoor mapping is likely to find obstacles in the form of cultural and political opposition. Many of those who openly
contribute to VGI projects for outdoor public environments will not want to publish maps of private indoor property. In

14
addition, if they do contribute this data to a VGI project, these maps cannot be edited by other contributors since they may
not have access. This simple example highlights accuracy, reliability, and precision as some of the key criticisms regarding
VGI data.
Year
Perce
ntage of active
contributors Number of Registered
Contributors Number of ways Number of nodes
2011 3.5% 501465 116196873 1280961903
2012 2.8% 1100215 159811148 1680385760
2013 1.50% 1824599 207118018 2108992829
2014 1.20% 1882817 262569075 2629122837
2015 1.00% 2371829 318959062 3126436219
2016 0.85% 3106987 445110741 3551080106
T
ABLE
6.
S
TATISTICS FOR THE NUMBER OF REGISTERED CONTRIBUTORS AND THE STORED WAYS AND NODES IN THE
OSM
DATABASE
.
The best option to improve coverage of indoor maps might be changing policies and legislation where necessary to
encourage more contributions to crowd-sourced data. Privacy is an on-going issue that needs to be included in these.
However, there are many public places, such as shopping malls, airports and universities, which already provide their map
online via their own web pages. These types of locations can be good targets to start the expansion of indoor maps.
Considering these issues (positioning, map coverage and privacy) it appears that indoor applications comprise quite a
challenging segment of LBS. In addition, there are some other challenges such as their complexity for modeling and analysis,
contextual information inference, data storage and streaming, which need a further level of customization for current LBS
services.
IV. D
ISCUSSION
Indoor LBS has not yet found its position in the market, despite the fact that people spend most of their time inside
buildings, e.g. offices and apartments. Indoor LBS faces several technical and non-technical challenges and this paper has
studied the three most important ones, according to a survey conducted, including indoor positioning, availability of indoor
maps, and location privacy.
In terms of positioning technologies, the usability analysis of current solutions for different segments of indoor LBS
market shows that there is a gap between the quality of positioning services and the requirements of indoor LBS applications.
This becomes particularly concerning when it comes to safety and security applications, which are potentially life-saving
such as emergency services. Multi-sensor positioning could provide a solution for indoor positioning but it is subject to
miniaturisation of more devices to be embedded in a size of a mobile phone, as the most widely used device for using indoor
LBS. There are also some promising results based on new technologies, such as quantum technologies, which requires more
tests and more importantly mass market (with lower cost) productions.
For indoor content, particularly maps as the essential type of contents for indoor LBS, there are still some long ways to go.
Storing indoor maps are somehow associated with the third biggest challenge of indoor LBS, i.e. privacy. What this paper
finds a relatively smoother start to improve the coverage of indoor maps, is crowd-sourcing the indoor maps of public places.
Crowd-sourced maps can hugely improve the coverage of indoor places, as the biggest issue for indoor maps unavailability
rather than quality. Also, it seems that in the era of social media networking, particularly new generation can have milder
privacy concerns and so this can help the development of indoor LBS. In addition, new/updated legislations and policies
regarding location privacy can make a big difference.
V. C
ONCLUSION
Indoor LBS is not commonly implemented in mobile services due to the many technical challenges that remain. This paper
has analysed the requirements and challenges of providing indoor LBS by reviewing the available literature and conducting a
survey. The main requirements of indoor LBS applications were determined and challenges were identified. Aspects related
to quality of service (including availability, accuracy, and cost) were identified as the major challenges. The development of
multi-sensor positioning services and new technologies such as BLE give potential solutions. The paper also highlighted the
most suitable existing solutions using an Analytic Hierarchy Process on the LBS application categories. The results of this
analysis shows that in some applications, such as emergency and security, there is actually no good option for indoor
positioning. WLAN is the technology that comes as the most suitable over all application categories. However, its relatively

15
low suitability value in specific areas indicates the need for improvement or the development of something superior.
VI. A
CKNOWLEDGMENT
This research was supported financially by EU FP7 Marie Curie Initial Training Network MULTI-POS (Multi-technology
Positioning Professionals) [grant number 316528].
The corresponding author has moved since the initial the submission of the paper. Her work, presented in this paper, has
been done at the Nottingham Geospatial Institute, The University of Nottingham.
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