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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.
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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) 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.
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
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
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.
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.
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
(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)
LBS researcher and LBS market analyst
(study LBS and related technologies, applications
and markets)
Other 7.55%
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.
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
Pedestrian Navigation
Path Finding And Routing
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
LB (Social) Marketing
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
Location Based Dealing
- Free or very inexpensive
- Medium to strong privacy preservation
LB Social Networking
LB Gaming
LB Fun Sharing
Find Your Friend
LB Chatting
LB Dating
- Medium to high availability (seamless indoors and
- 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 Q&A
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
Safety and Security
Emergency Services
Emergency Alert Services
Ambient Assisted Living
Security Surveillance
-Very high availability (seamless indoors and
- 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
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.
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.
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
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
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
Compressible media, such as sound and ultrasonic signals travel through a medium like air and the received strength or the
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.
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.
technology Stand-
aloneness Data
Accuracy Coverage
(range of the
Cost for users Cost of the
Infrastructure Computational
GNSS Stand-alone ~1Hz 4m – 7m Generally
£1-£100 (e.g. u-
blox LEA5H
Billions of Pounds (but
already existing) 150mW- 1.5W High
Pseudolite Stand-alone ~1Hz 3m-7m ~50km Locata receiver
~£5000/ IFEN
~£100000 per transmitter
~1W transmit power High
networks Stand-alone 1Hz-a
few Hz 1m-a few
hundreds of
~ A few km >£10(OMAP) Millions of Pounds (but
already existing) ~1W(TI OMAP) Medium
WiFi RSS Stand-alone 0.25Hz,
2m – 4m 10cm-50m HP Ipaq £77 20£-(more than £50) per
Access Point >1W, 700mW (for
>500mW for transmit
and 200mW for
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
(for UWB
~5m-175m £60 (for
ubisense tag
IP63 slim)-
Expensive laboratory
equipment >1W/ (500mW
transceiver)/ ~300mW
receiver and 600mw
RFID active Stand-alone 0.5Hz,
0.2Hz 1m-3m/ 30 – 100m ~£300 (I-Card
III interrogator),
>£500 M220
>£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
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
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,
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
Stand-alone ~50Hz 10cm-
~6m (depends
on tag
~£1 (marker)-
~£10(camera) £1 (marker)-£10
(camera) <50mW (for markers)-
165mW (for camera)+
Low (for
ent)/ high
(for user
with the
Infrared (IR)
Image feature
Stand-alone ~20Hz 0.2 – 0.8m ~6m- 10m ~£1
(thermopile) ~£1 per thermopile-
€8000 microbolometer
<50mW (thermopile) Low (for
ent)/ high
(for user
with the
Magnetometer Stand-alone
75Hz 1mm for
20cm for
1m magnetic
fingerprint map £2-£10 >£2*n <50mW High for
but low
for user if
a magnet
ic system Stand-alone 1Hz 1% of the
range ~ 5m- 20m >£1000 16 per mm^2 >1W Low
Light Image
marker Stand-alone
snapshots or
30Hz 1mm-30cm ~6m (resolution
dependent) ~£10- £500 >£10 for marker amount 200mW- ~2W High (if
Image feature
Stand-alone 5Hz-
30Hz ~10cm (1%
drift for
~6m (resolution
dependent) ~£1 for
odometer- £100
for camera
~£10-£100 per camera 50mW for odometer
and up to 1W for
ry and
On user
Assistive 50-
500Hz Ubiquitously Very low High
Environment Stand-alone 22Hz-
60Hz 4cm-40cm Ubiquitously Low ~£100 (per 3x2m^2 area)
Odometer Assistive 4 pulse
Ubiquitously Low ~150mW High
(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
Bluetooth Low Energy (BLE)
2. Wireless Local Area Networks (WLAN)-
3. (GNSS+INS)-13.3%
Wireless Local Area Networks (WLAN)
2. Bluetooth Low Energy (BLE)-10.25%
3. Mobile Network-8.47%
Wireless Local Area Networks (WLAN)
2. Camera-16.98%
3. Mobile Network -10.43%
Information Retrieval
2. Bluetooth Low Energy (BLE)-9.67%
3. Wireless Local Area Networks (WLAN)-
Safety and Security
2. Wireless Local Area Networks (WLAN)-
3. The rest are almost equally unsuitable
(suitability less than 5%)
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
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
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.
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 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 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.
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
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.
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
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
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
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
low suitability value in specific areas indicates the need for improvement or the development of something superior.
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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... Specifically for locating people in those environments, many location-based services (LBS) have been explored. Among them are the monitoring people in hospitals and homes, support in rehabilitation therapies, navigation for blind or visually impaired people, and navigation in shopping centers [55][56][57][58]. ...
... There is a wide variety of Indoor Location systems (ILS). The deployment of a particular system depends on factors such as privacy, cost, and required accuracy [58]. ...
... Their characteristics change according to the technologies they use [60,79,80]. Choosing an IPS should be made considering seven aspects: the characteristics of the indoor environment, the accepted level of precision, budget limitations, obtrusiveness, system complexity, robustness, and privacy [56][57][58]. When choosing the type of IPS to use in an ambient-assisted living (AAL) scenario, these aspects need to be considered. ...
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Hintergrund: Die Lokalisierung einer Person in einer Person im Innenbereich und das Erkennen ihrer Tätigkeit sind wertvolle Informationsquellen zur kontext-basierten Unterstützung. Diese Informationen sind insbesondere für Assistenzsysteme für alltägliche Tätigkeiten älterer Menschen wichtig. Die meisten Studien haben bisher die Lokalisierung im Innenbereich (IL) und die Tätigkeitserkennung (HAR) als isolierte Disziplinen behandelt. Folglich wurde der Zusammenhang zwischen dem Aufenthaltsort einer Person und deren Aktivität noch nicht vollständig ausgenutzt. Darüber hinaus sind Privatsphäre und Unaufdringlichkeit grundlegende Aspekte bei Ambient-Assisted-Living (AAL) Szenarien. Ziele: Implementierung eines dynamischen Systems für simultane Positionsbestimmung, Kartierung un HAR für AAL Szenarien. Methoden: Es wurde ein System aufgebaut, das Daten von Trägheitssensoren auf Höhe der Fußgelenke einer Person mit Lage- und Aktivitäts-bezogenen Daten von im Raum verteilten Bluetooth-Beacons verbindet. Dies wird durch Anpassung des Frameworks zur gleichzeitigen Lokalisierung und Mapping. Die Aktivitäts-Erkennungs-Komponente verwendet einen K-Nearest-Neighbor (KNN) Daten-Strom-Algorithmus. Der PedestrianDead-Reckoning (PDR) Algorithmus bildet die Grundlage der Innenraum-LokalisationsKomponente. Resultate: Das System wurde an 22 Personen evaluiert, von denen 11 ältere Erwachsene waren. Als Ergebnis wurde erreicht, dass das System ältere Personen mit einem durchschnittlichen Fehler von 1.02+-0.24m und Erwachsene mit einem Fehler vonlokalisieren kann 0.98 ± 0.36 m. Die Aktivitäten des täglichen Lebens wurden erkannt mit einem F1 von 88% für ältere Erwachsene, und einem F1 von 88,02% für Erwachsene erkannt. Es gab keine signifikanten Unterschiede bei der Aktivität-Erkennung und InnenraumLokalisierung. Schlussfolgerung: In dieser Arbeit wurde ein System entwickelt, das Nutzen aus der Beziehung zwischen Ort und menschlicher Aktivität zieht, um simultane und nicht-invasiv zu lokalisieren, zu kartieren, und die menschliche Aktivität im Innenbereich zu erkennen. Dies ist die erste Studie, die Bewerbung von Beacons als Eingangsvariablen verwendet, um menschliche Aktivitäten später als Landmarken in SLAM zu klassifizieren. Mit Hilfe dieser Informationen ist es möglich, den Aufenthaltsbereich einer Person zu kartieren und den Ort basierend auf der Karte abzugleichen. Der HAR wurde mit einem KNN DatenflussAlgorithmus realisiert, welcher wegen des geringen Speicherbedarfs auf jedem wearable lauffähig ist. Das vorgeschlagene System ist die Grundlage für künftige Projekte, um flexible Systeme zur Überwachung von IL und HAR, basierend auf zwei nicht-intrusiven Komponenten zu entwickeln: einem Inertial Measurement Unit (IMU) und Bluetooth beacons.
... In recent years, the rising demand for accurate and timely location-based services (LBSs) has attracted considerable interest from academics and the industry. Advanced positioning technology can provide better services such as indoor navigation and tracking, entertainment, location-based information retrieval, and emergency and safety applications [1,2]. ...
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Traditional magnetic-field positioning methods collect magnetic-field information from each spatial point to construct a magnetic-field fingerprint database. During the positioning phase, real-time magnetic-field measurements are matched to a magnetic-field map to predict the user's location. However, this approach requires a significant amount of time to traverse the entire magnetic-field fingerprint database and does not effectively leverage the magnetic-field sequence's unique patterns to improve the accuracy and robustness of the positioning system. In recent years, the application of deep learning for the indoor positioning of magnetic fields has grown rapidly, especially by using the magnetic-field sequence as a time series and a trained long short-term memory (LSTM) model to predict the position, directly avoiding the time-consuming matching process. However, the training of LSTM is time-consuming, and the degradation problem occurs as the stack of layers increases. This article proposes a temporal convolutional network (TCN)-based magnetic-field positioning system that extracts magnetic-field sequence features by preprocessing them with coordinate transformation, smoothing filtering, and first-order differencing. The proposed method is seamlessly applicable to heterogeneous smartphones. The trained TCN models are compared with the LSTM and gated recurrent unit (GRU) models, showing the high accuracy and robustness of the proposed algorithm.
... However, the common focus of industry and academia, the PNT (positioning, navigation and timing) technology in the GNSS signal denial environment, has not been solved, especially the indoor positioning technology in the PNT, which has not been able to achieve a fundamental breakthrough. Generally, indoor positioning technology includes active positioning means and passive positioning means [3,4]. Among them, active positioning needs to rely on infrastructure base stations and wireless access points in the environment, such as Wi-Fi [5][6][7], UWB (ultra-wideband) [8][9][10], sound [11][12][13], Bluetooth [14][15][16], etc., usually using localization methods such as TOA (time of arrival) [17,18], TDOA (time difference of arrival) [19], TOF (time of flight) [20], AOA (angle of arrival) [21] and fingerprint matching [22]. ...
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Aiming at the problems of the low robustness and poor reliability of a single positioning source in complex indoor environments, a multi-level fusion indoor positioning technology considering credible evaluation is proposed. A multi-dimensional electromagnetic atlas including pseudolites (PL), Wi-Fi and a geomagnetic field is constructed, and the unsupervised learning model is used to sample in the latent space to achieve a feature-level fusion positioning. A location credibility evaluation method is designed to improve the credibility of the positioning system through a multi-dimensional data quality evaluation and heterogeneous information auxiliary constraints. Finally, a large number of experiments were carried out in the laboratory environment, and, finally, about 90% of the positioning error was better than 1 m, and the average positioning error was 0.56 m. Compared with several relatively advanced positioning methods (Inter-satellite CPDM/Epoch-CPDS/Z-KPI) at present, the average positioning accuracy is improved by about 56%, 83.5% and 82.9%, respectively, which verifies the effectiveness of the algorithm. To verify the effect of the proposed method in a practical application environment, the proposed positioning system is deployed in the 2022 Winter Olympics venues. The results show that the proposed method has a significant improvement in the positioning accuracy and continuity.
... Although these biases seem small and vary slightly from device to device, they still lead to significant errors in the estimated ranges and affect the accuracy by tens of centimeters in the UWB-based RTLSs since the signals under measurement are moving at the speed of light. Such performance inefficiency is unsuitable for indoor RTLS applications that usually require stringent localization accuracy [29]. Usually, the measurement of the antenna delay values is done as a dedicated standalone procedure. ...
Full-text available
The ultra-wideband (UWB)-based real-time localization system (RTLS) is a promising technology for locating and tracking assets and personnel in real-time within a defined indoor environment since it provides high-ranging accuracy. However, its performance can be affected by the underlying antenna delays of UWB nodes, which act as a source of error during range estimations. Usually, measurement of the antenna delays is performed separately as a dedicated standalone procedure. Such an additional measurement procedure makes the UWB-based RTLS more tedious with manual interventions. Moreover, the air-time occupancy during the transmission and reception of signaling messages for range estimations between UWB node pairs also limits the serviceable capability of these networks. In this regard, we present a novel simultaneous ranging scheme that requires limited air-time occupancy during range estimations between UWB node pairs and also compensates for the error from the antenna delays. This paper provides a detailed mathematical modeling, system design, and implementation procedure of the proposed scheme. The effectiveness of the proposed scheme for locating a mobile node in an indoor environment is validated through experimental analysis. The results show that, compared to the state-of-the-art two-way ranging (TWR) method, the proposed scheme eliminates the requirement of dedicated standalone antenna delay measurement procedures of the nodes, increases air efficiency through the provision of simultaneous ranging, and provides relative root-mean-square errors (RMSEs) improvement for range and position estimations of approximately 54.52% and 39.96%, respectively.
... People need location-based-service (LBS) in such places, such as navigation service (Yan et al., 2021), urgency evacuation (Macatulad & Blanco, 2019). Indoor LBS has been studied for a few decades, but we still cannot fully enjoy the convenience brought by such service in our daily lives (Basiri et al., 2017;Cheema, 2018). For instance, indoor navigation is still very monotonous, which generally is the single-source shortest (distance/time) navigation path from one specific place to another (Yan et al., 2020). ...
Indoor navigation has been studied for many years, but it still has many limitations. In current navigation, pedestrians need to tell navigation systems the destination, because it is one of the preconditions for path planning. However, in some indoor cases, pedestrians cannot specify a destination because they have no information about where it is or even cannot be sure if there is a desired one. We believe that determining a service area is a possible way to handle such cases. For example, a service area that can be reached within a 2-min walk. In this paper, we propose an indoor service area determination approach for pedestrian navigation path planning. We demonstrate this approach in a shopping mall with multi-floors. The results show that it can successfully compute the reachable spaces and thereby helping people to select and arrive at the most appropriate destination. This approach is also useful for other indoor navigation applications within public buildings like offices, airports, theaters, hospitals, and museums where pedestrians would like to make a choice between multiple facilities of the same type, such as printers, registration desks, ATMs, AED, garbage bins, even exits.
... People need location-based-service (LBS) in such places, such as navigation service (Yan et al., 2021), urgency evacuation (Macatulad & Blanco, 2019). Indoor LBS has been studied for a few decades, but we still cannot fully enjoy the convenience brought by such service in our daily lives (Basiri et al., 2017;Cheema, 2018). For instance, indoor navigation is still very monotonous, which generally is the single-source shortest (distance/time) navigation path from one specific place to another (Yan et al., 2020). ...
Indoor navigation has been studied for many years, but it still has many limitations. In current navigation, pedestrians need to tell navigation systems the destination, because it is one of the preconditions for path planning. However, in some indoor cases, pedestrians cannot specify a destination because they have no information about where it is or even cannot be sure if there is a desired one. We believe that determining a service area is a possible way to handle such cases. For example, a service area that can be reached within a 2-min walk. In this paper, we propose an indoor service area determination approach for pedestrian navigation path planning. We demonstrate this approach in a shopping mall with multi-floors. The results show that it can successfully compute the reachable spaces and thereby helping people to select and arrive at the most appropriate destination. This approach is also useful for other indoor navigation applications within public buildings like offices, airports, theaters, hospitals, and museums where pedestrians would like to make a choice between multiple facilities of the same type, such as printers, registration desks, ATMs, AED, garbage bins, even exits.
... Indoor location-based services (ILBS) [1] enable the localization of individual users, which introduces promising application potentials in firefighting, cave exploration, etc. The approaches based on ILBS can be categorized into passive and active classes [2]. ...
Full-text available
With the development of indoor location-based services (ILBS), the dual foot-mounted inertial navigation system (DF-INS) has been extensively used in many fields involving monitoring and direction-finding. It is a widespread ILBS implementation with considerable application potential in various areas such as firefighting and home care. However, the existing DF-INS is limited by a high inaccuracy rate due to the highly dynamic and non-stable stride length thresholds. The system also provides less clear and significant information visualization of a person’s position and the surrounding map. This study proposes a novel wearable-foot IOAM-inertial odometry and mapping to address the aforementioned issues. First, the person’s gait analysis is computed using the zero-velocity update (ZUPT) method with data fusion from ultrasound sensors placed on the inner side of the shoes. This study introduces a dynamic minimum centroid distance (MCD) algorithm to improve the existing extended Kalman filter (EKF) by limiting the stride length to a minimum range, significantly reducing the bias in data fusion. Then, a dual trajectory fusion (DTF) method is proposed to combine the left- and right-foot trajectories into a single center body of mass (CBoM) trajectory using ZUPT clustering and fusion weight computation. Next, ultrasound-type mapping is introduced to reconstruct the surrounding occupancy grid map (S-OGM) using the sphere projection method. The CBoM trajectory and S-OGM results were simultaneously visualized to provide comprehensive localization and mapping information. The results indicate a significant improvement with a lower root mean square error (RMSE = 1.2 m) than the existing methods.
... A prevalent technology used for indoor positioning is Wi-Fi fingerprinting. The main reason Wi-Fi fingerprinting has become a great candidate for indoor positioning is the ubiquity of Wi-Fi access points to support wireless Internet connectivity [12]. In general, fingerprint-based IPS operates in two phases. ...
Full-text available
Indoor positioning systems have been of great importance, especially for applications that require the precise location of objects and users. Convolutional neural network-based indoor positioning systems (IPS) have garnered much interest in recent years due to their ability to achieve high positioning accuracy and low positioning error, regardless of signal fluctuation. Nevertheless, a powerful CNN framework comes with a high computational cost. Hence, there will be difficulty in deploying such a system on a computationally restricted device. Knowledge distillation has been an excellent solution which allows smaller networks to imitate the performance of larger networks. However, problems such as degradation in the student’s positioning performance, occur when a far more complex CNN is used to train a small CNN, because the small CNN does not have the ability to fully capture the knowledge that has been passed down. In this paper, we implemented the teacher-assistant framework to allow a simple CNN indoor positioning system to closely imitate a superior indoor positioning scheme. The framework involves transferring knowledge from a large pre-trained network to a small network by passing through an intermediate network. Based on our observation, the positioning error of a small network can be reduced to up to 38.79% by implementing the teacher-assistant knowledge distillation framework, while a typical knowledge distillation framework can only reduce the error to 30.18%.
The positioning using Wi-Fi signals and fingerprinting algorithms achieved a lot of attention lately. The main drawback of the fingerprinting localization is the process of radiomap creation, which is labour intensive and time-consuming procedure. Therefore, some solutions for crowdsourcing and dynamic map creation were proposed. However, the problem of these is that users usually don’t move regularly thru all the areas, which leads to undersampling of certain parts of the localization area. In this paper interpolation algorithms are used to increase radiomap density and thus reduce the problem with under-sampling. To evaluate the impact of interpolation on the performance of fingerprinting algorithms the NN, KNN and WKNN algorithms were tested on dynamic radiomap without interpolation as well as with radiomaps created using linear, inverse distance weight and Kriging interpolations. The paper shows the results achieved in the real-world scenario and shows the impact of the interpolation algorithms on the performance of the above-mentioned localization algorithms.
The phenomenon of volunteered geographic information is part of a profound transformation in how geographic data, information, and knowledge are produced and circulated. By situating volunteered geographic information (VGI) in the context of big-data deluge and the data-intensive inquiry, the 20 chapters in this book explore both the theories and applications of crowdsourcing for geographic knowledge production with three sections focusing on 1). VGI, Public Participation, and Citizen Science; 2). Geographic Knowledge Production and Place Inference; and 3). Emerging Applications and New Challenges. This book argues that future progress in VGI research depends in large part on building strong linkages with diverse geographic scholarship. Contributors of this volume situate VGI research in geography’s core concerns with space and place, and offer several ways of addressing persistent challenges of quality assurance in VGI. This book positions VGI as part of a shift toward hybrid epistemologies, and potentially a fourth paradigm of data-intensive inquiry across the sciences. It also considers the implications of VGI and the exaflood for further time-space compression and new forms, degrees of digital inequality, the renewed importance of geography, and the role of crowdsourcing for geographic knowledge production.
Conference Paper
This paper describes the development of a prototype floor sensor as a gait recognition system. This could eventually find deployment as a standalone system (eg. a burglar alarm system) or as part of a multimodal biometric system. The new sensor consists of 1536 individual sensors arranged in a \unit[3]{m} by \unit[0.5]{m} rectangular strip with an individual sensor area of \unit[3]{cm$^2$}. The sensor floor operates at a sample rate of \unit[22]{Hz}. The sensor itself uses a simple design inspired by computer keyboards and is made from low cost, off the shelf materials. Application of the sensor floor to a small database of 15 individuals was performed. Three features were extracted : stride length, stride cadence, and time on toe to time on heel ratio. Two of these measures have been used in video based gait recognition while the third is new to this analysis. These features proved sufficient to achieve an 80\% recognition rate.
Conference Paper
Commonly used Global Navigation Satellite Systems (GNSS) are inappropriate as Location Based Services (LBS) in indoor environment. Therefore research teams are developing different systems, which can be used as a suitable alternative. One of options is to use Inertial Navigation System (INS) which consists of inertial sensors and mathematic procedures. This concept has been known for a long time, but 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). In this paper we present PDR by using smartphone sensors. They can be classified as low-cost Inertial Measurement Unit (IMU), and have been compared with more precise and expensive Xsens IMU. 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. Particle Filter (PF) has been successfully used with map constraints to increase the accuracy of proposed location system. Presented results show that low-cost smartphone IMU combined with PF can be applicable as proper navigation system.
The concept of crowdsourcing is nowadays extensively used to refer to the collection of data and the generation of information by large groups of users/contributors. OpenStreetMap (OSM) is a very successful example of a crowd-sourced geospatial data project. Unfortunately, it is often the case that OSM contributor inputs (including geometry and attribute data inserts, deletions and updates) have been found to be inaccurate, incomplete, inconsistent or vague. This is due to several reasons which include: (1) many contributors with little experience or training in mapping and Geographic Information Systems (GIS); (2) not enough contributors familiar with the areas being mapped; (3) contributors having different interpretations of the attributes (tags) for specific features; (4) different levels of enthusiasm between mappers resulting in different number of tags for similar features and (5) the user-friendliness of the online user-interface where the underlying map can be viewed and edited. This paper suggests an automatic mechanism, which uses raw spatial data (trajectories of movements contributed by contributors to OSM) to minimise the uncertainty and impact of the above-mentioned issues. This approach takes the raw trajectory datasets as input and analyses them using data mining techniques. In addition, we extract some patterns and rules about the geometry and attributes of the recognised features for the purpose of insertion or editing of features in the OSM database. The underlying idea is that certain characteristics of user trajectories are directly linked to the geometry and the attributes of geographic features. Using these rules successfully results in the generation of new features with higher spatial quality which are subsequently automatically inserted into the OSM database.
Conference Paper
Location Based Services (LBS) market is growing rapidly, however it has faced several challenges and issues, including the availability of reliable positioning services seamlessly (indoors and outdoors), the privacy protection issues, and the relatively high demands for resources, such as high power consumption and cost. Among all the issues introduced to the markets of LBS, the non-technical issues can be easier to understand for many of ordinary users of LBS and they, consequently, can become yet bigger challenge to the development of LBS markets. Lack of social acceptance of the LBS applications can result in slowing down the growth of the market, if not failure. This paper reviews the non-technical issues of LBS market from users? perspective and evaluate the significance of their impact on the growth of the market based on the results of a survey conducted and the predictive analysis have been done.
Conference Paper
Indoor positioning is one of the biggest challenges of many Location Based Services (LBS), especially if the target users are pedestrians, who spend most of their time in roofed areas such as houses, offices, airports, shopping centres and in general indoors. Providing pedestrians with accurate, reliable, cheap, low power consuming and continuously available positional data inside the buildings (i.e. indoors) where GNSS signals are not usually available is difficult. Several positioning technologies can be applied as stand-alone indoor positioning technologies. They include Wireless Local Area Networks (WLAN), Bluetooth Low Energy (BLE), Ultra-Wideband (UWB), Radio Frequency Identification (RFID), Tactile Floor (TF), Ultra Sound (US) and High Sensitivity GNSS (HSGNSS). This paper evaluates the practicality and fitness-to-the-purpose of pedestrian navigation for these stand-alone positioning technologies to identify the best one for the purpose of indoor pedestrian navigation. In this regard, the most important criteria defining a suitable positioning service for pedestrian navigation are identified and prioritised. They include accuracy, availability, cost, power consumption and privacy. Each technology is evaluated according to each criterion using Analytic Hierarchy Process (AHP) and finally the combination of all weighted criteria and technologies are processed to identify the most suitable solution.
This chapter will discuss the interrelated concepts of privacy and security with reference to location-based services, with a specific focus on the notion of location privacy protection. The latter can be defined as the extent and level of control an individual possesses over the gathering, use, and dissemination of personal information relevant to their location, whilst managing multiple interests. Location privacy in the context of wireless technologies is a significant and complex concept given the dual and opposing uses of a single LBS solution. That is, an application designed or intended for constructive uses can simultaneously be employed in contexts that violate the (location) privacy of an individual. For example, a child or employee monitoring LBS solution may offer safety and productivity gains (respectively) in one scenario, but when employed in secondary contexts may be regarded as a privacy-invasive solution. Regardless of the situation, it is valuable to initially define and examine the significance of “privacy” and “privacy protection,” prior to exploring the complexities involved.
OpenStreetMap (OSM) data are widely used but their reliability is still variable. Many contributors to OSM have not been trained in geography or surveying and consequently their contributions, including geometry and attribute data inserts, deletions, and updates, can be inaccurate, incomplete, inconsistent, or vague. There are some mechanisms and applications dedicated to discovering bugs and errors in OSM data. Such systems can remove errors through user-checks and applying predefined rules but they need an extra control process to check the real-world validity of suspected errors and bugs. This paper focuses on finding bugs and errors based on patterns and rules extracted from the tracking data of users. The underlying idea is that certain characteristics of user trajectories are directly linked to the type of feature. Using such rules, some sets of potential bugs and errors can be identified and stored for further investigations.
Location-based services (LBS) are a new concept integrating a user's geographic location with the general notion of services, such as dialing an emergency number from a cell phone or using a navigation system in a car. Incorporating both mobile communication and spatial data, these applications represent a novel challenge both conceptually and technically. The purpose of this book is to describe, in an accessible fashion, the various concepts underlying mobile location-based services. These range from general application-related ideas to technical aspects. Each chapter starts with a high level of abstraction and drills down to the technical details. Contributors examine each application from all necessary perspectives, namely, requirements, services, data, and scalability. An illustrative example begins early in the book and runs throughout, serving as a reference.