self-mapping radio maps for location fingerprinting

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Wireless NetworksThe Journal of Mobile Communication,Computation and Information ISSN 1022-0038 Wireless NetwDOI 10.1007/s11276-014-0864-1

Self-mapping radio maps for locationfingerprinting

Gareth Ayres & Jason Jones

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Self-mapping radio maps for location fingerprinting

Gareth Ayres • Jason Jones

� Springer Science+Business Media New York 2014

Abstract Localisation of a mobile device in Wi-Fi based

indoor environments has received much attention and is an

important area of research, considering the uptake of

devices with built-in Wi-Fi and often blanket coverage in

enterprise. Most localisation techniques in this area require

an off-line calibration phase where a radio map is manually

built using the laborious efforts of administrators. This

radio map is then subject to becoming inaccurate with time

and requires refreshing as the equipment is upgraded and

replaced. We propose a system that can automatically build

and calibrate a radio map for use with location finger-

printing techniques to provide indoor and outdoor locali-

sation with Wi-Fi. This system first uses a self-mapping

technique that makes use of user roaming patterns to build

a weighted undirected graph of Access Point locations. We

then combine this initial map with a map built by users of

smartphones with built-in Wi-Fi and GPS to generate

anchor nodes. We show that the unique combination of

these two techniques provides a zero-configuration cali-

bration map for use with location fingerprinting techniques,

which not only saves time and effort in the calibration

phase, but provides a constantly fine-tuning and self-heal-

ing map.

Keywords Wireless � Localization � Visualization �Graph theory � Force-directed layout

1 Introduction

The recent explosion in popularity of mobile devices such

as the Apple iPhone and Google Android phones has

accelerated the use and deployment of Location Based

Services (LBS). LBS’s require different levels of granu-

larity of location data with some applications working

sufficiently well with low-grained location data such as a

town/city while others require much more fine-grained data

such as room/building.

The problem of localisation while outdoors has been

largely addressed with the use of three technologies, Global

Positioning System (GPS), mobile phone network locali-

sation and Wi-Fi localisation. GPS localisation has proven

very popular with consumers, with GPS chips being

embedded in many personal devices such as traffic navi-

gators, smartphones and sports watches. The GPS system

works through the use of a Global Navigation Satellite

System to provide timing data to devices from multiple

satellites. A device can then use this data to trilaterate its

position on the earth. The GPS system works well in open

spaces, but runs into problems in city centers where the

signal suffers from reflection.

Mobile phone network localisation has become a pop-

ular method to quickly locate a phone on a GSM network,

all be it with significantly less accuracy than GPS. The U.S

FCC 911 (E911) mandate has pushed the development of

these technologies to assist authorities tracking users in

times of an emergency [1].

Wi-Fi data can also usually be obtained from smart-

phones, and as a result this has begun to emerge as a

solution to outdoor localisation. It has become common

place for competing mobile OS providers to build their

own a priori map of Wi-Fi access points that is used to help

assist in the location of smartphones. The smartphone’s

G. Ayres (&) � J. Jones

College of Engineering, Swansea University, Swansea SA28PP,

UK

e-mail: [email protected]

J. Jones

e-mail: [email protected]

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DOI 10.1007/s11276-014-0864-1

Author's personal copy

then contribute new information back to the provider to

improve and add to the map.

Indoor localisation using Wi-Fi on the other hand is an

ongoing problem to which an adequate solution has yet to

be adopted by any mainstream smartphone developers.

There have been many proposed solutions, such as location

fingerprinting, which will be covered in Sect. 2.

In this paper we propose a novel system that augments a

self-mapping and calibrating Wi-Fi radio map derived from

mobile device roaming behavior, with a smartphone vari-

able accuracy radio map, to produce a zero-configuration

self-mapping radio map for use in indoor and outdoor

localisation.

The remainder of this paper is broken down as such:

Sect. 2 covers related work in indoor localisation, location

fingerprinting and zero-configuration radio maps. Section 3

provides an overview of the LocPriS system developed to

implement the concepts and produce results. Section 4

introduces the self-mapping using undirected weighted

graphs technique. Section 5 introduce the smartphone

variable accuracy radio map android application and

technique. Section 6 then brings Sects. 4 and 5 together to

present the Augmented Radio Map. Section 7 provides a

conclusion of the work.

2 Related work

Zero-configuration indoor Wi-Fi localisation is an active

area of research that has its roots in indoor localisation for

mobile sensor networks as well as mobile robotics. Both

these areas of research have a need to build a self-map of a

wireless environment and to locate themselves within it.

These areas of research combined with indoor localisation

in WLAN environments have produced much work. This

section will summarise the work in these areas related to

this paper.

2.1 Indoor localisation

Much work on localisation of mobile devices in an indoor

environment has been undertaken in order to provide

context-aware and location based services. The first areas

of research involved specialist hardware devices that made

use of TOA (time of arrival), TDOA (time difference) and

AOA (angle of arrival). These specialist setups made use of

infrared (Active Badges) [2], ultrasonic (Active Bat) [3]

and beacon based (Cricket) [4] high frequency wireless

communications to determine location. While these sys-

tems worked well, the requirement for specialist hardware

was a hurdle to mainstream adoption [5].

With significant research in localisation techniques and

other issues relevant to LBS’s focusing on indoor systems

with specialist hardware throughout the 1990s until early

2000 the next wave of research was initiated by Microsoft

with a paper they published called RADAR [6], which

details a number of methods of performing localisation

through the use of a emerging technology, wireless LAN’s

(Wi-Fi), without the need of specialist hardware. This

paper is discussed more in Sect. 2.2.

The direction of research began to shift during the early

2000s from using indoor location tags to existing Wi-Fi

infrastructure to provide LBS’s. The increase in popularity

of GPS and smartphones helped drive this change of

direction to focus on Wi-Fi localisation techniques.

2.2 Location fingerprinting

In 2001 Microsoft Research published a paper that detailed

a passive system called RADAR which made use of the

802.11 based Wi-Fi infrastructure for localisation, with

only additional software needed and no additional spe-

cialist hardware. This paper sparked a drive to improve

localisation of indoor devices using the native wireless

equipment used by 802.11b networks at the time.

In the RADAR system, the RF received signal strength

indicator values (RSS) is used as a measure of distance

between an Access Point (AP) and mobile node (MN). An

empirical/deterministic algorithm is described (Nearest

Neighbour in Signal Space NNSS) which is used to store

the RSS of each AP for a specific location in a Vector in a

off line phase. This off-line phase is used to create a radio

map. Each point that RSS values are added to the vector is

called a calibration point (CP). This information is used to

compute the 2D position by triangulation, with both an

empirical method and a signal propagation modelling

method used.

RSS based positioning has developed to generally use

three different techniques: Association/Cell ID based

methods, triangulation (pattern matching and probabilistic

filtering) and fingerprinting. Cell ID based is the simplest,

but yields a very low accuracy equal to that of the total

range of RSS from an AP. Triangulation through lateration

is more accurate with three or more AP’s, but is compu-

tationally demanding and requires knowledge of the exact

position of AP’s, and does not work well when considering

radio propagation/multi-path problems encountered by

non-line of sight (NLOS). However, for indoor positioning,

non-line-of-sight propagation and attenuations caused by

walls, other structures, and even people causes significant

fluctuations to RSS, which makes the simple path loss

models too inaccurate in many real life situations. Location

Fingerprinting has become an established area of further

research as it copes well with the problem of NLOS in Wi-

Fi by modelling the environment and making use of the

propagation characteristics by design.

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Typically two phases are required for location finger-

printing; an offline training phase to build a radio map of

calibration points (CPs) for all locations and an online

location determination phase where a radio map is refer-

enced by a device to determine its location.

The radio map, or a priori, is created during a calibration

phase that includes reading RSS values at predetermined

locations for a fixed period of time. RADAR suggested

manual or simulation calibration phases. Manual requires

walking around and taking readings at each CP while

simulation requires a detailed model of a building, both

taking considerable time and effort to achieve. It is also

usual to include building floor plans as images, and require

manually linking location to position on a floor plan

(labelling). It has been suggested that the linking of actual

location to radio map could be achieved through prompting

a user to label locations during the online phase instead of

the calibration phase [7]. This phase of calibrating the

system is also labour intensive and subject to errors when

AP’s move or fail.

The number of CP’s in a radio map, and the time spent

at each [8] has also been the subject of much work, in an

attempt to reduce the burden of the offline calibration

phase. It has been shown that more CP’s result in more

accurate location estimates but a larger search time and

computational cost. While less CP’s result in less accurate

location estimates but a smaller search time and are more

efficient [9].

It has also been reported that different devices report

differing RSS values at the same location as a result of the

different hardware and design properties of Wi-Fi chipsets

[10]. This, along with the fact that the orientation and

direction of devices can also return different readings,

results in a significant possibility of error in a radio map

[11].

Location fingerprinting techniques fall into two cate-

gories, probabilistic or deterministic. In deterministic

techniques typically the estimated location of a MN is

considered static in time and makes use of the Nearest

Neighbor techniques, with the Kth NN and Weighted NN

techniques proven to be successful in early works [6].

Probabilistic Algorithms have proven to be the most

popular technique; they consist of RSS data summarised

into Probability Density Functions (PDF). At each CP, a

radio map has a PDF for each AP approximated using a

Kernel Function or Histograms [12]. Using a random var-

iable to represent location estimation results in a smaller

radio map in memory and less computation time in the

localisation phase [13]. This combined with filtering using

Kalman or Bayesian filtering results in improved location

estimation using continuous random variables. This

approach makes use of previous location data, not just

static calibration points [12].

2.3 Reduced/zero-configuration radio maps

Accurate location estimation using measurements of signal

strength is still a difficult task due to the noisy character-

istics of signal propagation. Physical characteristics such as

reflection, refraction, diffraction and absorption means

signal propagation suffers from multi-path fading effects in

an indoor environment and RSS values frequently change.

With 802.11 techniques such as channel hopping, automatic

equipment and transmission power control and changes in

network layout an up to date radio map for use with location

fingerprinting is a research challenge of its own.

Frequently changing fingerprints result in the time/

labour intensive radio maps becoming inaccurate or

expired quickly, with a new costly offline recalibration

phase necessary. This challenge has been addressed by

projects such as RedPin [14] which make use of RSS fin-

gerprints and manual interaction from clients to continually

update a radio map with actual location on floor plans.

Reducing the size of radio maps and the number of

required CPs has been suggested as a way to speed up the

off line phase. The use of probabilistic estimation tech-

niques has resulted in smaller radio map requirements for

fingerprinting, but still requires linking to a floor plan or

map [15].

The link between a floor plan and a CP on a radio map is

sometimes called labelled data. There has been some work

to reduce the effort of labelling data when building radio

maps by allowing one floor to be manually labelled and

assume the other floors follow a similar layout [16].

Anchor nodes have been proposed as a solution to

provide zero-configuration, along with access points that

monitor each other’s RSS values [17]. The anchor nodes

however need to be manually entered and positioned before

a system is active and AP’s require custom layer 2 soft-

ware. If such an anchor node were to move or be replaced

with different hardware, it would have significant impact

on the reliability of such a system.

3 System overview

LocPriS is a modular extensible framework [18] that we

are developing to provide tools for the development, ana-

lysis, comparison and visualisation of LBS’s that preserve

privacy and security. The framework will also assist in the

development and testing of Location Based Services

through an exposed API (Fig. 1).

3.1 Visualising roaming with LocPriS

LocPriS can build a model of wireless access points (AP),

mobile nodes (MN) and the movements of the MN’s

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between AP’s. This model can then be analysed empiri-

cally or visualised in 2d/3d as in Fig. 2. The visualization is

produced using custom software written in Java, PHP and

Javascript. The D3.js framework is used for all 2d visu-

alisations, and the jME framework used for 3d.

3.1.1 Automatic labelling and grouping

Although trivial, the hostname of each AP can sometimes

be used to automatically label AP’s and group them. This

technique can be useful in enterprise environments where

multiple location/buildings/floors have similar hostnames.

These self-assigned labels or groups can then be used to

help eliminate or speed up the manual calibration phase.

Groups of similar hostnames can be presumed to rep-

resent similar geographic locations. Fulton-AP-F1-2 for

example could be inferred to represent a cluster of AP’s in

the Fulton building. These groups can then be labelled and

visualised instead of individual AP’s in order to present a

visualisation that is simpler to interpret and conceptualise.

This is shown in Fig. 2, where automatic groups for each

building are shown as red circles on a campus map which

are generated in LocPriS using manually entered locations.

The green lines in Fig. 2 represent the roaming between

groupings by a MN as it moves around the campus.

3.1.2 Association/disassociation data

As MN’s roam, they associate to different AP’s. These

associations and disassociations create events (SNMP

traps) in the wireless system which can be sent to LocPriS

and then stored in a database. These events contain addi-

tional information such as time, AP name and MN iden-

tifier. The LocPriS system can pseudo-anonymise this data

to enhance user privacy for use with location fingerprint-

ing. While it is the aim to discover the location of AP’s

using self-mapping, it is possible to set the latitude/longi-

tude of an AP manually in LocPriS. Figure 3 shows the

location of all AP’s at the Student Village mapped using

their lat/long location which was manually entered in order

to evaluate the effectiveness of self-mapping in later

sections.

3.2 Android application: variable accuracy location

data

Using smartphones it is now possible to generate much

location data with variable accuracy from different sources.

These sources can include GPS, the mobile phone network

LocPriSData Collection Module

(Real-Time data collection or Wireless Traces )Mobility Simulation Module

(Internal and External Simulation )

Visualisation Module(2D & 3D Visualisation)

Statistical Output Module(Output to CSV File)

Location Based Service Module / API

Location Privacy Module(Implementation of Privacy Techniques )

Fig. 1 The modular design of the LocPriS Framework

Fig. 2 Automatic groupings based on labels and fixed locations

(Color figure online) Fig. 3 Map of all AP’s in Student Village

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and Wi-Fi estimation. The specific use of Android and a

custom LocPriS application that has been developed is

discussed in further detail in Sect. 5.

4 Self-mapping using undirected weighted graphs

The LocPriS system receives real time association and

disassociation data about all connected MN’s from wireless

AP’s via the wireless controllers. This data is then for-

warded to the LocPriS server software and stored in two

mySQL database tables.

This data is generated by devices roaming between

AP’s. Each roam results in an association and disassocia-

tion message being sent to the LocPriS system using SNMP

packets. Figure 4 shows an example of a user in a typical

scenario walking past access points, between buildings,

with a device roaming for connectivity as the user moves.

This information can be used to build an undirected

weighted graph G with vertices V and edges E. Let an

access point be considered a vertex V, and the con-

nectivity/relationship between each access point as a

device roams considered the edge E. While the graph of

one user’s movements is directed, when multiple devices

over a larger time period are considered, the same edge

between vertices will be covered in both directions. So the

direction becomes irrelevant.

The edge weight W can then be defined as the time taken

to associate between access points. Given Fig. 4, access

points A1 and A2 are nodes, and the edge between the two

nodes signifies the relationship that node A2 was next

roamed to from node A1. The edge weight is W = 8 as there

was an 8 s time period between associations.

As many weights, from many devices will be fed into the

system for two given nodes, each with a potentially dif-

ferent weight, it is important to use the most effective

maximum weight. This is required to rule out some sce-

narios where invalid data would be generated, such as when

a device remains still or a device is switched off and then

back on. In order to calculate the most effective weights to

use, we recorded the GPS locations of the 204 access points

at the university’s student village, which is a private area of

land roughly quarter of a mile squared in area containing

130 houses and 204 AP’s. This can be seen in Fig. 3, with

each access point positioned on the map with a red dot.

We used 1 month of data from March 2013 and plotted

each nodes GPS location into a Euclidian plane and cal-

culated the distance between neighboring AP’s. We then

paired these data with the minimum and average time

recorded to associate between neighbor AP’s. The corre-

lation coefficient between the time taken (t) in seconds to

roam between two AP’s, and the displacement (d) between

those two AP’s in meters was then calculated to discover

the best maximum weight to use for future graphs.

Correlðt; dÞ ¼Pðt � �tÞðd � �dÞ

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPðt � �tÞ2

qPðd � �dÞ2

where �t ¼ average time; �d ¼ average displacement

This was undertaken for all the access points using all

the data from March 2013, and used maximum weights

ranging between 25 and 3,000 s. The weight is then said to

be the time taken in seconds to roam between two AP’s.

This gave us the plot which can be seen in Fig. 5, and

shows that the minimum time seen to roam between two

AP’s gives a better result than the average time. It also

shows the highest correlation coefficient is for a maximum

weight of 250 s.

Using the information discovered in Fig. 5, the best

maximum weight Wmax = 250 s is recommended, and used

throughout the rest of this paper. While the weight is

Fig. 4 One device roaming

0

0.1

0.2

0.3

0.4

0.5

0.6

0 500 1000 1500 2000 2500 3000

CORR

ELAT

ION

CO

EFFI

CIEN

T

SECONDS

Correla�on Min Correla�on Avg

Fig. 5 Correlation coefficient of weights

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derived from time, the unit of the weight once the graph is

built is meaningless, as the graph is dimensionless. Once

the graph is anchored in Sect. 6, the weights are used to

scale the graph and keep edges relative.

For the full 12 months of 2013 there were 50,539,780

associations recorded by LocPriS running on the Swansea

University wireless system, for all campuses. During this

time, there were 870 distinct AP’s identified in the system

and 21,382 distinct devices seen.

The sample data seen in Table 1 shows the information

stored in the LocPriS database in the association table. The

edge weight between V(AP1) and V(AP2) is W = AP2(Wi)

- AP1(Wi) = 1,304,467,963 - 1,304,467,955 = 8 s.

Figure 4 represents one devices movement, for a trivial

number of AP’s. The LocPriS system builds d subgraphs

Gd = (V,E), where d = {1…number of devices}, based on

the connectivity and mobility of a device d and then

compares the weights, adding only the smallest weight seen

between two edges to graph G, where W \ 250. So edge

G(E) will contain {W0…Wd} weights, and Ed(W) will

contain the smallest value of {W0…Wd}.

The AP Name is irrelevant to the Graph, but used only

as a label to aid interpretation of visualisations. In our

dataset the AP name is the hostname of the AP. Fortunately

the hostnames at Swansea are descriptive of the location to

differing degrees, but usually contain at least the building

name and floor. This makes grouping and automatically

labelling AP’s based on AP Name a future possibility. The

remainder of this paper will focus on data taken from the

academic year 2013–2014.

4.1 Roaming statistics and device characterisation

Devices roaming between access points produce the data

used to build the graphs for the radio map. Some inter-

esting statistics and trends have been observed when ana-

lysing the roaming statistics from the LocPriS system

which may affect the effectiveness of the data and tech-

niques used. We have analysed 12 months roaming data

from 2013 to 2014 from the Student Village.

Table 2 shows the top 17 device vendors in descending

order of association count, including a row for ‘All Ven-

dors’ which shows the total for all devices seen in 2013-14

at the Student Village. This table shows which device types

are popular on the wireless network at Swansea, and how

much of the data used to build the graphs came from which

device types. It also provides an insight into which of the

popular devices on campus have a high or low number of

Association/Device. The number of AP’s (Vertices) seen

Table 1 Sample associations data

AP name Device ID Timestamp

AP1 7899 1304467955

AP2 7899 1304467963

Table 2 Top device vendors by

association countVendor Association

count

Assoc

%

Edges Vertices Devices Assoc/

Device

All vendors 13,422,957 100.0 23,462 204 18,674 718.80

Apple 6,263,451 50.0 20,102 204 8,415 744.32

Samsung 1,606,796 12.8 15,784 204 1,985 809.47

Hon hai precision ind 937,605 7.5 3,276 204 1,284 730.22

Nintendo co., ltd. 813,108 6.5 3,366 191 71 11,452.23

HTC corporation 477,820 3.8 10,236 204 666 717.45

Liteon technology

corporation

427,762 3.4 2,345 204 781 547.71

Murata manufactuaring 401,243 3.2 10,840 204 614 653.49

Nokia corporation 376,124 3.0 8,628 204 355 1,059.50

Sony mobile

communications

365,596 2.9 8,952 204 756 483.59

Intel corporate 295,498 2.4 2,460 204 751 393.47

LG electronics 207,446 1.7 7,834 204 175 1,185.41

Research in motion 148,693 1.2 14,448 204 381 390.27

Asustek computer inc. 61,926 0.5 3,450 203 120 516.05

Huawei technologies 54,258 0.4 3,866 194 73 743.26

Motorola mobility 50,952 0.4 3,088 199 49 1,039.84

Gemtek technology 35,702 0.3 200 149 81 440.77

Wistron neweb corp 13,638 0.1 238 125 52 262.27

Total for listed vendors 12,537,618 100.0

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by each manufacturer (from a total of 204) and the number

of distinct associations between AP’s (Edges) are also

shown.

We have also calculated the correlation coefficient

between displacement and time for each device vendor type,

and broken down the two most popular vendors Apple and

Samsung for further analysis. It can be seen in Table 3 that

Apple devices account for 50 % of all wireless devices in the

Student Village, with Samsung making up nearly 13 %.

Students at Swansea can use any make device they wish on

the wireless network, and all are self-bought by students.

By using the wireless registration data it is possible to

brake the Apple vendor down into the known models. The

same applies for Samsung, but we have had to combine all

tablets/phones under Android as this was all that was

possible with the registration data. The totals for ‘All

Apple’ and ‘All Samsung’ is larger than the sum of the sub-

types as not all devices seen on the system were registered,

so these were not included in the granular analysis.

It can be seen from the data in Table 3 that the ‘Apple-

OSX’ and ‘Samsung-Laptop’ have significantly poorer

correlation coefficients than the mobile devices such as the

‘iPhone’. This is presumably a result of the difference in

use of mobile devices such as phones and laptops. It is also

likely a result of the behaviors of such devices, where

smartphones stay connected to wireless in a user’s pocket

as they roam, whereas a laptop would typically be shut

down or suspended when carried.

The devices with a correlation coefficient of less than

0.5 are given in italics, and greater than 0.5 in roman.

Using this information to remove the devices which pro-

vide a low or poor correlation may help improve the overall

accuracy of the map.

Other factors which may also play part in the roaming

decisions of device are badly written wireless drivers

which roam between near access points too aggressively, or

possible infrastructure problems in some locations.

4.2 Visualising roaming using LocPriS

Using the roaming data from LocPriS along with the

grouping of the AP data per building using the automatic

labelling of AP names, it is possible to quickly visualise the

mobility of devices to get an idea of roaming patterns.

Figure 6 shows a visualisation of associations of all devices

during a 1 h period, overlaid onto a Swansea Universiy

Student Village map. The association between two AP’s is

represented by a line. Each AP was manually positioned

using manually collected GPS locations and is represented

by a red circle.

However, removing the dependency of having location

for buildings and access points manually entered and

recorded is the goal of a self-mapping radio map.Table 3 Device type correlation coefficient

Vendor Devices Associations % Correl Coeff

All Vendors 18,674 100.0 0.61

All Apple 8,415 50.0 0.60

Apple-iPhone 4,298 N/A 0.61

Apple-iPad 753 N/A 0.62

Apple-OSX 135 N/A 0.29

All Samsung 1,985 12.8 0.64

Samsung-Android 1,321 N/A 0.65

Samsung-Laptop 90 N/A 0.03

HTC Corporation 666 3.8 0.63

Hon Hai Precision 1,284 7.5 0.45

Murata manufacturing 614 3.2 0.62

Nintendo 71 6.5 0.43

Nokia Corporation 355 3.0 0.63

Liteon Technology 781 3.4 0.26

Intel 751 2.4 0.32

Sony 756 2.9 0.64

RIM 381 0.6 0.51

LG Electronics 175 1.7 0.62

Wistron Neweb 52 0.1 0.27

Asustek Computer inc 120 0.5 0.57

Motorola Mobility 49 0.4 0.61

Huawei Technologies 73 0.4 0.54

Gemtek Technology 81 0.3 0.02 Fig. 6 One hour associations with automatic grouping and manual

entered AP locations (Color figure online)

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4.3 Graph drawing of the undirected weighted graph

All the associations and disassociations recorded by Loc-

PriS at the Student Village for March 2013 were used to

build an undirected weighted graph, with a maximum

weight of 250 s. The LocPriS system calculates the nodes,

edges and weights then outputs the result as a Graph G.

Using force-directed layout techniques, and the visual-

ization package D3.js, it is possible to visualise Graph G.

The graph in Fig. 7 represents the Self-Mapping Radio

Map (SMRM) generated from all the associations collected

for March 2013 on the Swansea University student village

wireless infrastructure.

This graph is an undirected weighted graph that is using

a maximum weight of 250 s to determine the minimum

weight of each neighbour relationship between AP’s.

The orientation of the SMRM is irrelevant, as without

anchor nodes there is no way to determine correct orien-

tation or if a flip/twist has occurred.

In Fig. 7, groupings and clustering of AP’s where there

is a relationship between edge weight of neighbouring AP’s

and the time taken to roam between them is becoming

evident.

The graph was visualized using the custom visualization

module in LocPriS, and can be edited and manipulated in

real time using a web browser.

The SMRM now requires a meaningful orientation and

reference points to link it to the real-world locations of

AP’s for use with further location fingerprinting techniques

or for immediate localisation. Section 5 will describe how

these reference points can be collected and defined in a new

map, with Sect. 6 then showing how the two maps can be

combined to produce a self-mapping radio map. The ori-

entation and positioning could also be achieved manually,

by an administrator dragging some anchor nodes into

position on a real map, but this would not achieve the zero-

configuration or continuously updating and correcting

functionality that the map in the next section helps achieve.

5 Smartphone variable accuracy based radio maps

The rise in smartphone’s with built-in location technolo-

gies such as GPS, mobile network and Wi-Fi localisation

has resulted in a boom of location based services. Fortu-

nately, smartphone’s with the Android OS come with a

SDK [19] that is freely available for application developers

to make use of the smartphone technology to gather loca-

tion information.

A LocPriS application has been developed for Android

which collects location data and Wi-Fi fingerprints and

sends them to the LocPriS system. This data is then used to

calculate anchor nodes (AN) that then form the Variable

Accuracy Radio Map (VARM).

5.1 LocPriS Android application

Developing the LocPriS application in Android has the

advantage that it can be easily distributed to one of the

many Android devices available to users, through the

market place. This application could then be used by many

users or administrators to help build a detailed radio map as

they roam day to day.

A HTC Hero running Android 2.1 was used for the

development and experimentation of the application as

seen in this paper, but the application should work on any

Android device.

The left screen shot in Fig. 8 shows the summary page

of the Android application. From this page it can quickly

be seen that there are a number of items of information

available including: the total movements detected, the total

AP’s seen by the application and the number of broadcast

SSIDs seen.

The right screen shows the detailed view from the

LocPriS application. The detailed view is used to get more

information and turn features on and off. The Refresh

button can be used to start a Wi-Fi scan, and use the last

known good location data once. The Send button will

access the data structures of the application and send the

data to the LocPriS server. The Auto On button indicates

the automatic location sensing is off, and can be turned on

by clicking the Auto On button. This allows a user to run

the application silently, and roam around while constantly

sending data to LocPriS server.

The data text visible is a debug/detailed summary out-

put. Information available to the application from the SDK

includes: The location with an estimated location accuracyFig. 7 Self-mapping radio map (SMRM) for March 2013

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in meters, the RSS value of the AP seen, the latitude and

longitude of the current location along with a timestamp

and the location provider type.

The location data comes from either the built in GPS on

the device, or the estimated location based on the mobile

network localisation technique provided by Android. This

uses the mobile telephony towers to calculate location, and

is generally a less accurate than GPS except for some dense

city centers where GPS is poor and there is a high con-

centration of mobile network towers.

5.2 LocPriS application calibration points and anchor

nodes

The LocPriS application was used around Swansea Uni-

versity Student Village to collect a sample of data. It is

envisaged that the application would only need to be used

by one site administrator, and even only once in order to

collect sufficient anchor nodes. The application could be

built as a service that is running silently on android devices

without user intervention.

A Calibration Point (CP) can be considered the esti-

mated location of a MN (running the LocPriS application)

when a RSS value reading, paired with GPS/Mobile loca-

tion data was taken (Location Fingerprint). A user running

the LocPriS Android Application walked on an anticlock-

wise path around the Student Village. Figure 9 shows the

path taken by the users, along with the CP’s (red pins in left

image) and the AP roaming history obtained by the

LocPriS system (right image). As the exact locations of

APs is already known in Fig. 9, this can be considered the

ground truth for AP locations.

Multiple CP’s with the same AP visible are then used to

compute the Anchor Node location, which is the estimated

location of the AP. This is achieved by using the location

of the CP with the lowest location accuracy error as the

AN. If multiple CP’s with similar location accuracy error

as found then the one with the lowest RSS value is used.

The location of the AN then become the location provided

by the CP, as derived from the android application.

It is possible to use the location of multiple CP’s and

techniques such as those described in RADAR [6] to per-

form trilateration to give a AN with a potentially better

location accuracy, but this is not explored in this paper.

The use of GPS or mobile network localisation to obtain

AN locations results in AN’s that are placed outdoors, or

on the periphery of buildings. Also limiting the maximum

location accuracy error to 5 m (typically GPS) will help

remove the creation of unsuitable AN’s.

The AN seen in Table 4 has had its location determined

by an android device with a GPS lock with an accuracy of

\5 m. This AN can then be used along with other ANs to

orientate and position other radio maps, as discussed in

Sect. 6.

Combining the anchor nodes along with the variable

accuracy location data then provides a Variable Accuracy

Radio Map (VARM) derived from android devices

reporting data back to the LocPriS server.

Fig. 8 Screen shots of LocPriS

application

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5.3 Visualising variable accuracy location data

The Android application reports location data with latitude

and longitude values, accompanied by an accuracy value.

This is the degree of accuracy in meters that the android

location service reports the location data is correct to within.

The data collected from the LocPriS application con-

tains this accuracy value, and it is important when calcu-

lating anchor node locations.

The markers in Fig. 10 visualise the location accuracy

estimated by the android application when sending location

data coupled with Wi-Fi data to the LocPriS server.

Again blue represents mobile network location, and red,

GPS estimated locations. The blue locations in this instance

are all large as mobile network localisation is not very

accurate at the Student Village. In a dense city environment

where traditionally GPS is not so accurate but there is

excellent mobile network coverage, the mobile network

location data may be the better option. The red location

estimates are generally much smaller at the Student

Village, with some instances where location using GPS

was given a poor accuracy value.

6 Augmented radio map

With the two independently derived radio maps built, it is

possible to combine them to produce a radio map that

Fig. 9 (Left) Calibration points

via GPS. (Right) Ground truth

from MN roaming (Color figure

online)

Table 4 An anchor node (Node-id 41) with a fixed location

\node id = ’’39’’ label = ’’ful-wap-fg-niche’’/[\node id = ’’41’’ label = ’’uh-aironet-f1-lift’’[\attvalues[\attvalue for = ’’latitude’’ value = ’’51.6100090742111’’/[\attvalue for = ’’longitude’’ value = ’’-3.98159444332123’’/[\/attvalues[\/node[\node id = ’’42’’ label = ’’pre-wap-gnd-1’’/[

Fig. 10 Variable accuracy radio map (VARM) (Color figure online)

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contains enough information on the location of access

points to perform indoor and outdoor localisation, or to

perform further Location Fingerprinting to improve accu-

racy. We call this an Augmented Radio Map (ARM). The

ARM is equivalent to the a priori map used traditionally in

location fingerprinting techniques.

The SMRM contains information useful to indoor and

outdoor localisation, but it lacks orientation and a link to

the real world locations. The smartphone VARM contains

orientation through the use of anchor nodes and helps

improve accuracy through GPS on buildings periphery

AP’s by linking anchor nodes to the real world locations.

By infusing the anchor node information into the

SMRM, it is possible to correctly orientate and position an

augmented radio map that contains information useful to

indoor and outdoor localisations and more importantly, as a

zero-configuration self-mapping radio map for use with

Location Fingerprint techniques.

The known GPS locations of the 204 AP’s at the student

village were used to anchor all nodes in a SMRM map to

their known locations, as seen in Fig. 3. Then a random

node was unanchored one at a time, and the average error

between the nodes location in the Augmented Radio Map

(ARM) and its known location was calculated and plotted

in Fig. 11. This shows the number of anchored nodes

required to maintain a specific level of error (distance in

meters) for the ARM map. While it may be assumed only a

small number of nodes are required to correctly orientate a

map, rarely will the three most ideally placed nodes be

selected as anchors. We also found that although the map

can be considered one graph, subgraphs that naturally

form, can be flipped/twisted even if the graph is correctly

orientated. The twisting and flipping of these subgraphs can

only be resolved with either anchor nodes in the subgraph

or more edges between the subgraph and supergraph.

Figure 11 shows a steady increase in error as nodes are

unanchored. We believe this is a result of the subgraph

flipping/twisting problem being corrected for each subgraph,

as well as the correction to AP locations as a result of

mapping 3D AP locations into a 2D plane. We have observed

that mapping buildings with multiple floors of APs directly

above each other into 2D results in a spread of vertices near

each other, instead of a tower of vertices atop each other. This

is a result of converting the 3D world into a 2D graph where

the edge weights are interpreted as distance between nodes in

the x/y dimension stead of the z-dimension.

The augmented radio map is a map of the AP’s in an

enterprise wireless deployment, both indoors and outdoors,

and will correct itself and improve its accuracy as more

calibration information is feed into the two source maps.

The accuracy of localisation is entirely dependent on the

quality of the two source maps, and any further finger-

printing techniques used. The more anchor nodes generated

by android application users will increase the accuracy of

the anchor nodes placed on the edge of buildings which are

used to orientate and position the self-mapping radio map.

Figure 12 shows the ARM overlaid onto a diagram of

Swansea University student village. The SMRM is posi-

tioned and orientated correctly using some of the anchor

nodes derived from the calibrations points of a user who

had walked around the student village with the application

running and collecting data. The blue dots in Fig. 12 show

the estimated location of AP’s, with the AP’s used as

anchor nodes coloured red.

While there are some obvious errors with the ARM, the

majority of AP’s are positioned atop/near the correct

building. Continuously adding more data to the source

maps should improve the ARM accuracy. The greater the

density of access points inside a building, combined with

sufficient roaming devices producing roaming data for

decreased weights, will also increase the accuracy of the

self-mapping radio map.

-10

10

30

50

70

90

110

1 14 27 40 53 66 79 92 105

118

131

144

157

170

183

196

DIST

ANCE

(MET

ERS)

UNANCHORED NODES

Error (distance ) / Anchors

Fig. 11 UnAnchored nodes/error (distance)

Fig. 12 Augmented radio map at the Student Village (Color figure

online)

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The augmented radio map exists in a real time computed

state in the memory of the LocPriS system. While the computed

SMRM graph data is stored in LocPriS in constantly updating

database tables, as is the VARM data. The LocPriS system then

provides access to the augmented radio map, and rebuilds it as

necessary and as the source radio maps change. LocPriS can

then be references by any fingerprinting systems or even used

directly for location estimation of wireless devices.

7 Conclusion

In this paper we introduce a method to generate a Self-

Mapping Radio Map to be used with location fingerprinting

and indoor localisation that makes use of the LocPriS

system and requires zero-configuration.

We introduce the novel approach of using the roaming

data generated by mobile devices moving between access

points as weights for a self-mapping radio map (SMRM) of

access point locations using undirected weighted graphs.

The best maximum weight in the graph to use was calcu-

lated as 250 s, and the minimum time taken to roam

between AP’s was show to be better than the average.

We then show the use of a smartphone application to

collect location and wireless fingerprints at calibration

points in order to calculate anchor nodes. These anchor

nodes form a variable accuracy radio map (VARM) that

can then be combined with the self-mapping radio map

(SMRM) to orientate and position the SMRM into a aug-

mented radio map (ARM) for use as an a priori with further

location fingerprinting techniques.

Through the use of 1 month’s wireless roaming data

from the Swansea University Student Village, we show how

our technique can produce a self-mapping radio map. Such

a system can not only be used to locate access points and

hence devices in indoor and outdoor locations, but it is self-

correcting and improving, and requires zero configuration.

Further areas of interest which we have not covered in

detail in this paper but which could benefit from deeper

investigation are what influences the best maximum weight

values used, if different scenarios or deployment environ-

ments influence this, and if different graph layout tech-

niques would help improve visualisation and accuracy. The

implications of mapping real world three dimensional data

to a 2D graph/map is also an interesting area. In our sce-

nario at the Student village most buildings contained only

one or two AP’s and floors, but in a scenario where

buildings contain multiple floors of AP’s this will introduce

a new area to the graph layout which warrants further work.

Device roaming behavior between access points proved to

be an interesting topic with some unexpected results. This

value being critical to the graph composition means that the

further testing of what influences devices roaming times

between AP’s would be interesting. We believe some unex-

pected roaming statistics could be the results of some user not

walking on campus but carrying a device on a bike or in a

vehicle. Also some Wi-Fi devices do not roam as expected,

possibly as a result of bad roaming algorithms or hardware.

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html

Gareth Ayres is a Ph.D. student

in the School of Engineering at

Swansea University and also

works as a Wireless Network

Officer for Information Services

and Systems (ISS) at Swansea.

He previously completed an

undergraduate degree at Swan-

sea before working in ISS, and

then completed a master’s

degree in communication sys-

tems before starting his Ph.D.

He now works and researches in

the area of wireless networking

and privacy and enjoys

combining the practical side of his work with ISS with the theoretical

aspect of his Ph.D.

Jason Jones received a B.Sc. in

Computer Science from Swan-

sea University in 1994. He

subsequently moved across to

the College of Engineering and

received his Ph.D. in High Per-

formance Visualisation in 1999.

After that he stayed in Swansea

as a post-doctoral researcher

working on a number of large

European projects involving the

major Aerospace companies in

Europe including BAE Systems,

Airbus and EADS. Dr. Jones

then took up the post of Centre

Manager for a new Virtual Reality Centre in the University for

3 years before finally moving back to the College of Engineering as

their High Performance Computing Research Manager.

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http://developer.android.com/sdk/index.html

http://developer.android.com/sdk/index.html

self-mapping radio maps for location fingerprinting

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