4a-4

2014 International Conference on Indoor Positioning and Indoor Navigation, 27th-30th October 2014

Dynamic Wi-Fi Fingerprinting Indoor PositioningSystem

Omar Costilla-ReyesDepartment of Electrical Engineering

College of EngineeringUniversity of North Texas

Denton, Texas, USAEmail: [email protected]

Kamesh NamuduriDepartment of Electrical Engineering

College of EngineeringUniversity of North Texas

Denton, Texas, USAEmail: [email protected]

AbstractIn this paper, a technique is proposed to improvethe accuracy of indoor positioning systems based on Wi-Firadio-frequency signals by using dynamic access points andfingerprints (DAFs). Moreover, an indoor position system thatrelies solely in DAFs is proposed. The walking pattern of indoorusers is classified as dynamic or static for indoor positioningpurposes. We demonstrate that the performance of a conventionalindoor positioning system that uses static fingerprints can beenhanced by considering dynamic fingerprints and access points.The accuracy of the system is evaluated using four positioningalgorithms and one access point selection strategy. The systemfacilitates the location of people where there is no wireless localarea network (WLAN) infrastructure deployed or where theWLAN infrastructure has been drastically affected, for exampleby natural disasters. The system can be used for search andrescue operations and for expanding the coverage of an indoorpositioning system.

KeywordsDynamic Wi-Fi fingerprinting, Indoor PositioningSystem, Signal Strength Based Indoor Positioning.

I. INTRODUCTION

Global positioning systems (GPS) use satellites that orbitthe earth to calculate outdoor location. Since its inception in1973, GPS has been widely used, commercially-speaking, formany applications ranging from military to commercial. Allindustrialized and technologically developed societies dependon GPS.

Since GPS requires a direct line of sight between thesatellites and a mobile device to correctly receive the signal,there has to be no obstruction between such parties for thistechnology to function correctly. As a consequence of thislimitation, it is not plausible to use GPS signals to localizesomeone inside a building. For this reason alternatives have tobe developed to effectively locate someone indoors.

From finding a shop in a large mall to finding a gateat an airport, indoor positioning systems (IPS) have proventheir importance. Several indoor localization technologies andtechniques have been proposed in recent years to solve thisproblem. None of those technologies have become ubiquitous.The Institute of Electrical and Electronics Engineers (IEEE)have not yet released specifications regarding indoor technolo-gies standards since, as of today, a solution does not exist thatcan solve this problem perfectly.

The technologies that can be used to solve the indoorlocalization problem, which ranges from using the wirelesslocal area network (WLAN) infrastructure to using ultrawideband (UWB) technology, have their own advantages anddisadvantages, which explains the lack of a clear winner at thispoint.

A. Motivation

Even though a standard for indoor positioning systemsis not available, as of today the most widely used indoorpositioning system technology is based on using the WLANinfrastructure deployed in buildings. The main reason behindthis trend is because of its low cost and extensive use. Theoriginal intention of the WLAN infrastructure is to provideinternet access to users within the building.

The work presented in this paper is based on WLANpositioning systems and the use of smartphones to improvethose systems. Smartphones have become increasingly popularin the last few years surpassing desktop computers in numberof sales [1].

The main contribution of this work is based on the ideaof taking advantage of the Wi-Fi hotspot feature embedded inmost smartphones. The hotspot feature is being used as an APthat create temporary access points from personnel who areinside buildings. Indoor positioning systems that have beendeveloped so far shortfall on taking advantage of this Wi-Fi feature. The approach of this work contributes by yieldingaccuracy superior to that of current WLAN indoor systems.

Improvement is needed in terms of accuracy for indoorpositioning systems currently based on WLAN infrastructure.There are many indoor positioning systems that exist usinga wide variety of technologies and there are advantages anddisadvantages for each of those systems. The aim of thisresearch is to show that the novel WLAN-based indoor posi-tioning system presented in this work provides better accuracycompared to existing systems.

B. Organization

This paper is organized as follows: Section II presents aliterature survey regarding techniques and algorithms for Wi-Fiindoor positioning systems, Section III presents the descriptionand design of the dynamic Wi-Fi Fingerprinting IPS, section


Fig. 1: Indoor positioning using Wi-Fi fingerprinting

IV presents the indoor user movement recognition method usedfor indoor positioning, section V presents the experimentalresults. The paper concludes in section VI presenting theconclusions and the future work.

II. RELATED WORK

This section emphasizes the concepts and algorithms ofthe the Wi-Fi fingerprinting technique for indoor positioning-based systems as it is the approach being used for the systemdeveloped in this work. The approach is presented in Figure 1[2]. Wi-Fi fingerprinting, a scene analysis technique, has beenshown to be a reliable way to localize people indoors since ituses infrastructure already deployed indoors.

A. Obtaining Indoor Position from Fingerprints

Wi-Fi fingerprinting associates a unique location inside abuilding to a fingerprint that gives that location a specificidentifier. The fingerprint is usually a feature of a signal inthe indoor environment.

The received signals at the mobile device emitted by oneor several transmitters can be used to infer the location of theuser. The location can be computed locally or remotely. Toobtain the position of a mobile device, a match needs to beperformed between the signal being read at the mobile devicein real time and those signals previously saved in a database.

B. RSSI for Fingerprinting

Any type of signal that can help differentiate a locationinside a building can be used as a fingerprint. For this work,the received signal strength obtained from nearby Wi-Fi accesspoints is used to characterize the fingerprint. The RSSI in noisefree environments can be modeled with the following equation:

RSSI = P R 10log10d (1)P is the transmitted power, is the path loss exponent whichfalls linearly and R is a constant that depends on the conditionsof the environment [3]. Due to noise in the environment, thisequation cannot be used for trilateration localization purposes.The RSSI from multiple access points can be employed toinfer the localization of the mobile device, which is the coreidea of the fingerprinting method.

Single samples taken from the RSSI received from nearbyaccess points are not sufficient to characterize a fingerprint. It

is necessary to obtain an average of the readings to successfullyidentify a fingerprint. The collection of access point averagereadings at one location is what characterizes one fingerprintlocation.

C. Derivation of Position from RSSI Fingerprints

To obtain the localization of a user indoors a matchingbetween the fingerprints from a training set and the fingerprintsbeing read in real time on the mobile device needs to beperformed. This process is called offline and online phases,respectively.

1) Offline Phase: During this phase, a survey of the indoorarea where the indoor positioning system is going to be de-ployed is obtained to create a training set of offline fingerprints.Each fingerprint contains a set of averages values from thenearby access points that characterize that location.

2) Online Phase: During this phase, the mobile device iswithin the indoor positioning system coverage. At the begin-ning the position of the mobile device is currently unknown. Tocalculate the position, the device reads the RSSI measurementsfrom the near access points and creates a vector with theaverage of these readings; then it compares the values obtainedwith the ones saved on the offline survey using a positioningalgorithm, the algorithm returns the approximated location.The process of obtaining a location via a positioning algorithmare explained in the following section.

D. Indoor Positioning Algorithms

Once data is captured during the offline phase, an algorithmcapable of processing the data to approximate the true locationof the user is needed; this is a very important aspect of anindoor positioning system, since according to the algorithmchosen, the performance of the positioning system will beaffected substantially.

For WLAN-based indoor positioning systems there are 2type of algorithms to infer the location of the user given thedata obtained by the positioning technology, the deterministicand probabilistic approaches.

1) Deterministic Algorithms: A fundamental property of adeterministic algorithm is that by giving the same set of inputsignals the output of the algorithm will always be the same.

In order to obtain the location of a user using a deter-ministic approach, the Euclidean distances between the offlinefingerprints and the online fingerprint needs to be obtained.

Assuming N offline fingerprints, the Euclidean distance(D) between the ith measured online fingerprint fi and the ithoffline fingerprint can be calculated as:

D =

Ni=1

|ri fi|2 (2)

This distance must be calculated between the online fin-gerprint and all the existing offline fingerprints, the smallestdistance is used to infer which offline fingerprint is selected toinfer the location of the user, as a consequence, the coordinatesof the selected offline fingerprint determine the location of theuser.


Bahl et al. [4] proposed the first indoor positioning systembased on Wi-Fi fingerprinting. They used a deterministicapproach for their proposed system.

2) Probabilistic Algorithms: Roos et al. [5] were the first topurpose a probabilistic-based algorithm for Wi-Fi fingerprint-ing. They estimate the likelihood of a fingerprint distributionto obtain the approximated location of the user.

Given a vector of locations v of fingerprints and a signalvector s, the element from v selected is the one obtained from:

if P (vi|s) > P (vj |s) for i, j = 1, 2, 3, ..., n, j 6= i (3)

P (vi|s) denotes the probability that the user is located atposition vi given the online fingerprint s.

p(vi) denotes the probability that the user is in locationvi. The selection of the fingerprint is based on posterioriprobability.

If Bayes rule is used to solve this problem, and assuming anequal probability between locations expressed as: P (vi|s) =P (vj |s) for i, j = 1, 2, 3, ..., n The following decision rulebased on the likelihood that P (s|vi) is the probability that thesignal s is received. P (vi) given that the user is located atlocation vi.

The estimation of the location vi can be obtained asfollows:

P (vi|s) = P (S|vi)P (vi)P (S)

(4)

If considering that P (S) is constant for all v the previousequation can be rewritten as:

P (Vi|S) = P (S|vi)P (Vi) (5)

The estimated location v is the one that attains the maxi-mum probability when

v = arg maxvi

[P (vi|s)] = arg maxvi

[P (S|vi)P (vi)] (6)

E. WLAN Access Point Selection Strategies

Each access point (AP) available in the environment hasits own contribution to the positioning system, there are APsthat help in the performance of the system and there are APsthat decrease the performance of the system. Discarding theAPs that decrease the performance is needed, it is desirable toinclude only the APs that improves the performance.

A variety of access point selection strategies have been ex-tensively studied in the existing WLAN fingerprinting locationliterature [6] [7] [8].

Youseff et al. [6] propose a joint probabilistic techniquefor indoor positioning; and presents an AP selection strategycalled MaxMean, were a few access points from all thatare available in the environment with the strongest RSSI areselected for positioning. Chen et al. [7] presents an access pointselection strategy called InfoGain based on selecting theAPs with the highest discriminating power. The discriminativepower of the ith AP is obtained as the reduction of entropyas described in the following equation:

infoGain(APi) = H(G)H(G|APi) (7)

An AP with high discriminative power helps in efficientlydifferentiate fingerprints from one another. Chen et al. alsointroduces a random access point selection strategy that isindependent from the signal strength called RandMean.

The MaxMean approach is used in this work as accesspoint selection strategies with the aim of increasing the accu-racy of the system.

III. DYNAMIC WI-FI FINGERPRINTING INDOORPOSITIONING SYSTEM

A. Introduction

It is commonplace that people who perform daily activitiesindoors stay at the same place, for example, students in aclassroom, professors at their offices, receptionists in a lobbyarea and so on. Moreover, they usually frequent the sameplaces while indoors, that is, students going to the sameclassroom and professors having a meeting in the same roomat a specific time of the day. The work presented in this paperaims to take advantage of those observations and patterns tocreate an IPS that relies solely on Wi-Fi hotspot signals fromdevices carried by occupants and also to improve the accuracyof existing IPS based on Wi-Fi fingerprinting.

The adoption of smartphones has grown exponentiallyall over the world [1], due to their extensive functionalityand decline of price. The level of adoption allows for theinference that a large number of people who stay indoors usesmartphones. In this text the term passive users refer to peoplethat perform their daily routine activites indoors.

Most smartphones have a Wi-Fi hotspot feature that allowssharing the smartphones internet connectivity with nearbydevices via Wi-Fi; in this scenario, the smartphone behavesas an AP. Unlocked smarphones as the Nexus 5, that haveinstalled the Android original firmware developed by Googlecan activate the Wi-Fi hotspot feature without having a Wi-Fihotspot plan with the phones carrier. In these scenarios theuser is asked to activate the service once a request to connectto the internet is being made from a device using the Wi-Fihotspot feature.

B. Dynamic Access Points and Fingerprints (DAF)

At every place where passive users become stationary, theycreate a dynamic fingerprint. The dynamic fingerprint containa set of access points already available in the infrastructureand a set of dynamic access points, which are created fromthe signals coming from other passive users when they are ina stationary state with smartphone being used as an AP. DAFsare modified in real time according to the dynamics of the IPS.

1) Creation of DAFs: The sum of fixed Wi-Fi access pointsand the temporary access points created by stationary usersconstitute a dynamic fingerprint. The term dynamic is used aschange is being made to the system every time a stationary useris added or removed, which depends on the movement patternof the stationary users. Each dynamic fingerprint contains a setof dynamic access points that are added to, or removed fromthe fingerprint according to the moving pattern of the stationaryuser. A dynamic access point is associated to the availabilityof the Wi-Fi signal coming from a passive user. The dynamicfingerprints have the particularity that they are only created at


location where passive users are static; those locations can beinferred by the analysis of the movement patterns of the users.

2) update of DAFs: The dynamic fingerprints are updatedwhen a user is added into the system and considering thechange of state of the stationary users.

A record of fingerprints and accelerometer data can beexpressed as Rt = f(F,Ac) where F represents the Wi-Fisignal fingerprint and Ac represents the recorded accelerometerdata. if n access points are available in the building, then thefingerprint can be represented as F = [f1, f2, ..., fn] where fidenotes the RSS value of the ith access point.

In order to categorize the motion of the user as dynamicor static the accelerometer readings Ac was saved over timefor further analysis.

The training data for our system is the initial collection ofdata containing the dynamic fingerprints associated with thestatic and dynamic accelerometer data, after several samplesare obtained from stationary users.

C. Improvement of the Performance of an IPS

The performance of the proposed dynamic IPS was eval-uated in terms of accuracy as the dynamics of the systemchanges, compared with the standard Wi-Fi fingerprintingpositioning system and when more users contribute to the IPS.

The accuracy of the system was evaluated using an accesspoint selection strategy and indoor positioning algorithmswhich include probabilistic and non probabilistic approaches.The testing was performed at the University of North Texas(UNT) Discovery Park computer science and engineering(CSE) department. The results in terms of accuracy are pre-sented in this paper.

D. Configurations of the Proposed IPS

There are many buildings were there is no WLAN in-frastructure or where not many fixed Wi-Fi access points aredeployed; in this case, an IPS can be created with the approachpresented in this work. two configurations are presented usingthis approach.

1) No WLAN infrastructure deployed and passive usersavailable: An ad-hoc wireless network can be created fromonly Wi-Fi hotspots when there is no Wi-Fi infrastructuredeployed. This particular network is presented in Figure 2.Nodes from N1 to N22 represent a static stationary user sharingthe mobile Wi-Fi hotspot connectivity from his phone. Everynode can detect the signal of every other node when all thenodes become static.

In order to determine when a user is stationary or dynamic,machine learning algorithm were used for prediction.

The nodes have only 2 states:

Moving Node (deactivated)When the nodes start moving they are not beingconsidered in the indoor positioning system.

Static Node (activated)When a node is static its received signal strength isconsidered to help localize other nodes.

N1

N3

N4

N2

N12

N6

N7

N8

N9

N13N11 N14

N15

N16

N17

N18N19N20N21N22

N23

N24

N25

N26

Fig. 2: Ad-hoc wireless network consisting of Wi-Fi hotspotnodes

Source node MAC address RSSI

N2 00:24:6c:c1:c1:80 -53N3 00:1a:1e:85:a4:11 -67N4 00:1a:1e:87:04:c2 -67N5 00:1a:1e:85:a4:02 -60

TABLE I: Pair of mac address and RSSI from four nodes atnode one

Table I is an example of a fingerprint created at N1 whensignals from 4 other nodes are available (static). This caseimplies that the rest of the nodes are in a moving state(dynamic).

Chen et al. [7] reports that at least 3 access points arerequired for a WLAN fingerprinting to function correctly, sofingerprints with fewer than 3 fingerprints were not consideredfor positioning.

All the fingerprints combinations of at least 3 other nodesare calculated at the server, the server coordinates the contentof the dynamic fingerprints according to the movement patternof the users.

2) WLAN infrastructure and stationary users available:Figure 3 shows a combination of fixed Wi-Fi nodes with Wi-Fihotspot nodes. The white color represents Wi-Fi hotspot nodesand black color represents fixed Wi-Fi nodes.

This configuration has the following types of fingerprints:

Fixed Wi-Fi fingerprintsFingerprints created solely from fixed Wi-Fi nodes aredeployed as a backup system when no Wi-Fi hotspot

N1

N3

N4

N2

N12

N6

N7

N8

N9

N13N11 N14

N15

N16

N17

N18N19N20N21N22

N23

N24

N25

N26

Fig. 3: Combination of fixed Wi-Fi nodes with Wi-Fi hotspotnodes


nodes are available. This case exemplifies a standardnon-dynamic fingerprinting indoor positioning system.

Wi-Fi hotspot fingerprintsThe fingerprints created from signals coming fromWi-Fi hotspots nodes from stationary users containfixed Wi-Fi access points as well, which are alreadydeployed in the infrastructure. These types of finger-prints have the particularity to be updated in real time,as users move. This configuration require the creationof all possible combinations of dynamic fingerprintsavailable.

E. Android Mobile Application for the Online and OfflinePhase

For this work an online, offline andriod aplications and aserver were developed using the Java programming language.Part of the source code is based on the open-source platformAirplace [12]. Android was selected as the mobile platformfor the IPS since it provides more hardware manipulation thaniOS; also Android devices are used more extensively in theworld than iOS devices.

1) Offline android app: The offline android app generates aradiomap which contains a set of fingerprints. The fingerprintscan contain fixed Wi-Fi access points and dynamic accesspoints. The fingerprints can be created via crowdsourcing(several users) or by a single user. In the case of crowd-sourcing, the server appends and combines all the fingerprintscreated by several users to create the final radiomap that isused for positioning. The users can select how many samplesper fingerprint they want to collect and the time betweensamples can also be adjusted. The users then must upload thecollected fingerprints to a main server for further analysis anddistribution.

2) Online android app: Using the online android app, theuser has to connect to a main server and retrieve the stored fin-gerprints that were created in the offline phase; also, specific-algorithm parameters that yield the lowest calculated accuracyfrom a testing data set are used for real time positioning usingfour possible indoor positioning algorithms.

The user can select the algorithm used for positioning inreal time. The parameters for the lowest accuracy provided bythe server cannot be modified.

3) Server: The server, also developed in Java, receives thedynamic and non-dynamic fingerprints captured by the users.The server processes the raw fingerprint data by calculatingthe mean of the fingerprints obtained at the same locationand then it calculates the algorithm-specific parameters for theprobabilistic and deterministic approaches; depending on thevalue of those parameters the accuracy of the system changeas it is presented in the results section. The parameters withthe best accuracy for each algorithm is returned to the userfor real-time positioning. The parameters selected cannot bechanged by the online application.

F. Calculation of Accuracy

The accuracy is measured by obtaining an average posi-tioning error expressed in meters. Since it is unfeasible and

impractical to obtain the average positioning error empirically,a large set of test data is used for this purpose.

The error is obtained by calculating the error of a large setof test data, the test data contain a collection of fingerprintsassociated to a location; each test data fingerprint is processedby a positioning algorithm. The obtained estimated location iscompared with the real location by calculating the Euclideandistance between the real and the estimated positions to obtainthe deviation between the original and the estimated value.

The average positioning error relies on the true and esti-mated location to calculate a value that correctly expresses theaccuracy of the system.

The process to obtain the average positioning error is bythe summation of positioning errors obtained per location anddivided by the total number of locations, as it can be expressedin the following equation:

APE =

Ni=1

PEi

NPm (8)

APE represents the average positioning error expressed inmeters, PEi represents the positioning error of the ith locationexpressed in meters and NP is the total number of positions.

The position error is calculated as the euclidean distancebetween (xi, yi), which is the real location and (xj , yj) whichis the approximated location. The PE is obtained as:

PE :

(xi xj)2 + (yi yj)2 (9)

G. Algorithms Used for Positioning

In this subsection, the characteristics of the positioningalgorithms used for the dynamic Wi-Fi IPS are presented.

To evaluate the results 4 algorithms are used for posi-tioning. Two deterministic, namely the K-Nearest Neighbor(KNN) [4] and the weighted K-Nearest Neighbor (WKNN)[13] algorithms and two probabilistic namely the maximuma posteriori (MAP) [14] and the minimum mean square error(MMSE) [5] algorithms.

Those algorithms were selected since they exemplify thedeterministic and probabilistic approaches in a weighted andnon-weighted manner.

1) K-Nearest Neighbor and Weighted Nearest NeighborAlgorithms: The algorithm considers N offline fingerprints f,the online fingerprint vector is represented by r, and a vectorof approximated locations l can be obtained by calculating theeuclidean distance between each element i of the online andoffline fingerprints.

Calculating the inverse of the euclidean distance the weightw of each approximated location can be obtained as:

wi =1

|fi ri| (10)

The approximated location l can be obtained as:

l =

Ki=1

wiKj=1

wj

li (11)


The approximated location l is ordered according to in-creasing the distance between the offline and online finger-prints |fi ri|

For the K-Nearest Neighbor algorithm:

K 1 and w is expressed as wi = 1k

(12)

For the weighted K-Nearest Neighbor algorithm:

K 1 and w is expressed as wi = 1|fi ri| (13)

2) Variation of Parameters for Deterministic Algorithms:The value of K neighbors is varied from 1 to 15 at theserver; then the K that returns the less positioning error isselected, and the value changes according to the estimatedpositioning values obtained by the deterministic algorithmsfrom the testing data.

3) Maximum A Posteriori and Minimum Mean SquareError Algorithms: The probabilistic approach is based oncalculating the probability of location l given the signal s as:

P (li|s) = P (S|li)P (li)P (S)

=P (S|li)P (li)l

i=1

P (S|li)P (li)(14)

The MAP algorithm obtains the estimated location l as:

l = arg maxli

[P (s|li)p(li)] (15)

The MSSE algorithm obtains the estimated location l as:

l = E(l|s) =l

i=1

lip(li|s) (16)

4) Variation of Parameters for Probabilistic Algorithms:For each location of the radiomap a probability or likelihoodof the user being at that specific location is assigned accordingto the similarity between the online and offline fingerprints.

The probability P is obtained using the following equation:

P =

ni=1

e

(v1i v2i )22 (17)

where v1i and v2i are the ith values from the RSSI of the

radiomap (offline fingerprint) and the RSSI values being ob-served (online fingerprint), respectively.

For the experiments performed in this paper, the equation(17) is used to vary the values of the parameter from 1 to15. The that returns the less positioning error is selected.The value of changes according to the estimated positioningvalues obtained by the probabilistic algorithms. The testingdata is used to determine which parameter returns the fewesterrors for the positioning algorithm.

5) Importance of the variation of Parameters in the De-terministic and Probabilistic Algorithms: Shin et al. [15] pro-posed the enhanced weighted K-Nearest Neighbor algorithmthat improves accuracy of an indoor positioning system byvarying the number of K neighbors considered for positioningin real time, during the online phase of the system.

The premise for studying indoor positioning systems with avarying K is that there should only be considered K neighborsthat are at a small distance to the location of the user. Thenumber of nearest neighbors relevant for positioning dependon the type of indoor area. For example in a corridor there arefewer nearest neighbors relevant for positioning than the onesavailable in an open area.

In the case of the probabilistic algorithms the ith probabil-ity of a location li given the signal s changes according to thevalue of the parameter . The optimal value of also dependsin the area used for positioning.

The accuracy results presented, depend on the change of theK for the deterministic algorithms and for the probabilisticalgorithms. The accuracy results are expressed as an averagepositioning error.

6) Performance of Deterministic and Probabilistic Algo-rithms: According to the literature [16], The probabilistic al-gorithms perform better than the deterministic algorithms. Forthis work, the type of algorithm is not the only characteristicthat affects the performance of the algorithms, but also theparameters k for the deterministic and for the probabilisticapproaches.

H. Applications of the proposed dynamic IPS

1) Natural disasters: The WLAN infrastructure in build-ings can severally be affected as a result of a natural disaster; inthose situations, the approach presented in this work could bethe only available option for indoor positioning. The positionsystem can help a rescue team find people trapped indoors.The rescue team will be able to use an existing dynamicpositioning system if the trapped users indoors are able toshare their mobile Wi-Fi hotspot connectivity when the Wi-Fiinfrastructure is damaged.

2) Increase the coverage of an existing IPS: The coverageof an already deployed IPS can be expanded if the approachpresented in this paper is considered in the case when creatingWi-Fi hotspot nodes where the WLAN infrastructure is notavailable.

IV. INDOOR USER MOVEMENT RECOGNITION

Figure 4 shows the movement pattern of 3 users indoorsthat are represented with 3 different colors (red, green andblue). The circles represent when users are moving and thesquares represent when the users are static.

A. Stationary user is static

The Wi-Fi hotspot feature is only activated when the useris at a static position, This allows for having a constant Wi-Fisignal strength received at the fingerprints to characterize themcorrectly.


B. Stationary user is moving

When the user starts moving between places, the AP isswitched off and the access point signal is removed at thefingerprint, since variation on the received signal strength isnot useful for positioning.

C. Step Detection Using the Accelerometer

In order to infer when a user is moving or not moving andto eliminate small changes in the step detection, Jimenez et al.[9] demonstrate a robust approach to step detection:

First, the magnitude of the acceleration ai for every ob-tained acceleration sample i is expressed as:

ai =a2xi + a

2yi + a

2zi (18)

The local acceleration variance is obtained to improve stepdetection and to remove gravity (noise):

2ai =1

2w + 1

i+wj=iw

(aj aj)2 (19)

where aj is a local mean acceleration value, obtained by

aj =1

2w+1

i+wq=iw

aq , and w defines the size of the averaging

window. for detecting the swing phase and stance phase athreshold is expressed as:

A1i =

{Threshold 1, ai>Threshold 10, otherwise

(20)

for the swing phase, and as:

A2i = Threshold 2, if ai


V. EXPERIMENTAL RESULTS

A. Dynamic WI-FI Fingerprinting System at UNTs CSE De-partment Using MaxMean

In this experiment a dynamic Wi-Fi fingerprinting IPSdeployed at the CSE department of UNT is analyzed.

1) Selected Building: The selected buildings to test thesystem is the CSE building located at UNTs Discovery Park.The selected area is shown in Figure 6.

(a) Offline fingerprints (b) Testing fingerprints

Fig. 6: Offline and testing fingerprints at the CSE department

The dimensions of the selected area are 35.97 meters wideby 33.53 high. The dimension are considered in the offline andonline android apps to calculate the average positioning error.10 access points are selected from all the fixed Wi-Fi accesspoints available using the MaxMean access point selectionstrategy. For this experiment 10 dynamic access points fromvolunteers were available for positioning. As explained insection III, 4 algorithms are used to evaluate the accuracyof the system: 2 deterministic (KNN and WKNN) and 2probabilistic (MAP and MMSE).

2) Location of the DAF: In Figure 7 are shown the locationof the 10 DAF at the CSE department.

Fig. 7: Location of DAF at the CSE department

3) Results of the IPS from the Original (Non-Dynamic)System to Adding 10 DAF (Dynamic): In this subsection theresults from the non-dynamic system (original) to the addition

of 10 DAF into the system are presented in Figure 8 toFigure 10.

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Fig. 8: Average positioning error of the non-dynamic IPS

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Fig. 9: Average positioning error considering 2 DAF

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Fig. 10: Average positioning error considering 5 DAF

4) Summary: The best accuracy per experiment, from theoriginal IPS experiment to adding 10 DAF, are presented inFigure 11. The best accuracy is plotted for each trial; also theparameter that yielded the best accuracy is shown.

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OriginalMMSE = 10

DAF 1MMSE = 10

DAF 2MMSE = 10

DAF 3MMSE = 10

DAF 4MMSE = 10

DAF 5MMSE = 10

DAF 6MMSE = 10

DAF 7MMSE = 10

DAF 8MMSE = 10

DAF 9MMSE = 10

DAF 10MMSE = 10

Ave

rage

po

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ing

err

or

(m)

Fig. 11: Best estimate of location in all experiments and thecorresponding parameters

As it can be observed in Figure 11, the best accuracy forthe IPS is obtained when 5 DAF are considered; consideringmore than 5 access points decrease the accuracy of the system.The MMSE at parameter 10 performed better in all the trialswhen compared with other algorithms as expected.


B. Dynamic WI-FI Fingerprinting Consisting of DAF only atUNTs CSE Department

In this experiment, the results of the dynamic IPS consist-ing only of dynamic access points and fingerprints are analyzedat the CSE department shown in Figure 6. The results areshown in Figure 12 and Figure 13. The availability of theDAF is varied from 1 to 10 in a step of 1 DAF. The locationof the DAFs is the same as in the previous experiment.

12

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

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Ave

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Fig. 12: Average positioning error of the dynamic IPS consid-ering 2 DAF

0

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

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Fig. 13: Average positioning error of the dynamic IPS consid-ering 10 DAF

1) Summary: In Figure 14 can be observed the best resultsfor all the experiments considering all parameters and allalgorithms, the results show only the parameters that yieldedthe best accuracy per experiment; the accuracy is increased asmore DAFs were considered into the system.

0

2

4

6

8

10

12

14

DAF 2MMSE = 2

DAF 3MMSE = 2

DAF 4MMSE = 2

DAF 5MMSE = 2

DAF 6MMSE = 2

DAF 7MMSE = 2

DAF 8MMSE = 2

DAF 9MMSE = 2

DAF 10MMSE = 2

Ave

rage

Po

stio

nin

g Er

ror

(m)

Fig. 14: Best estimate of location in all experiments and thecorresponding parameters considering DAF only

The results presented in Figure 14 show that when con-sidering a system solely of DAF the accuracy is better as thenumber of DAF increase. In the experiment performed for thispaper the DAF was varied from 1 to 10, the algorithm thatperformed better was the MMSE with parameter 2.

The original IPS at the CSE department yields an APE of3.4 meters. When adding DAF into the original system the

accuracy of the system is increased, with a minimum APE of2.4 meters for 5 DAF. When considering more than 5 DAFthe accuracy of the system decreases.

When considering a DAF only IPS the APE varies from13 meters to 4.4 meters for 2 and 10 DAF respectively. Theresults show that when adding more DAF into the system theAPE decreases.

VI. CONCLUSIONS AND FUTURE WORK

In this paper, a novel approach to improve accuracy of anindoor positioning system based on Wi-Fi fingerprinting waspresented. The results of this work also show improvement foran indoor positioning system. The approach takes advantage ofthe AP option in most smartphones to create dynamic accesspoints and fingerprints. The accelerometer embedded in mostsmartphones was used to predict the movement patterns of theusers as static or dynamic using machine learning algorithms.

2 deterministic and 2 probabilistic algorithms were used tocalculate the position of the users while indoors at the CSEdepartment of the Discovery Park building at the Universityof North Texas. For each algorithm a set of parameters wheretested and the best parameter that returned the highest accuracywas the one used for real time indoor positioning.

In this paper, the MaxMean access point selection strate-gies, was used to select a set of access points from all theavailable for the purpose of improving indoor positioning.

Our experiments show that a limit of 5 DAF are needed toobtain improvement of an existing indoor positioning system.The accuracy was decreased when considering more than 5DAFs. Since the availability of the dynamic fingerprints andaccess points change over time, as users are added or removedto the system, there exists an high probability that at leasthalf of the total number of DAFs are available for positioning,which are the number of DAFs needed to maximize theaccuracy of the system.

In the case of an indoor positioning system consistingsolely of DAFs; The more the DAFs were considered, thebetter the accuracy that was obtained for the system. Theresults show that when adding more DAF into the indoorpositioning system the APE decreases which is better in termsof accuracy.

As future work, the inclusion of other ambient signals canbe studied to increase the efficiency when characterizing thefingerprints to decrease the chances to provide an incorrectlocation indoors. Example of those signals can be the earthmagnetic fields or the frequency modulated signals, which areavailable in the environment. It is important to mention thatwhen considering more ambient signals a better accuracy isachieved, but this comes with a cost in terms of increasing thepower consumption of the mobile devices and other factorsused for indoor positioning, since the complexity of the systemincreases.

The fusion of the dynamic Wi-Fi indoor positioning systemwith other technologies can be studied, for example BluetoothBLE, RFID tags or UWB sensors, in order to increase theperformance of the positioning system. The selection of thetechnology should be made wisely according to the needs of


the user, since each technology has its own advantages anddisadvantages.

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