research article scalability optimization of seamless

Research ArticleScalability Optimization of Seamless Positioning Service

Juraj Machaj, Peter Brida, and Jozef Benikovsky

Department of Telecommunications and Multimedia, FEE, University of Zilina, 010 26 Zilina, Slovakia

Correspondence should be addressed to Juraj Machaj; [email protected]

Received 1 December 2015; Revised 13 April 2016; Accepted 27 April 2016

Academic Editor: Daniele Riboni

Copyright © 2016 Juraj Machaj et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Recently positioning services are gettingmore attention not only within research community but also from service providers. Fromthe service providers point of view positioning service that will be able to work seamlessly in all environments, for example, indoor,dense urban, and rural, has a huge potential to open new markets. However, such system does not only need to provide accurateposition estimates but have to be scalable and resistant to fake positioning requests. In the previous works we have proposed amodular system, which is able to provide seamless positioning in various environments. The system automatically selects optimalpositioningmodule based on available radio signals.The system currently consists of three positioningmodules—GPS, GSM basedpositioning, and Wi-Fi based positioning. In this paper we will propose algorithm which will reduce time needed for positionestimation and thus allow higher scalability of the modular system and thus allow providing positioning services to higher amountof users. Such improvement is extremely important, for real world application where large number of users will require positionestimates, since positioning error is affected by response time of the positioning server.

1. Introduction

In past few years, positioning of mobile devices becameimportant topic not only for researchers but also for serviceproviders. Service providers can benefit mainly from novelindoor positioning systems and systems that can estimateposition of users seamlessly in various environments. Mostof these systems are, however, still only in development andtesting stage [1]. Nowadays almost all mobile devices haveintegrated at least one Global Navigation Satellite System(GNSS) receiver, most commonly Global Positioning System(GPS). GNSSs can be used mainly in the outdoor environ-ment to estimate position of mobile user. Nevertheless, accu-racy of GNSS based positioning is not always high enoughto provide reliable position estimates for Location BasedServices [2]. Positioning error of user-grade GPS receiver caneasily be higher than 100m in urban environmentmainly dueto signal blockage and multipath propagation. Therefore, itis important to introduce backup solutions based on othertechnologies even for the outdoor environment.

On the other hand, positioning in indoor environmentis even more demanding on accuracy and GNSS receivers

are commonly not able to estimate position in these envi-ronments. Currently there has been a lot of different systemsproposed to solve indoor positioning problem, based onradio networks [3–6], inertial measurements [7, 8], imageprocessing [9], or magnetic measurements [10].

Indoor positioning based on inertial measurementsseems to be quite promising; however, Inertial MeasurementUnit (IMU) sensors integrated in the mobile devices sufferfrom higher errors. This is caused by the fact that integratedIMUs are of low cost and thus low quality. It is also widelyknown thatwith inertial positioning the accuracy of estimatesdecreases due to integration of noisy measurements overthe time [8]. This may lead to extremely inaccurate positionestimations.

Positioning using magnetometer measurements can pro-vide accurate results but requires calibration measurementsperformed in the positioning area in various directions,since magnetic field is orientation dependent [11]. Anotherproblem is processing of themeasurements, since inmagneticbased localization static measurements cannot be used anddifferences in movement speeds of various users have to beeliminated [12]. On top of that, processing huge database of

Hindawi Publishing CorporationMobile Information SystemsVolume 2016, Article ID 9714080, 11 pageshttp://dx.doi.org/10.1155/2016/9714080

2 Mobile Information Systems

RSS 1RSS 2RSS 3

RSS N

Vector of signalcharacteristics

Radiomap area

RP1

RP2

RP3

RPK

.

.

.

Figure 1: Radiomap principle.

measurements is currently problematic, since it can introducedelays in position estimations [13].

Results achieved by the optical positioning systemsare quite interesting; however relatively high computationpower is needed for image processing algorithms to estimateposition and implementation of optical tags is commonlyrequired in the area of positioning [14, 15]. Implementationof optical tags may be either problematic or even impossiblein some areas.

On the contrary, wireless networks are widely deployedin both indoor and outdoor environments and therefore canprovide signals that might be used for position estimation.In our paper we decided to use positioning based on GSMand Wi-Fi signals, since most of currently used devices haveimplemented receivers for these networks. Moreover, signalsfrom these networks are almost ubiquitous in both indoorand outdoor environments.

We have previously proposed modular positioning sys-tem [16] that is able to automatically switch between variouspositioningmodules (GNSS,GSM, andWi-Fi) based onqual-ity ofmeasured signals. In the developedmodular positioningsystem the position of mobile device is estimated by local-ization server, in case that GPS signals are not presented orquality of measurements is low.When alternative positioningis required measurements from Wi-Fi and GSM networksare collected and preprocessed in the mobile application andsent to the localization server which estimates position ofmobile device. In this paper we will introduce algorithm thatwill enable deployment of the modular system by reducingresponse times of the positioning server.

In this paper we will propose optimization algorithm,which allows better scalability of the modular positioningsystem and thus allows providing positioning service formore users by reducing complexity of position estimationprocess and response time.This algorithmwill play importantrole in the system, since response time is crucial parameterof positioning service. The main contribution of the paperis proposal and testing of transmitter reduction algorithm.From the results presented in the paper it is clear thatproposed algorithm has positive impact on complexity ofsystem in case that a large number of transmitters aredetected.Moreover, removal of transmitters with low RSS didnot have negative impact on the localization accuracy.

The rest of paper is organized as follows: developedmodular system together with related work is presented inSection 2. Section 3 describes the proposed optimizationalgorithm. Testing scenarios will be presented in Section 4,Section 5 is focused on discussion of the achieved results, andSection 6 will conclude the paper.

2. Related Work and ModularPositioning System

In this section modular positioning system will be describedtogether with principles and algorithms that are alreadyimplemented.The system is based on fingerprinting position-ing, since it is the most common and reliable positioningframework used in both GSM and Wi-Fi networks. Finger-printing seems to significantly outperform other positioningframeworks, especially in environments with Non-Line ofSight (NLoS) and strong multipath propagation. Therefore,we will firstly describe fingerprinting framework.

All fingerprinting based positioning systems require cal-ibration phase in order to create radiomap of area wherepositioning will be performed. Calibration phase, which canbe in some literature referred to as offline phase, representsa necessary step in fingerprinting framework. During thisphase radiomap database is created and stored at the local-ization server.

In principle, localization area can be divided into smallareas, which are defined by a reference point [17]. Duringthe radiomap construction RSS samples are collected at eachreference point. Vector of RSS measurements, that is, finger-print, consists of RSS measurements from all transmitters inthe communication range and can be defined as follows:

𝑆𝑗 = (𝛼1, . . . , 𝛼𝑁𝑗, 𝑐𝑗, 𝜃𝑗) 𝑗 = 1, 2, . . . ,𝑀, (1)

where 𝑁𝑗 is the number of transmitters detected at the jthreference point, M is the number of reference points, 𝛼𝑖 areRSS values, 𝑐𝑗 represent coordinates of jth reference point,and parameter vector 𝜃𝑗 can contain additional information,which may be used during the position estimation. The ideaof radiomap database construction is depicted in Figure 1.

During the online or localization phase position of themobile device is estimated based on measured RSS vector. In

Mobile Information Systems 3

principle, fingerprint is measured by the mobile device andsent to the localization server. Localization server use imple-mented algorithms to compare fingerprint received from themobile device with fingerprints stored in the radiomap.

In our system we have implemented deterministic algo-rithms from Nearest Neighbor family. We decided to useNN family algorithms, as these algorithms can perform atthe same level as statistical algorithms and in some casescan even outperform other approaches [18]. Moreover, theirperformance is not affected by accuracy of statistical modelwhich is important in case when positioning system isassumed to be implemented in heterogeneous environments,that is, outdoor and different indoor environments.

Deterministic algorithms are, basically, based on assump-tion that RSS values at the receiver are not random anddepend on position of mobile device [19]. From this assump-tion it is clear that position of mobile device can be estimatedby finding the highest similarity between the fingerprintreceived from themobile device and fingerprints stored in theradiomap. In such case position estimate is given by

�� =∑𝑀𝑖=1 𝜔𝑖 ⋅ 𝑐𝑖

∑𝑀𝑖=1 𝜔𝑖

, (2)

where 𝜔𝑖 is a nonnegative weighting factor [17]. Weights arecalculated as inverted value of the Euclidean distance betweenfingerprint received from the mobile device and fingerprintsstored in the radiomap database. Since Euclidean distance ismost widely usedmetric inNNalgorithms, it will also be usedin our experiments.

The estimator of formula (2), which keeps the 𝐾 highestweights and sets the others to zero, is called the WKNN(Weighted K-Nearest Neighbors) method [19]. WKNN withall weights 𝜔𝑖 = 1 is called the KNN (K-Nearest Neighbors)method.The simplest method, where𝐾 = 1, is called the NN(Nearest Neighbor) method [20]. In [21] it was found thatWKNN and KNN methods commonly outperform the NNmethod, especially in cases when𝐾 is set to 3 or 4.

The modular localization system was proposed to belogically one level above the structure of standard localizationsystem. This means that all its components have to bedeveloped according to the goals and requirements of thesystem.

The modular localization system [16] can be representedas an integrated set of components that provide localizationservice for its users. The system should be able to providesimultaneous access to the services formultiple users in orderto be commercially interesting. Modular localization systemis developed as centralized and thus all position estimatesare computed at the localization server. The system is fullyautonomous, since all communication is handled by theexisting telecommunication networks and there is no need toimplement new infrastructure.

The idea ofmodular localization systemwas developed onthe assumption that GPS (Global Positioning System), GSM,and Wi-Fi could provide seamless positioning in heteroge-neous environments as can be seen in Figure 2. The systemwas proposed with these positioning modules, because allof required technologies are commonly implemented in

BTS 1 BTS 2

BTS 3

AP 1

AP 2

GPS satellite

Wi-Fi access point

GSM base station

AP 3

Mobile device

Radio link

Figure 2: Example of environment suitable for implementation ofmodular positioning system.

Presentation layer

Application logic layer

Service layerSe

curit

y la

yer

Man

agem

ent l

ayer

Figure 3: Functional layers in the modular localization system.

standard mobile devices. The system is developed as an openplatform and new positioning modules can be implementedaccording to requirements, for example, Bluetooth, Zig-Bee,and so forth.

In principle the modular localization system can bedivided into three components, that is, mobile devices,existing network infrastructure, and localization server, thatcommunicate with each other and have their own respon-sibilities. The system can be divided into individual layerswith different functions. These layers are classified into threelevels. In principle, each layer is allowed to communicate onlywith neighboring layers. However, security and managementlayers have to be presented at all levels and therefore cancommunicate with any of the three layers. All layers in thesystem are shown in Figure 3.


Start

Is GPS available?

Performmeasurements from

GPS, GSM, and Wi-Fi

Is Wi-Fi betterthan GSM?

Estimate positionusing Wi-Fi

End

Is positionestimated?

Is positionestimated?

Position is unknownReturn positionfrom GSM module

Return positionfrom Wi-Fi module

Return positionfrom GPS module

Estimate positionusing GSM

Yes

No

Yes

Yes

Yes

No

No

No

Figure 4: Flowchart of modular localization algorithm.

The highest presentation layer can be described as appli-cation interface. The layer is responsible for user interactionand visualization of the position estimates in various modes.

The core of the modular positioning system can be foundin the application that logic layer contains. This layer isrepresented bymodular localization algorithm (MLA), whichis responsible for handling of localization requests with allnecessary signal information from GPS, GSM, and Wi-Fi. Itis also responsible for selection of the most appropriate plat-form communication with the mobile devices.The algorithmis depicted in flowchart in Figure 4.

The lowest layer, service layer, is represented by thelocalization and optimization algorithms implemented at thelocalization server and is also responsible for communicationbetween localization server and mobile device.

The idea is that the MLA checks GPS availability and, incase that GPS signals are available, it returns the position tothe mobile device from GPS measurements. However, it isobvious that GPS will not be available in all environmentsor due to missing GPS hardware or there will not be enoughvisible satellites. In such case Wi-Fi or GSM measurementswill be used for position estimation. In this step the algorithmcomputes number of transmitters above the minimum mea-surable signal level for both Wi-Fi (𝑁AP) and GSM (𝑁BTS)

networks. If 𝑁AP is higher than or equal to 3, Wi-Fi basedpositioning is selected. The threshold of 3 transmitters (APsor BTSs) was chosen, since we assume that signals fromat least 3 transmitters are needed to clearly define positionin 2D space. Lower amount of transmitters can be usedfor positioning; however, it is assumed that this may havenegative impact on localization accuracy. This assumption isbased on the fact that with less than 3 transmitters numberof reference points with similar RSS fingerprints will beincreased. This may have negative impact on positioningperformance; therefore when less than three transmitterswere detected for a given network type alternative positioningsolution should be used.

In case that GPS signals cannot be used for positioningand bothWi-Fi andGSMmeasurements provide enough datato estimate position, theWi-Fi based positioning is preferred.This is due to the fact that previous experimental results showthat Wi-Fi based positioning can achieve better accuracywhen compared to GSM.

In the service layer algorithms for position estimationare implemented. These algorithms are NN, KNN, andWKNN algorithms together with optimization algorithms.Since modular localization system will cover significantlylarger area compared to traditional fingerprinting based


Select areas with atleast one

transmitter match

Select areas withhighest number of

transmitter matches

Order vectors bydistance to current

fingerprint

Select K-nearestvectors

Calculate positionestimate

End

Start

Get currentfingerprint

Radiomap

Weightfunction

K

Figure 5: Flowchart of the fingerprinting with 2-phase map reduc-tion algorithm.

localization systems, it is crucial to reduce complexity ofposition estimation process. Another important reason forreduction of the complexity of positioning process is abilityto provide positioning service to higher number of users [16].

In the first step the 2-phase map reduction algorithmwas implemented to the system. The idea of the algorithmis to reduce area from which reference points are selectedfrom the radiomap database. Algorithm can be divided intwo phases; in the first phase the relevant areas are foundbased on RSS data from fingerprint measured by the mobiledevice. During the second phase reference points are selectedfrom the relevant areas, in order to reduce complexity ofthe position estimation process. Flowchart of the positioningalgorithm can be seen in Figure 5.

In the algorithm, the weights are computed as invertedvalue of Euclidean distance between measured RSS samplesand RSS samples stored in the radiomap database. During thefirst phase of the algorithm all vectors contain at least one oftransmitters from fingerprint measured by a mobile device.This operation can be easily performed via SQL languageand this initial filtering reduces the radiomap.This radiomapreduction is depicted in Figure 6.

BTS1 BTS2

BTS3

Mobile device

BTS: 1, 2, 3

BTS: 1, 2

BTS: 2

BTS: 2, 3

BTS: 3

BTS: 1, 3

BTS: 1

Figure 6: Phase 1 of map reduction algorithm, areas selected basedon availability of radio signals.

In Figure 6 the situation when mobile device does notdetect signals from BTS 1 is shown. Areas chosen by the firstphase of the algorithm are marked with diagonal pattern. Itcan be seen that in this stage also areas where signals fromBTS1 should be detected were chosen.

In the second stage the map reduction algorithm selectsthe most appropriate areas from the relevant areas chosenduring the first stage, based on comparison of numberof transmitters in the radiomap fingerprints that matchtransmitters detected by the mobile device. In this steponly reference points with the highest number of matchingtransmitters are selected from the radiomap. The output ofthe second stage of the algorithm is shown in Figure 7.

In the algorithm the highest number of matches isselected, so the algorithm can handle power fluctuations thatcan cause themeasured fingerprint from themobile device tocontain more (or less) transmitters than was detected at thereference points during the radiomap construction.

3. Proposal of Optimization Algorithm forReduction of Transmitters

Commonly fingerprint vector components contain variousRSS values from all transmitters in the communicationrange. Such values can achieve any value betweenmeasurableminimum and maximum in particular radio network. It hasbeen proven that in Wi-Fi networks the weakest signalscan have negative impact on achieved positioning accuracy[22]. The main idea of proposed algorithm is to filter outRSS samples that have low or negative impact on accuracyand thus reduce computing power needed by the server tocompare RSS vectors and estimate position of mobile device.

Generally, sensitivity threshold for downlink in GSM900network is −113 dBm [23]; however minimum signal strength


Mobile device

BTS1 BTS2

BTS3

BTS: 1, 2, 3

BTS: 1, 2

BTS: 2

BTS: 2, 3

BTS: 3

BTS: 1, 3

BTS: 1

Figure 7: Phase 2 of map reduction algorithm area selected basedon number of detected transmitters.

detected in Wi-Fi networks may vary on different devices.During our previous experiments, with devices from variousmanufacturers, the minimum detected power levels were−113 dBm for GSM networks and between −100 dBm and−110 dBm for Wi-Fi networks, which proves correctness ofthe theory stated in previous sentence. The experimentswere performed in the anechoic chamber with 3 differentsmartphones, namely, SonyXperia Z3, Samsung S4, andHTCWildfire S. Measurements were focused to detect differencesin receiver sensitivity and characteristics. In the proposedalgorithm for reduction of transmitters we decided to setthe threshold 10 dB above the minimum detected powerlevels, that is, −103 dBm and −90 dBm for GSM and Wi-Fi,respectively. In Wi-Fi networks we have assumed minimumdetected power level to be−100 dBm, since this value has beenreported by all devices we have tested. However, introducingsuch thresholds may cause some fingerprints that are mea-sured far from transmitters to not have sufficient amount ofdata to be used for position estimation.

It is assumed that, similar to trilateration based position-ing, information about RSS from at least𝑁+1 transmitters isneeded in order to estimate position in𝑁-dimensional space.Since our modular positioning system estimates position in2D space, RSS measurements from at least 3 transmittersare needed to estimate the position of mobile device. Thus3 transmitters represent the lowest required amount for thesystem to operate. Obviously, if there are more transmitterswith RSS values higher than the threshold then all of thosewill be used for position estimation, since using higher num-ber of transmitters may have positive impact on localizationaccuracy and enable positioning in 3D environment. Theproposed algorithm has to be as simple as possible sincethe main goal is reduction of the complexity of positionestimation process. The flowchart of the proposed algorithmfor reduction of transmitters can be seen in Figure 8.

In the first step the algorithm removes all RSS measure-ments below the threshold from fingerprint vector. In thesecond step the algorithm checks if reduced vector containsenough transmitters in order to perform positioning. In casethat reduced vector does not contain enough transmittersthe algorithm will choose three strongest transmitters fromthe original fingerprint vector. This step is introduced due tothe assumption that it is still better to estimate position withlower accuracy than to have no position estimate at all. Thesereduced RSS vectors are afterwards used in position estima-tion process. The algorithm process RSS samples from singlenetwork (GSM or Wi-Fi) are based on selected localizationmodule. Currently the system does not use combination ofheterogeneous signals; however, research of possible signalcombinations will be conducted in future work.

4. Experimental Scenarios

Performance of the proposed algorithm was tested in realworld conditions in both GSM andWi-Fi networks. Modularpositioning system was deployed in the area of University ofZilina in both indoor and outdoor environments.

During the experiments we decided tomeasuremore RSSsamples for each fingerprint, so we can decrease positioningerrors caused by the signal fluctuations by use of localmean RSS value for the position estimation. The number ofmeasurements to achieve stable local mean RSS would be20 and more; however, the value of 5 provides pretty similarresults as can be seen from results in Figure 9 with durationof each iteration 4 times faster. The results were achieved byexperimental measurements with RSS data collected during50 independent position estimates.

Positioning error is calculated as average value of meanvalues of all independent trials. From Figure 9 it is clear thatthe higher number of measurements significantly improveslocalization accuracy. However, it can also be seen thatchanges are not so dramatic when the number of measure-ments used to calculate local mean RSS is more than 5.

During the experiments ground truth position was esti-mated by accurate GPS receiver (as reference value). In themodular positioning system theGPSmodule was disabled, sowe were able to assess performance of positioning solutionsbased on radio networks.The radiomap was created using anHTCWildfire S smartphone during the evening, when therewas lownumber ofmoving reflectors, that is, people, cars, andso forth. Measurements were performed dynamically duringthe walk around the campus of the university. Final radiomapis depicted in Figure 10.

It can be seen that there is a difference between referencepoints stored in GSM and Wi-Fi radiomaps. This differenceis given by the fact that at some reference points there wereno enough Wi-Fi access points in the communication range;therefore, these reference points were excluded from theradiomap database.

Radiomap consists of total 937 reference points for GSMand 547 reference points for Wi-Fi positioning. In average 5BTSs and 15 APs were detected at these points.


transmitters

transmitters

Removetransmitters under

threshold

Use 3 strongesttransmitters

Position cannot beestimated

Estimate position

End

Start

Yes

No

NoVector has ≤3

Vector has ≤3

Figure 8: Flowchart of the proposed algorithm.

NNWKNN

5 10 15 20 25 30 35 400Number of RSS samples

33.5

44.5

55.5

66.5

Posit

ioni

ng er

ror (

m)

Figure 9: Impact of number of measured samples on positioningerror.

In the first scenario impact of the proposed algorithm onaccuracy of the localization system was investigated. Duringthe positioning phase the position of mobile device wasestimated at 177 positions randomly chosen in the area wherelocalization map was created.

In the second experimental scenario we decided to testhow the proposed algorithm affects response time of thepositioning server. During the testing the localization serverwas established as a virtual machine at laptop with Intel i3processor and 4GB RAM running Windows 8.1 OS. Thisimplementation was sufficient for the testing, since thistesting was focused mainly on behavior and characteristicsof the positioning system. These are assumed to be inde-pendent on hardware equipment. The only difference causedby the differences in hardware and software equipment is

assumed to be in absolute numbers. During the experiment,localization requests were generated and sent to the local-ization server and response time was measured. Localizationrequest generated in the testing can be divided into valid andinvalid requests.

Valid localization request contained measured data thatwere collected in the localization area and localizationserver was able to estimate position of mobile device fromthese data. On the other hand, invalid localization requestscontained measurements from transmitters that were notpresented in radiomap.Therefore, localization server was notable to find match between transmitters in measured finger-print and transmitters presented in the radiomap database.

5. Achieved Results and Discussion

In this section results achieved during the experimentalscenarios will be presented and discussed. Results achievedduring the first testing scenario, which was aimed at eval-uating impact of the transmitter reduction algorithm onlocalization accuracy, are presented in Table 1.

From Table 1 it can be seen that system without imple-mented optimization algorithmsmarked as basic fingerprint-ing achieved extremely poor results in terms of accuracy. Itis important to note that Wi-Fi based positioning achievedworse results compared to GSM based positioning, which isprobably caused by the fact that a high number of APs withextremely low RSS power were detected at reference pointsthat are far from each other. It can also be seen that afterapplication of map reduction the positioning accuracy wassignificantly improved. This is caused by the fact that signals


Figure 10: Radiomap used in experiments for GSM and Wi-Fi networks, respectively.

Table 1: Impact of proposed algorithm on positioning error in outdoor environment.

Network Estimator Positioning error [m]Basic fingerprinting With map reduction With map reduction and reduction of transmitters

GSM NN 144.78 ± 78.15 89.67 ± 64.61 89.67 ± 64.61GSM KNN 110.44 ± 71.02 92.42 ± 50.90 92.42 ± 50.90GSM WKNN 120.63 ± 69.18 85.10 ± 61.45 85.10 ± 61.45Wi-Fi NN 178.47 ± 47.57 18.20 ± 10.74 18.20 ± 10.74Wi-Fi KNN 181.24 ± 29.60 21.66 ± 13.18 21.66 ± 13.18Wi-Fi WKNN 181.42 ± 29.60 19.22 ± 8.70 19.22 ± 8.70

Table 2: Impact of proposed algorithm on response time of GSM based positioning.

Number of requests 𝑇 [s]Basic fingerprinting With map reduction Map reduction and reduction of transmitters

1 valid request 0.68 ± 0.06 0.55 ± 0.17 0.59 ± 0.2010 parallel valid requests 3.44 ± 0.37 2.84 ± 0.95 3.12 ± 1.00100 parallel valid requests 30.76 ± 8.87 25.79 ± 10.81 29.67 ± 13.821 invalid request 0.86 ± 0.14 0.006 ± 0.001 0.006 ± 0.00110 parallel invalid requests 4.32 ± 0.66 0.017 ± 0.006 0.018 ± 0.006100 parallel invalid requests 39.15 ± 12.27 0.220 ± 0.085 0.202 ± 0.080

from the strongest transmitters were used to preselect pointsfrom radiomap database.

In addition it can be seen that implementation ofproposed algorithm did not have impact on localizationaccuracy. This proved our assumption that the transmitterswith highest RSS values have the most significant impacton performance of the system, if high number of trans-mitters can be detected and thus it is possible to removemeasurements from weakest transmitters without sacrificingpositioning accuracy. In our measurements in average 15 APsand 5 BTSs were detected in eachmeasurement forWi-Fi andGSM network, respectively. These numbers were achievedafter application of the transmitter removal algorithm.

The results achieved during the second scenario forboth valid and invalid requests for GSM and Wi-Fi basedpositioning are presented in Tables 2 and 3, respectively.The requests on the localization server were generated by

application and the number of requests that will be sentto the server in parallel (at the same time) was changed.In Tables 2 and 3 the results for 1, 10, and 100 parallel requestsare shown. Localization requests were sent to the localizationserver 100 times and time between each group request andlast localization server response was measured. Based onmeasured times, mean of response time and its standarddeviationwere computed and are presented in Tables 2 and 3.

From Table 2 it can be seen that proposed algorithmdoes not have any significant impact on response time ofGSM based positioning. It can even be stated that additionaldelay was introduced by the algorithm when compared tofingerprinting with map restrictions. This may be causedmainly by the fact that amount of transmitters detected in thefingerprinting vectors are quite low for GSM fingerprinting.This is caused by the network infrastructure and reuseof frequencies in GSM network. During our measurement


Table 3: Impact of proposed algorithm on response time of Wi-Fi based positioning.

Number of requests 𝑇 [s]Basic fingerprinting With map reduction Map reduction and reduction of transmitters

1 valid request 4.31 ± 2.13 0.76 ± 0.60 0.27 ± 0.2810 parallel valid requests 23.14 ± 11.34 3.96 ± 3.33 1.20 ± 1.32100 parallel valid requests 289.86 ± 147.66 45.79 ± 41.90 12.64 ± 15.451 invalid request 4.49 ± 2.37 0.014 ± 0.007 0.009 ± 0.00510 parallel invalid requests 24.15 ± 12.22 0.053 ± 0.033 0.027 ± 0.020100 parallel invalid requests 308.78 ± 178.89 0.505 ± 0.327 0.240 ± 0.174

the highest number of detected BTSs was 7, which is givenby the network design and the fact that smartphone used wasonly able to detect BTSs from one network provider.

It is important to note that invalid requests, that is,requests with data only from transmitters not present inthe radiomap database, were processed much faster. This iscaused by the fact that implemented optimization algorithmfor radiomap reduction was not able to find any area with thesignals from the given transmitters and therefore the posi-tioning process was unsuccessful. In the basic fingerprinting,that is, without optimization, the server has to comparereceived data with data stored in the radiomap. However,since measurements do not contain data from known trans-mitters the position of mobile device will not be estimated.The optimization algorithm may therefore help to improvethe performance of the positioning system significantly incase that some of the users send positioning requests fromthe area where positioning service is not available.

On the contrary, results achieved with Wi-Fi based posi-tioning module show significant improvement in responsetime of the positioning system. From Table 3 it can be seenthat proposed algorithm decreased response time three timescompared to positioning with only map reduction algorithm.This is given by the fact that fingerprint vectors measured inWi-Fi networks contain higher number of RSS value belowthe threshold. This is given by the fact that most of APs areplaced indoors and thus radio signal is attenuated by walls ofbuilding.Moreover, communication range inWi-Fi networksis significantly smaller compared to GSM network.

Similarly to GSM based positioning also for Wi-Fi basedpositioning the processing times for invalid requests aresignificantly lower after implementation of optimizationalgorithm. In this case even reduction of transmitters helpedto reduce response time of the server, which is caused bythe fact that less data has to be transmitted and processed.Based on the results achieved for Wi-Fi positioning it canbe stated that negative impact of transmitter reduction onresponse time inGSMnetworkwas caused by the lownumberof detected transmitters. Therefore algorithm for transmitterreduction should be used only in cases when the number ofdetected transmitters is assumed to be higher. In GSM basedpositioning this can be achieved by scanning signals fromBTSs of various service providers.

In the next stepwe decided to find dependency of numberof parallel positioning requests on response time of thelocalization server. For this reason we decided to use small

20 40 60 80 100 120 140 1600Number of parallel localization requests

0

0.5

1

1.5

2

2.5

Resp

onse

tim

e (m

s)

×104

Figure 11: Impact of number of valid parallel requests on responsetime of localization server after implementation of proposed algo-rithm.

increments of number of valid parallel requests. Achievedresults are shown in Figure 11.

FromFigure 11 it can be seen that dependency of responsetime of the positioning server on number of parallel validrequests is almost linear. Small deviations from linear char-acteristics can be caused by processes running on the back-ground of operating system. It is important to note that thistesting represents the worst case scenario; when all requestsare parallel in real implementation localization requests areassumed to be more random.

Moreover, it is important to note that during the testingphase localization serverwas implemented as virtualmachinerunning at a laptop, which does not have high computationpower and further improvement of response time can beexpected if the system is implemented at a dedicated server.

6. Conclusion

In the paper novel optimization algorithm for reductionof transmitters was proposed to reduce response time oflocalization server in modular positioning system. The mainidea behind the algorithm is to reduce complexity of positionestimation and thus provide service that will have lowestpossible latency, since high latency of position estimation cansignificantly reduce quality of user experience. The proposedalgorithm was tested in real world conditions.

From the achieved results it can be seen that proposedalgorithm does not affect accuracy of position estimates. Itcan also be concluded that proposed optimization algorithm


has potential to reduce complexity of positioning system,especially when large numbers of transmitters with low RSSvalues are presented in area of positioning as can be seenfrom time measurements for Wi-Fi based positioning. Onthe other hand, it can be stated that algorithm might havenegative impact on complexity of localization system if thenumber of removed transmitters is low, as can be seen fromtime measurements for GSM based positioning. In this casethe response timewas increased.This is caused by the fact thatin GSM network number of detected transmitters cannot behigher than 7, if device allows scans only from one networkoperator. In the most of performed measurements signalfrom only one transmitter was removed by the algorithm.

It is possible to conclude that the proposed algorithmfor reduction of transmitters may improve response time ofpositioning service, if applied correctly, that is, when largenumber of transmitters with low RSS can be detected inthe localization area. Since most of currently used devicescan only scan RSS samples from a single service provider,the algorithm should not be implemented in GSM basedlocalization as it has negative impact on response time of thelocalization system.

From the results provided it can be seen that novelalgorithm from transmitter removal has positive impact oncomplexity of the system, when implemented in Wi-Fi basedpositioning. In the Wi-Fi based positioning system, themobile device may sense large number of RSS samples fromdifferentAPs. Part of these samples could be removedwithoutsignificant impact on the positioning accuracy especially ifthe RSS is low. Another positive impact of the algorithm is thefact that, by removal of transmitters with low RSS, less datahas to be transferred between mobile device and localizationserver. Further improvement of the achieved results maybe possible by implementation of the localization server ondedicated server or the cloud.

Competing Interests

The authors declare that they have no competing interests.

Acknowledgments

This work has been partially supported by the Slovak VEGAgrant agency, Project no. 1/0263/16, by project Broker Centreof Air Transport for Transfer of Technology and Knowl-edge into Transport and Transport Infrastructure, ITMS26220220156, and by Centre of Excellence for Systems andServices of Intelligent Transport II, ITMS 26220120050,supported by the Research & Development OperationalProgramme funded by the ERDF.

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