SSD: A Robust RF Location Fingerprint Addressing Mobile ...1 SSD: A Robust RF Location Fingerprint Addressing Mobile Devices’ Heterogeneity A.K.M. Mahtab Hossain, Yunye Jin, Wee-Seng

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SSD: A Robust RF Location FingerprintAddressing Mobile Devices’ Heterogeneity

A.K.M. Mahtab Hossain, Yunye Jin, Wee-Seng Soh, and Hien Nguyen Van

✦

Abstract — Fingerprint-based methods are widely adopted for indoorlocalization purpose because of their cost-effectiveness compared toother infrastructure-based positioning systems. However, the popularlocation fingerprint, Received Signal Strength (RSS), is observed todiffer significantly across different devices’ hardware even under thesame wireless conditions. We derive analytically a robust location fin-gerprint definition, the Signal Strength Difference (SSD), and verify itsperformance experimentally using a number of different mobile deviceswith heterogeneous hardware. Our experiments have also consideredboth Wi-Fi and Bluetooth devices, as well as both access-point-basedlocalization and mobile-node-assisted localization. We present the re-sults of two well-known localization algorithms (K Nearest Neighborand Bayesian Inference) when our proposed fingerprint is used, anddemonstrate its robustness when the testing device differs from the train-ing device. We also compare these SSD based localization algorithms’performance against that of two other approaches in the literature thatare designed to mitigate the effects of mobile node hardware variations,and show that SSD based algorithms have better accuracy.

Index Terms —Location fingerprint, signal strength difference (SSD),Wi-Fi, Bluetooth, indoor localization, positioning system, heterogeneousdevices.

1 INTRODUCTION

A CCURATE indoor location determination is an indispens-able building block of various context-aware services

and ubiquitous environments. Geometric approaches requireantenna arrays with large number of array elements ontransceivers to achieve good accuracy, which incur high hard-ware cost [2]. On the other hand, fingerprint-based approaches,utilizing signal parameters provided by off-the-shelf wirelessdevices, are widely adopted for indoor localization purpose fortheir cost-effectiveness.

In a typical fingerprint-based system, a set of “traininglocations” are chosen in the service area. During an off-line“training phase”, location-dependent signal parameters, mostcommonly received signal strength (RSS) values, are measuredand recorded at each training location as the fingerprint forthat particular location. During the online localization phase,

A.K.M. Mahtab Hossain is with the Internet Education and ResearchLaboratory (intERLab) of Asian Institute of Technology (AIT) (E-mail:[email protected]), Yunye Jin and W.-S. Soh are with the Departmentof Electrical and Computer Engineering, National University of Singapore(E-mail: {g0700214, elesohws}@nus.edu.sg). Hien Nguyen Van is with theDepartment of Electrical and Computer Engineering, University of Maryland(E-mail:[email protected]).This work was published in part at IEEE MASS 2007 [1].This research work is supported by National Research Foundation projectgrant NRF2007IDM-IDM002-069 on “Life Spaces”.

various methods utilizing the recorded data can be appliedto estimate the target device’s location when the online RSSvalues of the device are collected.

Various commercially available hand-held devices and wire-less access points (APs) are capable of reporting RSS. Ingeneral, the RSSs are mostly reported in dBm values. How-ever, these devices usually come with many different hardwaresolutions, even for the same wireless technology. Regardless ofwhether a device’s signal strengths as perceived by the APs areused to denote the device’s location fingerprint, or the reverseapproach in which the APs’ signal strengths as perceived bythe device (i.e., mobile node (MN)) are used, such fingerprintsmay differ significantly with the device’s hardware even un-der the same wireless conditions [1], [3]–[5]. This is oftenobserved in existing popular wireless technologies, such asWi-Fi or Bluetooth. The presence of power control feature insome mobile devices further complicates the issue [3]. As aresult, a positioning system that relies solely on RSS to definelocation fingerprints generally does not perform well acrossheterogeneous devices.

The need for a robust location fingerprint is obligatory forany fingerprint-based localization algorithm, no matter howsophisticated the algorithm is. In [1], we proposed a robustlocation fingerprint, namely,Signal Strength Difference (SSD),which was shown to outperform the traditional RSS fingerprintin terms of robustness across heterogeneous mobile devices,both analytically and experimentally. In this paper, we analyzethe robustness of SSD more elaborately, using several off-the-shelf Wi-Fi and Bluetooth devices.

In existing localization literature, we usually encounter twodifferent approaches to collect the signal strength samples,namely,AP-based, where the RSS is measured at the AP, andMN-assisted, where the RSS is actually measured at the MNitself. In order to verify SSD’s robustness, we need to considerboth of these scenarios. However, we have only considered theAP-based scenario in [1], both for analysis and experiments.In this paper, we show that, regardless of whether the signalstrength samples are collected at the APs or at the MN, SSD isa more robust location fingerprint compared to the traditionalRSS.

We consider two separate experimental testbeds for Wi-Fiand Bluetooth. The Bluetooth testbed follows the AP-basedapproach while the Wi-Fi testbed follows the MN-assistedapproach. Both testbeds include concrete walls, cubicles, peo-ple, etc., representing the indoor environment more practically,

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compared to our initial lecture theater testbed in [1]. We alsocompare SSD with two other robust location fingerprints [5],[6] that are argued to mitigate the effects of MN’s hardwarevariations.

The rest of the paper is organized as follows. We discussour idea of defining a robust location fingerprint in Section 2.We provide a brief description of related works in Section 3.In Section 4, we present experimental findings supporting ourclaims. Finally, we depict in Section 5 the conclusions drawn,and our future work.

2 ROBUST LOCATION FINGERPRINT

Our research focuses on providing cost-effective location esti-mation in the indoor environment, utilizing existing infrastruc-ture. Due to widespread availability of Wi-Fi and Bluetoothnetworks within buildings, we choose both these RF wirelesstechnologies for our analysis and experiments.

RSS is the most common RF signal parameter used aslocation fingerprints for Wi-Fi since it was first proposed in[7]. For Bluetooth, both “Received Signal Strength Indicator(RSSI)” and “Link Quality (LQ)” have been previously usedas location fingerprints; but generally, positioning systems thatare solely based on Bluetooth have reported poor accuracy. Adetailed analysis of the available Wi-Fi and Bluetooth signalparameters can be found in [8] and [9], respectively. Based ontheir analysis, it is apparent that all these signal parametershave specific usage according to their own respective tech-nologies, which may render them inappropriate as locationfingerprints. Among all the signal parameters available, RSSis argued to be the most viable option as location fingerprintfor both Wi-Fi [8] and Bluetooth [9]. In this section, wededuce our location fingerprint, the SSD, and analyticallyprove its superiority over RSS, in terms of the system’s overallrobustness against heterogeneous mobile devices.

In existing localization literature based on location finger-prints, the signal strength samples are either collected at theAPs, or at the MN that needs to be located. The AP-basedapproach has the advantage of not requiring any modificationof the MNs’ devices before the latter can be tracked. Onthe other hand, the MN-assisted approach could better ensurethe security and privacy of the MN. In both approaches, thesamples’ signal strength values collected over a small time-window are generally averaged to obtain the traditional RSSlocation fingerprint.

The RSS location fingerprint is influenced by a particulartransmitter-receiver pair’s hardware-specific parameters, suchas antenna gains. Consequently, having a different transmitter-receiver pair compared to the training phase would likelyproduce a different RSS signature at the same location [4].

In this section, we show that, rather than utilizing theabsolute signal strength (RSS) as location fingerprint, the dif-ferences of signal strengths perceived at the APs or at the MNwould actually provide a more stable location signature for anymobile device irrespective of its hardware used. We contendthat, in this way, the transmitter-receiver pair’s hardware effectis mitigated.

Suppose P (d) and P (d0) denote the received signalstrengths at an arbitrary distanced and a close-in reference

distanced0 from the transmitter, respectively, for a particu-lar transmitter-receiver pair. From the log-normal shadowingmodel [10], we get,

[P (d)P (d0)

]

dB

= −10β log(

d

d0

)+ XdB. (1)

The first term on the right hand side (RHS) of (1) defines thepath loss component (β is the path loss exponent), while thesecond term reflects the variation of the received power at acertain distance (XdB ∼ N(0, σ2

dB)). Eqn. (1) can be rewrittenas,

P (d)|dBm = P (d0)|dBm− 10β log(

d

d0

)+ XdB. (2)

Depending on the hardware used at both the AP and theMN, the perceived power at a reference distance (i.e.,P (d0))varies, as a result of hardware-specific parameters, such asantenna gains. Therefore, the perceived RSS at a distancedis also hardware-dependent. This explains why RSS is not arobust location fingerprint, although it is commonly used inthe existing literature.

To simplify our discussion, let us first focus on the AP-based approach, where the MN is the transmitter, while theAP is the receiver. Rather than using absolute RSS values aslocation fingerprints, the difference of the RSS values observedby two APs (i.e.,SSD) can be used to define a more robustsignature for a transmitting mobile device. In order to explainanalytically, letP (d1) andP (d2) denote the RSSs of a mobiledevice’s transmitted signal as perceived at two different APs(AP1 and AP2) which are at distancesd1 and d2 from themobile device, respectively. We assume that, all the APs havethe same hardware properties, since it is quite common for aninstitution to choose the same brand and model for all theirAPs in the building. Consequently, using (2), we can write thefollowing for AP1 and AP2, respectively:

P (d1)|dBm = P (d0)|dBm− 10β1 log(

d1

d0

)+ [X1]dB, (3)

andP (d2)|dBm = P (d0)|dBm− 10β2 log(

d2

d0

)+ [X2]dB. (4)

Subtracting (4) from (3), we obtain,[P (d1)P (d2)

]

dB

= −10β1 log(

d1

d0

)+ 10β2 log

(d2

d0

)

+ [X1 −X2]dB. (5)

Eqn. (5) denotes SSD’s expression, which is free fromP (d0). Based on the above analysis, we claim that SSD ismore robust against device hardware variations, comparedto traditional RSS in denoting the location fingerprint whenthe signal strength samples are collected at the APs. Inthe following sections, we explain it in a more detailedway. We also inspect the case of MN-assisted localizationwhere the signal strength samples are actually collected atthe MN. Here, we have applied the shadowing model of RFpropagation in our analysis which is a common practice inexisting indoor localization literature for the sake of analyticaltractability [11], [12]. The shadowing model has also beenused to model indoor RF propagation in popular “Wireless

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Communications” textbooks [10], [13]. Nevertheless, we alsoprovide an alternative analysis using multipath propagationchannel model in Appendix A, where SSD could also beshown to be free from the effect of variations in MN’shardware-dependent transmitter/receiver power gain for bothAP-based and MN-assisted localization.

Note that, although it is common for Wi-Fi communicationinfrastructure in most campus and industrial buildings to haveAPs with the same brand and model, it is not a mandatorycondition for our proposed SSD fingerprint to work in practice.As we will show in the following analysis, as long as eachAP remains constant for both the training phase and thelocalization phase, the proposed scheme is able to eliminatethe hardware differences caused by device heterogeneity.

2.1 Signal Strength Samples Collected at APs (AP-based Approach)

Consider the same scenario as above but with the assumptionthat the reference power, i.e.,P (d0) of (2), can be evaluatedusing the free space propagation model as follows [13],

P (d0)|dBm = 10 log(

PMNGMNGAPi λ2MN

16π2d20L

), (6)

wherePMN is the MN’s transmitted power,GMN is the MN’santenna gain,GAPi is theith AP’s antenna gain,L is the systemloss factor, andλMN is the transmitted carrier’s wavelength.

Using (6), both (3) and (4) can be rewritten respectively as,

P (d1)|dBm = 10 log

(PMNGMNGAP1λ

2MN

16π2d20L1

)

− 10β1 log(

d1

d0

)+ [X1]dB, (7)

and P (d2)|dBm = 10 log

(PMNGMNGAP2λ

2MN

16π2d20L2

)

− 10β2 log(

d2

d0

)+ [X2]dB. (8)

In order to compute SSD, subtract (8) from (7), we have,[P (d1)P (d2)

]

dB

= 10 log(

GAP1L2

GAP2L1

)− 10β1 log

(d1

d0

)

+ 10β2 log(

d2

d0

)+ [X1 −X2]dB (9)

The expression of SSD for the AP-based approach in (9)does not contain any MN-dependent term. Therefore, the SSDwould be entirely free from any influence caused by the MNs’hardware variations. Moreover, even if different APs havedifferent antenna gains and system loss factors, as long asthese settings for each individual AP remain consistent acrossboth training and localization, SSD will achieve consistencybetween the offline and online fingerprints.

2.2 Signal Strength Samples Collected at MN (MN-assisted Approach)

We consider the same scenario as above, except that thesignal strength is now measured at the MN rather than at

the APs. Subsequently, (7) and (8) take the following forms,respectively,

P (d1)|dBm = 10 log

(PAP1GAP1GMNλ2

AP1

16π2d20L1

)

− 10β1 log(

d1

d0

)+ [X1]dB, (10)

and P (d2)|dBm = 10 log

(PAP2GAP2GMNλ2

AP2

16π2d20L2

)

− 10β2 log(

d2

d0

)+ [X2]dB. (11)

In order to compute SSD in this scenario, subtract (11) from(10), we have,

[P (d1)P (d2)

]

dB

= 10 log

(PAP1GAP1λ

2AP1

L2

PAP2GAP2λ2AP2

L1

)

− 10β1 log(

d1

d0

)+ 10β2 log

(d2

d0

)

+ [X1 −X2]dB (12)

Again, in the MN-assisted approach, the SSD is entirelyfree from the influence caused by MNs’ hardware variations.Although the SSD expression is affected by different APs’configurations such as power settings, antenna characteristics,and operated channels, as long as the configuration for eachindividual AP remains consistent across both training andlocalization phases, the SSD will achieve consistency betweenthe offline and online fingerprints.

Furthermore, even if the APs were to switch to differentchannels (e.g., changing from channel 1 to channel 11 for802.11g) from the training phase, the changes in theλ’s of(12) will not be significant [14]. It should also be noted that,the samples gathered at the MN can be derived from thebeacon frames that come from the APs [4]. Since these framesare generally sent using some default power setting, we canapproximate thatPAP1 ≈ PAP2.

Although the SSD is robust against device heterogeneity,an important trade-off needs to be made when it is used toreplace the RSS as a location fingerprint - the SSD fingerprintvector is always one dimension lower than the RSS fingerprintvector for the same number of APs. In order to understand this,suppose there areN APs within the range of a mobile device.Since each AP yields one RSS reading, the resulting RSSfingerprint vector hasN elements. On the other hand, althoughthere are

(N2

)different SSD values resulting from theN RSS

readings, only(N − 1) of these are independent. Hence, anSSD fingerprint vector contains only(N − 1) elements. Thesmaller dimensionality potentially puts it at a disadvantagecompared to an RSS fingerprint vector (if all else remainsthe same). This implies that, if the same device were to beused for both training and online localization phases, then theuse of RSS fingerprint vectors could yield better localizationaccuracy than SSD fingerprint vectors. Nevertheless, it wasfound in [11] that whenN is large (N > 5), an increasein RSS fingerprint vector’s dimensionality no longer resultsin any significant improvement of the localization accuracy.

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Therefore, the effect arising from the slightly smaller dimen-sionality of the SSD fingerprint vector should also becomeinsignificant whenN is large.

In many practical scenarios, a localization system is in-tended to track heterogeneous devices, and hence, we wouldexpect the user devices to be frequently different from thetraining device. As reported in [1], [3]–[5], different devicestend to report quite different RSS values at the same location.Under such circumstances, the use of RSS as a locationfingerprint usually results in significant deterioration of thelocalization accuracy. The SSD, in contrast, is able to maintainits good localization accuracy across heterogeneous devices.As we will show in our experimental results in Section 4.4,with four APs, it is observed that the localization accuracyobtained from using SSD fingerprints is only slightly lowerthan using RSS fingerprints when the same device is usedfor both training and online localization phases. However,in the more practical case in which different devices areused for training and online localization respectively, the SSDoutperforms RSS significantly even though the SSD fingerprintvector has a smaller dimensionality.

3 RELATED WORK

The current research efforts for indoor positioning systemscan largely be divided into two main categories: i) those thatrequire specialized hardware (e.g., RF tags, ultrasound re-ceivers) and extensive deployment of dedicated infrastructuresolely for localization purpose [15]–[17], and ii) those thatutilize the location-dependency of easily measurable signalparameters (e.g., received signal strength). The latter approachaims to build a positioning system by leveraging on an existinginfrastructure (e.g., Wi-Fi networks) [7], [18]–[25] in a cost-effective way.

A few works in the second category above exploit RSSdirectly for distance estimation. One example is [19], in whichdistance and path loss exponent are jointly estimated in a leastsquare approach. Another example is [26], which converts Sig-nal Strength Differences (SSDs) into distance measurementsusing signal propagation model. Then a hyperbolic positioningalgorithm [26] based on the distance differences is suggestedto locate a base transceiver station (BTS) in cellular com-munications environment. However, the robustness of theseschemes in the presence of hardware heterogeneity is notaddressed. In contrast, our work focuses on fingerprint-basedpositioning systems. Due to space limitation, we only providean overview of some existing approaches under this category.More in-depth discussions can be found in [27], [28]. Locationfingerprinting techniques became popular with RADAR [7],mainly because of the unavailability of appropriate radio signalpropagation models for indoor environments. It also openedthe door for many different approaches to be applied for theindoor localization problem. For example, Nibble [18] is oneof the first systems to use a probabilistic approach for locationestimation. Local linearization technique and factor graph havebeen employed in [29] to model the mapping between RSSand location, based on the training data. To date, Ekahau’sPositioning Engine [30] claims to be the most accurate location

system with one-meter average accuracy and a short trainingperiod. Note that, Ekahau is a commercial positioning system,and there exists no scientific evaluation to verify their claim.

The effects of different devices’ hardware variations onRF location fingerprint have gained little attention in thelocalization literature so far. As discussed before, existingworks generally use the same mobile device during bothtraining and testing phases, thereby, invoking similar setups(i.e., transmitter-receiver pair) in both cases. However, [1], [3]–[5], [31], [32] have observed that the location fingerprints (i.e.,RSSs) produced by using different mobile devices vary quitesignificantly from one another even under the same wirelessconditions. Haebarlenet al. [4] try to accommodate various de-vices by having a benchmark training database taken with onlyone device. For other devices, they require a set of linear RSSconversion formulae, which translate the RSSs of those devicesinto the benchmark device’s RSSs. These linear conversionformulae are obtained by laboriously experimenting with eachsupported device to discover its RSS relationship with thatof the benchmark device. Kjærgaard [31] follows a similarapproach, and also discusses a method to tune the parametersof the linear conversion formula automatically instead ofmanual calibration. In their subsequent work, Kjærgaard [32]identifies that the hearability problem (i.e., a mismatch of theset of APs that can be heard by different NICs) might affectthe performance of their scheme, and suggests that the NICused for collecting fingerprints (i.e., their benchmark devicein the formula) should be the one that can hear the mostAPs. Tao et al. [3] utilize signal strength difference as alocation fingerprint like our approach. Their motivation was tofind the locations of rogue machines with different hardwareconfigurations and varying transmitting powers. They haveonly provided experimental results based on the idea, withoutany intuition or analysis about why the differences in signalstrengths could work successfully in their scenarios. On thecontrary, our work gives both the detailed analysis and theexperimental results as to why the SSD could be regardedas arobust location fingerprint, for both AP-based and MN-assisted localization approaches.

Around the same time when we proposed the use of SSDas a robust location fingerprint in [1], another work [5] alsoattempted to use a fingerprint that is related to signal strengthdifferences. Specifically, it explored the use of normalizedlogarithmic signal-strength ratios, termed as Hyperbolic Lo-cation Fingerprint (HLF) [5] that is experimentally motivated.However, it does not provide any theoretical analysis as to whythe HLF mitigates the hardware variation effects. Althoughthe logarithm function is monotonic, the relationship betweenHLF and SSD is not as subtle as it seems. To appreciate thedifference, one needs to take an analytical approach. The HLFapproach is equivalent to taking the logarithm of (3) and (4),and then combining them. It can be easily seen that the result-ing expression is not totally free fromP (d0), unlike our SSD’sexpression in (5). Since heterogeneous devices are likely tohave differentP (d0), the HLF is unable to fully mitigatehardware variations from a theoretical standpoint. As will bedemonstrated in our experimental results in Section 4.4.3, theSSD indeed outperforms the HLF when heterogeneous training

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TABLE 1The list of Wi-Fi and Bluetooth devices used as MN and AP in our experimental testbeds

Technology MN Devices AP Devices

Testbed 1 Wi-FiIntel PRO/Wireless 2200BG Linksys WRT54GAtheros AR242x 802.11abg Cisco Aironet 1200

Testbed 2 Bluetooth

Ranger’s BT-2100 (Class 1)

Ranger’s BT-2100Billionton’s USBBT02-B (Class 2)Acer n300 PDA (Class 2)Motorola V3xx Phone (Class 2)

Fig. 1. Our Wi-Fi experimental testbed (MN-assisted localization) – all the training locations are marked as shadedpoints.

and testing devices are used.Another method, termed Ecolocation [6], uses ordered se-

quence of RSS measurements rather than the absolute RSSsto constitute a unique location fingerprint. IfP (di) andP (dj)denote the RSSs at APi and APj , which are at distancesdi

and dj from the MN, respectively, then aconstraint of thesequence is defined as,

P (di) > P (dj) ⇒ di < dj . (13)

First, the constraint set for each grid point is calculated usingthe RHS of (13). Only the locations ofreference nodes(i.e.,APs) are required in this phase – no signal strength collectionsurveys are necessary. During location determination phase,the ordered sequence of RSSs collected at the APs is trans-lated into the ordered sequence of distances using (13), andsubsequently matched against the constraint set of each gridpoint calculated beforehand. The centroid of the grid pointswhere the maximum number of constraints are matched isreturned as the location estimate. We believe that, owing toMNs’ hardware variations and varying transmission powers,both P (di) and P (dj) should be affected in a similar way.Therefore, the constraint (13) is expected to remain intact

over different MNs. Consequently, Ecolocation could be robustagainst MNs’ hardware variations as well. Hence, we alsocompare SSD’s performance with that of Ecolocation in ourexperiments.

4 EXPERIMENTAL STUDY

We first describe our experimental testbeds and data collectionprocedure in Section 4.1 and 4.2, respectively. Then, we listin Section 4.3 some assumptions that we have made for ourexperiments. Finally, in Section 4.4, we present our results andfindings.

4.1 Testbed Setup

We have two experimental testbeds. Table 1 lists the devicesused in our testbeds.

Testbed 1 is a Wi-Fi testbed located inside a laboratory ofour campus (see Fig. 1) that spans over an area of382 m2.It has three separate rooms (divided by walls), where oneis a discussion room, and the other two include many smallcubicles.

We have mainly used Linksys WRT54G routers as our APs.The locations of these APs are marked as stars in Fig. 1.

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��

Fig. 2. Our Bluetooth experimental testbed (AP-based localization) – all the training locations are marked as shadedpoints.

Additionally, we have also utilized measurements from theCisco Aironet 1200 series routers for some of our experimentsthat provide wireless connectivity in the building. Note that,this testbed follows anMN-assistedapproach where the MNitself retrieves the signal strength information.

Testbed 2 is a Bluetooth testbed located within anotherlaboratory of our campus (see Fig. 2) that spans over an areaof 214 m2, and includes many small cubicles. In this testbed,we have used four Aopen MP945 Mini PCs as our APs whichare placed near the ceilings. The locations of these APs aremarked as stars in Fig. 2. Each MP945 is equipped with BT-2100 Class 1 Bluetooth adapter which scans for Bluetoothpackets by issuing inquiries periodically. This testbed emulatestheAP-basedpositioning system where the signal strengths areactually measured at the AP side.

4.2 Data Collection Procedure

In our two testbeds, there are 466 and 337 training pointsor grids, respectively. The training process involves placingthe mobile device at each training point, and collecting data.Our front-end of the signal strength collection program has aJava Graphical User Interface (GUI), which allows the userto load the map and click on the location to be trainedconveniently. We have collected our measurements duringafternoons over10 working days. As mentioned before, thesettings and surroundings of both testbeds include concretewalls, cubicles, movement of people, etc., and represent theindoor environment more practically compared to our initialtestbed in [1].

In Testbed 1, we have utilized tcpdump [33] to capturethe signal strength at the MN. We first put the MN’s NIC

into “monitor mode”, and continuously cycle through the non-overlapping Wi-Fi channels 1, 6 and 11, where it stays on eachchannel for 10 ms. Concurrently, we run tcpdump to snoopall the 802.11 packets from the air. Later on, we ran somescripting programs on the tcpdump’s actual output to retrievethe required RSS information from our desired APs. In thecase of Bluetooth, we log onto the mini PCs using SecureShell (SSH) and make the APs issue Bluetooth inquiries whichthe mobile device responds to. The Bluetooth signal strengthinformation retrieval program is written utilizing the HCI APIof BlueZ [34] protocol stack. In each case (Bluetooth or Wi-Fi), the packet information is transferred to our central server’sdatabase from the APs (i.e., mini PCs) or the MN. The centralserver is also responsible for calculating the location duringthe testing phase. Our signal strength collection programs areinvoked externally from the Java program when we click onthe locations to be trained on the map. Note that, our Bluetoothadapters provide the absolute RSS values of the inquiryresponse packets, rather than the RSSI values as stipulated bythe Bluetooth Core specification. At each location, stationingourselves with an MN for 1 minute would give enough samples(200 to 300) in case of Wi-Fi for every AP, whereas forBluetooth, we would have to stand for 2 or 3 minutes to gatherthe same number of samples. We aim to take many samples ata particular location because we want to prove statistically thatSSD is better in the experimental results. However, collecting20 samples per location was observed to provide comparableresults to even collecting 200 to 300 samples per location incase of SSD.

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4.3 Assumptions

Here, we list the assumptions that we have made for ourexperiments:

i) Whenever we have used RSS as location fingerprintfor certain experiments, we have assumed it to be normallydistributed at any particular location in our paper. Thoughsome works defy this phenomenon, others lend support toit [35]. We assume each RSS value in the location fingerprintto be a normal random variable characterized by only itsmean and standard deviation. Similar to [4], our experimentalresults also suggest that it is a reasonable approximation, assignificant improvement cannot be achieved even if we wereto utilize histogram representations of RSS. However, we haveused the histogram representation for HLF and the histogram’sbin size is selected to be 0.02 as suggested by [5].

ii) We have chosen two well-known algorithms in thelocalization literature, namely, K Nearest Neighbor (KNN) [7]and Bayesian Inference [4], in order to test our ideas. Our keyintention is to show that our ideas are quite generic and canbe helpful irrespective of the choice of algorithms. For theKNN algorithm, we chose the value ofK empirically, similarto prior works [7]. While applying Bayes formula, the prioriprobabilities are assumed to be uniformly distributed.

iii) In order to apply probabilistic models, one assumptionthat has widely been used is the independence of RSS valuesof different APs [18], [21]. This assumption is justifiablefor a well-designed network where each AP runs on a non-overlapping channel. Kaemarungsi and Krishnamurthy haveperformed experiments in [35] to evaluate the correlation fac-tor among the APs’ RSS values in the presence of interferenceand they have strengthened this claim as well. Thus, we havealso adopted their vindication.

4.4 Experimental Results and Findings

4.4.1 Justification of SSD as a Robust FingerprintFor this experiment, we have chosen various mobile deviceswhich are listed in Table 1 to inspect their effects on both RSSand SSD location fingerprints. In Testbed 1, we conducted thesignal strength survey by plugging two different Wi-Fi NICs(Intel PRO/Wireless 2200BG and Atheros AR242x 802.11abg)into our laptop. Since our Testbed 1 emulates the MN-assistedlocalization scenario, we actually collected the signal strengthsamples at the MN rather than at the APs. In Testbed 2, wehave selected four different Bluetooth devices and measuredtheir signal strengths at the APs (i.e., mini PCs). The Acern300 PDA and the Motorola V3xx phone have integratedClass 2 Bluetooth chips, whereas the USBBT02-B Class 2adapter and Ranger’s BT-2100 Class 1 adapter were pluggedinto a laptop during the experiments. In both testbeds, we havepicked 20 random training points and stationed the devices atthose locations, while ensuring that we have collected enoughsamples at the MN (Testbed 1) or APs (Testbed 2) for all thedevices.

Fig. 3(a) shows the RSSs as perceived by two different Wi-Fi NICs (i.e., MNs) from packets transmitted by a LinksysWRT54G router (i.e., AP) while Fig. 3(c) depicts the SSDsbetween two such routers’ signals as perceived by the two

NICs. Only Linksys routers’ data have been presented here forbrevity, as Cisco routers’ measurements have yielded similartrends. Fig. 3(b) shows the RSSs as perceived by a Bluetoothadapter (i.e., AP) from packets transmitted by several Blue-tooth devices (i.e., MNs) while Fig. 3(d) depicts the SSDs ofthese MN devices’ transmitted packets as perceived by twoBluetooth APs.

From our experimental results, we see that, the RSS per-ceived by a certain MN (MN-assisted) or AP (AP-based)varies significantly across different mobile devices at eachtraining location. This has repercussion in their use as fin-gerprints because the RSS fingerprint vectors collected duringthe training phase will be strongly dependent on the mobiledevice used. Most existing works perform both their trainingand testing phases using the same device, thereby, ignoringthis practical issue. On the contrary, the SSD remains quiteconsistent across different mobile devices in our experiments.This readily complies with our analysis in Section 2.

4.4.2 Comparison between SSD and RSS as LocationFingerprintAs pointed out in the previous section, the use of the sameMN for both training and testing phases may have biasedthe reported results of the existing fingerprinting techniques.To investigate further, we conducted experiments inside bothour MN-assisted Wi-Fi (Testbed 1) and AP-based Bluetooth(Testbed 2) testbeds to visualize the effects of MN’s hardwarevariations.

In order to inspect the “same device” effect, we utilizedIntel’s NIC for both training and testing phases in Testbed1. Among the466 training grids as shown in Fig. 1,200of them are selected randomly as training points while theremaining266 are kept for testing purpose. We then run ouralgorithms (i.e., KNN and Bayesian) to obtain the localizationerrors. We repeat this procedure for101 times in order toobtain all the errors for different combinations of training andtesting samples, and finally obtain the cumulative probabilitygraph of Fig. 4(a). In Testbed 2, we utilized Ranger’s BT-2100 Class 1 adapter for both training and testing phases.In this particular testbed,200 of the 337 training grids asshown in Fig. 2 are selected randomly as training points, whilethe remaining137 are kept for testing purpose. We follow asimilar approach as the one described for Testbed 1 in order toobtain the cumulative probability graph of errors in Fig. 4(b).Note that, only Bayesian algorithm’s results are presented herefor brevity. The results obtained using KNN algorithm hasdemonstrated similar trends.

In order to inspect the “different device” effect, we utilizedtwo different Wi-Fi NICs as listed in Table 1 for Testbed 1.The Intel NIC’s collected data at466 grids as shown in Fig. 1are kept as training data while the Atheros NIC’s collecteddata at244 of the466 grids are utilized for testing purpose. InTestbed 2, we have utilized four different Bluetooth devicesas listed in Table 1 for collecting measurements at the 337locations as shown in Fig. 2. We set aside Ranger’s BT-2100Class 1 adapter’s data set as our training samples, while theremaining (3 × 337) = 1011 samples from the other threeClass 2 devices are used for testing. The resulting cumulative

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Fig. 3. Comparison between RSS and SSD for both Wi-Fi and Bluetooth considering various mobile devices.

probability graphs of localization errors are shown in Fig. 4(c)and 4(d) for Wi-Fi and Bluetooth, respectively.

The error performance when using the same device for bothtraining and testing can be visualized in Fig. 4(a) and 4(b) forWi-Fi and Bluetooth, respectively. In this case, the RSS basedalgorithms perform slightly better than its SSD counterparts.As explained earlier in Section 2, the SSD fingerprint vectorhas a smaller dimensionality compared to the RSS fingerprintvector (3 versus4 for the case of4 APs). This puts SSDat a slight disadvantage when the same device is used forboth training and online localization. Moreover, one may alsoargue that SSD has higher variance than RSS. Using (2)and (5), and assuming thatX1 and X2 are independent andidentically distributed Gaussian with varianceσ2

dB, RSS and

SSD are distributed asN(P (d0)|dBm− 10β log

(dd0

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andN(−10β1 log

(d1d0

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(d2d0

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For the same device, we notice that the means of both RSSand SSD do not change, and the variance of RSS is actuallylower than that of SSD.

However, in practical scenarios, a localization system isusually intended to track heterogeneous devices, and hence,the better performance of RSS only occurs occasionally whenthe user device happens to be the same as the device used

for training. In practice, it is more often for the users to carrydifferent devices from the training device. It can be easily seenfrom the Gaussian approximations of RSS and SSD that themean of RSS varies depending on different MNs’ hardwaresince it includesP (d0), while SSD’s mean still remains thesame. As we will see, the practical hardware dependencyissue overshadows the disadvantage of the larger varianceand smaller dimensionality of the SSD fingerprint, based onour experimental results shown below, using commonly foundcommercial devices.

Let us investigate the more common scenario, where theuser devices are different from the training device. FromFig. 4(c), and 4(d), it is apparent that the hardware variations ofthe MN have adverse effects on the RSS-based localization’sperformance for both Bluetooth and Wi-Fi. We further noticethat, this issue is prevalent regardless of whether the RSSis measured at the APs for AP-based localization, or atthe MN for MN-assisted localization. On the contrary, SSDbased localization has much better accuracy than RSS basedlocalization in the presence of hardware variations in both ourWi-Fi and Bluetooth experiments (see Fig. 4(c), and 4(d)).It is also noteworthy to compare Fig. 4(a) against Fig. 4(c),as well as Fig. 4(b) against Fig. 4(d). As can be seen, theaccuracy of SSD based localization remains almost the same

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(d) Bayesian algorithm’s performance (AP-based, different devices for train-ing and testing phases).

Fig. 4. Comparison of error performance using RSS vs. SSD as location fingerprint for Wi-Fi and Bluetooth when thetesting phase is conducted with either the same training device ((a) and (b)) or a different training device ((c) and (d)).

in the respective comparisons. This implies that SSD basedlocalization is invariant to the mobile device being used,regardless of whether it is the same as the training deviceor not. This agrees with our analysis in Section 2 that SSD isfree from hardware-dependent effects.

4.4.3 Comparison of SSD with Other Robust LocationFingerprints

In order to compare SSD with other robust location finger-prints, we consider two different combinations of training andtesting data for both Wi-Fi and Bluetooth.

Among the 466 locations of our Wi-Fi testbed, we firstkeep the Atheros chipset’s data collected at 244 locationsas training samples. The Intel chipset’s data collected at theremaining 222 locations are used for testing purpose. For thesecond combination, we just swap our training and testingdata. In other words, the 222 locations’ Intel data are usedas training samples whereas the 244 locations’ Atheros dataare kept for testing. The results of the experiments can bevisualized in Fig. 5(a) and 5(b), respectively. Some numericalvalues (e.g., percentiles and average) of these two figuresare listed in Table 2. The training and testing locations of

both combinations are chosen in a way that they are spreaduniformly over the whole testbed.

For our Bluetooth testbed, we first keep the Class 1 device’sdata collected at 171 among the 337 locations as trainingsamples, whereas the three Class 2 devices’ data collected atthe remaining 166 locations are used for testing purpose. Forthe second combination, the 166 locations’ Class 2 devices’data are used as training samples whereas the 171 locations’Class 1 device’s data are kept for testing. The results ofthe experiments can be visualized in Fig. 6(a) and 6(b),respectively. Some numerical values of these two figures arealso listed in Table 3. Similar to our Wi-Fi testbed, the trainingand testing locations of both combinations are chosen in a waythat they are spread uniformly over the whole testbed.

For the case of Wi-Fi, it is evident from Fig. 5 and Table 2that, SSD based techniques are better than the other twoschemes (HLF and Ecolocation) described in Section 3 thatcould also mitigate the MNs’ hardware variation effects tosome extent. Similar conclusions could also be drawn from ourBluetooth experimental results, as can be seen from Fig. 6 andTable 3. Although we have utilized both KNN and Bayesianalgorithms for performance comparison, we only show the

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Fig. 5. Bayesian (RSS, HLF and SSD are used as location fingerprints) and Ecolocation algorithm’s performance forMN-assisted localization (Wi-Fi).

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Fig. 6. Bayesian (RSS, HLF and SSD are used as location fingerprints) and Ecolocation algorithm’s performance forAP-based localization (Bluetooth).

figures for the Bayesian algorithm’s results here for brevity.However, the numerical results from both Bayesian and KNNalgorithms are listed in Table 2 and 3 for Wi-Fi and Bluetooth,respectively.

As explained earlier in Section 3, the HLF relies on normal-ized logarithmic signal-strength ratios for location fingerprint-ing, which is shown analytically to be still vulnerable to MN’shardware heterogeneity. This explains why our SSD basedalgorithms perform better than the HLF based algorithms.Nevertheless, the HLF based algorithms are still comparablymore robust than the RSS based algorithms.

Ecolocation performs even worse than the RSS based al-gorithms for both our Wi-Fi and Bluetooth experiments. Thiscan be attributed to the following reasons: i) Ecolocation ismainly targeted at localizing inexpensive sensors and is shownto perform better than other localization algorithms found inwireless sensor networks [6]. Its main advantage lies in the factthat it requires no time-consuming signal strength collectionsurveys in the location space, whereas all the other algorithmsconsidered in our experiments require the use of offline

training data. ii) RSS measurements cannot be translated intodistances accurately in the real world. Therefore, uncertaintiescould arise while using (13) as discussed in [6]. Moreover,since we only have four APs in each testbed, the number ofconstraints (i.e.,

(42

)) at each grid point is also quite limited.

For fairer comparisons with the other schemes, we modifyEcolocation by making use of the offline training data, and callthe resulting scheme “Modified Ecolocation”. The constraintset for each grid point of the modified algorithm consistsof the ordered sequence of RSS values collected during thetraining phase instead of the distance constraints as discussedin Section 3. The ordered sequence of RSSs collected duringthe online localization phase is now directly compared witheach grid point’s constraint set without the need for translationinto distance constraints using (13). The experimental resultsshow that the performance of our modified Ecolocation issignificantly better than the original Ecolocation scheme, andit also outperforms the RSS based algorithms. However, itsperformance is still inferior to our SSD based algorithms.

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TABLE 2Percentile values and averages of errors (in meter) when various fingerprints are considered for Wi-Fi (MN-assisted)

Training Dataset: Atheros, Testing Dataset: IntelAlgorithm (Fingerprint) 25th Percentile Median 90th Percentile Average

KNN (RSS) 3.42 5.04 7.90 5.06KNN (HLF) 1.61 2.61 5.34 2.92KNN (SSD) 1.60 2.58 5.15 2.86Bayes (RSS) 3.47 4.95 7.72 5.04Bayes (HLF) 1.64 2.63 4.86 2.80Bayes (SSD) 1.58 2.53 4.74 2.73Ecolocation 4.36 7.15 11.46 7.08

Modified Ecolocation 1.74 2.61 5.34 3.01Training Dataset: Intel, Testing Dataset: Atheros


Modified Ecolocation 1.85 2.90 5.29 3.10

TABLE 3Percentile values and averages of errors (in meter) when various fingerprints are considered for Bluetooth (AP-based)

Training Dataset: Class 1, Testing Dataset: Class 2Algorithm (Fingerprint) 25th Percentile Median 90th Percentile Average


Modified Ecolocation 1.84 2.95 5.05 2.99Training Dataset: Class 2, Testing Dataset: Class 1


Modified Ecolocation 1.90 2.96 5.14 3.10

5 CONCLUSIONS AND FUTURE WORK

In this paper, we define a robust location fingerprint, theSSD, which provides a more robust location signature com-pared to the traditional RSS in the presence of mobile nodehardware heterogeneity. Both our theoretical analysis andexperimental studies have shown that, regardless of whetherthe signal strength samples are collected at the APs (AP-basedlocalization) or at the MN (MN-assisted localization), SSDbased localization algorithms outperform those based on thetraditional RSS fingerprints, as well as several other techniquesthat are designed to mitigate the effects of MNs’ hardwarevariations. This conclusion could not be drawn in our earlywork in [1] where only AP-based analysis was carried out. Inthis paper, we also considered two different testbeds for Wi-Fi and Bluetooth which emulate MN-assisted and AP-basedlocalization, respectively. The settings and surroundings ofboth testbeds represent an indoor environment more practicallycompared to our initial lecture theater testbed of [1] which onlyconsidered an AP-based localization approach.

We point out two future directions. First, although previousworks on Bluetooth-based localization have largely provideddiscouraging results [36], or required the aid of additionalwireless technologies [20], our experience with Bluetoothshows that it is a promising technology as well that re-quires more investigation. Second, more experiments couldbe conducted in testbeds with different setup and size toexplore SSD’s viability across different settings. Moreover,investigating the impact of testbed’s grid size, and the samplecollection procedure’s effects (e.g., fewer samples at eachgrid) on our SSD based algorithms could certainly provideinteresting future work directions.

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A.K.M. Mahtab Hossain received his B.Sc.in Computer Science and Engineering fromBUET (Bangladesh University of Engineeringand Technology) in 2003. He then received hisM.Engg in Computer Science from AIT (AsianInstitute of Technology, Thailand) in 2005, andPhD in Electrical and Computer Engineeringfrom NUS (National University of Singapore) in2010. He joined the Internet Education and Re-search Laboratory (intERLab) of Asian Instituteof Technology (AIT) as a Research Specialist in

May, 2010. His current research interests include indoor localization,mobile IP, and wireless ad-hoc networks.

Yunye Jin (S’09) received the B.Eng degree(Hons) in computer engineering from NationalUniversity of Singapore in 2007. He is currently aPh.D. candidate in the Department of Electricaland Computer Engineering, National Universityof Singapore. His research interests are in thealgorithms and theoretical performance analysisfor indoor tracking and localization systems.

Wee-Seng Soh (S’95–M’04) received theB.Eng. (Hons) and M.Eng. degrees in electricalengineering from the National University of Sin-gapore (NUS) in 1996 and 1998, respectively. In1998, he was awarded the Overseas GraduateScholarship by the National University of Sin-gapore to study at Carnegie Mellon University,Pittsburgh, PA, where he received the Ph.D.degree in electrical and computer engineering in2003. Since 2004, he has been with the Depart-ment of Electrical and Computer Engineering,

National University of Singapore, where he is currently an Assistant Pro-fessor. Prior to joining NUS, he was a Postdoctoral Research Fellow inthe Electrical Engineering and Computer Science Department, Univer-sity of Michigan. He has served on the Technical Program Committees(TPC) of 20 conferences, and has also served as a TPC co-chair forICCS’08 and WUnderNet’11. He is currently serving as an area editorof Computer Communications (Elsevier) journal. His current researchinterests are in wireless networks, underwater networks, and indoortracking/localization techniques.

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Hien Nguyen Van received the B.Eng. (Hons)degrees in electrical and computer engineer-ing from the National University of Singaporein 2007. Since 2008, he has been a doctoralstudent with the Computer Vision Lab (Centerfor Automation Research) at the University ofMaryland, College Park. His current researchinterests are in statistical video analysis, com-pressed sensing and its applications to patternrecognition.

Documents

SSD: A Robust RF Location Fingerprint Addressing Mobile ...1 SSD: A Robust RF Location Fingerprint Addressing Mobile Devices’ Heterogeneity A.K.M. Mahtab Hossain, Yunye Jin, Wee-Seng