IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 8, NO. 12, DECEMBER

DECODE: Exploiting Shadow Fadingto DEtect COMoving Wireless DEvices

Gayathri Chandrasekaran, Mesut Ali Ergin, Marco Gruteser, Member, IEEE Computer Society,

Richard P. Martin, Jie Yang, and Yingying Chen

Abstract—We present the DECODE technique to determine whether a set of transmitters are comoving, i.e., moving together in close

proximity. Comovement information can find use in applications ranging from inventory tracking to social network sensing and to

optimizing mobile device localization. The positioning errors from indoor RSS-based localization systems tend to be too large, making it

difficult to detect whether two devices are moving together based on the interdevice distances. DECODE achieves accurate

comovement detection by exploiting the correlations in positioning errors over time. DECODE can not only be implemented in the

position space but also in the signal space where a correlation in shadow fading due to objects blocking the path between the

transmitter and receiver exists. This technique requires no change in or cooperation from the tracked devices other than sporadic

transmission of packets. Using experiments from an office environment, we show that DECODE can achieve near-perfect

comovement detection at walking speed mobility using correlation coefficients computed over approximately 60-second time intervals.

We further show that DECODE is generic and could accomplish detection for mixed mobile transmitters of different technologies (IEEE

802.11b/g and IEEE 802.15.4), and our results are not very sensitive to the frequency at which transmitters communicate.

Index Terms—Comovement, localization, Received Signal Strength Indicator (RSSI), shadow fading, correlation coefficient.

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1 INTRODUCTION

MANY location-aware applications benefit from higher-level information about the movements of transmit-

ters. One instance of such higher-level information iscomovement, which describes whether a set of transmittersis moving together on a common path. Comovementinformation could be used to infer containment relation-ships, indicating, for example, that two devices are ownedand carried by the same person, or that several tagged objectsare placed on the same pallet. It could also be used to infersocial relationships if the transmitters are carried by differentpersons or for optimizing localization system performance.

While it is straightforward to derive comovementrelationship from position coordinates and trajectoriesgenerated by a localization system, sufficiently accurateand precise data are not always available. Indeed, ourevaluation of a Bayesian WiFi localization system (M1) [1]shows that the location estimation errors lead to bias andvariance in the euclidean distance between two comovingtransmitters, making detection of comovement difficult.Global Positioning System (GPS) accuracy is also frequently

degraded in urban canyons [2], and even if signals areavailable, GPS receivers are not commonly used in portabledevices due to their high energy consumption. For indoorenvironments, localization systems require the presence ofmultiple landmarks or receivers, which adds infrastructurecost. Coarse comovement information can also be obtainedfrom connectivity through short-range radios [3]. This,however, requires tracking software to be installed on allmobile devices; it cannot easily be inferred throughinfrastructure solutions alone.

1.1 Overview of DECODE

In this paper, we propose the DECODE technique whichdetects comovement through correlated signal variationsover time rather than directly measuring the signaldifference between two transmitters. The technique caneither work in signal space, using Received Signal StrengthIndicator (RSSI) values, or in position space, using locationcoordinates derived from the signals.

DECODE can exploit commonalities in signal powervariations, because certain fading patterns of comovingtransmitters are similar. The wireless communicationsliterature [4] distinguishes shadow and multipath fadingeffects that attenuate or amplify a signal in addition to thepath loss due to communication distance. Shadow fadingrefers to obstacles in the environment that attenuate thetransmitted signal when it travels through the object. Themagnitude of this effect depends on the material and widthof the object (e.g., about 10 dB attenuation was observedwhen an outside antenna was moved inside of a vehicle [5]).Multipath fading describes the effect that objects in theenvironment reflect and scatter the transmitted signal, sothat the signal often arrives at the receiver along multiplepaths. The signal components constructively or destruc-tively interfere, leading to fast changes in received signalstrength if the position of the receiver changes by merely

IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 8, NO. 12, DECEMBER 2009 1663

. G. Chandrasekaran, M.A. Ergin, and M. Gruteser are with the WirelessInformation Networks Laboratory (WINLAB), Technology Centre of NewJersey, Rutgers, The State University of New Jersey, 671 Route 1 South,North Brunswick, NJ 08902-3390.E-mail: [email protected], {ergin, gruteser}@winlab.rutgers.edu.

. R.P. Martin is with the Department of Computer Science, RutgersUniversity, Hill Center, 110 Frelinghuysen Rd. Piscataway, NJ 08854-8019. E-mail: [email protected].

. J. Yang and Y. Chen are with the Department of Electrical and ComputerEngineering, Stevens Institute of Technology, Hoboken, NJ 07030.E-mail: [email protected].

Manuscript received 21 Nov. 2008; revised 29 Apr. 2009; accepted 25 June2009; published online 28 July 2009.For information on obtaining reprints of this article, please send e-mail to:[email protected], and reference IEEECS Log Number TMC-2008-11-0468.Digital Object Identifier no. 10.1109/TMC.2009.131.

1536-1233/09/$25.00 � 2009 IEEE Published by the IEEE CS, CASS, ComSoc, IES, & SPS

one-half the wavelength of the communication frequencyused (about 59 mm for ISM Band 2.4 GHz [6] can result insignal strength changes exceeding 20 dB). As transmitters orreceivers move, the time-varying attenuation due to theseeffects will be unique for each path in space. Two receiverscomoving with a separation of less than one-half wavelengthcan be trivially detected because they will experience nearlyidentical signal power curves (assuming same transmissionpower and antennas). For high communication frequenciesin the unlicensed band, however, only few transmitters willbe sufficiently close to allow such straightforward detection.Thus comovement detection has to allow significantdifference in signals due to multipath fading.

Thus, this paper presents the DECODE technique, whichdetects comoving transmitters by correlated signal changesintroduced by the shadow fading component in measuredsignals. While the multipath component of the signal differs,transmitters separated less than a few meters will often stillobserve commonalities in shadow fading since larger objectsin the environment tend to block all direct signal paths to thecolocated transmitters. To isolate the shadow fading com-ponent, DECODE first extracts periods of high signalvariance from the observed signal strength traces over time.When operating directly in signal space, DECODE removeshigh-frequency multipath components of the signal andcalculates a correlation coefficient over the filtered signal. Ahigh correlation coefficient indicates comovement of thetransmitters. When operating in location space, it calculatescorrelation over a time-series trace of coordinates reportedby a localization algorithm. Localization algorithms typicallyaverage signals over time, and thus, also largely filter outmultipath effects. Shadow fading can manifest itself as errorsin the localization output, which DECODE can exploit. Onekey advantage of applying DECODE in signal space is that,in typical indoor or urban outdoor environments whereshadow fading exists [7], DECODE requires only onereceiver to detect comovement, while localization systemsrequire signal measurements from multiple receivers.

1.2 Uses of Comovement Information

Many applications can benefit from comovement informa-tion. Some of the important ones are:

. Mapping Devices to Persons: Many location-awareapplications such as Friend finders are trackingdevices that act as a proxy to infer the position of thedevice owner. The proliferation of mobile devicesand distinct radio technologies on each mobiledevice make monitoring this mapping of devices totheir owners increasingly cumbersome. For example,as a mobile device moves from an outdoor to an in-building location, it may be tracked by a variety ofdifferent technologies each using a different deviceidentifier (usually a radio MAC address). Bymonitoring comovement of different transmitters, alocalization system may be able to infer whichdevices belong to the same owner, or whichaddresses represent the same device.

. Social Network Mining: Recent work [3] has soughtto infer social relationships from mobile deviceconnectivity patterns. Applications for such techni-ques include automatically determining access con-trol policies and viral marketing. Current techniques

monitor Bluetooth advertisement messages to deter-mine when and how long devices from differentowners meet. This requires software on mobiledevices. The comovement techniques could alsoextract this information through external observa-tions (from a communications base station).

. Localization Optimizations: Knowing that twomobile devices move together helps collaborativepositioning mechanisms that provide lower energyconsumption or better accuracy. For example, onedevice could power down its GPS receiver toconserve energy, while the other device’s receiverstill provides accurate position updates. In challen-ging environments for localization, position esti-mates may also be improved through redundancy.

The remainder of the paper is organized as follows: inSection 2, we review related research and Section 3 presentsthe DECODE technique. In Section 4, we discuss ourexperimental methodology and results. Section 6 discussesthe advantages of the DECODE system in signal spacecompared to the location space and the effects of environ-mental mobility on DECODE’s performance. Concludingremarks are given in Section 7.

2 BACKGROUND

2.1 Related Work

The previous works on detecting colocated and comovingobjects have either been based on absolute location of thetransmitters obtained using localization indoors and GPSoutdoors or from proximity sensing using short rangeinfrared (IR) or Bluetooth devices. We know of no otherwork that infers colocation or comovement directly fromsignal strength measurements. In this section, we classifythe related work into three main categories.

Mobility detection. Several earlier studies have concen-trated on distinguishing mobile and stationary transmitters.Sohn et al. [8] determine mobility from GSM traces usingseven different metrics one of which is the variance in SignalStrength which is similar to our approach. Similarly,Muthukrishnan et al. [9] discuss detecting mobility fromRSSI in WLAN. LOCADIO [10] again used variance to detectmobility and combined it with a two state Hidden MarkovModel (HMM) to eliminate oscillations between the staticand mobile states. We build on this work—detectingmobility is an integral component of the DECODE technique.

Proximity-based inference. Proximity based colocationinference techniques mainly consist of using short-range IRor Bluetooth devices to estimate distance between thetransmitters. The Reality Mining project [3] [11] usedBluetooth-capable GSM phones to record the other nearbyBluetooth devices and transmit them to the central server forinferring social interaction patterns. SpotOn system [12]used radio signal attenuation to estimate the relativedistance between the special tags. Though these techniqueslook attractive for colocation detection, they require trackingsoftware on the devices themselves and are effective only fordetecting devices that have the same technology. Ourscheme is more generic as it involves measurement of RSSIwhich is common to GSM, WLAN, Zigbee, and Bluetooth.

Distance threshold detection. This baseline detectiontechnique involves estimating the locations of differenttransmitters and deriving conclusions about comovement

1664 IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 8, NO. 12, DECEMBER 2009

based on the distance between the estimated positions of thetransmitters. Recent efforts have resulted in a plethora ofmethods to determine the locations of transmitters. Want etal. [13] used infrared and [14] employed ultrasound toperform localization. However, both of them requiredspecialized infrastructure to be deployed for performinglocalization. On the other hand, in spite of meter-levelaccuracy [15], using RSS [1], [16], [17] is an attractive approachbecause it can reuse the existing wireless infrastructure.

RADAR [16], the first algorithm for IEEE 802.11 trans-mitters in this category, uses RF Fingerprint information(vector containing known locations of transmitter alongwith a measure of the observed signal strength at differentreceivers) observed at three receivers and performs a nearestneighbor matching algorithm to determine the location ofthe transmitters with a three meters median accuracy.Elnahrawy et al. [1] use Bayesian learning algorithm on RFfingerprints observed at three or more receivers to obtain amedian 802.11 localization accuracy of 3-4 meters. The mostaccurate 802.11 location system to date is [18] which uses theHidden Markov Model and Bayesian inference derived fromobservations at nine different receivers yielding a medianaccuracy of 1 meter. Further, the average localizationaccuracy employing RSS in a 802.15.4 (Zigbee) network[17] and an active RFID system [19] is about the same withmedian errors around 3-4 m when using four receivers.

While the recent papers [20], [21] have reported a higheraccuracy localization techniques, these techniques requiretransmitters to perform synchronized communicationswhich is not common across typical transmitters that weanalyze in this paper. Further, these papers have notreported the accuracy in a mobile environment questioningits applicability for the detection of comovement.

Intuitively, one can derive comovement information withthreshold detection on the distance between two transmit-ters. Compared to DECODE (in signal space), all theselocalization systems require three or more receivers to workin concert, whereas DECODE can be used even with onlyone receiver. In addition, the accuracy results reported forWiFi localization raise questions about the precision of sucha detection approach. We will further address this questionin the next section.

2.2 Accuracy of Distance Threshold Detection

If current indoor localization systems can provide suffi-ciently accurate location coordinates, one would detectcomovement based on the distance between the twotransmitters remaining below a threshold of a few meters.To verify this intuition, we conduct an experiment in anoffice environment with coordinates reported by a WiFilocalization system using the RSS bayesian localizer M1 [1].M1 is a lateration-based Bayesian algorithm which encodesthe relationship between the RSS and 2D cartesian locationcoordinates using a simple log-distance propagation model.Using a training set (a vector of RSS for different knownðx; yÞ locations), M1 determines the propagation parametersfor each of the receivers. It then derives the joint probabilitydensity of ðx; yÞ as a function of the observed RSS for thepoint to be localized and uses the mean of the derived pdfto estimate the unknown location ðx; yÞ. M1 has been shownto provide qualitatively comparable accuracy to currentstate-of-the-art WiFi localization algorithms [1].

In our experimental setup, two IEEE 802.11g (WiFi)transmitters send 10 packets per second on the samechannel, while moving together with about 6 inch separa-tion within the office space at a constant speed of 1 m/sec.Four receivers recorded the observed Received SignalStrength (RSS) from this transmitter pair. We localize thesecomoving pair of transmitters in the 85 m� 50 m cubicleoffice environment using M1 every second, based on theaverage RSS reported over the last second. More details onthe testbed setup are provided in Section 4.1.

Fig. 1 plots the euclidean distance in geographic spacebetween the localized points for the pair of comovingdevices over time. We can see that the distance variesbetween 0.3 and 12 m with a mean distance of 2 m. Thishigh variance in the euclidean distance can be attributed tothe following causes:

1. Typical RSS-based localization algorithms exhibit arelatively high median localization error of 3 m evenunder no mobility. This localization inaccuracy canincrease with mobility thereby resulting in highdistance variance.

2. The Localization algorithm estimates X and Y forevery transmitter independently before the eucli-dean distance metric combines the estimated Xand Y from each transmitter. It is possible thaterrors add up temporarily. It is also possible that abias in the estimated values for one of theparameters could result in continuously higheuclidean distance estimates.

These high distance errors suggest that the distancethreshold detection approach cannot accurately determinecomoving transmitters. This further motivates the DECODEtechnique, which we will describe next.

3 DECODE SYSTEM DESIGN

The environment in which wireless communication takesplace affects the received signal power (or signal-to-noiseratio). The key idea underlying the DECODE technique isexploiting shadow fading, i.e., signal attenuation due toobjects blocking the path of communication. Two transmit-ters in close proximity will be similarly affected by surround-ing buildings, furniture, or passing people. Therefore, theobserved signal power from these transmitters should becorrelated. This similarity in signal strength, in turn, shouldalso translate to correlations in localization errors.

CHANDRASEKARAN ET AL.: DECODE: EXPLOITING SHADOW FADING TO DETECT COMOVING WIRELESS DEVICES 1665

Fig. 1. The euclidean distance between the localized ðX; Y Þ positions fora comoving transmitter pair.

DECODE captures these similarities by calculating thecorrelation coefficient over a time-series trace of signalstrength or location coordinate values. The correlationcoefficient measures the strength of a linear relationshipbetween two random variables. Thus, the correlationcoefficient captures similarities in the changes of two values,even if the absolute values are different. DECODE uses thePearson’s product moment correlation coefficient [22], apreferred method for quantitative measures such as theRSSI traces used. For comparison, we also evaluated anothermeasure of correlation, Spearman’s Rank correlation coeffi-cient [23]. Unless otherwise mentioned, correlation coeffi-cient will refer to Pearson’s product moment correlationcoefficient rxy in the remainder of this paper. For n sampleseach from two random variables X and Y , it is defined as

rxy ¼Pxiyi � nx yðn� 1ÞSxSy

; ð1Þ

where Sx and Sy are the sample standard deviations. Thecorrelation coefficient lies in the interval ½�1; 1�, where 0indicates no correlation, þ1 indicates maximum positivecorrelation, and �1 indicates maximum negative correla-tion. We empirically determined a correlation coefficientthreshold of 0.6 (see Section 4.4), and values that exceed thisthreshold indicate comovement.

Received signal strength, however, also significantlyvaries due to multipath fading. It can introduce receivedsignal strength changes of more than 20 dB between locationsseparated only by half the wavelength of the carrierfrequency, if no line-of-sight path to the transmitter isavailable. These variations render the similarities due toshadow fading difficult to detect. To address this challenge,DECODE calculates a moving average over signals, whichacts as a low-pass filter to reduce or remove multipath effects.

Movement also helps detection of shadow fadingsimilarities, because comoving transmitters will experiencereceived signal strength changes due to shadowing atsimilar points in time (e.g., two comoving transmitterswould pass a building corner at the same time). Intuitively,higher speed of the transmitters will increase the frequencyof these changes, and thus, facilitate comovement detection.Therefore, DECODE will focus on periods of high signalvariance, which typically correspond to movement.

Fig. 2 illustrates the system design and key processingsteps of the DECODE system, which can use received signalstrength or location coordinates for estimation. Bothapproaches share a number of common data collectionand preprocessing steps.

In both cases, the receiver measures the received signalstrength for packets emitted from transmitters. It reports atransmitter identifier, signal strength, and a reception timestampfor each observation to the DECODE processing unit,usually over an existing wired network infrastructure. Inour prototype, we have implemented DECODE by mon-itoring the RSSI values reported for each packet receptionby the receiver. RSSI has been shown to be a good indicatorof channel quality [24]; hence, it should provide adequateinformation about fading patterns. RSSI is also availableacross all wireless technologies, which allows measuringcomovement across different transmitters. For each trans-mitter, DECODE first performs time alignment and inter-polation to facilitate later processing in the face of missing

samples. It then extracts periods of high signal variance,which are likely to correspond to movement of transmitters.

This is followed by RSS or location-specific processingsteps. Finally, correlation coefficients are calculated for eachtransmitter pair and correlation values exceeding a speci-fied threshold indicate comovement of a transmitter pair.

In the following sections, we give details of the common,RSS-specific, and location-specific components of DECODE.

3.1 Common Components

The common preprocessing steps include time alignment

and extraction of high variance periods.Time alignment. The following comovement detection

seeks to compare RSSI values observed at the same timefrom different transmitters. The packets originating fromtransmitters attached to different devices may not besynchronized in time. Even if one attempts to synchronizetransmitters attached to the same device, the inherentchannel access delays will cause packets from thesedifferent transmitters to arrive at the receiver at slightlydifferent times. Depending on wireless channel conditions,packets are also lost due to collisions or path loss. Thus, thetime alignment step synchronizes the samples receivedfrom two transmitters. Given the packet traces for twotransmitters, our implementation matches every packetfrom the first transmitter with the last prior packettransmission from the second transmitter. If a sample ismissing from the second transmitter, this procedure


Fig. 2. System diagram and data flow.

replaces the missing sample with the last observed samplefrom the second transmitter.

Extracting high variance periods. Recall that DECODEfocuses on periods of mobility because during theseperiods, it can observe correlated signal changes due toshadow fading, and during these periods it can filter outmultipath fading. Several techniques have been proposed todetect mobility [8], [9], [10], [25]. Of these, we choose thestraightforward signal strength variance threshold detec-tion technique. DECODE divides the RSSI traces intoblocks. It then extracts and concatenates all blocks wherethe variance exceeds the specified threshold. We empiri-cally determined the variance threshold to be three (seeSection 4.6 for further discussion) and a suitable block sizeof 5 seconds for variance calculation.

3.2 RSS Estimation Components

If DECODE operates using RSS data, this is followed byfiltering out multipath fading and computing correlationover RSS values.

Filtering out multipath fading. While fading is commonin communication channel, the fast fading component wherethe signal varies in amplitude and phase over short periodsof times does not contain useful information about theshadowing profile of the environment. The variance due tofast fading should thus be removed from the RSSI traces toallow calculation of correlation primarily over shadowfading components. DECODE uses a moving windowaveraging process with a window size of 10 packets (1 sec).Fig. 3 shows an example of this filtering effect. Beforefiltering the received RSSI values vary by about 10 dB ontime scales of less than 100 ms. After processing, only slowvariations remain, which are expected from shadow fading.

Comovement detection. The final step involves calculat-ing correlation coefficient on the processed signal strengthvalues from the transmitter pair under consideration. If theresulting correlation coefficient exceeds a certain threshold,we classify the transmitter pair to be comoving. We givedetails on determining this threshold in Section 4.

3.3 Location Estimation Components

The location-based estimation approach calculates the samecorrelation metric over time-series location coordinate data,but it requires data from several receivers to be available

and a calibrated localization system. Our localizationsystem relied on an existing signal map of the building,which discretizes spaces and contains an observed signalstrength vector (each value corresponding to a differentreceiver) for each known location.

RSS fingerprint generation. The input to the locationsystem is a fingerprint, an R� Ts matrix containing RSSvalues, where R refers to the number of receivers (four inour setup) and Ts to a time window in seconds. To generatethese fingerprints, receivers report the transmitter identifier,signal strength, and a reception time stamp for everytransmitted frame to DECODE. After generating a time-aligned sample for the transmitter pairs at each of thereceiver and extracting the high variance periods, theresulting RSS samples for each transmitter-receiver pairare averaged over 1-second intervals and entered into thefingerprint matrix (one matrix per transmitter). If theinterval contains no observations for a specific transmit-ter-receiver pair, the fingerprint generator fills in a localiza-tion algorithm-specific default value of �99.

Localization. We use a Bayesian solver [1] called M1 toperform localization. M1 is initially provided with a signalmap (or training set) containing measurements from 88different locations in 2D space within the building wherewe carried out the experiment. M1 then transforms eachfingerprint matrix into a 2� Ts matrix of cartesian locationcoordinates over time, one location estimate per second.

Comovement detection. The final step involves detect-ing comovement from the (X, Y ) estimations at everysecond for different transmitters. To verify whether a pair oftransmitters move together, we estimate their similarity inX or Y coordinates using correlation coefficient. If thecorrelation coefficient for X or Y is over a certain threshold,we declare the transmitter pairs to be moving together.While it may be possible to combine the inference about thecorrelation in X and the correlation in Y , we do not addressthis in this paper.

4 RESULTS

4.1 Experimental Methodology

The measured environment is a typical office environmentwith partitioned cubicle offices. The experiments wereperformed during normal office hours where one couldexpect dynamic changes in the environment as a result ofthe mobility of the people within the office. We set up bothIEEE 802.11b and IEEE 802.15.4 receivers within the officespace and place them at strategic locations as shown bystars in Fig. 4. The WiFi receivers(landmarks) in these fourlocations operated in promiscuous mode in 2.4 GHz, ISMBand Channel 1 to capture all the packets in this particularchannel. A Tmote Sky mote configured as receiver wasattached to each of the landmarks to capture packetsoriginating from Zigbee transmitters. These motes operatedin 2.4 GHz, ISM Band Channel 16.

We used four IEEE 802.11b cards and four Tmote Skymotes as transmitters where a pair of WiFi cards and a pairof motes were placed together in the first laptop and theother pair of WiFi cards and motes were placed together inthe second laptop as illustrated in Fig. 5. The motes werebattery powered. The WiFi cards were connected to theconfigured APs and pinged the AP at the rate of 10 packets/sec with a transmit power of 15 dBm. And the motes were


Fig. 3. Smoothing data to remove fast fading.

configured to transmit packets at the rate of 10 packets/secat 0 dBm. We use the ORBIT infrastructure for capturingand logging each IEEE 802.11 and IEEE 802.15.4 packet fromthese transmitters to be stored in a SQL database. For eachpacket, we logged the transmitter’s MAC address (Mote IDin case of motes), the receiver’s MAC address (Mote ID incase of motes), RSSI and the time when the packet wascaptured. We also recorded the ground truth about whichtransmitter pairs were moving together along with thespeed and the start and the end times of the different staticand mobile periods of these transmitters manually. We notethat we set up pairwise transmitters in our experiments toshow how DECODE works, but our approach could beapplied to a set of transmitters that are comoving.

Two experimenters carried one laptop each (containingtwo WiFi and two motes) and conducted the experiment.The total experiment lasted for 1 hour with alternating staticand mobile periods as shown in Fig. 7. The authors werewalking at 0.3 m/sec (1 ft/sec) for about 20 minutes. We callthis experiment period Slow Mobility. We chose very slowspeeds because this represents the most challenging case.The same experiment was repeated once more where themoving speed of the transmitters was increased from 0.3 to1 m/sec (normal human walking speed). We refer to thissecond experiment period as Walking Speed Mobility. Werefer to these experiment traces as the complete traces.

To analyze the effect of mobility on the results, we thenalso create mobile-only traces by extracting and concatenat-ing the two 10-minute mobile periods into a 20-minutemobile trace. Using this technique, we both create a slow-mobile and a walk-speed mobile trace. We then use a time-based sliding window of time interval Ts seconds to sliceeach of the above data sets into overlapping test traces. Wevary the time interval Ts from 10 to 400 seconds in steps of10 seconds. For, example Ts ¼ 100 s would generate 1,101test traces of duration 100 seconds from the 1,200 seconds ofdata. We used these different sliced data sets with differenttime intervals Ts in our results to report the detection ratesand false positives.

4.2 Evaluation Metrics

We will evaluate the effectiveness of RSS-based DECODE inthe following three categories: 1) performance evaluation interms of the detection rate of comoving transmitters and thecorresponding false positive rate, 2) sensitivity study underdifferent packet sampling rates and various correlationcoefficient thresholds, and 3) generality investigation acrossdifferent correlation methods and wireless networks.Finally, we will study the effects of mobility detection onthe performance of DECODE.

4.3 Effectiveness of DECODE’s RSSI-BasedDetector

To evaluate the performance of DECODE, we first examinethe detection rate and the false positive rate of determiningthe comobile transmitters. Fig. 6 depicts the detection rateand the false positive rate as a function of time with respectto each receiver for the IEEE 802.11 network for both SlowMobility as well as Walking Speed Mobility experiments.

We compute the correlation coefficient for the samplesaccumulated over the last Ts seconds and if the computedcorrelation coefficient is larger than 0.6, the pairs oftransmitters are declared to be comobile. Otherwise, thesepairs of transmitters are declared to be not moving together.A detailed discussion of the choice of the threshold ispresented in Section 4.4. In our 20 minutes of mobile trace,we repeat the above procedure for all the generated datasubsets of duration Ts seconds. We then estimate detectionrate as the percentage of times DECODE correctly reportscomobility when the pairs of transmitters are indeedmoving together and False positive rate as the percentageof times DECODE incorrectly reports comobility when thetransmitters are not moving together.

Fig. 6 shows that in both the Walking Speed Mobility andSlow Mobility experiments, DECODE is able to detect allcomoving and noncomoving pairs over all the data subsetsaccurately. We can also see that increasing the observationtime Ts improves the comobility detection rate whilereducing the likelihood of observing spurious matches.

We found that the mobility speed also has an impact onthe time required to achieve high detection rate and lowfalse positive rate. In the Walking Speed Mobility experi-ment, it takes about 130 seconds to detect all comoving datasubsets. Whereas it takes around 370 seconds to achieve thesame in the Slow Mobility experiment. This suggests thatwith higher speed, more shadow fading effects can beobserved within a shorter duration, leading to improveddetection performance.

The results of the Slow Mobility experiment representdetection performance of DECODE under challenging


Fig. 4. Floorplan of the experiment environment and the node

placement.

Fig. 5. Nodes and the transmitters used in experiments.

conditions. For the rest of this section, we provideanalysis by using the Walking Speed Mobility experimentsince it represents more typical scenarios for devicescarried by humans.

4.4 Sensitivity to Sampling Rate and CorrelationCoefficient Threshold

We now study the sensitivity of our scheme with respect tothe different correlation coefficient thresholds and samplingrates, which we define to be the “packet transmission rateper transmitter.” To this end, we further process theWalking Speed mobile trace and extract 0.5, 1, 5, and10 packets every second from the trace to generate data setscorresponding to sampling rates of 0.5, 1, 5, and 10 packetsper second (pps), respectively. These four data sets arefurther sliced into several data subsets with time interval Tsseconds similar to our previous study for estimatingdetection and false positive rates.

Fig. 8 presents the detection rate and false positive rateas a function of time for packet sampling rates of 0.5, 1, 5,and 10 pps observed at receiver-2 (we do not present theresults from other receivers as the performance is verysimilar). The threshold of the correlation coefficient isempirically determined to be 0.6. We found that for thesampling rates of 1, 5, and 10 pps, the time taken to achieve100 percent detection rate and 0 percent false positive rate issimilar—about 130 seconds. With the low 0.5 pps, the timeto reach 100 percent detection rate increases marginally to150 seconds. This is encouraging as it indicates that

DECODE is not very sensitive to sampling rates in the1 pps range. This insensitivity can be because a highersampling rate would not provide additional gain comparedto lower sampling rate as long as the lower sampling rate isfast enough to capture the “shadowing events.”

This insensitivity to sampling rate also allows reducingthe overall channel utilization, in a system design that relieson explicitly transmitted beacons to allow comovementdetection. The transmission overhead would be negligible.For example, assuming a minimum packet length of29 Bytes (28 Bytes of Frame and 1 Byte of Payload), an802.11b station transmitting one packet per second at11 Mbits/s PHY rate takes 603:27 �sec [26] which accountsonly for 0.06 percent of channel utilization.

We next analyze the sensitivity of DECODE to thecorrelation coefficient thresholds � . Choosing an appropriate


Fig. 7. The experimental procedure.

Fig. 6. IEEE 802.11 network: effectiveness of DECODE in terms of detection rate and false positive rate. Plots for the (a) Walking Speed Mobility

experiment and (b) Slow Mobility experiment.

Fig. 8. IEEE 802.11 network: sensitivity of DECODE versus samplingrate.

threshold will allow our detection scheme to be robust tofalse detections. Fig. 9 presents the detection rate and thefalse positive rate for � equaling 0.4, 0.5, 0.6, 0.7, and 0.8. Asexpected, we observe that the detection rate takes longer toreach 100 percent as the threshold goes up, while the falsepositive rate drops to 0 percent quicker. The threshold � ¼0:6 achieves the best balance with a false positive rateremaining below 10 percent at all times and the detectionrate reaching 100 percent nearly as fast as the smallerthresholds 0.4 and 0.5. Hence, we chose a threshold of 0.6 forall other experiments.

4.5 Generality of RSSI-Based DECODE

We now study the generality of DECODE in using differentcorrelation methods to determine comoving transmittersand its generality across both IEEE 802.11 as well as IEEE802.15.4 networks.

Different correlation methods. We compare our correla-tion coefficient method (i.e., Pearson’s product momentcorrelation coefficient) with Spearman’s rank correlationcoefficient in Figs. 10a and 10b for the IEEE 802.11 networkand the IEEE 802.15.4 network, respectively. The correlation

coefficients are computed for all the comoving andnoncomoving pairs of transmitters. Note that we refer tothe Pearson’s product moment correlation coefficientmethod as correlation coefficient in the figure.

We observed that both the correlation coefficientmethods perform similarly for the comoving and thenoncomoving pairs of transmitters. For the comoving pairs,the correlation coefficients from both methods are above0.6, while for the non-comoving pairs, both have values ofcorrelation coefficient below 0.2.

Different wireless networks. Fig. 11 presents the resultsof correlation coefficient calculated across an 802.11transmitter and an 802.15.4 transmitter.

We found that the correlation coefficients for comovingpairs for both the 802.11 as well as for the 802.15.4 areconsistently high (larger than 0.6) across all receivers. This isbecause when there is an obstruction to the Line-of-Sightsignal component due to walls and other objects, both theWiFi and the mote transmitters experience similar shadow-ing effect as they are placed close enough. Though the actualamount of the degradation of signal differs, the relativeeffects are the same. Since Pearson’s correlation coefficientmethod removes the sample mean from its estimation,similar relative performance is enough to capture comovingtransmitters. This result is strong evidence that ourapproach is generic across different networks.


Fig. 9. Sensitivity of DECODE to correlation coefficient threshold. Wepick a threshold of 0.6 for comovement.

Fig. 10. Comparison of correlation coefficient methods for WiFi and Mote radio pairs. (a) IEEE 802.15.4 network and (b) IEEE 802.11 network.

Fig. 11. Correlation coefficient for colocated Mote and WiFi.

4.6 Significance of Mobility Detection for DECODE

In this section, we examine how mobility detection impactsthe performance of DECODE.

Effects of mobility detection. Fig. 12 plots the correla-tion coefficient at all four receivers for comoving transmit-ters in the Walking Speed Mobility experiment. Thecorrelation coefficient is computed over the entire durationof the experiment as well as just over the mobile periods.

We found that the mobility detection helps in increasingthe values of the correlation coefficient for comovingtransmitters by an average of 20 percent. During staticperiods, the comoving transmitters do not experiencesignificant changes in shadow fading, but may experiencesmall scale fading effects that differ from one transmitter tothe other (if the separation is more than about 6 cm (�=2) for2.4 GHz). Thus, including static periods in the calculationstends to reduce overall detection performance, particularlyif the static periods are long compared to the mobile periods.

These results support our approach of first extractingmobile (high variance) periods.

Thresholds for variance and window size. For mobilitydetection, there are several metrics available as shown in[8]. However, we found that using a simple metric, varianceof RSS, is sufficiently effective. Further, two parameters areimportant when using the RSS variance to detect mobility:the threshold of variance and the number of RSS samples on

which the variance is calculated. Fig. 13 plots the trade-offbetween the detection rate and the false positive rate fordifferent variance thresholds and different window sizes forthe comoving WiFi transmitter pair.

We observed that the variance threshold of three has thehighest detection rate with false positives less than10 percent for all window sizes. We choose a window sizeof 50, where the detection rate is over 96 percent and thefalse positive is less than 1 percent. We estimate thecorrectness of these parameters across all transmitters tocheck the result consistency. Fig. 14 plots the detection rateand the false positive rate for mobility detection across therest of the six transmitters including both WiFi and motetransmitters. The results from Fig. 14 prove that our resultsare consistent across all the transmitters with high detectionrate and less than 10 percent false positive rate under awindow size of 50 and a variance threshold of three.

4.7 Comovement Detection in Location Space

As pointed out in Section 2, the euclidean distance betweenthe pairs of transmitters is not a very accurate estimator forcomovement detection. In this section, we evaluate theDECODE correlation estimation applied to individualcoordinates in location space.


Fig. 13. Effects of variance threshold and sample window size on mobility detection.

Fig. 12. 802.11 network: calculation of the correlation coefficient over theentire experimental period and over the mobile periods only. There is a20 percent improvement in the correlation coefficient values whenapplied over mobile periods only.

Fig. 14. Mobility detection of comoving transmitters under windowsize ¼50; variance threshold ¼ 3.

Fig. 15 plots the localized X and Y positions over timefor a pair of comoving WiFi devices that were attached tothe laptop 1 and moving at a speed of 1 m/sec. We canobserve that the X and Y coordinates estimated by thelocalization system for the two comobile transmitters arevery similar, but some differences exist. These differencescan be attributed to the sensitivity of the localizationalgorithm to small scale fading, which can affect bothtransmitters differently and resulted in the high variance ineuclidean distance, as was shown in Fig. 1.

However, by calculating the correlation coefficient overthe localized X position and the correlation coefficient overthe localized Y positions, we can achieve similar detectionperformance to the signal space technique. This is possiblebecause the correlation coefficient can ignore the absolutevalues and can capture the relative trend in the way the Xand Y coordinates vary (e.g, shadow fading is likely to leadto similar localization errors for both transmitters).

We evaluate the total time taken to achieve a 100 percentdetection rate and 0 percent false positive rate. We define thedetection rate as the percentage of times the correlationcoefficient computed for a comoving pair is above 0.6 andfalse positive as the percentage of times the correlationcoefficient for a noncomoving pair is above 0.6. Fig. 16 plotsthe detection rate and false positives as a function of time.Note that for simplicity, we have calculated correlationseparately for the X and Y coordinates. We can see that ittakes nearly 200 and 90 seconds for theX and Y coordinates,respectively, to achieve a 100 percent detection rate with0 percent false positive rate. The corresponding time takenby DECODE in signal space was 130 seconds. While thesetimes are comparable, there are several advantages of usingsignal space DECODE over location space DECODE—wediscuss them in the Section 6.

5 SIMULATION WITH DIFFERENT CHANNEL

PARAMETERS

After observing encouraging results in the experimentalindoor environment, we evaluate now whether theseexperimental results as presented in Section 4 are consistentwith results from simulation models and whether they canbe generalized to indoor and outdoor environments withdifferent propagation parameters. We also analyze theeffect of shadow fading on the detection time.

Our simulation methodology involves generating thereceived power at a receiver from three transmitters, two ofwhich are moving together on the same path and the thirdtransmitter following a different path. To allow comparisonwith the experimental results, the path taken by the movingtransmitters in the simulation was derived from theexperiment paths described in Section 4.1.

The simulator generates received power levels asfollows: From [27], we know that the received power at areceiver from a transmitter can be modeled as:

P ðdÞ ¼ P0 � 10� log10

d

d0

� �þ S� þ �; ð2Þ

where d is the distance between the transmitter and thereceiver; P0 is the received power at the reference distanced0 from the transmitter; � is the path loss exponent; S�represents shadow fading (i.e., correlated shadowing)which follows zero mean and � standard deviationGaussian distribution and � is the random noise.

To simulate correlated shadowing, the S� for differ-ent positions must satisfy the following exponentialconstraint [28]:

E½S�ðPiÞS�ðPjÞ� ¼ �2e�Dij=Dc ; ð3Þ

where S�ðPiÞ and S�ðPjÞ are the shadow fading at locationPi and Pj, respectively. Dij is the distance between thepositions Pi and Pj. Dc represents the decorrelationdistance, which can range from 1-2 m indoors to manytens of meters outdoors. We generate such correlatedGaussian random variables S� by multiplying uncorrelatedGaussian random variables with the upper triangularmatrix from a Cholesky decomposition of the correlationmatrix [29]. In our case, the correlation matrix is initializedwith the desired correlation values e�Dij=Dc between eachtransmitter (position) pair.

As shown in Table 1, we considered four scenarios withdifferent propagation parameters, two for indoor environ-ments and two for outdoor environments. For the indoor


Fig. 16. Effectiveness of correlation coefficient applied over the localizedX and Y locations for comovement detection.

Fig. 15. Localized X and Y positions for a pair of comoving WiFidevices.

environments, we chose standard deviation of the receivedpower by measuring in our experiment environment. Sincethis standard deviation combines both correlated shadow-ing and random noise, we simulate two indoor scenarioswith different assumptions on the level of shadowing andnoise. While Gudmundson’s exponential [28] decay modelhas been proposed for medium to large cellular networks inthe outdoor environments, Jalden et al. [7] have shown thatthis exponential model can be adapted for analyzing thespatial correlation arising from shadowing in the indoorenvironments. We obtained the other indoor and outdoorparameters including the propagation exponent from otherreported measurements [7], [30]. The last column of thetable also shows the result, the total time taken for detectingcomovement without false positives. The results showsimilar detection times across all four scenarios, indicatingthat DECODE is not very sensitive to propagation para-meters. This is encouraging and shows that DECODE canbe expected to also work in outdoor environments withtypical parameter settings.

While the simulation results show slightly lower detec-tion times, 80-108 seconds compared to 130 seconds in theexperiment, the results are on the same order of magnitude.The difference can be attributed to modeling and measure-ment inaccuracies. We measured the standard deviation inpower (4.3 dB) within the office environment several monthsafter conducting the DECODE experiments. Also, thesimulation model assumes that power measurements followa Gaussian distribution NðP0 � 10� log10ðdn=d0Þ; �2

RSSÞ,which may not be fully accurate.

The indoor results also show that increasing the corre-lated shadowing reduces the overall detection time from110 seconds to 81 seconds. A similar trend can be observed inthe outdoor results. This indicates that the presence ofcorrelated shadow fading leads to faster detection and isbeneficial for DECODE.

6 DISCUSSION

In this section, we discuss the feasibility of detectingtransmitters that are static and located within close proximity.We continue the discussion by giving out the advantages ofoperating in the signal space in comparison to the locationspace. We finally conclude this section by discussing theimpact of missing samples on comobility detection.

6.1 Feasibility of Detecting Colocation

The comovement detection results described so far raise thequestion whether the DECODE technique can also be usedto detect stationary colocated transmitters. Ostensibly, anenvironment with high surrounding mobility could lead tosimilarly high signal variance even though the transmittersand receivers are stationary, because the moving objects can

temporarily block transmission paths, which changesshadow and multipath fading patterns.

To investigate whether human mobility in a cubicleoffice environment is sufficient to also allow detection ofcolocated stationary transmitters, we performed an experi-ment where a pair of mote transmitters were attached to themain doorway within the WINLAB office, which is an areawith frequent human traffic (it is located next to a printerand water cooler providing additional traffic).

Table 2 shows the correlation coefficients obtained forthe stationary transmitter pairs by each of the receivers overa 2,000-second interval (the transmitters actually movedwhen the door was opened, but this occurred only twice inthis period). Note that all correlation coefficients are farbelow the 0.6, our correlation threshold for comovementdetection. Note also that some of the receivers showcorrelation coefficient values near zero, which suggests thatreducing the detection threshold would not be veryeffective. Thus, these results show that in a typical officeenvironment, surrounding mobility is unlikely to inducesufficient correlated fading to allow use of the DECODEtechnique for detecting colocated transmitters (even withthe extended 2,000-second measurement period, comparedto the 130-second period that was sufficient for comove-ment detection as shown previously).

6.2 RSSI-Based versus Location-Based Detection

While accuracy of DECODE in both signal and location-space is similar, applying DECODE in signal-space pro-vides several advantages, particularly if location informa-tion is not needed for other applications. However, there arechallenges to be addressed before one could assumelocalization systems are sufficient for the purposes ofcolocation detection:

. Generality: Most localization systems use thealready computed training set to determine thelocation associated with any fingerprint. However,this approach requires the TX power settings duringthe training and the testing phase to be same inorder to estimate the correct location. With a widevariety of wireless devices in the environment, thisrequirement makes localization technique highlysensitive and error prone, while RSS-based comove-ment detection is more agnostic to these issues. Also,different radio technologies may need different


TABLE 2Correlation Coefficient for the

Time Interval t ¼ 3;100 seconds to t ¼ 5;100 seconds

TABLE 1Summary of the Parameters Used in Simulations along with the Total Detection Time

localization systems with different accuracy limits,making colocation detection for radios belonging todifferent technologies nontrivial using these sys-tems, while we showed that the RSS-based techniquecan be used across wireless technologies.

. Localization Overheads: Calculating absolute loca-tion of a device takes time and requires signalinformation from multiple points of contact (e.g.,three reference points for trilateration), which maynot be available at all times.

. Infrastructure Costs: Investing in the localizationinfrastructure, including the equipment costs as wellas maintaining signal maps, beacon or landmark(receiver) positions, etc., might be costly. In compar-ison, the RSS-based comovement detection techni-ques only requires a single receiver.

6.3 Impact of Missing Samples on ComobilityDetection

As explained in Section 3.1, the time alignment step involvesreplacing the missing samples from a transmitter with its lastobserved sample. While this step aids in comparison ofsignal strengths from transmitter pairs, excessive replace-ment of missing samples could overstate the correlationbetween transmitter pairs. To this end, we analyze thepercentage of times the missing samples have been replacedduring this step for a WiFi-WiFi pair at Receiver-1.

Fig. 17 shows that very few consecutive packet lossesoccur. Eighty percent of packets have been correctlyreceived. Fifteen percent are intermittent single packetlosses and only 5 percent of packets are consecutive packetlosses. We also verified these packet loss rates across all theother transmitter receiver pairs and found similar results.Since DECODE uses a moving window average over1 second of received packets to remove multipath fading,interpolation of these packet losses has little effect on thecorrelation results.

7 CONCLUSION

In this work, we presented DECODE, a system that detectscomoving wireless devices. DECODE’s strategy is foundedon exploiting the similarity in shadow fading for thepackets transmitted from a set of transmitters. We showedthat our technique can work in both the signal space and itscorresponding location space, but that the signal spaceapproach provides the key advantage that only a singlebase station is needed. Our approach was general in that itcould detect comovement of wireless transmitters withdifferent radios. We validated our approach through

simulations for various indoor and outdoor environments.We further demonstrated that it works for both IEEE802.11b/g WLAN and IEEE 802.15.4 Mote devices in realindoor environments.

Given 130 seconds of mobile data, DECODE can achievea true positive rate of 100 percent with 0 percent falsepositive estimated over 1,071 data subsets. However, a keyfinding of this work is that mobility is critical for ourapproach, and that the DECODE’s effectiveness scales withboth the time and speed of the devices mobility. We alsoshowed that DECODE’s performance is insensitive to thesampling rate and a sampling rate of 1 packet/second for130 seconds was sufficient to achieve a 100 percentdetection rate and 0 percent false positive rate.

Finally, because DECODE’s effectiveness is quite sensi-tive to mobility, we examined using the RSSI variance formobility detection. We found this technique to have amobility detection rate of over 96 percent with thecorresponding false positives to be less than 1 percent.Therefore, detecting mobility has a straightforward solutionand does not limit the DECODE system.

ACKNOWLEDGMENTS

This material is based upon work supported by the USNational Science Foundation under Grant No. CNS0627032. This work is supported in part by the US Army,Picatinny, under Contract No. W15QKN-05-D-0011.

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Gayathri Chandrasekaran received the MSc(Tech) degree in information systems from BITSPilani, India. She is currently working toward thePhD degree in the Department of ComputerScience at Rutgers University and is a researchassistant in the Wireless Information NetworkingLaboratory (WNLAB) since 2006. Her researchinterests include pervasive computing, wirelessdevice localization, and experimental evaluationof networking protocols. She is a recipient of the

University Fellowship at the Ohio State University between the years2004-2006 and the JNCASR summer research fellowship in the years2003 and 2004.

Mesut Ali Ergin received the BS degree incontrol and computer engineering from Istan-bul Technical University, Turkey, in 1999 andthe MS degree in computer engineering fromBogazici University, Turkey, in 2003. He iscurrently working toward the PhD degree inthe Department of Electrical and ComputerEngineering at Rutgers, the State Universityof New Jersey, and is a graduate assistant inthe Wireless Information Networks Laboratory

(WINLAB) since 2004. His research focus is on the diverse aspectsof wireless and mobile networking systems.

Marco Gruteser received the PhD degree incomputer science from the University of Color-ado at Boulder. He is currently an assistantprofessor at the Wireless Information NetworksLaboratory (WINLAB), Rutgers University. Hisresearch interests include location-aware net-working and building reliable, secure, andprivacy-aware communication systems for vehi-cular networks. He is a member of the IEEEComputer Society and the ACM.

Richard P. Martin received the BA degree fromRutgers University and the MS and PhD degreesin computer science from the University ofCalifornia at Berkeley. He is an associateprofessor of computer science at Rutgers Uni-versity. He is a member of the Wireless NetworkInformation Laboratory (WINLAB). His currentresearch interests include wireless device loca-lization and human factors in dependablecomputing. His awards include the Best Paper

Award at the 2004 IEEE Conference on Sensor and Ad HocCommunication Networks as well as a CAREER award from the USNational Science Foundation (NSF). He has served as an investigatoron grants from the Defense Advanced Research Projects Agency, theUS National Science Foundation, and IBM.

Jie Yang received the BE degree in automaticcontrol from Beijing Institute of Technology,China, in 2004. He was in the PhD program inthe Department of Automatic Control of BeijingInstitute of Technology from 2005 to 2007. He iscurrently working toward the PhD degree in theElectrical and Computer Engineering Depart-ment at Stevens Institute of Technology. Hisresearch interests include the areas of informa-tion security and privacy, wireless localization,

and location-based services (LBS).

Yingying Chen received the PhD degree incomputer science from Rutgers University. Sheis currently an assistant professor in the Depart-ment of Electrical and Computer Engineering atStevens Institute of Technology. Her researchinterests include wireless and system security,wireless networking, and distributed systems.She is the coauthor of the book SecuringEmerging Wireless Systems (Springer, 2009).Prior to joining Stevens Institute of Technology,

she was with Bell Laboratories and the Optical Networking Group,Lucent Technologies. She received the IEEE Outstanding ContributionAward from the IEEE New Jersey Coast Section each year from 2000 to2005. She was the recipient of the Best Technological Innovation Awardfrom the Third International TinyOS Technology Exchange in 2006.


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