EM-ID: Tag-less Identification of Electrical Devicesvia Electromagnetic Emissions

Chouchang YangDisney Research

Pittsburgh, PA 15213email: jack.yang@disneyresearch.com

Alanson P. SampleDisney Research

Pittsburgh, PA 15213email: alanson.sample@disneyresearch.com

Abstract—Radio Frequency Identification technology hasgreatly improved asset management and inventory tracking.However, for many applications RFID tags are considered tooexpensive compared to the alternative of a printed bar code,which has hampered widespread adoption of RFID technology.

To overcome this price barrier, our work leverages the uniqueelectromagnetic emissions generated by nearly all electronic andelectromechanical devices as a means to individually identifythem. This tag-less method of radio frequency identificationleverages previous work showing that it is possible to classifyobjects by type (i.e. phone vs. TV vs. kitchen appliance, etc). Acore question is whether or not the electromagnetic emissionsfrom a given model of device, is sufficiently unique to robustlydistinguish it from its peers.

We present a low cost method for extracting the EM-ID from adevice along with a new classification and ranking algorithm thatis capable of identifying minute differences in the EM signatures.Results show that devices as divers as electronic toys, cellphonesand laptops can all be individually identified with an accuracybetween 72% and 100% depending on device type.

While not all electronics are unique enough for individualidentifying, we present a probability estimation model thataccurately predicts the performance of identifying a given deviceout of a population of both similar and dissimilar devices.Ultimately, EM-ID provides a zero cost method of uniquelyidentifying, potentially billions of electronic devices using theirunique electromagnetic emissions.

I. INTRODUCTION

The 5¢ RFID tag was famously claimed to be the tippingpoint for the RFID industry that would lead to wide spreadadoption of the technology. While many of the innovations incost reduction Sanjay Sarma outlined in 2001 [1] have come topass, the 5¢ price point has remained elusive at any quantity.Presently, EPC Gen2 tags (ISO-18000-6c) can be readilypurchased individually for ∼90¢ online and industry trademagazines report, that in large volumes, tags can be purchasedin the 7¢ to 15¢ range [2]. While both academia and industrycontinue to make strides to reduce the cost of traditional UHFRFID tags, the reality is that due to inflationary forces theopportunity for a 5¢ tag may have already passed us by.

In an effort to further reduce cost and tag complexity,researchers are focusing on developing chip-less [3], [4] andantenna-less [5], [6] RFID tags. The general rule of thumbbeing that; a third of the cost of the tag is the integratedcircuit (IC), the second third is the antenna inlay, and the finalthird represents the cost of bonding the two elements together.

Fig. 1. Depiction of an IT professional scanning the unique electromagneticnoise generated by the unmodified laptops in order to determine their uniqueIDs and recover their asset management information.

Therefore by eliminating one of these elements it should bepossible to further reduce the cost of RFID tags.

However, all of these RF methods of uniquely identifying anobject rely on adding some form of tag. For many applications,RFID is simply dismissed as being too expensive comparedto the alternative of using optical identification in the formof printed barcodes or QR codes. While barcodes are oftenconsidered to be “free” they do take up valuable real estate onprint media and/or require printed stickers that are manuallyapplied to objects; both of which represent non-zero costs.This begs the question: Can Radio Frequency Identificationever cost less then a barcode?

This paper argues yes it can; and in fact there exists a subsetof over a billion electronic devices that already have a uniqueradio frequency identity and are simply waiting to be read. Weintroduce a tag-less Radio Frequency Identification methodthat uses the unique electromagnetic signatures emitted byelectronic devices as a means to identify individual objects,even of the same type and model.

Traditionally Electro-Magnetic Emissions (EME) have beensimply thought of as incidental system noise that must be keptbelow a certain threshold to meet governmental regulations –the fact is that EME is highly structured and a direct mani-

Fig. 2. Diagram depicting the process of uniquely identifying an electronic device (in this case a unknown iPhone 6) based on its low frequency electromagneticemissions. Panel A shows a low cost EM-ID reader recording the EM signature shown in panel B. This signature is thresholded to remove noise and thedevice’s EM-ID is extracted as depicted in panel C. In order to identify what type of device it is, a classification algorithm is done in panel D. Once theobject type is known, it is compared to all other iPhone 6s in the database in order to recover its unique ID, as shown in panel E.

festation of the system circuits that generate it. Furthermore,variations in the manufacturing process at all levels, from theintegrated circuits, to passive components, and board levellayout all provide further EME differences between devices,even of the same model.

Figure 1 depicts an application scenario where an IT pro-fessional scans an unmodified electronic device (such as alaptop) to extract its unique EM-ID, which is then entered ina database. Since the EM-ID is persistent over time, the objectcan be scanned at a later date and its EM-ID is then comparedto a database for identification. This unique identificationmethod is free, in the sense that it already exists, cannot beremoved, or be easily tampered with, but it does require thatthe device is powered on to generate the EME signal.

Previous work has shown that EME can be used to identifygeneral classes of objects, for instance household appliances,computing devices, power tools, automobiles, etc. [7], [8].However, using these methods it is not possible to uniquelyidentify objects of the same model nor is it possible to assignthem individual IDs.

This work introduces a $10 electromagnetic emissionsreader based on a software defined radio and a signal pro-cessing pipeline that is capable of robustly and repeatedly ex-tracting the unique EM-IDs of individual devices. An overviewof the system is presented in section II, along with a detaileddescription of our new signal processing and ranking algorithmused to identify individual objects in section IV. Since theEME signals emitted by a device is an emergent propertyand not simply a fixed digital number, section V presentsa mathematical description of the probability distribution ofthe EME of a device and our ability to disambiguate similardevices in terms of the probability of successful identification.Lastly, experimental results are presented in section VI thatshow a wide range of devices of different complexity levels– from toys to cell phones and laptops – can all be uniquelyand repeatedly read and identified.

II. SYSTEM OVERVIEW

This section provides a general overview of the processof scanning, classifying and identifying individual devicesbased on their electromagnetic signatures. Subsequent sections

provide a deep dive into the algorithms needed for fine-grainedobject identification, and details on predicting the identificationperformance of a given set of devices.

A. EM-ID Reader Hardware

While lab equipment such as spectrum analyzers and high-speed oscilloscopes have traditionally been used to capturethe electromagnetic signatures emitted by electronic and/orelectromechanical devices, this work utilizes a software de-fined radio module based on the RTL-SDR, which works inconjunction with a smartphone or laptop to form a portableand low cost EM-ID scanner.

The RTL-SDR [9] is sold online for $10 (USD) andis based on the Realtek RTL2832 chip which performs I-Qdemodulation and digitizes the IF signal with high speed 8-bitADCs. The system streams raw data to a host computer viaUSB. In order to sample the low frequency EMI, the RF frontend chip was removed and a WD2142 transformer is used tofeed the raw EMI into the RTL2832 as previously describedin [7].

The internal digital mixer and low-pass filter providesa selectable frequency window from 0 to 28.8 MHz at amaximum sampling rate of 3.2 MHz. Since most EM signalsexist at the low frequencies of this range, the sampling rateis set to 1 MHz allowing the system to observe EM signalsfrom 0 ∼ 500 kHz. Figure 2 panel A, shows a smartphonebeing scanned. An antenna consisting of an electrically shortmonopole is used to capture the EMI signals, which are fedto the modified RTL-SDR, and then processed by a host PC.

B. EM-ID Extraction

Once the EMI signal is digitized and sent to the host PC,it is converted to the frequency domain as shown in figure2, panel B. In order to extract an EM-ID from the raw FFTdata the low magnitude noise must be removed. This is doneby setting a threshold, which is 1% higher then the differencebetween the peak and average signal magnitude: Threshold =(peak−mean) ·1%+mean). Data points above the thresholdare stored in an array of frequency and magnitude pairs thatrepresents the EM-ID of the device, as shown in figure 2, panelC. While the number of frequency peaks is dependent on the

device type, typical EM-IDs have a length of 1,000 to 2,000elements.

Once the EM-ID of a device has been recorded it can bestored in a central database along with other asset managementinformation such as asset number, manufacturer, model, owner,etc. Since an EM-ID is based on random variations in themanufacturing of the electronic device it cannot be knowna priori. Thus each device must be registered in a database,which is a typical process for most RFID applications.

C. Category Classification and Device Identification

As with all RFID systems, the ID number is simply ameans of linking a physical object to a database of attributes,or to trigger an event/action. Since the EM-ID cannot storecustom user information, the challenge is to link the EM-IDof a unknown scanned object to the correct entry stored inthe database of EM-IDs. The process of quickly searching adatabase is more complicated for EME based identificationsince the EM-ID is an emergent statistical property of thesystem rather then a digitally stored ID number.

As will be described in detail in section IV the EM-ID isthe result of a probability distribution. While it will be shownthat the probability distribution from one device to the nextis indeed unique and non-overlapping (and thus sufficient foridentification), for the purpose of this system overview it isimportant to remember that from one read to the next theremay be a small perturbation in a device’s EM-ID. Therefore,to uniquely identify an individual device (that exists in thedatabase) there is a two-stage ranking process starting withcategory classification and then device identification.

The goal of the category classification stage is to determinewhat type of device the unknown object is, thereby greatlyreducing the search space for the identification stage. Asdescribed in Laput et. al. [7] category classification based onsupport vector machines (SVM) can be done robustly acrossa wide variety of devices. In this paper, we implemented aranking system based on the cosine similarity function, whichgreatly reduces the computation complexity and eliminatesthe need for training. As shown in figure 2, panel D, thefrequency components of the EM-ID of the unknown iPhone6, is compared to examples of each of the subcategories. Theresults are ranked and the high matching categories are flagfor further examination in the device identification stage.

To give some intuition into the effectiveness of this approachfigure 3 shows the frequency distribution of five exampledevices (MacBook Pro, toy lightsaber, florescent lightbulb,LCD Screen, and an iPhone 6). Visually, it is easy to see thatthe spikes along the 500kHz frequency range are identifiableand unique to each object. Once this data is thresholded,the resulting frequency and magnitude pairs are recorded androbust similarity scoring can be done.

The final stage of the process is device identification whichis depicted in figure 2, panel E. Here the goal is to disam-biguate one device from others of the same type and model;for instance one iPhone 6 from a population of iPhone 6s. Thisis a much more challenging task, as the frequency distribution

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Fig. 3. Representative frequency domain plots from 0 to 500kHz of the EMsignals from five different categories of devices. The distinctive patterns are amanifestation of the circuit topology and are unique to each device category.

will have a much higher likelihood of overlapping. As willbe described in detail in section IV, both the frequency andmagnitude components of the EM-ID will be used alongwith the cosine similarity function to uniquely determine theidentity of the unknown objects.

III. MITIGATING EM-ID READER VARIATIONS

As with all radio receivers, different EM-ID readers havevariations in signal sensitivity as well as different local oscil-lator precisions and offsets. Even when using the same EM-IDreader to rescan a given device, variations in reader antennaplacement and distance can result in differences in the absolutemagnitude of the EM signal. These variations can result inmeasurement uncertainty in both frequency and magnitude.The following section presents methods for mitigating theseissues, thus allowing multiple EM-ID readers to be usedinterchangeably.

A. Frequency Uncertainty

Since the local oscillator (LO) on the EM-ID reader is usedas a reference to measure the frequency components of thereceived electromagnetic emissions from the device under test,variations in the LO over time or from reader to reader, willresult in mis-measured frequencies. For example, in this work,the EME signals are sampled at a rate of 1 MHz and an FFTwindow size of 217 is used when converting from the timedomain to the frequency domain. This results in a frequencyresolution (or frequency bin width) of 7 Hz. However, thelocal oscillator used on the RTL-SDR has an accuracy of only10 PPM (parts per million) and at 1MHz this results in anuncertainty of 10 Hz. Thus, it is possible that a signal source of100 Hz will be measured by one reader at 110 Hz and anotherreader at 90 Hz, resulting in a mismatch between the respectivefrequency indexes in the recorded EM-ID. While changingsystem parameters such as FFT window size, sampling rate,and oscillator accuracy can improve the system, the underlyingproblem still exists. In typical radio systems this issue isovercome by having the transmitter send a pilot tone that thereceiver’s Phase Lock Loop can lock on to. However, no suchsignal exists for an EM-ID reader due to the emergent natureof EMI signals.

To mitigate the LO issue, a pseudo pilot tone approachis used where the frequency component with the maximumsignal strength of the first EM-ID is used to align to the secondEM-ID. Since the amount of frequency uncertainty is known,only peaks that are within +/-10 Hz are used for alignment,thus greatly reducing false matches for unrelated frequencyspikes. For example, consider an EM signal which has multipletones scattered over the frequency spectrum, two differentEM-ID readers may measure the strongest frequency tonedifferently at 100 Hz and 110 Hz. The frequency compensationalgorithm can shift all the 110Hz data by a -10 Hz offset.Therefore, after frequency offset compensation, two differentmeasurements reach a consensus in terms of frequency mea-surements such that later their similarity can be evaluated.

B. Magnitude Uncertainty

The magnitude of recorded EM signals can very signifi-cantly based on the EM-ID reader’s sensitivity and the distanceand placement of the reader antenna to the device under test.To deal with this issue, the signals are normalized to a unitvector for similarity evaluation in later sections. Generallyspeaking, measurements can be taken robustly without theuser worrying about aligning the antenna as long as it istouching or is tapped on the object of interest. For instancewhen scanning an iPhone, it simply needs to be placed onthe reader. Alternatively when scanning a MacBook Pro trackpad, the EM-ID reader antenna should touch the pad and notthe LCD screen, as that would be a different measurement.This does make the reasonable assumption that there is notsignificant frequency dependent attenuation caused by userantenna placement or drifts in the analog front end of thereader.

IV. FINE-GRAINED CLASSIFIER FOR SIMILARITYMEASURES

Similarity measures between datasets have been studied inseveral fields [10]. These techniques include Euclidian dis-tance, cosine similarity and relative entropy – these approachesquantify the similarity of two vectors in high dimensional dataspace [11], [12]. For example, cosine similarity computes thescore between two vectors [13], [14]; the higher the score ofcosine similarity, the more similar the datasets. In this section,a two stage cosine similarity algorithm is used to first classifythe category of an unknown device and then determine its trueidentity by correctly determining which entry in the EM-IDdatabase it belongs to.

A. Cosine Similarity

Cosine similarity gives a score based on the similarity of twovectors in higher dimensional space. Consider a time seriesEM signal x = [x1, x2, x3, ..., xn] sensed by an EM-ID reader.The frequency transform of this EM signal is X =F{x} andrepresents the EM signal’s frequency distribution. The vectorX=[X1, X2, X3, ..., XN ] represents the EM signal data in thefrequency domain where each element Xi (0 ≤ i ≤ N )refers to the signal strength at the ith frequency bin. Similarly,

another unknown EM signal after frequency transformation isY. By viewing each EM signal X and Y as n-dimensionalvectors, the cosine similarity yields a score calculated by:

C.S.(X,Y) :=X ·Y‖X‖ ‖Y‖

=

N∑i=1

XiYi√N∑i=1

X2i

√N∑i=1

Y 2i

(1)

A high value of cosine similarity corresponds to two similardatasets. Since each element Xi and Yi for 0 ≤ i ≤ Nrepresents absolute magnitude, the values Xi and Yi are alwayspositive – this results in a cosine similarity range between0 to 1. A similarity score of 0 implies two vectors areorthogonal to each other, while a score of 1 indicates the twovectors are identical. Given two known EM signals X and Yobtained from an electronic object “E” and “G” respectively,and an unknown EM signal X′, using cosine similarity wecan identify which object the unknown vectors correspondsto. If C.S.(X,X′) > C.S.(Y,X′), then the cosine similarityindicates the unknown EM signal X ′ is from the same sourceas X . Hence, the result identifies the unknown signal X′

as the electronic object “E”. Similarly, if C.S.(X,X′) <C.S.(Y,X′), the unknown EM signal X′ is identified as theelectronic object “G”.

1) Gain difference vs. Similarity: Equation (1) implies thatall gain differences can be mitigated because each vector Xand Y are normalized by their total energy ‖X‖ and ‖Y‖respectively. To illustrate this concept, consider a time seriesEM signal x′ that has α times more gain than x (x′ = αx).By applying the fact that the same gain exists in the frequencydomain as X′ = αX, the cosine similarity can be obtained by

C.S.(X′,Y) :=X′ ·Y‖X′‖ ‖Y‖

=αX ·Y

α ‖X‖ ‖Y‖= C.S.(X,Y)

(2)

Equation (2) indicates that the signal strength, in termsof gain coefficient, will be normalized through the cosinesimilarity calculation. Thus, once the EM signals for givendevices are established in a database, the cosine similarity canevaluate each new unknown EM signals even when measuredin different positions and orientations, or by different EM-IDreaders.

B. Category Classification

When EM signals are from different device categories, mostof their frequency bins do not overlap. This can be seen infigure 3, where the Macbook Pro trackpad, lightsaber, flores-cent lightbulb, LCD screen, and iPhone 6 have completelydifferent frequency distributions. Since their internal circuitstructures are completely different, different categories haveno or few common frequency bins. When two vectors haveno common frequency bins, the cosine similarity returns tozero. For example, two EM signals from different categorieswith frequency distribution X = [X1, X2, X3, 0, 0, ..., 0] and

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Fig. 4. Frequency domain plots from 0 to 500kHz of the EM signals for fiveidentical toy lightsabers. Since the same circuit is used in each device thefrequency distributions are similar to each other, although close inspectionshows that indeed there are small differences.

Y = [0, 0, 0, Y1, Y2, Y3, 0, 0..., 0] have zero scores. If EM sig-nal vectors are from different objects but are within the samecategory, their frequency distributions usually have a higherfraction of common frequency bins because of their similarcircuitry. The lighsaber example shown in figure 4 yieldsa very similar frequency distribution and a higher fractionof overlapping frequencies. Consider two EM signal vectorsfrom the same categories as X = [X1, X2, X3, 0, 0, ..., 0]and Y = [0, Y1, Y2, Y3, 0, 0, ..., 0], their cosine similarity isX2Y1+X3Y2

‖X‖‖Y‖ and returns a non-zero score. As a result, differentobjects within the same category can have a higher cosinesimilarity than objects from different categories. By leveragingthis property, we can first identify the class of device by usinga known vector for each category. For example, comparingan EM signal of an unknown device to representative EM-IDvectors from each category of devices in the database willresult in a set of similarity scores. The highest score willcorrespond to the category of the unknown device which canthen be used to narrow the search space when determining theidentity of the object among objects of the same category andmodel in the database.

C. Fine-grained Identification

Once the class of a device is determined, it is then comparedto all known objects in the database of the same class using thecosine similarity function to determine its identity. The highestscore is used to return the object’s true ID. For example, anunknown object is first compared to one representative of eachdevice class in the EM-ID database. If the highest scoring EM-ID vector is the lightsaber class then the unknown devicesis compared to all lightsabers in the EM-ID database. Thesystem will then return the ID of the lightsaber with the highestprobability match based on the cosine similarity function.

V. PREDICTING IDENTIFICATION SUCCESS RATE

Since the electromagnetic emissions from electronic devicesare an emergent property of the system and not specificallydesigned to be unique, there is the possibility that the EMemission spectrums of closely related devices will overlapcausing identification errors. To investigate this issue weemploy the Euclidean distance function [15] to analyze a large

population of EM signatures for a set of devices, and thenby modeling them as a Gaussian distribution we are able tocalculate the probability of successfully identifying a givendevice.

A. Performance Analysis via Euclidean distance

When comparing two vectors the Euclidean distance func-tion computes the same information as the cosine similarityfunction but its output is a linear vector rather then angle whichmakes it more applicable for plotting and manipulating prob-ability distributions. Given two EM-IDs represented by an ndimensional vector of frequency and magnitude pairs (X,Y).The Euclidean distance can be calculated by first taking theunit vector of each ux= X

‖X‖ , uy= Y‖Y‖ and then calculating

the distance between them d(ux,uy) = ‖uy − ux‖. Smallervalues of d(ux,uy) represent EM-ID vectors that are closelyaligned, while larger distance numbers indicate vectors thatare dissimilar.

We now return to the previous example of five identical toylightsabers as shown in figure 4 which consists of five knownEM signals A, B, C, D and E. With the goal of showing howrobustly lightsaber A can be identified out of the population offive devices; 300 scans of test data are taken of lightsaber Aand denoted by Ai where i = 1, 2, ..., k represents each trial.Computing the Euclidean distance of all 300 trials of Ai acrossthe five known elements in the data base (A, B, C, D and E)results in 1,500 similarity measurements. These measurementsare then normalized and plotted as the probability histogramdepicted in figure 5. For instance the red block of histogramdata shows all 300 distance measurements between lightsaberAi and A (i.e. measurements against itself). As expected thedistance between the test data Ai and A is smaller then thedistances reported for the other lightsaber, B maroon, C green,D blue and E khaki. Furthermore since the red histogram forA does not overlap with any of the other lightsabers this showsthat it is sufficiently unique, compared to its peers, such thatit can be robustly identified without errors.

While the toy lightsaber is an example of a class of deviceswhere it is easy to identify individual instances of the givendevice this is not always the case. As can be seen in figure 6,which shows a histogram of the measured Euclidean distanceof five identical Apple Macbook Pros. As in the previousexample 300 test scans of Macbook Pro B’s trackpad (Bi) wasscanned and the Euclidean distance between it and each of thefive Macbook Pros in the database (A, B, C, D and E) arecalculated and plotted resulting in 1,500 data points. Ideally,MacBook Pro B (shown as a green histogram) should havethe smallest distance when compared to others four MacBookPros. However, some of MacBook Pro’s E histogram (shownin khaki) overlaps with the MacBook Pro B which resultsin an increased probability of identification errors. These twoexamples show that the higher the degree of overlap of thehistogram (i.e. the higher the similarity between devices) thehigher the probability of classification and identification errors.Likewise if the histograms do not overlap and have widemargins the probability of errors is quite low.

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Fig. 5. A histogram of Euclidean distances between an unknown testlightsaber Ai and the five known lightsabers A, B, C, D and E who’sEM-IDs are stored in a database. In these example lightsaber A is correctlyclassified as matching Ai since it has the shorted distance and its probabilitydistribution does not overlap with any other lightsabers.

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Fig. 6. The histogram distances distributions of EM signal are constructedthrough Macbook Pro trackpad. The distances of the pair of EM signalscollected from Macbook Pro B has some overlapping areas with the distancebetween the pair of Macbook Pro Bi and E such that the identificationfailure happens. The error performance can be predicted through Gaussiandistribution models.

B. Gaussian Distribution Prediction

This section describes a method for quantifying the prob-ability of successfully identifying a device based on thedistribution of Euclidean distances. By modeling the histogramof distance data as a Gaussian distribution, as shown in figure5 and 6, the statistical information of all Euclidean distancescan be used to predict the systems ability to correctly identifyobjects based on their EM signal from a database of EM-IDs. Consider µS and σS , which are the mean and standarddeviation of all distances from the same electronic deviced(ux,ux′i

), while µD and σD are the mean and standarddeviation for all distances from different devices but withinthe same category d(ux′i

,uy). The probability of success canbe computed as:

PS = Prob.[ d(ux,ux′i) < d(uy,ux′i

) ] (3)

Equation 3 presents the probability of success by finding alldistances from the same device that are smaller than thosefrom different devices. Therefore, once µS , σS , µD and σDare obtained, the success rate PS can be calculated throughthe Gaussian distribution model for each electronic device as:

PS =

∫ ∞−∞

∫ ∞x

1

σSσD2πe− (x−µS)2

2σ2S e

− (y−µD)2

2σ2D dxdy (4)

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µS−3σS

∫ µD+3σD

x

1

σSσD2πe− (x−µS)2

2σ2S

− (y−µD)2

2σ2D dxdy

Figure 7 illustrates the probability of accurately classifyingan unknown device when the database contains overlappingGaussian probability distributions. The blue curve representsthe d(ux′i

,ux) distribution while the brown curve representsthe d(ux′i

,uy). The area shaded in blue indicates a TruePositive identification, where an unknown device is correctlyclassified as matching the test vector stored in the database.The area shaded in brown indicates a True Negative iden-tification, where an unknown device is correctly classifiedas not matching the test vector stored in the database. Theregion of space the falls under both the blue and browncurves that is shaded in red is a concatenation of the FalseNegative and False Positive identification, and represents anerror. Thus the more the two Gaussian distributions overlap,and the larger the red region, the higher the probability ofmiss classifying the unknown device. Therefore to calculatethe probability of successfully classifying an unknown device,one must calculate the area of the blue and brown shadedregions using equation 4.

Since 99.73% of the Gaussian distribution area is within3 standard deviations of distance, the integration area canbe reduced to obtain the approximate probability of success.Thus, this leads to one important result: a 100% successrate, or an error-free identification, must have the followingproperty:

ErrorFree : (µD − µS) > 3× (σD + σS) (5)

To achieve error free identification as defined in 5, therelative distance of the mean between the blue and browncurves must be more than 3 total sigma (σD +σS). Thisproperty can be used to predict why the lightsaber is errorfree while the Macbook Pro trackpad’s EM signals have someidentification failures. As will be shown in the results section,the equality (µD − µS) > (3 × (σD + σS)) is true for thelightsaber category, but not the Macbook Pro category.

VI. EXPERIMENTAL RESULTS AND PERFORMANCE

In order to evaluate the effectiveness of the proposedEM based identification system both category and individualdevice identification has been tested. Additionally the successrate prediction algorithm is also used to estimate performancewhich is then compared to measured results.

Fig. 9. Confusion matrices showing the likely hood that a particular device can be positively identified out of a population of devices of the same model.Note that the column denoted PS next to each matrix shows the predicted success rate of identifying each individual device.

Fig. 7. The successful rate of identification can be estimated through theintegral of true positive and true negative.

For testing, five different categories of electronic deviceswere chosen ranging from simple toys to laptop computers. Foreach device type identical versions of the same model wereused for testing the system’s ability to disambiguate similar

Fig. 8. Confusion matrix for category classification, showing that theEM-ID algorithm can reliably determine device type based solely on itselectromagnetic emissions.

electronic devices. A total of 40 devices were tested including:5x General Electric fluorescent tube light bulb (Model: GE-F54W), 5x Hasbro lightsaber toy (Model: A4571), 20x DellLCD 24 inch screen (Model: U2413F), 5x Apple iPhone 6(Model: A1549), and 5x Macbook Pro Retina Mid-2014. Forboth the iPhone 6 and Dell LCD screens the same imagewas displayed on each of the five respective units during EMmeasurements. For the five Macbook Pros the trackpad wasscanned with the EM-ID reader, although other parts of thelaptop such as the screen and keyboards could also be usedas a secondary measurement for redundancy.

To generate the EM-ID database each of the 40 deviceswas scanned once and their respective EM-IDs where stored.To show that the EM emissions from these devices are stableand persistent overtime the EM-ID database was collected onOctober 27, 2015 and the testing was conducted five monthslater on March 14, 2016. In order to show the reliability ofthe EM-ID system to both determine a device’s category andits individual identity, each of the 40 devices was scanned 30times and compared to EM-IDs in the database, resulting in1,200 total trials.

The results from category testing are shown in figure 8. Inorder to reduce computational complexity only one EM-IDfrom each of the five categories was used for comparison. Forexample when testing an unknown device, only one devicefrom each category was needed for comparison. Results showthat the cosine similarity function produced robust categoryclassification accuracy across the 1,200 trials with a totalaccuracy of 100%.

Once an unknown device’s category has been determinedthe second, and more challenging, task is to determine whichof the five devices of the same model it corresponds to. Again,the cosine similarity function is used to determine the degreeto which the EM-ID candidate vectors are aligned and the

one with the highest score is chosen as a match. Results areshown in the five confusion matrices in figure 9. Five identicalmodels of the lightsaber, MacBook Pro, florescent lightbulb,and iPhone 6 were each tested for 30 trials. For the HasbroLightsaber the identification algorithm perfectly identified theindividual device 30 times (i.e. Lightsaber A was correctlyidentified as Lightsaber A). The five MacBook Pros had anaverage identification accuracy of 94.6% and the GE florescentlightbulb had an average accuracy of 86%. Fortunately, 20units of the Dell LCD screen was available for testing andeach unit was scanned 30 times. The results shows and averageidentification accuracy of 94.7%.

The results for the iPhone 6 are less reliable with an averageaccuracy of 71.2%. This is primarily due to iPhone “C” beingcompletely miss-categorized as iPhone “B”. As described inthe text since the electromagnetic emissions generated by adevice are an emergent property it is not possible to ensurethat EM-IDs are always unique and never collide. Fortunatelythe algorithm for Predicting the Identification Success Rate(presented in section V was able to accurately predict thesefailures. This is shown in the columns labeled PS next tothe confusion matrices shown in figure 9, which accuratelypredicts the success rate. This is an important result since thesuccess rate prediction PS was made based only on the originalEM-ID database created in October. Thus when a user enters adevice into the database the success rate prediction algorithmcan be run and can alert the user if the new device is uniqueenough to be read or if an alternate strategy is needed.

Error free identification performance can be predicted byusing the Euclidian distance information in Table I, which listeach categories average distance and standard deviation. Forinstance, for the lightsaber µD - µS = 2.69 × 10−3 while3× (σD +σS) = 1.098× 10−3. Thus by using the criteria forerror free identification performance defined in equation (5),the lightsabers satisfies the equality µD - µS > 3× (σD+σS)such that it has error free identification performance.

TABLE IAVERAGE EUCLIDEAN DISTANCE FOR EACH CATEGORY: (UNIT:10−3)

Category iPhone6 MBPR Screen Lightsaber LightsAvg:µS 3.341 8.521 5.72 3.703 32.173σS 0.33 0.2356 0.483 0.253 1.867

Avg:µD 3.452 10.571 8.124 6.393 33.406σD 0.324 0.3325 0.351 0.113 1.763

VII. CONCLUSION

This paper proposes a method for individually identifyingelectronic devices, without the need to add bar codes are RFIDtags, simply by measuring the electromagnetic noise theygenerate when powered on. These electromagnetic emissionsare captured using a low cost (∼$10) hand held reader andcan reliably extract the EM-ID of the device under test.

This work presents a new computationally lightweight al-gorithm that can determine the similarity in the EM-IDsof devices and shows an increase in category classificationaccuracy over previous work. More importantly for the first

time, this work tackles the more challenging task of individ-ually identifying an unknown device out of a population ofthe same model. Testing was done on electronic devices assimple as a light saber toy and as complex as a smart phonewith identification accuracy ranging from 100% to 71.2%respectively.

While not all classes of electronic devices are guaranteedto be individually identifiable the success rate predictionalgorithm presented here can accurately predict the likelihoodthat a device can be identified out of a population of the samemodel. Ultimately the signal processing and mathematicalframe work established here, lays the foundation for utilizingelectromagnetic emissions for identification purposes.

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