Design and Evaluation of a Wireless Magnetic-basedProximity Detection Platform for Indoor Applications

Xiaofan Jiang, Chieh-Jan Mike Liang, Kaifei Chen, Ben Zhang, Jeff HsuJie Liu, Bin Cao, and Feng Zhao

Microsoft Research AsiaBeijing, P.R. China, 100080

{fxjiang, liang.mike, v-kaiche, v-benzh, v-jehs, liuj, bincao, zhao}@microsoft.com

ABSTRACTMany indoor sensing applications leverage knowledge of relativeproximity among physical objects and humans, such as the notionof “within arm’s reach”. In this paper, we quantify this notionusing “proximity zone”, and propose a methodology that empiri-cally and systematically compare the proximity zones created byvarious wireless technologies. We find that existing technologiessuch as 802.15.4, Bluetooth Low Energy (BLE), and RFID fallshort on metrics such as boundary sharpness, robustness against in-terference, and obstacle penetration. We then present the designand evaluation of a wireless proximity detection platform basedon magnetic induction - LiveSynergy. LiveSynergy provides sweetspot for indoor applications that require reliable and precise prox-imity detection. Finally, we present the design and evaluation ofan end-to-end system, deployed inside a large food court to of-fer context-aware and personalized advertisements and diet sug-gestions at a per-counter granularity.

Categories and Subject DescriptorsB.0 [Hardware]: General; B.4 [Hardware]: Input/Output & DataCommunication; H.4.m [Information Systems Applications]: Mis-cellaneous

General TermsDesign, Experimentation, Measurement

KeywordsLocalization, Magneto-Inductive, Tracking, Virtual Zone

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1. INTRODUCTIONLow-power wireless technologies are often used for both com-

munication and proximity sensing. Many applications rely on short-range radio to restrict which receivers can receive their transmis-sions. This property is particularly useful in person-scope appli-cations, where human wearable devices can discover and interactwith other devices embedded in the environment without explicithuman interventions.

To make these applications intuitive to human users, the discov-ered objects in the environment must be within the personal inter-action sphere, i.e., “within arm’s reach”. For example, a computermay automatically wake up when a user sits in front of the desk.But, it should not react to people walking past the cubicle. A de-partment store may tag a cloth rack to beacon the sizes and materialinformation of clothes hanging on the rack. This information onlyneeds to reach people who can see and touch these clothes. A homeappliance (e.g., refrigerator or microwave) may change its user in-terface when a person stands in front of it. All these applicationsshare the same architecture: wireless beacons are attached to sta-tionary objects, and mobile nodes carried by human communicatewith them when in their proximity. To make these applicationsrobust, beacon nodes must create predictable and stable coveragezones for mobile nodes to discover. In fact, fuzzy zone boundariesintroduce uncertainty to the user location, and can lower the userexperience with false positives, such as in the case of targeted ad-vertisement. Or, it can lower security such as in the case of buildingdoor access.

Many typical low power communication technologies, such asBluetooth 4.0 (a.k.a. Bluetooth Low Energy or BLE) and ZigBee(with 802.15.4 as the physical layer) have difficulties maintainingrobust communication zones. The 2.4 GHz band is prone to exter-nal interferences. Human bodies, Wi-Fi activities, and metal ob-jects nearby can significantly alter the signal propagation patternsand the receiving noise floor. While many previous research studiesthese effects from the perspective of point-to-point communicationlink quality, such as bit-error rates and data goodput [8, 19], littlehas been done to quantify the impact on proximity sensing at thehuman interaction scale. For example, humans react to messages inthe order of seconds. As long as a node can be discovered within afew seconds, there is no perceivable difference for the applications.On the other hand, human interactions are less tolerable to obstruc-tions, such as human body or any object blocking the view, and thereach between senders and receivers.

One of the key challenges for characterizing and comparing acrosswireless proximity technologies is to define comparison metrics.Practically, one can sample the waveform propagation field withonly discrete receivers. In this paper, we first propose a method-ology to reconstruct spatial and temporal patterns from wireless

receivers. Then, we apply a machine learning technique based onsupport vector machines (SVM) to infer parametric models fromdiscrete samples. With model parameters defining the white, grey,and black zones, the comparison is resilient to temporary noise anddisturbance.

Using this methodology, we empirically characterize and sys-tematically compare the proximity zones across several technolo-gies: BLE, 802.15.4, and 900 MHz RFID reader. Results show thatnone of them can produce robust proximity zones for person-scopeapplications. We then investigate an alternative wireless beaconbased on magnetic-inductive (MI) coupling. Humans, furniture,and most construction materials do not affect magnetic propaga-tion pattern. Thus, MI proximity zones are much more intuitiveand practical for human interactions than those using existing tech-nologies.

MI coupling components used to suffer from size, cost, and powerinefficiency. However, recent development in the hardware, espe-cially passive keyless entry (PKE) in the automotive industry, hasmade it feasible to integrate MI communication (MIC) onto mote-sized devices. To this end, we design and implement LiveSynergy,a MIC-capable platform with both MI transmitter and receiver. Weevaluate the proximity zone generated by this new kind of wirelesscommunication. The comparison to other alternatives shows sig-nificant advantages in zone consistency over time, boundary sharp-ness, and robustness to human obstruction. We also discuss our ex-perience from deploying a LiveSynergy-enabled system in a cafete-ria to deliver personalized nutrition and fitness information to din-ers at each food counter.

In summary, this paper makes the following contributions:

• We propose methodologies that empirically and systemati-cally compare the proximity zones created by various wire-less technologies.

• We design, implement, and evaluate a magnetic-inductionbased wireless proximity sensing platform - LiveSynergy.

• With real data traces, we show that MI solution exhibits sig-nificantly more robust zones than BLE, 802.15.4 and RFIDalternatives.

• We share the experience from deploying LiveSynergy in anreal-world application.

The rest of this paper is organized as follows: in Section 2, wesurvey related work in wireless proximity sensing. In Section 3, wegive an empirical definition of proximity zones and how to comparethem. After comparing the proximity zone for RF-based technolo-gies in Section 4, we motivate and describe our design of LiveSyn-ergy (Section 5). We evaluate LiveSynergy’s proximity zone prop-erties in Section 6 and demonstrate a real-world application deploy-ment at cafeteria in Section 7.

2. RELATED WORKProximity detection can be achieved either directly by proximity

sensing, or indirectly by inferring their absolute positions. Manyproximity sensing are initiated by humans, for example, using RFIDscanning [5], near field communication (NFC) touching, 1-D or 2-D barcode scanning, or optical sensing such as bokode [12]. Thesetypes of technologies require explicit actions from human, addinga level of inconvenience. Proximity sensing can also be initiatedby the environment, typically through computer vision techniquesusing conventional cameras [2, 20] or Kinect-like depth cameras.

Indirect sensing technologies are hard to achieve proximity ac-curacy. Positioning services such as GPS are not feasible indoors as

they require line-of-sight to satellites. Ultrasound-based localiza-tions [16, 6] do not penetrate solid objects. Infrared-based systems,such as ActiveBadge [21], have difficulty localizing objects withfluorescent lighting or direct sunlight, and have an effective rangeon the order of meters, offering poor granularity for small spaces.Inertia sensors based approaches, such as double-integration of ac-celerometer data, are often inaccurate due to large DC/offset er-rors, and require frequent recalibration [3]. Wi-Fi fingerprintingtypically requires a time-consuming training phase and costly re-calibration [23, 1]. Wi-Fi also suffers from multi-path effect andRaleigh fading, leading to inaccurate location estimates [18]. Us-ing pressure floor sensors for localization do not scale to large sys-tems [13] and they do not work for lightweight objects such as thoseon the table.

These technology are either too fine grained, requiring explicituser actions for discovery, or too coarse grained to pinpoint objectsor people within the vicinity of a few meters. As a result, currentapplications have to make compromises, such as requiring a personto actively swipe a security card when entering a building, or inac-curately localizing people based on association to the same Wi-Fiaccess point.

With increasing attention in this domain, several technologieshave emerged as leading choices for proximity detection indoors.Bluetooth Low Energy (BLE) is a part of Bluetooth 4.0 specifica-tion, targeting directly at low energy application. While it suffersfrom the same interference problem as other technologies on the2.4 GHz band, it has the unique advantage of being standard onnewer mobile phones. Long-range RFID is another technology de-signed for locating objects. While it has the advantage of usingpassive tags, it is easily affected by obstructions, and often requiresheavy post-processing for outlier reduction [22]. Devices based on802.15.4 (PHY and MAC layers of the Zigbee specification) havealso been used extensively, particularly in the sensornet commu-nity. In this paper, we evaluate these three technologies with respectto our application requirements.

LiveSynergy leverages dynamic magnetic fields for proximitysensing, which inductively couple the beacon and the receiver, alle-viating much of the interference issues. The theory of operation formagnetic tracking has been explored initially by Raab et. al. [17],and has been commercialized for high-end motion tracking appli-cations [15]. These commercial devices are typically expensive andnot optimized for proximity detections at scale. In [9], Markhamet. al. used magneto-induction for tracking underground beavers.Their system uses a relatively large antenna (5m × 1m), which isnot practical in indoor scenarios, but provided the inspiration forour work. We compare our platform against 802.15.4, BLE, andlong-range RFID, and evaluate it under a real application deploy-ment.

3. PROXIMITY ZONEThe desired length of “an arm’s reach” is application dependent.

For example, in the scenario of automatic computer-login, the de-sirable range of detection is perhaps the size of the cubicle or theimmediate space in front of the monitor; in case of targeted adver-tising at the shopping mall, the desired detection range could bethe two-meter space in front of a particular brand of merchandise.While the detection range is moderate in these applications, thesharpness and consistency of the detection boundary are of greatimportance, and directly impact the application performance anduser experience. For example, in the first scenario, if the region ofdetection has a large “grey” area where detections happen sporadi-cally and unpredictably, the computer may not identify the correctuser in a timely manner.

Figure 1: Example proximity zone from single dimensionaldataset.

In this paper, we refer to this detection space as the proximityzone. Being able to accurately specify the “proximity zone” of anobject or space, and reliably tell when someone enters and exitsthis zone are paramount for these applications. In the rest of thissection, we first list a set of evaluation metrics for the proximityzones common to this class of person-scope applications. Basedon these metrics, we propose an empirical definition of “proxim-ity zone”, which allows us to better evaluate and compare varioustechnologies.

3.1 Evaluation MetricsThe following are the metrics for evaluating proximity zones:

• Boundary sharpness: The boundary of the proximity zoneshould be as binary as possible - either in or out. The regionof sporadic detections, or the “grey” zone, should be mini-mal.

• Boundary consistency: Proximity detection should be con-sistent over time. This requires the technology to be robustagainst temporal interferences such as external noises and en-vironmental variations.

• Obstacle penetration: Since both the beaconing node andthe listening node can be mobile, e.g., being carried by hu-mans, the radio technology should be robust against obstruc-tions such as the human body. Specifically, the radio recep-tion should not change significantly regardless locations onthe body.

• Additional metrics: A number of additional properties alsoimpact their real-world applicability, such as the range andthe geometric shape of the zones, the beaconing frequencyachievable by the transmitters, the power consumption of thedevices, the form-factor of the mobile tag, and the cost of theoverall system.

3.2 Empirical DefinitionSince waveform propagation from beacons is a physical phe-

nomenon, it is more useful to characterize the zones from the re-ceiver’s point of view. We propose an empirical definition for prox-imity zones, inferred over the packet reception ratio and the trans-mission distance. Unlike ranging, where RSSI or LQI is sometimesused to infer distance information, in the case of proximity detec-tion, the event of successfully receiving a packet is the primaryindicator of whether the receiver is within the proximity zone of abeacon 1.

While this definition relies on empirical data collected at the re-ceiver, and is only a sampling of the space, it provides a practicalmetric for evaluation against real application requirements.

Conceptually, a proximity region consists of three adjacent zones:the white zone, the grey zone, and the black zone. It is also equiva-lently defined by two boundaries: the white/grey boundary and thegrey/black boundary. Figure 1 shows the one-dimensional view of1However, we do use RSSI in deciding the primary zone in the caseof overlapping zones (c.f. Section 7)

a proximity zone based on measurement data along a single axis.With additional data, the same methodologies described below canwork on two-dimensional and three-dimensional proximity zones.After empirically determining proximity zones, we can then eval-uate the above metrics of boundary sharpness, consistency and soon.

The classification and evaluation procedure starts from collectingpacket reception statistics at various distances from the transmitter.Then, these statistics allow us to classify and determine whethereach location falls in the white, grey, or black zone. Finally, weapply a machine learning technique based on support vector ma-chines (SVM) to infer the continuous coverage of the zones fromthese discrete samples.

3.2.1 Classification of PointsAssume a beacon broadcasts at a fixed frequency f , for a total of

α packets from time t to time t′. Let:

P = a point in space at a distance of (px, py, pz)from the beacon

W = detection windowPRRτ = packet reception rate measured between τ and τ +W

where τ ∈ [t, t′ −W )

We first define the indicator function I(τ) as:

I(τ) =

{0, if PRRτ 6 ε

1, if PRRτ > ε

where ε is the PRR threshold

Then,

Color(P, t, t′) = white,

if∏τ

I(τ) = 1 : ∀τ ∈ [t, t′ −W )

Color(P, t, t′) = grey,

if∑τ

I(τ) > 1 and∏τ

I(τ) = 0 : ∀τ ∈ [t, t′ −W )

Color(P, t, t′) = black,

if∑τ

I(τ) = 0 : ∀τ ∈ [t, t′ −W )

Here P represents the location of the receiver node. W definesthe sliding window size in computing all possible PRRs over theduration of the dataset at P . We collectively term these sliding-window PRR values as WPRR. I(τ) labels each sliding windowinto 1 if the corresponding PRR is above an application specificthreshold ε, or 0 if otherwise. Using this indicator function, we canlabel P as “white” if all sliding windows are 1, “grey” if some butnot all are 1, and “black” if all are 0. This definition allows us tolabel points around a beacon into {white, grey, black}, accordingto how likely beacons are detected by a receiver at that point oversome period of time [t, t′].

We note that WPRR is fundamentally different from computing asingle PRR over the entire dataset at P and then evaluating whetherPRRp > γ. Specifically, looking at a single PRR over the entiredataset does not consider the uniformity of successful packet recep-tions over time. Most applications can tolerate losing a few packetswithin a given time window, as long as the receiver hears at leastone beacon in that time window.

Additionally, if ε is set to 0, it is equivalent to stating I(τ) =1 if the time between consecutive packet receptions is less than

the detection window W , which is the minimum requirement fordetection to happen. In the context of the sliding window, havingε = 0 equates to having the inter-packet arrival time to be alwayssmaller than the detection windows W . In this specific form, onecould build a histogram of the inter-packet arrival times and seeif any falls beyond the detection window W . However, to allowapplications to specify the degree of likelihood of detection, weleave ε as a user definable parameter.

3.2.2 Classification of ZonesGiven a set of points P and their colors, we can then find the

boundaries that divide the proximity regions into three zones ofwhite, grey, and black colors. There are multiple approaches togrouping points into zones. Due to the nondeterministic nature ofthe boundaries of the proximity zone, we take statistical learningapproaches to find these boundaries. We could consider the bound-ary detection problem as a classification problem. We first definetwo boundaries.

• The white/grey boundary separates the white points {P |Color(P, t, t′)= white} from the grey points {P | Color(P, t, t′) = grey}.We use notation fw/g as the classifier that fw/g(x) > 0 forany white point x and fw/g(x′) < 0 for any grey point x′.We use equation fw/g(x) = 0 to represents the decisionboundary.

• The grey/black boundary separates the grey points {P |Color(P, t, t′)= grey} from the black points {P |Color(P, t, t′) = black}.Similarly, we use notation fg/b as the classifier that fg/b(x) >0 for any grey point x and fg/b(x′) < 0 for any black pointx′. Equation fg/b(x) = 0 represents the decision boundary.

Based on the boundaries, we infer the three proximity zones asbelow.

• Grey zone: {x|fw/g(x) > 0 and fg/b(x) < 0}. Be-tween the white/grey boundary and grey/black boundary.

• White zone: {x|fw/g(x) < 0}. Inside the white/grey bound-ary is the white zone.

• Black zone: {x|fg/b(x) > 0}. Outside the grey/black bound-ary is the black zone.

We use support vector machines (SVM) as the classifier to findthe boundaries of the zones in this paper. SVM seeks maximum-margin hyperplane to separate two classes. The objective of classi-cal SVM is shown in Equation 1.

w = argmin ||w||2

s.t. yi(wTxi − b) ≥ 1, for all i(1)

where w and b are the parameters to define the hyperplane to sepa-rate the two classes.

To accommodate application-specific requirements, we introducetwo user-definable parameters in our classification model.

One parameter is the error tolerance, which indicates how cleanthe zones should be. There could be multiple boundary candidatessometimes. For example, we could choose one smooth boundarywith some misclassified points. At the same time, we could alsohave a non-smooth boundary without any misclassified point. Interms of learning, it is a tradeoff between training loss and regu-larization. We could specify the cost parameter C for misclassifiedexample to choose the best decision boundary for the specific sce-nario.

Another parameter is the strictness of a zone. We may expectthe white zone, as well as the black zone, to contain no grey points.We should notice that although strictness is related to error toler-ance, however, strictness is non-symmetry. For example, while werequire no grey point be misclassified to white or black, we allowwhite or black points to be misclassified as grey. In terms of theclassification problem, strictness could be controlled by the differ-ence of costs for false positive and false negative. We could set thecost parameter (or the cost of false positive)C and the cost for falsenegative C′ to be different. Formally, given C, we could define thestrictness parameter γ to be γ = 1− C

C′ .For our problem, we adapt the classical SVM model. By slack-

ing on the hard constraints for error tolerance and adding the costparameters, we formulate the problem as,

w =argminf

{||w||2 + C(

l∑yi=1

(1− yif(xi))+)

+ C′(

l∑yi=−1

(1− yif(xi))+)} (2)

where (z)+ = max(z, 0). Parameter C and C′ are the costs formisclassification of false positive and false negative, respectively.

Since our zone boundary might not be linear, we apply the ker-nel trick in the SVM problem to obtain nonlinear boundaries. Weuse the RBF kernel as the kernel function. In this case, the decisionboundary could be specified as {x|fw/g ≡ 〈φ(x),w〉 = 0} for theclassifier fw/g , where φ(x) is the feature mapping function for theRBF kernel. Similarly, for the classifier fg/b, the decision bound-ary is {x|fg/b ≡ 〈φ(x),w〉 = 0}. With the boundaries defined,we could compute the sizes of each zone for our evaluation pur-pose. Because the black zone is not closed, it can not be computed.In fact, the zone of the greatest interest is the grey zone. However,computing the area of grey zone is non-trivial since its boundariesmight be irregular. Section 6.4 presents the heuristics we use tocompute the area.

The SVM also provides information on how well boundaries fitthe training data in terms of loss. Therefore, we could further cap-ture the fitness of proximity zones by the classification accuracy.

In summary, user can specify the following input parameters tothe classification model:

• Error tolerance: Corresponds to the cost parameter C inSVM, which specifies a trade-off between smoothness of theboundary and mis-classification.

• Strictness γ: ComputesC′ = C ·γ+C to specify the appli-cation’s tolerance to false positives (i.e., sporadic detectionsoccurring outside the nominal zone range) and false nega-tives (i.e., missing detections inside the nominal zone range).For example, for applications that cannot tolerate any miss-ing detection or false detections, γ can be set to 1 to coercethe model to include all grey points into the grey zone.

Together with the learning model in (2), we could obtain the bound-aries, as well as the following metrics,

• Size of the white and grey zone, which can be computed nu-merically based on the boundaries.

• Boundary sharpness, defined as the area ratio of the whitezone to the sum of white zone and grey zone, size(white)

size(white)+size(grey).

• Fitness of proximity zones, which provides a measure ofhow well the zone boundaries fit the data, or a confidencemeasure of the proximity zone classification.

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Figure 2: (top) the WPRR at different distances, and (bottom) zone boundaries, for the corresponding radio

4. EVALUATION OF EXISTING TECHNOLO-GIES

Using the procedure for determining proximity zones in Sec-tion 3, we evaluate some of the common radios for beaconing:802.15.4, Bluetooth Low Energy (BLE), and RFID.

4.1 Boundary Sharpness and ConsistencyTo characterize grey zones of each radio, we collected packet

reception data at different positions from the transmitter as outlinedbelow. We primarily focus on single dimensional data here since itcan clearly illustrate our results. We defer the analysis of data fromtwo dimensions to later sections.Experiment methodology: The hardware setup consists of a pairof TI CC2540 BLE dev boards (transmitting on 2.4 GHz at 0 dBm),a pair of TelosB motes with 802.15.4-compliant TI CC24240 radio(transmitting on 2.4 GHz at 0 dBm), and a Impinj Speedway R1000RFID reader (transmitting on 902 MHz at 8 dBm).

Experiments take place on a floor inside a typical office building,and the floor contains cubicles separated by semi-metallic wallsand metallic over-head shelves. For each experiment, we positionthe receiver at various locations along a line from the beacon ona small hallway. The distance intervals are adaptive depending onthe range of the technology. At each location, the packet recep-tion data is collected over a period of 200 seconds. WPRR is thencomputed using a windows size of 3 seconds and an ε of 0. Usingthe algorithm described in Section 3, each point is then assigned acolor from {white, grey, black}, and the white/grey and grey/blackboundaries are computed for each experiment with a strictness pa-rameter of 0.99.

Finally, to capture the time-variant nature of the radio due to en-vironment changes, we repeated the experiments three times through-out the day: morning, afternoon, and night.Results: Figure 2 shows the WPRR at each sampling location andzone boundaries for all three radio technologies at different timesof the day.

From the PRR vs. distance figures on top of Figure 2, we ob-serve that the average WPRR for 802.15.4 fluctuates significantlyover a large distance between 5 and 20 meters, suggesting that theboundary is not sharp spatially. The confidence interval also varieswith distances, suggesting that packet reception changes signifi-cantly over both space and time. In comparison, average WPRRfor BLE is a little better than 802.15.4 spatially, leading to slightly

shorter grey zones. However, BLE has a relatively large confidenceinterval outside the white zone, signifying that the packet receptionis not uniform over time. This suggests that BLE is more suscep-tible to bursty packet loss, in which case BLE requires a largerwindow, or human wait time, for the proximity detection to be re-liable in the grey zone. However, inside the white zone, BLE hasa smaller confidence interval than 802.15.4, possibly because BLEemploys forward error correction rather than error detection. RFID,in comparison, has much smaller confidence intervals, which sug-gests that packet reception is consistent over time, and results insmaller grey zones. An interesting observation is the dip at around1.75 m. Further experiments with different heights at the same dis-tance reveal that this dip is isolated at that height, and is thereforean effect of the physical environment such as the multi-path effect.We summarize the boundary sharpnesses of all three in the table atthe end of this subsection.

While we can see that RFID is more consistent than 802.15.4 andBLE over short periods of time from their respective WPRR con-fidence intervals, to better understand each radio’s tolerance to en-vironmental changes, we consider data across three different timesof the day. From Figure 2, we can see that both 802.15.4 and BLEhave large variances in WPRR curves and zone boundaries, whileWPRR curves for RFID almost overlap. An explanation is that both802.15.4 and BLE occupy the unlicensed 2.4 GHz band shared withmany office equipments, such as Wi-Fi, whereas RFID operates onthe fairly quiet 902 MHz band. We further quantify consistency interms of the standard deviations across zone boundaries betweenthe three trials:RMS(σ(borderwhite/grey), σ(bordergrey/black))

A smaller RMS value is preferable. We summarize consistenciesin the table below.

802.15.4 BLE RFIDBoundary sharpness 0.32 0.70 0.77Boundary consistency 0.12 0.13 0.13

4.2 Human Obstacle PenetrationPrevious studies have shown that human body can significantly

affect the packet reception of 802.15.4 [11]. This section extendsthe experiments to include BLE and RFID.Experiment Methodology: The user carries the receiver in theright pants pocket, which is one of the most common locations tocarry mobile devices. At each distance from the transmitter, we

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Figure 3: Packet reception ratio when the user, with the receiver in the right pants pocket, is in different body orientations

calculate PRR from 500 packets as the user changes the body ori-entation by 90◦ each round.Results: In Figure 3, y-axis corresponds to different orientations ofthe user with respect to the beacon. “Face” means the user is facingthe beacon at 0◦; “farside” means the user is −90◦ with respectto the original orientation, standing between the receiver and thebeacon; “back” means the user is at 180◦ with his back facing thebeacon; and “nearside” sideways at 90◦. x-axis is distance from thebeacon, and color of the block represents PRR (white=1). Proxim-ity zones from the same time period is added as a reference. Thisfigure shows that human body has a significant impact on all threeradio technologies. PRR for 802.15.4 is almost zero beyond one ortwo meters, regardless of orientation. BLE has PRR of 1 in the ini-tial 20 meters, but drops sharply to zero for the rest 16 meters in theoriginal white zone, also independent of orientation. PRR for RFIDis zero for all distance at the “farside” and “back” orientations. Theother two orientation are slightly better with 50% receptions. Thisresult suggests that none of these three technologies are suitable forproximity detection of humans.

4.3 Additional MetricsSignal propagation and geometry: Beaconing platforms basedon 802.15.4 and BLE radios usually use omni-directional antennas,with typical transmission range of about 100 m. However, as previ-ously shown, their signals attenuate with amoeba-like propagationpatterns [10], which is not desirable for applications requiring aconsistent boundary.

Off-the-shelf RFID readers are usually coupled with directionalantennas, with a range in the neighborhood of meters. Compared to802.15.4 and BLE, the radio propagation attenuates more evenly indifferent directions. RFID antennas usually have a radiation angleless than 180 degrees.Form Factor and Costs: Form factor and costs play a huge rolein realizing ubiquitous deployments. While RFID can produce amore consistent and smaller grey zone, 802.15.4 and BLE have ad-vantages in both form factor and costs. Specifically, long-rangeRFID readers have a relatively large antenna due to the lower ra-dio frequency, but the cost of long-range RFID readers range fromhundreds to thousands of dollars, such as Impinj Speedway R1000.

5. LIVESYNERGY PLATFORMSection 4 shows that existing beaconing technologies are not

ideal for reliable and precise proximity detection. Fundamentalproblems such as external interferences motivate us to explore dif-ferent beaconing hardware technology, rather than software tricks.In this section, we first discuss magnetic induction, then present aplatform we developed for proximity detection - LiveSynergy.

5.1 Magnetic Induction CommunicationMagnetic induction communication (MIC) has a different elec-

Figure 4: (left) Pulse, and (right) Link

tromagnetic radiation pattern and properties than most existing bea-coning technologies.

First, MIC is near-field with a sharp signal drop off. Specifi-cally, signals of far-field solutions such as Bluetooth attenuate atthe square of distance from the transmitting antenna, or 1/r2, near-field signals attenuate at 1/r3. This implies that the transition zoneof magnetic fields is relatively small, and results in a “sharper”boundary given a specific receiver sensitivity. Section 6 shows thatLiveSynergy can produce a boundary in the sub-meter range.

Second, the physical environment has less impact on MIC sig-nals. MIC is immune from radio frequency (RF) interference whilemost existing technologies mentioned in Section 4 operate on thecrowded 2.4 GHz spectrum. In addition, the magnetic signal doesnot attenuate over non-metal obstacles, nor suffer from multi-patheffect. As a result, MIC signal propagates and attenuates evenly inall directions and consistently over time.

5.2 Platform DesignApplying MIC to real-world proximity detection, we developed

a platform called LiveSynergy with a transmitter – Pulse, and a re-ceiver – Link. Specifically, spaces or physical objects can be instru-mented with Pulses that beacon modulated magnetic fields to en-code unique IDs. Links carried by humans read the signal strengthof the magnetic field and demodulate the signal for the Pulse ID.Both Pulse and Link are equipped with 802.15.4 radios for datacommunication. In additional, similar to [7], Pulse is powered di-rectly by AC, and has the ability to measure power and performactuation, which is useful for some applications. The rest of thissection discusses the hardware design choices of Pulse and Link inmore detail.

5.2.1 Pulse TransmitterPulse (c.f. left of Figure 4) consists of four primary hardware

components: microcontroller (MCU) and radio, magnetic transmit-ter tuned at 125kHz, energy metering, and a mechanical relay foractuation.

Magnetic Transmitter: Pulse generates the dynamic magnetic fieldin three stages. First, the MCU encodes the payload data and tog-gles its IO pins according to the bits. Then, a driver circuit am-plifies the toggling pattern to provide sufficient power. Finally, theantenna transmits the signal as magnetic pulses in the air. The restof the discussion focuses on each of these stages.

The magnetic transmitter software stack on MCU largely deter-mines the data transmission rate and reliability through the choiceof modulation scheme and maximum transmission unit (MTU).Preliminary results showed that simple duration-based on/off key-ing modulation does not achieve high reliability. We opted Manch-ester encoding, as the self-clocking nature reduces the chance ofseveral types of data failure. In addition, preliminary results sug-gested an MTU larger than 16 bytes can significantly impact thePRR. Finally, the bit signal duration can impact the reliability orthe transmission rate if the duration is too short or long respec-tively. In our system, a data rate of 2730 bps provides the sweetspot.

The I/O pin from the MCU, signaling at 3.3V, connects to the in-put of a buffer powered at 12VDC; the output of the buffer connectsto the input of an inverter, also powered at 12VDC. The two endsof the MI antenna are connected to the outputs of both the bufferand the inverter. This design effectively doubles the voltage swingacross the antenna. In addition, power-limiting resistors are put inseries with the antenna to artificially decrease the range, if needed.

The antenna size and geometry partially determine the transmis-sion range. In [9], a relatively large antenna (5m × 1m) is usedto obtain a range of 5m. However, such a large antenna is notpractical in indoor scenarios. Pulse has a PCB mountable antennaspecifically designed for PKE applications, with a dimension of8cm× 1.5cm.Microcontroller and Radio: To mitigate interference and noiseissues that often exist in modern office buildings, and to ensureadequate RF range, we opted the sub-1 GHz band instead of thepopular 2.4 GHz spectrum. Our radio stack conforms to 802.15.4c,a 779 Mhz PHY layer amendment to the 802.15.4 standard.

To simplify our RF design phase and ensure optimal RF per-formance, we use the SuRF core module from PeoplePower [14],which integrates TI CC430 and RF matching network (balum).CC430 is a SoC with MSP430 MCU (with 32 KB ROM and 4 KBRAM) and CC1101 low-power radio chipset. We designed a com-pact 1 dB monopole PCB antenna based on the TI reference de-sign [4].

5.2.2 Link ReceiverLink (c.f. right of Figure 4) is a mobile device carried by hu-

mans to receive and decode the IDs from dynamic magnetic fieldstransmitted by Pulses. Link is battery-powered inside a 9.2cm ×5.8cm × 2.3cm enclosure. The three primary hardware compo-nents include MCU and radio, 3D magnetic coil, and wake up chip.Since Link and Pulse share the same MCU and radio, the discus-sion below focuses on the latter two.

The 3D receiver coil is tuned to 125 kHz, and sensitive in allthree dimensions providing spatial freedom to the human carryingthe Link. Since magnetic field is not significantly attenuated bynon-ferrous materials, the signal reception is similar regardless ofLink’s location on the body. The outputs from the coil are smallelectrical signals in x, y, and z dimensions, and they are connectedto a low-power wake up chip, AS3932. AS3932 reduces analogcircuitry with an integrated programmable gain amplifier (PGA),offloads computation by digitizing the analog magnetic signals, andreduces MCU power consumption by firing a wakeup signal onlywhen a magnetic field with valid preamble is decoded.

0 1 2 3 4 5 60

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Figure 5: WPRR vs. distance for morning, afternoon, andnight, and associated 1D proximity zones.

Upon being waken up by the AS3932 chip, the MCU uses its in-ternal clock to periodically capture the value of the AS3932 outputpins. After the software stack successfully verifies the 16-bit CRC,it delivers the payload data to the application.

Finally, we note that the magnetometer inside existing mobilephones use Hall elements and can theoretically detecting dynamicmagnetic fields (in addition to static fields). Unfortunately, de-tailed inspection of popular magnetometer chips reveal that dy-namic magnetic field is filtered out in hardware to reduce “noise”from the earth magnetic field. It is our hope that sensor manufac-turers can lift this limit and enable MIC on future mobile phones.

6. EVALUATION OF LIVESYNERGYThe evaluation methodology is similar to that used in Section 4.

We place a pair of Pulse and Link to perform the test at three differ-ent times of the day. In each trial, the Pulse is fixed in position andtransmits an ID using MI at 5Hz. We position the Link at differ-ent distances from the Pulse, with variable intervals to cover morepoints inside and near the grey region. For each distance, Pulsetransmits 1,000 packets; the Link records successful receptions, to-gether with timestamps and RSSI. In addition, for every position,we rotate Pulse with respect to the Link, at angles from 0◦ to 180◦

in 30◦ increments. While this is still a small subset of all the de-grees of freedom between a pair of antennas, this at least enablesus to create proximity zones in both 1D and 2D space.

6.1 Boundary Sharpness and ConsistencyThe top part of Figure 5 shows the WPRR vs. distance graph

for MI in a single dimension, collected at three different times ofthe day. In comparison to 802.15.4 (Figure 2(a)) and BLE (Fig-ure 2(b)), packet reception ratio of MI is significantly better – it ismore uniform, as indicated by shorter confidence intervals; and itis more consistent over time, as indicated by the similar averageWPRR readings across three trials. In comparison to RFID (Fig-ure 2(c)), the variance of WPRR readings is about the same, but MIexhibits better signal decay characteristics than RFID. There arestill some fluctuations in MI’s WPRR curve since ferrous materials

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Figure 6: Body orientation vs. distance

in the environment do affect magnetic propagation, but much lesssevere than RFID. The bottom part of Figure 5 shows the proxim-ity zone for MI, computed using our definition in Section 3. Thisfigure shows that the zone boundary for MI is sharp in all threetime periods of the day, with a boundary sharpness of 0.82, whichis much better than 802.15.4, BLE, and RFID. From this result,we can conclude that LiveSynergy, using magnetic induction as thebeaconing signal, is able to generate proximity zones with the mostconsistent and the sharpest boundaries among current technologiesstudied in this paper.

To better differentiate MI from RFID, we compare results in twodimensions, as described in more detail in Section 6.4.

6.2 Human Obstacle PenetrationFigure 6 shows the PRR vs. distance with the Link being carried

in the right pant pocket at different orientations from the Pulse.This figure shows that human body has very little impact on theMI signal propagation. In contrast, 802.15.4, BLE, and RFID areeasily affected by human bodies and other obstacles, as seen in Fig-ure 3. This is because those technologies operate at much higherfrequencies than MI, and also because MI is inductively coupled.This property makes MI ideal for applications where a mobile “tag”needs to be carried by a human. On the hand, MI signal is blockedby ferrous objects, which may be a problem for some applications,but desired for some others (e.g., applications using ferrous mate-rials as natural zonal boundaries).

6.3 Additional MetricsGeometry: From Figure 7, we can observe that the proximity

zone of Pulse in two dimensions extends to all directions, coveringall 360◦. This same observation also applies to 3D.

Range: The maximum range (i.e., radius) is around 5m, but itcan be artificially decreased by limiting the antenna output power.This range is desirable for a range of indoor applications as de-scribed in Section 1.

Beacon rate, power, size, and cost: We summarize these met-rics in the table in Section 6.5.

6.4 Comparisons to RFID in 2DBecause long-range RFID exhibits the sharpest boundary among

all previous technologies, as seen in Section 4, we compare MI withRFID in more detail here. Using packet reception data measuredin the x-y plane, we were able to find the zone boundaries in 2Dby applying our methodologies in Section 3. As Figure 7 shows,in two dimensions, the white/grey and grey/black boundaries arecurves, and zones are represented as areas.

From the MI proximity zone on the left, we can see that the MI’swhite zone is relatively circular, centered at the MI transmitter, witha small grey zone surrounding it. In contrast, RFID has a much nar-

Figure 7: Proximity zones for MI and RFID in 2D space

rower proximity zone with an angle less than 180◦. This geome-try is not as desirable for applications that require omni-directionalcoverage. The grey zone of RFID is also much bigger than that ofMI. We used Monte Carlo method to estimate the areas of whiteand grey zones for both MI and RFID, and found that the boundarysharpness ratio for MI to be 0.70, and for RFID to be 0.25. Thisshows that MI has a much sharper boundary than RFID. Further-more, the range of MI is about 5m omni-directionally where RFIDis only about 1m in the right half plane. Combined with the advan-tages MI has over RFID in terms of consistency and body penetra-tion, MI is the more viable proximity detection solution than RFIDfor many indoor applications.

6.5 SummarySummary of evaluation results for LiveSynergy/MI:

Boundary sharpness 0.82Consistency 0.03Penetration ExcellentGeometry OmnidirectionalRange 5mMax beacon rate 50HzPower 19.75mATag form factor 9cm x 6cm x 2cmCost (tx, rx) ($50, $30)/pc @ 100 units

7. APPLICATION DEPLOYMENTResults from Section 6 show that magnetic induction communi-

cation (MIC) is a viable solution for reliable and precise proxim-ity detection. This section discusses a real-world deployment at alarge cafeteria to provide personalized advertisements and diet sug-gestions. The discussion starts by giving the deployment overview,discussing the end-to-end system, and then presenting deploymentresults and experiences.

7.1 Deployment OverviewProviding personalized shopping experience has been a recurring

application in the ubiquitous computing community. On one hand,shops can deliver targeted advertisements and coupons; on the otherhand, shoppers can receive suggestions based on their shopping his-tory and preferences. We highlight the potential of MIC communi-cation in such applications by instrumenting the cafeteria inside alarge company with Pulses, as seen in Figure 8. Diners carry Links,which are associated to their mobile phones running our mobile ap-

Figure 8: A Pulse is installed at the Teppanyaki counter, pro-jecting a proximity zone of 5m radius. Links carried by dinersin their pockets detect this zone as they enter.

plication. Food counters in the cafeteria are organized by types offood offered. The proximity readings are maintained at a back-end server, and used to deliver real-time targeted advertisementsand coupons for the particular food counter that the customer isnear. The entire user experience depends on performance of theLiveSynergy system in detecting diners, and how targeted adver-tisements and coupons are delivered.

Figure 10 shows Pulses at five different locations in the cafeteria:the entrance, the “Japanese” counter, the “A La Carte” counter, the“Teppanyaki” counter, and the “Vegetarian” counter. Pulses bea-con twice per second, while Links report MIC beacons heard onceevery second. Aggregating beacon reports help lower the wirelessnetwork load, and help resolving cases where beacons from multi-ple Pulses are heard. Specifically, Link reports the Pulse ID withthe highest signal strength in the current detection time window.

The back-end server performs a simple time-window thresholdfiltering on the location readings to differentiate between passing-by and staying. Using this data, the server can push context-awareinformation to the customer in a timely manner, through a nativeapplication running on the mobile phone.

7.2 System Architecture

Figure 9: End-to-end LiveSynergy system architecture

Our system architecture (c.f. Figure 9) includes three main com-ponents: the LiveSynergy platform for detecting when humans en-ter and exit cafeteria counters; a networking and data representa-

Figure 10: Movement trails from three customers in the cafete-ria deployment.

tion layer enabling communication among counters, services in thecloud, and app clients; and an application layer consisting of a mo-bile application for providing feedback and visualization.Networking: Our network needs to be reliable since interactionsbetween humans and physical objects rely on reliable data exchange,and it should be low latency to support timely feedback to users. Wedesigned our network to meet these requirements by using single-hop at the wireless sensor layer, reliable proxy gateway, and RESTover IPv6. Nodes (Pulses and Links) directly connect to the edgerouter via single-hop, and communicate using UDP over IPv6. Ap-plications, as well as multiple services hosted in the cloud, commu-nicate with the edge router using HTTP over Ethernet.Data Representation and Web Services: To enable interoperabil-ity between applications and services provide by our system, wedesigned a simple message format based on JSON, which is com-mon in RESTful web services. Similar to XML, JSON is text-based, human-readable, self-describing, and language independent.However, compared to XML, JSON is light-weight with a smallergrammar and simpler data structure.

7.3 Deployment ResultsFigure 10 shows the floor-map of the cafeteria, labeled with both

the locations of each food counter, and the movement trails of thethree diners in our study. Diners enter the cafeteria from the en-trance at the lower left corner at different times. Each diner takesa different route and visits various food counters on the way. Thetrails are indicated by three different colors, and direction is in-dicated by arrows of the same color; purple-shaded circles repre-sent proximity zones of Pulses (approximated to perfect circles inthis figure), with radius equaling the mean of the actual ranges ofPulses. As our ground truth, we recorded a video as the customerswalk around the cafeteria purchasing food. The video is times-tamped so that we can correlate events in the footage to the actualproximity detection data we collected.

Figure 10 also overlays our deployment data over the floor-map.Circles of varying sizes indicate locations of detections, using datareported by Links as they intersect the proximity zones of Pulses.The size is proportional to the RSSI recorded by the Link, and redlines connecting green circles to the center of the purple circles

Figure 11: Windows Phone 7.5 application

indicate which Pulse that particular Link detects (i.e. being associ-ated with). To avoid clustering the figure, we only draw red linesfor the first detection after the “green” customer enters a new Pulsezone.

From this figure, one can observe a few key results. First, theboundaries of the Pulse proximity zones are sharp, with no falsepositives or negatives. The detections occur consecutively withineach proximity zones projected by Pulses. There is a little delaybetween entering of a zone and the time of first detection, as seenby the slight offset between the edge of the purple circle and the firstgreen circle. This delay is also observed at the exit of the zone. Thisis expected since Links run a simple window-based filtering beforereporting the detection. This delay is well within the tolerance.Second, the detections are consistent for all three customers as theyenter and exit the five zones, as evident from the detection locationsfor all three customers – the three different colors of detections cir-cles all exist inside the same purple circles (+ the detection delay).Additionally, we observe that RSSI is indeed proportional to thedistance between the Link and the Pulse. This allows the system tocorrectly resolve overlapping zones by “associating” to the closestproximity zone. For example, the “green” customer is first associ-ated with the “Japanese counter”, then switched (correctly) to the“A La Carte counter” in the overlapping area of both zones.

All three diners installed our native Windows Phone 7.5 applica-tion. Figure 11 shows “green” customer’s screenshots as he walkspast various food counters, as labeled by A, B, and C. First, afterhis Link detects the Pulse ID of the entrance and forward to theserver, our web service sends a notification message to his mobilephone. Then, the phone displays a notification at the top of the

screen without interrupting the foreground application. Clickingthe notification opens the main screen of our mobile applicationwith the menu2 and promotional items. At the “Japanese counter”,the web service pushes counter-specific coupons (tuna sashimi).Then, at the meat-heavy “Teppanyaki counter”, the diner receivesa diet alert of excessive meat consumption based on his purchasehistory, with the advice of vegetarian dishes instead.

This simple mobile application demonstrates that the LiveSyn-ergy proximity detection platform enables useful context-aware ap-plications, without the need for heavy post-processing. We plan tobuild a personal energy footprint application and a cardless secu-rity entry system based on our LiveSynergy platform in the nearfuture.

8. CONCLUSIONSIn this paper, we study the proximity zones established by wire-

less beacons in person-scale applications. We first propose a method-ology based on sampling and classification techniques that enableus to compute zone boundaries and other metrics such as boundarysharpness, consistency, and body penetration. Using this methodol-ogy, we empirically evaluate three prominent technologies: 802.15.4,BLE, and 900MHz RFID, using data collected over a large pe-riod of time in both x and y dimensions. We show that 802.15.4and BLE have large grey regions and are inconsistent over time.While RFID has sharp boundaries, it suffers heavily from atten-uation by obstacles such as the human body. To overcome theseshortcomings, we describe the design, implementation, and evalu-ation of a magnetic-induction based proximity sensing platform –LiveSynergy, which creates sharp and consistent boundaries that isnot affected by human obstacles. These characteristics make MIsignificantly more robust than BLE, 802.15.4, and RFID alterna-tive. Finally, through a real-world deployment, we demonstrate thatLiveSynergy is able to successful support human interacting with asmart environment. As future work, we plan to explore the possi-bility of integrating MI into regular mobile devices and expandingits applications.

9. ACKNOWLEDGMENTSWe want to thank Caiquan Liu for his contributions in the early

phase of the project, and Andrew Markham for his helpful feed-back. And we would like to thank our shepherd, Anthony Rowe,and the anonymous reviewers for their feedback and insightful com-ments.

2Dish items in A2 are pre-populated and are not the actual menu.We plan to synchronize the menu with the cafeteria in the future.

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