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Computer Communications 31 (2008) 1067–1077
Bluebot: Asset tracking via robotic location crawling
Abhishek Patil a,*, Jonathan Munson b, David Wood b, Alan Cole b
a Kiyon, Inc., 9381 Judicial Drive Suite 160, San Diego, CA 92121, United Statesb IBM T.J. Watson Research Center, 19 Skyline Drive, Hawthorne, NY 10532, United States
Available online 31 January 2008
Abstract
From manufacturers, distributors, and retailers of consumer goods to government departments, enterprises of all kinds are gear-ing up to use RFID technology to increase the visibility of goods and assets within their supply chain and on their premises. How-ever, RFID technology alone lacks the capability to track the location of items once they are moved within a facility. We present aprototype automatic location sensing system that employs a robot, with an attached RFID reader, which periodically surveys thespace, associating items it detects with its own location determined with the Wi-Fi positioning system. We propose three algorithmsthat combine the detected tag’s reading with previous samples to refine its location. Our experiments have shown that our position-ing algorithms can bring a two fold improvement over the positional accuracy limitations in both the RFID reader and the posi-tioning technology.� 2008 Elsevier B.V. All rights reserved.
Keywords: Asset tracking; RFID; Wi-Fi location positioning; Automatic location sensing
1. Introduction
Consider a typical public library. Each book has itsown place in a particular shelf. Usually, readers like totake several books (from different shelves) and browsethrough them until they find the right book. While somereaders manage to return the unwanted books to the cor-rect shelf, many of them either leave the books in somecorner of the library or place them back in the wronglocation. This latter situation is hard to detect and canbecome a librarian’s nightmare. A similar situation existsin many retail stores where the customers can tryout sev-eral items before deciding which one to buy. In mostcases, the customer never returns the tested item to itscorrect shelf. Some kind of automated tracking mecha-nism is required. The problem of asset tracking is notrestricted to libraries or small retail stores. Many compa-
0140-3664/$ - see front matter � 2008 Elsevier B.V. All rights reserved.
doi:10.1016/j.comcom.2008.01.041
* Corresponding author. Tel.: +1 858 412 0080.E-mail addresses: [email protected] (A. Patil), [email protected].
com (J. Munson), [email protected] (D. Wood), [email protected](A. Cole).
nies are realizing the importance of increasing the visibil-ity within their supply chain. Asset tracking – knowingwhat you have and where it is located – is essential forthe smooth operation of large manufacturing companies.It also helps big retailers (like Wal-Mart) isolate bottle-necks in their supply chain, reduce overstocking or locatespoiled cargo. Several government and military organiza-tions are always on the lookout for cheaper (and moreefficient) ways to track their assets and equipment. Auto-matic location sensing is the key to realizing these effi-ciencies and knowledge. One of the most well-knownpositioning systems is GPS which relies on satellites totrack location. However, due to its dependence on thesatellites, GPS lacks the ability to accurately determinelocation inside buildings. In order to achieve locationtracking inside buildings, researchers and industry haveproposed several systems, which differ with respect tothe technology used, accuracy, coverage, frequency ofupdates and the cost of installation and maintenance[2,4,6,11–15]. Steggles and Cadman [17] provide a goodcomparison of various RF-tag-based location sensingtechnologies. Many of the current location sensing
Table 1Tracking systems taxonomy
Continuousin time
Continuous in space
YES NO
YES GPS, TDOA, EOTD,Wi-Fi signal strength,etc.
Simple ‘‘presence” technologies(e.g., cellular system where cell-ID is reported as the cellphone’slocation)
NO BlueBot Fixed Beacon (e.g., EZ-Pass,Bluetooth)
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systems are radio based (Wi-Fi [1,5,12,13,16], Bluetooth[3]). By using base station visibility and signal strengthor time of flight, it is possible to locate Wi-Fi deviceswith an accuracy of several meters.
In recent years, RFID technology has attracted con-siderable attention. It is emerging as an important tech-nology that is reshaping supply chain management.RFID not only replaces the old barcode technologybut also provides a greater degree of flexibility in termsof range and access mechanisms. For example, an RFIDscanner can read the encoded information even if the tagis concealed (this might be for either aesthetic or securityreasons). Several companies (like Wal-Mart, CVS, etc.)are proposing to use RFID for identifying large lots ofgoods at pallet and carton level. Usually passive tagsare preferred for tagging goods as they are muchcheaper, long lived, lightweight and have a smaller footprint. However since passive tags work without a bat-tery, they have a very small detection range. CurrentRFID systems are portal based where tagged items arescanned either when they enter or leave a facility. Thisscheme does not provide any information about theexact location of the item once it is moved away fromthe portal. The prototype system described in this papercombines (passive) RFID technology and a Wi-Fi(802.11b) based continuous location positioning systemto provide a periodic asset-locating sweep. Although,our system uses Wi-Fi based location positioning, itcan work with any continuous positioning technology.The prototype system not only identifies but also pro-vides location information of every RFID-tagged itemin the sweep space. A portable system (e.g., laptop orPDA) running a Wi-Fi client and connected to an RF-reader is mounted on a robot that moves autonomouslythrough the space. As the robot moves, the RF-readerperiodically samples which tags are detectable. At eachsample time, the robot’s position is obtained from thepositioning system. For each detected tag, given the esti-mate of the robot’s current position, knowledge of thereader’s physical detection range, and the robot’s posi-tion estimates at previous detections, an algorithm com-putes an estimate of the tag’s position. In summary, ourexperiments with the prototype system show that we areable to estimate positions of tagged entities to within1.5 m, given an accuracy of the raw positioning systemof about 4 m. We experimented with different positionestimation algorithms and found that certain algorithmswork better than others when the raw positioning systemis capable of giving better accuracy.
The rest of the paper is structured as follows. InSection 2, we survey related work in the area of loca-tion tracking in indoor environments. Section 3describes the BlueBot setup in detail along with ourthree positioning algorithms. Section 4 looks at experi-mental results for different scenarios. And finally, Sec-tion 5 concludes the paper and presents directions forfuture research. For additional details, we encourage
our readers to refer to our technical report [18] on thistopic.
2. Asset tracking technologies
Typically, asset tracking technologies are gearedtowards tracking items that individually have high value(e.g., emergency medical equipment or a surgeon). Theseitems require continuous tracking and justify the use ofexpensive tracking equipment. However, in many track-ing applications (e.g., the library scenario described ear-lier) the object being tracked is either too small orinexpensive to justify the use of a tracking system withhigh per-item cost. And in fact, many of these applica-tions do not require continuous tracking. We believethere are many applications in which it is valuable toknow the precise location of an asset, yet it is sufficientfor an asset’s location to be updated on a periodic basis– nightly, for example. Our BlueBot system is gearedtowards such applications.
We characterize different tracking technologies by dis-tinguishing between continuity in space and continuity intime, as illustrated in Table 1. A technology that pro-vides space-continuous position estimates is able to pro-vide a position estimate that falls anywhere within thespace of interest – GPS is an example. A technology thatprovides time-continuous position estimates is able toprovide position estimates at any point in time (intervalsbetween estimates limited only by the performance of thesystem). Most well-known positioning technologies arecontinuous in both space and time. An example that iscontinuous in time but not in space is ‘‘cell-ID,” inwhich the location of an entity as assumed to be thatof the wireless base station with which it is communicat-ing. The location is known to be somewhere in the cov-erage region of the base station but at no greaterresolution than that. An example that is neither continu-ous in space nor in time is a typical RFID deployment,such as the EZ-Pass toll collection system, in which thelocation of a tagged item is known only at the timesthe item is identified by a reader. BlueBot, which pro-vides position estimates continuous in space but not intime, represents a new point in the taxonomy. As such,it points the way to tracking solutions that provide the
Fig. 1. Intermec PCMCIA reader and passive tag.
A. Patil et al. / Computer Communications 31 (2008) 1067–1077 1069
precise location estimates needed by some applications,but by sacrificing continuity in time, at a much lowercost.
A variety of indoor position determination systemshave been proposed by different research groups andindustry. Some of the earlier attempts used infrared(IR) technology. Active Badge, developed at OlivettiResearch Laboratory (now AT&T Cambridge), useddiffuse infrared technology [11] to realize indoor loca-tion positioning. However, due to the line-of-sightrequirement and short-range signal transmission,researchers realized that infrared technology is not anoptimal solution. Other projects include the Active Batssystem at AT&T Research Laboratory which usedultrasound technology [2], the Cricket Indoor LocationSystem [4] at MIT which used a combination of ultra-sound and RF technologies, TinyOS RF motes at UCBerkeley, and the Cooltown [6] project at HewlettPackard. In recent years and because of its ubiquity,emphasis has focused on radio frequency (RF) technol-ogy as a basis for position determination. Techniquessuch as Differential Time of Arrival or simple signalstrength measurement at various sensors are employed.RADAR [12] is an RF-based system for locating andtracking users inside buildings. It uses standard 802.11network adapters to measure signal strengths at multi-ple base stations positioned to provide overlapping cov-erage in a given area. The system combines empiricalmeasurements and signal propagation modeling in orderto determine user location. In contrast, WhereNet [10]uses timing signals transmitted from tags to a networkof receivers. It is based on the same 2.4 GHz band asthe 802.11 and Bluetooth systems, but uses a dedicatedstandard protocol (ANSI 371.1) optimized for low-power spread-spectrum position determination. Aero-Scout [1] uses an 802.11-based time difference of arrival(TDOA) location solution. It requires the same radiosignal to be received at three or more separate points,timed to a few nanoseconds and processed using theTDOA algorithm to determine the location. UnlikeAeroScout and other TDOA based systems, the Ekahau[5] Wi-Fi positioning system computes the location of aclient device by applying a probabilistic model to thesignal strengths measured at the Wi-Fi client device.The indoor environment must be calibrated a priorito provide the positioning engine with a signal strengthmap of the room. Ekahau ‘‘tags”, consisting of Wi-Fidevices running the Ekahau client, continually sendtheir signal strength vector to the positioning enginewhich keeps track of each device’s location. UltraWideband (or UWB) is a new technology that hasentered the arena of indoor location sensing. Unlikeconventional RF systems, UWB systems are much lessaffected by multipath distortion. The Ubisense [9] sys-tem uses UWB and is based on time of arrival ratherthan signal strength and claims to have an accuracy of6 in. (15 cm) in 3D space at a confidence level of 95%.
The system uses UWB tags (called UbiTags), which arefixed to the items being tracked.
3. The Bluebot system
The following sections provide an overview of our Blue-bot system and proposed algorithms.
3.1. Wi-Fi positioning system
We use an off-the-shelf Wi-Fi (802.11b) positioningsystem to track our mobile client. The client (shown inFig. 3) consists of the RFID reader, client machineand robot. The Wi-Fi positioning system computes thelocation by using signal strength information as per-ceived by the client device being tracked. The manufac-turer of the positioning engine advertised an averageaccuracy of 1 m (3.5 ft); however, our experiments withthe system showed that this was a best case scenariounder ideal conditions. The positioning system had tobe calibrated before location scanning could begin. Thecalibration process establishes an RF-signal strength plotof the area into the positioning system’s engine. Becauseour tag-position estimation algorithms use characteristicsof the positioning technology used to track the robot, wecarried out two sets of experiments to examine the per-formance of our Wi-Fi positioning system. The two setsdiffered in the density of calibration points and the sizeof the experiment area. We encourage our audience toread our technical report [18] (Section 4) for a detailedanalysis of the positioning system.
3.2. RFID system
In our system, we use the Intermec [7] PCMCIARFID reader (Fig. 1), which operates at a frequency of915 MHz. The reader can be connected to any devicethat has a PCMCIA port. For our experiments, wehad the reader connected to a laptop. However, a smal-
Fig. 2. RF characteristic of reader antenna.Fig. 4. Samples for a particular tag during robot’s random walk.
1070 A. Patil et al. / Computer Communications 31 (2008) 1067–1077
ler device, such as a PDA, or a custom-built PC, can beused for additional portability. The reader is attached toa directional antenna, which has a very short range (5 ftmax). The reader’s antenna is placed facing the ceilingsuch that its maximum gain is in the vertical direction.The RF characteristics of the reader would approximatea vertical cone with its vertex on the reader’s antenna.We observed that tags at a height of 4 ft were detectedwith 90% (or higher) probability when the reader waswithin 0.5 m of the tag (Fig. 2). Due to the rectangularbase of the reader’s antenna, the RF characteristics hada slight elliptical shape. For all our experiments we haveconsidered the reader’s RF characteristics to be acircular.
3.3. Bluebot setup
Our system uses the Roomba Robotic Floorvac [8] asthe robot that moves autonomously in the sweep space.
Fig. 3. BlueBot setup.
The Roomba uses intelligent navigation technology toautomatically move around the room without anyhuman direction. The Roomba expects to cover 90% ofthe room. A server machine running the positioningengine (PE) tracks our client device (laptop/PDA) thatsits on the robot. Fig. 3 depicts the BlueBot setup. ThePE is calibrated to work with the access points placedin the corners of the experiment room. The RFID readeris connected to the client device and records all the tagsthat it detects as the Roomba moves to different cornersof the room. In our experiments, the tagged items wereplaced at a height of approximately 4 ft from the ground.Whenever the reader detects a tag, the client machinesends a message containing the tag’s id to the server.The server then notes the current position of the clientand associates it with the detected tag. Our algorithmscombine this sample with the previous samples to refinethe position of the tagged item over time. As seen fromFig. 4, a tag will be sampled only when the RF-readerenters its coverage area. Fig. 5 gives a logical flow chartof the system. It should be noted that due to the randommovement of the robot, consecutive samples for the sametag might not be equally spaced in time. Experimentalresults in later section confirm this.
3.4. Algorithms
As mentioned before, the positioning system reportedthe X, Y coordinates and an error estimate ee in meters.We have seen before that the RF characteristics of thereader are in the form of a cone expanding outwards inthe vertical direction. With the tags placed at 4 ft fromthe ground, the reader’s detection circle was determinedto have a radius (r) close to 0.25 m. A circle drawn withcenter at (X,Y) and radius (R) of ee + r (Fig. 6) will includethe tag being tracked. We call this circle the confidence cir-
cle. We define the following:
Fig. 7. Intersection of the different ‘sample’ circles converges to the taglocation.
TagID, Robot’s Position, Timestamp, Sample no.
RF Reader Position Determination
Technology
Visible Tags Current Location
Algorithm for computing the location of tagged items
Robot
Hardware
Software
LocationSensingSystem
RF Reader Client
Fig. 5. Logical flowchart of the robotic crawler system.
Fig. 6. Total radius is the sum of the reader coverage and the uncertaintycircle (error estimate circle).
A. Patil et al. / Computer Communications 31 (2008) 1067–1077 1071
t – represents the tag with id t.Nt – is the total number of samples for a given tag, t.(Xti,Yti) – is the location estimate for sample i of tag t.eeti – is the error estimate for the positioning report forsample i of tag t.r – is a constant representing the read radius of theRFID reader.Rti = eeti + r – is the radius of the confidence circle forsample i for tag t.C[(Xti,Yti),Rti] – is the confidence circle for sample i oftag t.
With these in mind, we provide three algorithms to com-pute the location.
Intersection algorithm: Intersection of several confidencecircles provides a finer estimate of a tag’s position. We rep-resent the tag’s location as the centroid of the boundingbox of this intersection area. As the number of samplesincreases, the intersection area decreases (Fig. 7), thusimproving the accuracy of the (tag’s) calculated location.The estimate location then is:
ðX t; Y tÞ ¼ Centroid ðBounding BoxðC½ðX t1; Y t1Þ;R1�\ C½ðX t2; Y t2Þ;Rt2� \ . . . \ C½ðX Nt; Y NtÞ;RNt�ÞÞ
The precision of this algorithm is inversely proportionalto the size of the intersection region. Smaller intersectionsimply higher probability distribution.
Weighted averages: Here we create an algorithm thatcomputes the location coordinates of the tagged entityas a weighted average of the reader’s locations when itdetected the entity. The weight of each location estimateis inversely proportional to the square of the errorradius.
ðX t; Y tÞ ¼Xf1=ee2
i � ðX i; Y iÞgh i X
1=ee2i
� �.
1072 A. Patil et al. / Computer Communications 31 (2008) 1067–1077
The positioning system’s location estimates having asmaller error radius tend to be closer to the tag. Therefore,by using 1=R2
i , the algorithm is able to give higher weight tosample points that are closer to the tag. The accuracy ofthis algorithm depends more on the estimated position(Xi,Yi) as reported by the positioning system and also toa large extent on the distribution of samples around thetagged entity. We assume that with enough samples, thiswill be averaged out.
Plain averages: An algorithm that computes the loca-tion coordinates of the tagged entity as the statisticalaverage of the reader’s location when it detected theentity.
ðX t; Y tÞ ¼XðX ti; Y tiÞ
h i.Nt
The accuracy of this algorithm is similar to weightedaverage algorithm. However, since this algorithm doesnot take into account the error estimate of the positioningsystem, the errors in the estimated location this algorithmwill be slightly higher compared to the estimated errorfor the weighted average.
4. Bluebot performance
We used a large open 12.5 m � 13.75 m room (Fig. 8) tocarry out a series of experiments to examine the perfor-mance of our prototype system.
Fig. 8. BlueBot setup with non-uniform placement of tags.
4.1. Experiment set A
In the first round of experiments, the positioning enginewas calibrated for the entire room and the system’s accu-racy was around 4 m. The tag placement for this set ofexperiments is as shown in Fig. 8. We recorded the perfor-mance of the three algorithms for this setup for four differ-ent runs of the experiment. Fig. 9 shows the performancefor one such run. It is easy to see that the positioning accu-racy of the three algorithms greatly varies between tags.Part of the reason is due to the large variation in the num-ber of detections for each tag. In our experiments westarted the Roomba from the center of the experiment area.However, we noticed that since it moved in a random fash-ion, the number of ‘detection’ samples for each tag is not auniform distribution. We expect that the averaging effect(from several runs of the experiment) to smooth out thelarge variation. Fig. 10 shows the time distribution betweenconsecutive detections. Since the number of samples per tagper run is different, the values (per run) have been normal-ized to be on the same scale.
We define the term error distance as the differencebetween the estimated location and the actual location ofthe tagged item. The three positioning algorithms start witha large error distance and as they get more samples, theyslowly converge to the actual location of the tag. In orderto quantify the performance of our system, we define a con-vergence point for each tag (in each run of the experiment).Convergence point can be thought of as the start of thesteady state for a tag. Beyond this point, there is no signif-icant change in the computed coordinates for that tag. Forexample, the convergence values for Fig. 9 are shown inTable 2. We use convergence values to find the accuracyof our system.
Table 3 shows the average time and the average numberof samples at which the algorithms converged for each runof the experiment. Table 4 and Fig. 11 show the mean andmedian at convergence for each of the three algorithms. Aswe can see from Fig. 11, the Intersection algorithm wasable to position the tags with accuracy close to 1.5 m. Thisis almost a threefold improvement in the accuracy providedby the positioning system. The two averaging algorithms(plain and weighted), however gave an average error closeto 3.5 m.
We investigate the reason why the intersection and theaveraging algorithm did not converge to same error distance.As an example, we look at Fig. 12 which shows the perfor-mance of the Wi-Fi positioning system and each of the threealgorithms at every sample (for experiment set A tag B –Fig. 9). The two averaging algorithms depend more on thepositioning system’s accuracy and hence, their performanceis close to positioning system’s accuracy (which was close to4 m). Referring to Fig. 12a, one would expect a uniform dis-tribution of the estimate (from positioning engine) aroundthe actual 15.2 m; however that was not the case. The posi-tioning engine has an offset close to 3.5 m in the estimatedX (actual X = 15.2 m, Estimated X from positioning engine
Error Distance wrt Samples (Tag A)
0
0.5
1
1.5
2
2.5
3
3.51 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Sample No.
Met
ers
Plain Weighted Intersection Err Est
Error Distance w.r.t Samples (Tag B)
0
1
2
3
4
5
6
7
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65
Sample No.
Met
ers
Plain Weighted Intersection Error Estimate
Error Distance wrt Sample (Tag C)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81
Sample No.
Met
ers
Plain Weighted Intersection Err Est
Error Distance wrt Samples (Tag D)
0
1
2
3
4
5
6
7
1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86
Sample No.
Met
ers
Plain Weighted Intersection Err Est
Fig. 9. Error distance convergence w.r.t. sample no. for each tag.
Distribution of consecutive samples
0
10
20
30
40
50
60
0-30 30-60 60-90 90-120 150-120 180-150 210-180 240-210 270-240 300-270 >=300
Time (30 Sec Intervals)
No
. of
Sam
ple
(N
orm
aliz
ed)
Exp Run 1 Exp Run 2
Fig. 10. Sample distribution w.r.t. time for two different experiment runs.
Table 2Convergence values for Fig. 9
Tag SampleNo.
Time(s)
Plain(m)
Weighted(m)
Intersection(m)
A 25 1414 1.527 1.510 0.224B 22 1380 3.478 3.267 1.642C 19 2430 3.286 3.265 1.4868D 20 1271 4.5123 4.386 2.583
A. Patil et al. / Computer Communications 31 (2008) 1067–1077 1073
varies between 11.5 m and 13 m). For the averages algorithmto converge to the actual location, the offset from the posi-tioning engines needs to be uniformly spread above andbelow the actual location. In this case, the offset was alwaysbelow; hence the large error. The intersection algorithm onthe other hand is based on the intersection of several confi-dence circles. The intersection algorithm gives an area wherethe tracked entity is located. To better represent the result of
Table 3No. of samples and time (s) at convergence
Run No. No. of samples at convergence Time at convergence (s)
Mean Median Std. Dev. Mean Median Std. Dev.
1 21.5 21 2.645 1623.75 1397 540.952 13.25 14 3.774 1178.25 1169.5 448.9733 24.25 23 8.845 1706 1553.5 442.6214 12.75 11.5 6.238 916.75 879 261.515
Average 17.9375 17.375 5.3755 1356.188 1249.75 423.514
Table 4Error distance at convergence for each algorithm
Run No. Plain averages algorithm Weighted averages algorithm Intersection algorithm
Mean Median Std. Dev. Mean Median Std. Dev. Mean Median Std. Dev.
1 3.201 3.382 1.239 3.107 3.266 1.188 1.484 1.564 0.9692 3.120 2.977 1.129 3.088 2.928 1.135 1.735 1.701 0.8613 3.241 3.513 1.334 3.162 3.360 1.295 1.571 1.587 0.7594 3.117 3.055 0.793 3.073 3.067 0.745 1.289 1.059 0.576
Average 3.170 3.232 1.124 3.108 3.155 1.091 1.520 1.478 0.791
Error Distance at Convergence for each algorithm
0
0.5
1
1.5
2
2.5
3
3.5
4
1 2 3 4
Experiment Runs
Err
or
Dis
tan
ce (
m)
Plain Avg (Median) Weighted Avg (Median) Intersection (Median)
Fig. 11. Error distance at convergence for each algorithm.
Fig. 12. Comparison of computed/estimated coordinates with the actual coordinates.
1074 A. Patil et al. / Computer Communications 31 (2008) 1067–1077
Fig. 13. BlueBot setup with uniform placement of tags.
Table 5Location coordinates of tags as computed by the three algorithms (forFig. 14)
Tag ID Actual (X,Y) Plain Weighted Intersection
A (1,2) (1.55,1.86) (1.43,1.80) (2.99,3.02)B (3,2) (2.94,1.45) (2.86,1.50) (4.01,2.47)C (5,2) (4.35,1.29) (4.80,1.33) (6.24,2.35)D (7,2) (4.99,1.31) (5.32,1.41) (6.62,2.22)
A. Patil et al. / Computer Communications 31 (2008) 1067–1077 1075
the algorithm, the center of the bounding box around theintersection area is reported as the computed location. Aclose examination of the two graphs shows that the error esti-mate for the first few samples is high resulting in larger con-
Error Distance wrt Sample No. (Tag A)
0
1
2
3
4
5
6
7
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37
Sample No.
Err
Dis
tan
ce (m
)E
rr D
ista
nce
(m)
Plain Weighted Intersection Err Est
Error Distance w.r.t Sample No. (Tag C)
0
1
2
3
4
5
6
7
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58
Sample No.
Plain Weighted Intersection Err Est
a b
c d
Fig. 14. Error distance convergence w
fidence circles. The intersection of these circles gives a largearea, the centroid of which is reported as the calculated loca-tion. As the experiment proceeds, the error estimate (ee)decreases. Later on, more circles (with smaller radius) inter-sect and the intersection area slowly shrinks the centroiduntil a point where all the new circles overlap the intersectionarea or the intersection area is so small that there is not muchchange in its centroid. This is when the intersection algo-rithm converges.
4.2. Experiment set B
For this setup, the positioning engine was calibrated towork with the smaller experiment area and the calibrationpoints were closely spaced. This setup gave accuracies closeto 2.5 m. Fig. 13 shows the placement of the tags. In this sce-nario, the positioning technology gave a better accuracy andhad lesser variation in the error estimate. As a result, ouraverages algorithms performed better giving us accuraciesclose to 1 m (Fig. 17). Table 5 compared the actual and com-puted coordinates by each algorithm. In most cases, the aver-ages algorithms surpassed the performance of intersectionalgorithm by at least 0.5 m and Fig. 15 shows the computedlocation of the tags at the end of an experiment run (same runshown in Fig. 14). Table 6 shows the statistical averages for
Sample No.
Error Distance w.r.t Sample No. (Tag B)
0
1
2
3
4
5
6
7
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49
Err
Dis
tan
ce (m
)
Plain Weighted Intersection Err Est
Sample No.
Error Distance wrt Sample No. (Tag D)
0
1
2
3
4
5
6
7
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55
Err
Dis
tan
ce (m
)
Plain Weighted Intersection Err Est
.r.t. to sample no. for each tag.
Table 7Error distance at convergence for each algorithm
Run No. Plain averages algorithm Weighted averages algorithm Intersection algorithm
Mean Median Std. Dev. Mean Median Std. Dev. Mean Median Std. Dev.
1 1.250 1.230 0.5382 1.650 1.696 0.915 3.057 2.682 1.9372 0.955 0.822 0.521 1.085 1.028 0.479 2.009 2.031 0.6593 0.829 0.758 0.286 0.684 0.621 0.219 1.252 1.187 0.7594 1.468 1.031 1.377 1.587 1.048 1.500 2.476 2.140 1.294
Average 1.126 0.960 0.679 1.252 1.098 0.778 2.199 2.010 1.162
Table 6No. of samples and time (s) at convergence
Run No. No. of samples at convergence Time at convergence (s)
Mean Median Std. Dev. Mean Median Std. Dev.
1 12.25 12.5 2.753 678.5 580 285.4872 8.25 8.5 2.5 716 715.5 134.8063 29.75 31.5 6.184 1335.75 1358.5 679.2374 21.5 21 3.415 1538.25 1651.5 498.893
Average 17.938 18.375 3.713 1067.125 1076.375 399.606
1076 A. Patil et al. / Computer Communications 31 (2008) 1067–1077
the time and number of samples at which the algorithms con-verged for each run of the experiment. Table 7 show themean, median and standard deviation at convergence foreach of the three algorithms. Fig. 16 shows the time distribu-tion between consecutive detections.
Distributio
0
5
10
15
20
25
30
35
40
0-30 30-60 60-90 90-120 150-120 18
Time (30 Se
No
. of
Sam
ple
s (N
orm
aliz
ed)
Exp Run 1
Fig. 16. Sample distribution w.r.t. time
Fig. 15. Position of tags as computed by the three algorithms (for Fig. 14).
A special note about run 1 in this set: during this run,access point 1 was blocked by a folded ping-pong tableplaced in front of it. This change in the environment alteredthe signal strength map of the room and hence affected thepositioning system’s accuracy. As a result, the three algo-rithms have a relatively higher error distance. This illus-trates the inability of the (signal strength based)positioning system’s to adapt to change in environmentalconditions. Some tracking applications may require recali-bration of the room to accommodate the change in the sig-nal strength pattern.
5. Conclusion and future research
In this paper, we have presented an inexpensive auto-mated indoor asset tracking system called BlueBot. Thisprototype system works by making use of any off-the-shelf
n of samples over time
0-150 210-180 240-210 270-240 300-270 >=300
c Intervals)
Exp Run2
for two different experiment runs.
Error distance at convergence for each algorithm
0
0.5
1
1.5
2
2.5
3
1 2 3 4
Experiment Run
Err
or
Dis
tan
ce (
m)
Plain Avg (Median) Weighted Avg (Median) Interesection (Median)
Fig. 17. Error distance at convergence for each algorithm.
A. Patil et al. / Computer Communications 31 (2008) 1067–1077 1077
location positioning system and passive RFID technology.Beyond simply providing a novel mechanism to tracktagged items, our experiments have shown that our posi-tioning algorithms can bring a threefold improvement onthe raw accuracy provided by the positioning technology.We also found that the intersection algorithm worked bet-ter than the two averages algorithm when the positioningsystem was not very accurate. In the case where the posi-tioning system had high accuracy, the averages algorithmout-performed the intersection algorithm. We are look atnew algorithms that can average out the offset in the rawlocation reported by the positioning system.
In our current system, there is a high variation in theamount of time and number of samples required to con-verge to a certain level of accuracy. In the future, we areplanning to reduce this variation by using a robot thatcan be controlled to move in a directed pattern. We haveconsidered employing a feedback system; so that the direc-tion of the robot is controlled by the RF system. A dual-variable gain antenna system can be used such that the highgain antenna controls the movement of the robot until itslow gain partner sees the tag being hunted. We noticed thatsignal strength based Wi-Fi systems are easily affected bychanges in the surroundings. In the future we plan to makeuse of systems that are immune to environmental changes(perhaps TDOA based positioning systems) and analyzethe performance of our BlueBot system. Another approachwould be to continue to use Wi-Fi signal strength, but addreference RFID tags through the environment that couldbe used to remove any positional offsets that might be pres-ent relative to the original calibration. We are also lookingat various ways to make 3D positioning possible.
Acknowledgments
We thank Sastry Duri and Amaresh Rajasekharan atIBM Watson for their assistance and ideas. We also thankIntermec for donating a PCMCIA card reader and itssoftware.
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