RFID indoor location identification for construction projects

Automation in Construction xxx (2013) xxx–xxx

AUTCON-01615; No of Pages 13

Contents lists available at SciVerse ScienceDirect

Automation in Construction

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RFID indoor location identification for construction projects

Ali Montaser ⁎, Osama Moselhi 1

Department of Building, Civil & Environmental Engineering, Concordia University, 1455 de Maisonneuve Blvd. West, Montréal, Québec H3G 1M8, Canada

⁎ Corresponding author. Tel.: +1 514 848 2424x7037E-mail addresses: [email protected] (A. Monta

[email protected] (O. Moselhi).1 Tel.: +1 514 848 2424x3190; fax: +1 514 848 796

0926-5805/$ – see front matter © 2013 Elsevier B.V. Allhttp://dx.doi.org/10.1016/j.autcon.2013.06.012

Please cite this article as: A. Montaser, O. M(2013), http://dx.doi.org/10.1016/j.autcon.2

a b s t r a c t
a r t i c l e i n f o
Article history:Accepted 21 June 2013Available online xxxx

Keywords:RFIDIndoor location identificationRSSITriangulationProximityMaterial tracking

This paper presents a low cost indoor location identification andmaterial trackingmethodology for constructionprojects using Ultra High Frequency (UHF) passive Radio Frequency Identification (RFID) technology. Onsitelocation aware information is an emerging area that focuses on automating delivery of spatial informationpertinent to location of materials, workforce, and equipment. This spatial information can be used to deriveknowledge about construction project status. A two-step algorithm is presented to automate the process oflocation estimation and material tracking in near-real-time. In this methodology, a number of passive RFIDtags are distributed onsite where work is progressing, and a mobile RFID reader is carried by a worker onsite.Each passive RFID tag is deployed as a reference point with a known location (landmark) within a predefinedzone. Reference tags of known locations are used to determine the location of the worker and eventually locateand track surrounding materials. The methodology uses Received Signal Strength Indicator (RSSI) for signalmeasurements. Two localization methods (triangulation and proximity) were used to identify the location ofthe worker. Testing this methodology was carried out on an actual construction jobsite, where five test bedswere setup at different locations and within different construction time spans. In addition, one test bed wasset up in a lab environment. The results presented in this study demonstrate the potential for a low-cost methodfor location estimation andmaterial tracking of indoor construction. The results show amean error of 1.0 m and1.9 m for user location identification and material tracking using the triangulation method, respectively. Theresults also show a mean error of 1.9 m and 2.6 m for location identification of the worker and for materialtracking using the proximity method, respectively. The proposed methodology detects the zones of workerand material location with 100% accuracy.

© 2013 Elsevier B.V. All rights reserved.

1. Introduction

Object localization is used to determine the absolute and/or relativelocation information of objects with given observations and spatialrelationships between these objects and a set of known references [1].The Global Positioning System (GPS) has well met the need for outdoorlocation sensing, with centimeter accuracy [2–4]. However, when itcomes to indoor areas, GPS is not reliable due to poor reception ofsatellite signals. As well, GPS is expensive for deployment to automatetracking of individual material items [5]. A wide range of technologieswere used for indoor location sensing such as infrared positioningsystems [6], indoor GPS-based solutions [7], Ultra Wide Band (UWB)[8–10] andWireless Local Area Network (WLAN) [11]. Signal measure-ments used in indoor location sensing technologies are Received SignalStrength Indicator (RSSI), Angle of Arrival (AOA) and Time of Arrival(TOA) [12]. TOA measures signal travel time between the source andreceiver on a designated channel and the system has to predefine the

; fax: +1 514 848 7965.ser),

5.

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oselhi, RFID indoor location013.06.012

velocity of the signal on that channel [13]. AOA is a category of signalmeasurement, which considers the direction of signal propagation[14]. Signal strength has a close relationship with the distance betweenthe sender and receiver. Certain types of signals such as radio frequency,ultrasound, and vibration can be attenuated on the transmissionmedia,and the localization system can estimate spatial information using thedegree of signal attenuation. RSSI measurements are made so that thedistance can be estimated using a path loss model. Signal strengthmeasurement based localization systems, have two main advantages:cost effectiveness and straightforward implementation [15].

RFID technology is also used in this respect. RFID data can be storedin tags and retrieved with readers that can communicate with thesetags, using radio frequencywaves [16–18]. Time and angle of arrival sig-nal measurement methods are not used for RFID location identificationbecause signals are affected by their respective multipath effect [25]. Liand Becerik-Gerber (2011) [20] conducted a comparative study of eightindoor location sensing technologies taking into consideration accura-cy, affordability, line of sight, wireless communication, context inde-pendence, on-board data storage, power supply, and wide applicationin the building industry. Based on that study they concluded that RFIDtechnology is the most suitable indoor location sensing technology.Choi (2011) [15] arrived at the same conclusion; stating that passiveUHF RFID based localization overcomes the drawbacks of conventional

identification for construction projects, Automation in Construction

http://dx.doi.org/10.1016/j.autcon.2013.06.012

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Unlabelled image


Unlabelled image

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LocalizationDatabase

RFID DataCollection

Triangulation ORProximity Method

Identify UserLocation

Change Location

11

22

33

44

55

66

StepOneStepOne

Triangulation ORProximity Method

Identify MaterialLocation

StepTwoStepTwo

77

88

99

Fig. 1. Main components of the developed methodology.

2 A. Montaser, O. Moselhi / Automation in Construction xxx (2013) xxx–xxx

indoor localization systems. According to Aryan (2011) [9], UWB haschallenges associated with its installation and its use in constructionjobsites. For example, it requires repeated calibrations especiallywhen the power supply is down. Furthermore, the study showed thatUWB measurement accuracy is highly dependent upon the line ofsight of the point to be located. This accordingly has to do with thepositions of the UWB receivers that cover the area under study andcalls for rectangular configuration of these receivers to insure higheraccuracy. Such a requirement may not be fully attainable in manyconstruction sites.

Start

Assign RFID Reference Tags to eachZone at each Floor Landmarks

Identify Coordinate (xi,yi) for each ReferanceTag

End

Reference Tag ID, Zone& Coordinate (xi.yi)Localization

DB

Fig. 2. The process of deploy

Please cite this article as: A. Montaser, O. Moselhi, RFID indoor location(2013), http://dx.doi.org/10.1016/j.autcon.2013.06.012

Three main methods have been developed to locate an objectusing RFID: triangulation, proximity and scene analysis. Triangulationis a technique of determining the location of an object, based on geo-metric properties. Triangulation determines the position of an objectby measuring its distance from several reference positions [21]. Theproximity method requires the measurement of the nearness of a setof neighboring reference points, which have fixed and known locations,and are close to the target [9]. Thus, the proximity algorithm guaranteesthe most simple and easy implementation for object localization [20].The scene analysis technique estimates the location of a signalsource using a pre-observed data set about themonitoring scene. How-ever, it requires extra information and data storage to maintain pre-observation and is not practical for dynamically changing environmentssuch as construction jobsites [11,22]. Most RFID literature focuses ondeployment of active RFID tags for tracking without localization[23–25], trackingwith localization [26] or outdoor localization support-ed by the Global Positioning System (GPS) [27]. However, active tagsare expensive and have a limited battery life time (5–10 years) [28].In addition, the use of active tagsmay result in undesirable interference,in view of their relatively wide range and likely obstruction objectsonsite during construction. So, short read range passive RFID tagscould reduce the impact of obstructions in case of using them on zonelevel. Although, the deployment of passive RFIDs entails the deploy-ment of a larger numbers of tags than active RFIDs, its lower costmakes it even more economically feasible than active RFIDs.

Tracking materials and accessing onsite related information can bechallenging tasks in view of the dynamic nature of onsite operationsincluding material delivery and utilization [29]. Onsite materialmanagement was identified as one of the areas that has the greatestpotential for improvement and the greatest positive impact on engi-neering construction work processes [30]. Unlike methods presentedin the literature, which are focused on built facilities and not onconstruction jobsites, the proposed methodology enables practicalapplications during construction. To identify the capabilities of thedeveloped method and its limitations, it was applied during theconstruction of a building project in the Montreal area. The resultsvalidate the effectiveness of the developed method for locationidentification and for tracking materials on site. The developed

sos

ing RFID reference tags.


image of Fig.�1

image of Fig.�2


Fig. 3. Path loss regression model.

3A. Montaser, O. Moselhi / Automation in Construction xxx (2013) xxx–xxx

methodology utilizes RFID along with two RSSI based localizationmethods (triangulation and proximity) and a specially designed rela-tional database to capture, store and process the transmitted signals.

2. Proposed methodology

The developed methodology utilizes passive RFIDs and localizationmethods integrated in a two-step algorithm supported by a speciallydesigned relational database to identify locations of worker(s) whoare equipped with RFID readers and materials onsite. The main compo-nents of the developed methodology are outlined in Fig. 1. In the firststep of the algorithm, the worker location is identified making use ofthe captured signals from a set of passive reference tags deployed on

Fig. 4. Sketch illustrating the pro


site and processing them using the triangulation or proximity method.In the second step of the algorithm, material location is identifiedmaking use of the mobile worker locations, which were identified inthe first step, and the captured signals from material tags; similarlyprocessed as in the first step using the triangulation or proximity meth-od. RFID reference tags are used as a reference point with a known loca-tion (landmark) within a predefined zone. In this approach, passiveRFID tags are distributed on the jobsite, and mobile worker(s) areequipped with RFID reader(s). In this study reinforced concretecolumns, shear walls, curtain walls and wall edges were used as land-marks for the placement of reference tags. The known locations of thereference tags are used to estimate the location of the user, based onthe RSSI received from these tags.

Locations of reference tags are identified with subscript (i), andthe location of the workers (site personnel who carry the mobileRFID readers) is identified with subscript (j). It should be noted thatmaterials are tagged in the fabricator's shop and hold the same IDas that in the 3D Building information Model (BIM) for the building.Prefabricated materials such as electromechanical equipment, curtainwalls, precast concrete panels, etc. are tagged using encapsulatedrugged tags attached via screws or epoxy adhesive according to thespecification of each material. For packed materials such as woodendoors and frames, gypsum board panels, etc., they are tagged usingprinted label RFID tags. One tag is for the pack itself to include informa-tion about the packed quantity and another printed label tag attached toeach item inside the pack, if possible. For items such as bricks, it willnot be beneficial or possible to tag each item. Loose materials such asconcrete or excavated materials will not be tagged — other methodsshould be used to track this type. Subscript (k) is used for trackedmaterial temporal location onsite and subscript (f) is used to representthe respective final locations of the tracked materials. Fig. 2 illustratesthe process of deploying RFID reference tags, which start with assigningRFID reference tags to each zone's landmarks. The coordinates (xi,yi) ofeach reference tag (i) are then stored in the database knowing that allreference tags are deployed at the same height, which is 1.5 m fromfloor level. This step is performed one time per floor and is used asinput for location identification and tracking purposes.

posed two-step algorithm.


image of Fig.�3

image of Fig.�4


Calculate Average (RSSI)i for eachReference Tag at Current Location Lj

Convert Average (RSSI)i to Weight Wi

Calculate Current Location (Xj,Yj)

W = f(RSSI)

Xj = f(xi,W)Yi = f(yi,W)

Proximity Method

Fig. 6. Flowchart for user location identification using the proximity method.


The triangulation method requires a path loss model to convertRSSI to distance (D). Therefore, a set of experiments were conductedby the authors to develop a path loss model. The model was devel-oped using 6704 data sets of laboratory experiments. Each data setconsists of a number of signals captured at a specific distance. Linearregression was carried out using the average of the captured signals'strength and the associated distance. Distance (r) was varied at aninterval of 10 cm. The developed relation is represented by Eq. (1)(See Fig. 3). For more details about path loss, different regressionmodels and the basis for deploying the linear model refer to Razaviet al. (2012) [19].

r ¼ –0:1618 RSSI–5:2863 ð1Þ

Start

RFID Reader Current Location Lj Set j = 1

Start Collecting Data Using MobileRFID Reader at t = to

Calculate Average (RSSI)i for eachReference Tag at Current Location Lj

Convert Average (RSSI)i to Distance ri

Move to Next LocationSet j = j + 1

Date, Time &Location Lj (Xj,Yj)

End

RFID DB

Stop Collecting Data at t = to + ΔtDat

aC

olle

ctio

n

Path LossModel

r = f(RSSI)

Is All ZonesCovered?

Yes

No

Filter Reference Tags

Draw Circle at each Sensed Reference Tag(xi,yi) with Radius ri

Identify the Intersection Area for theGenerated Circles

OneIntersectionArea Only?

Calculate the Centroid for the IntersectionArea, Current Location (Xj,Yj)

Yes

Calculate ∑ (r) for eachIntersection Area

No

Select the intersectionarea with Minimum ∑ (r)

Trilateration Method

Append

Generate Report

Fig. 5. Flowchart for user location identification using the triangulation method.

Please cite this article as: A. Montaser, O. Moselhi, RFID indoor location identification for construction projects, Automation in Construction(2013), http://dx.doi.org/10.1016/j.autcon.2013.06.012

image of Fig.�5

image of Fig.�6



3. Location of the mobile worker

The first step in the developed algorithm is to identify the worker'sstationary location using signals captured from reference tags asshown in Fig. 4-a and the flowchart shown in Fig. 5. The worker Lj at agiven location (Xj,Yj) operates his RFID reader at a time to and capturesthe signals received from the reference tags and materials at that loca-tion and time. This process is repeated at a set of time intervals; referredto here as Δt. In the field experiments, Δt ranged from 15 to 30 s. Thetag ID is used to distinguish RFID reference tags from material tags. Arelational database was developed to filter these tags based on their

S

Filter MaterUser Lo

Count # of SensLet k

Se

Does msense

than 2

Location Couldbe RoughlyIdentified

YesYes

NoNo

Is k

Date, Time &Material Location Lk

(Xk,Yk)

YesYes

Calculate Average (R

Convert AveragePath LossModel

r = f(RSSI)Draw Circle at each

Ra

Identify the InterGenera

OInterArea

Calculate the CentrArea, Materia

Yes

Calculate ∑ (r) for eachIntersection Area

No

Select the intersectionarea with Minimum ∑ (r)

Trilateration Method

Genera

Fig. 7. Flowchart for material location identi


respective IDs, and to ensure the use of signals, in the first step of thedeveloped algorithm, from related reference tags only. If a tag ID is fortracking materials, then it will be stored in the database for laterprocessing in the second step.

The triangulation method determines the position of the mobileworker by measuring his distance from several reference tags. Allreadings collected from each reference tag are averaged and convertedinto an equivalent distance (ri), using Eq. (1). When the localizationalgorithm identifies at least a set of three distances from different refer-ence tags, the algorithm generates circles; their respective centers arethe known positions of the reference tags (xi,yi). The intersection of

tart

ial Tags Data &cation Data

LocalizatrionDB

ed Material Tags k=1 to n

t k = 1

aterial kd in morelocations?

= n?

End

Move to Next LocationSet k = k + 1

NoNo

App

end

SSI)j for each LocationLj

(RSSI)j to Distance rj

Location Lj (Xj,Yj) withdius rj

section Area for theted Circles

nesectionOnly?

oid for the Intersectionl Location (Xk,Yk)

te Report

fication using the triangulation method.


image of Fig.�7


Calculate Average (RSSI)j for eachLocation Lj

Convert Average (RSSI)j to Weight Wj

Calculate Material Location (Xk,Yk)

W = f(RSSI)

Xk = f(Xj,W)Yk = f(Yj,W)

Proximity MethodProximity Method

Fig. 8. Flowchart for material location identification using the proximity method.


these circles determines the expected signal source's location. In casethe intersection is not in a point, but rather an area, the center of gravity(C.G.) of that area is used instead. Accordingly, the coordinates ofthe worker could be calculated (Xj,Yj). It is worth noting that, in manycases, there is more than one intersection area. In such cases, the

Material Has1

XfXfNameName

ArrivalDate

ArrivalDate

PricePrice

YfYf

ProjectNo.

ProjectNo.

QuantityQuantity

IDID

DeploZones

IDID

Floor No.Floor No.

1

Located

AreaArea

Entity AttributeAttribute PP

RFIDSignals

DateDate IDID

TimeTime RSSIRSSI

PP

Fig. 9. Entity relationship (ER) diagram


following heuristic rule is applied. Rule: “if more than one area of inter-section exists, then the C.G. of the area formed by the circles having theleast summation of their radii is used”. This rule acts as a useful filter ofnoisy out-of zone-signals. The user moves to the next location in thesame zone or another zone on site and the same procedure is repeated.The generated locations Lj (Xj,Yj) will be stored in the database withtheir corresponding time for further utilization in the second step ofthe algorithm.

The proximity method can also be applied using the same processfollowed in the triangulation method. However, the proximity methoduses Received Signal Strength Indicator (RSSI) as a weighting methodto express how near the reader is to the reference tags. RSSI is ameasurement of the power of the received radio signal. Therefore, thehigher the RSSI number (or the less negative in some devices) is, thestronger the signal; indicating that the mobile worker is closer to thattag. The readings collected for each reference tag were averaged andconverted into a related weight (Wi), which represents how muchcloser the reader is to that tag (See Fig. 5). The coordinates of theworker(Xj,Yj) are calculated using Eq. (2). Fig. 4-a, depicts the worker standingin a zone surrounded by three reference tags having coordinates(x1, y1), (x2,y2) and (x3,y3) and corresponding average signal strength

MaterialTag

Mobile RFIDReader

Read

M

1

1

IDID

XkXk YkYk

DateDate

DateDate

TimeFromTimeFrom

YjYj

XjXj

yReference

Tag

IDID

xixi

M

yiyi

HasM

1

rimaryKey

rimaryKey

Relationship

TimeTo

TimeTo

Capture

(RSSI)j(RSSI)j

Serial #Serial #

eriod IDeriod ID

M 1

N

for the designed RFID database.


image of Fig.�9

image of Fig.�8


a) RFID mobile reader

b) RFID encapsulated tag

d) RFID tag printer

c) RFID label tag

Fig. 11. RFID hardware used.

Fig. 10. (a) Case study I (b) Case study II.


(RSSI)1, (RSSI)2 and (RSSI)3. This provides sufficient data to determinehis location (X1,Y1) (Fig. 6).

xj ¼ ∑ni¼1xi�Wi

∑ni¼1Wi

& Yj ¼∑n

i¼1yi�Wi

∑ni¼1Wi

ð2Þ

4. Material location

After successful delivery of materials on site, it is distributed for useat different locations on site. While themobile worker's location can becalculated in a straightforward manner as described earlier in step oneof the algorithm, this is not the case for material location. The signalsreceived from a material may indicate that it is near the user, but thedirections of the received signals are unknown. The user moves to

Table 1Characteristics of test beds.

Test bed # Test bed 1 Test bed 2 T

Case study I I IDate 01/12/2010 03/12/2010 0Location Jobsite (3rd floor) Jobsite (2nd floor) JoTotal number of predefined locations 18 18 1Total no. of samples 418 494 4Covered area (m2) 75.24 75.24 7No. of deployed tags 24 24 2Average covered area (m2/#) 3.135 3.135 3


another location to capture RFID signals and repeats step one of thealgorithm to determine his new location. At this second location, theuser again receives tagged material signals, and material location canbe roughly estimated based on the region between these two locations.However, to increase the accuracy of calculating material locations Lk,the user moves to a third location and repeats the procedure performedat the previous two locations. After moving to the third location andreceiving material signals from that location, the triangulation or prox-imity method is applied to identify material location Lk based on theidentified locations of the mobile worker.

The triangulation method determines the position of the materialby measuring its distance from previously-identified locations of theworker. All collected material readings from each worker locationare averaged and converted into an equivalent distance (r), usingEq. (1). When the localization algorithm identifies a set of at least

est bed 3 Test bed 4 Test bed 5 Test bed 6

I I II8/12/2010 01/3/2011 14/04/2011 24/12/2011bsite (3rd floor) Jobsite (2nd floor) Jobsite (3rd floor) Lab environment8 15 15 4551 729 438 59155.24 108 120 614 25 33 25.135 4.32 3.6363 2.44


image of Fig.�11

image of Fig.�10



three distances from different locations of the mobile worker, thealgorithm generates circles, the centers of which are the respectivepositions of the mobile worker (Xj,Yj). Similarly, by applying the pro-cedure described earlier for worker's location, material locations are

Fig. 12. Test bed 3 Case Study I a) Setup b) Grap


calculated. Fig. 4-b shows the sequential steps of the procedureapplied and Fig. 7 shows the flowchart for the proposed method ofmaterial location identification using the triangulation method, whichrepresents the second step in the proposed two-step algorithm.

hical representation for results c) Pictures.


image of Fig.�12



As shown in Fig. 4-b, the usermoves to another location, for exampleL2 (X2,Y2) and the RFID reader again receives signals from material (k).Also, when moving to a third location L3 (X3,Y3), the reader keeps onreceiving a signal form the same material (k). After receiving a signalfrom material (k) from at least three different locations, the locationof material (k) can be calculated (Xk,Yk). Fig. 8 shows a flowchartfor the procedure used for material location identification using theproximity method and using Eq. (3) to calculate the coordinates ofeach material being tracked. It should be noted that the three differentlocations should not be on the same line. Another reason for choosingthree different locations instead of only two is that thematerial locationwould be on the line connecting the two locations and so would beautomatically calculated as being closer to the locationwhere the signalis stronger,whichmay give rise to higher errors. This process is repeateddaily during the worker's walkthrough for data collection. At that point,material location could be identified and tracked on a daily basis. Mate-rial temporal location, Lk is compared to material final location Lf

2.41.81.20.6

Median

Mean

11.00.90.8

95% Confidence Interval

Summary for User Loc

a

2.12.01.91.81.7


Summary for User Lo

b

Median

Mean

2.41.81.20.6

Fig. 13. Summary of statistical analysis results for all te


captured from 3D BIM in order to know whether the material is placedand/or installed in its final location or still in handling processes.

Xk ¼ ∑3j¼1Xj

�Wj

∑3j¼1Wj

& Yk ¼ ∑3j¼1Yj

�Wj

∑3j¼1Wj

ð3Þ

5. Localization database

The developed RFID localization database is at the core of the pro-posed methodology. It contains relatively structured data relationshipsand schema designs. It is used for filtering different types of tags and fordata processing. Relational database management systems are bettersuited for the application at hand. The database consists of six entitiesor tables. The entities of the database are RFID signals,material, materialtag, reference tag, mobile RFID reader and zone as shown in Fig. 9. RFID

3.0

1.2.1

1st Quartile 0.6776Median 0.90603rd Quartile 1.3387Maximum 3.0229

0.8625 1.1534

0.7636 1.1207

0.4062 0.6168

A-Squared 0.84

P-Value 0.028

Mean 1.0080StDev 0.4897Variance 0.2398

Skew ness 1.58505Kurtosis 4.92245N 46

Minimum 0.3795

Anderson-Darling Normality Test

95% Confidence I nterv al for Mean

95% Confidence I nterv al for Median

95% Confidence I nterv al for StDevs

ation Triangulation

2.32.2


1.6905 2.1196

1.6938 2.2296

0.5993 0.9100

A-Squared 0.31

P-Value 0.543


Skew ness 0.131495Kurtosis -0.764336N 46

Minimum 0.6605





cation Proximity

3.0

st beds user location a) Triangulation b) Proximity.


image of Fig.�13


Fig. 14. Comparison between triangulation and proximity method (Case Study I).

Fig. 15. Material tracking using triangulation and proximity method (Case Study II).


signal entity is considered the main repository for the captured RFIDdata. It consists of six attributes and they are serial number, period ID,ID, data, time and RSSI. The serial number is automatically generatednumber to represent how many records have been captures by theRFID mobile reader. Period ID is used to filter each of the data setspertinent to worker's location. As such, the period ID increases incre-mentally as the worker moves from one location to another. A manyto one relationship connects RFID signal and mobile RFID reader entity.

Mobile RFID entity represents the location of the mobile worker,which is calculated in near-real-time using the procedure describedabove and the quires and data stored for the RFID signal entity. Thisentity has five attributes: date, time from, time to, Xj and Yj. A oneto many relationship connects the mobile RFID entity and materialtag entity. Material tag entity characterizes RFID tags attached tomaterials, and it has five attributes. The most valuable attributes inthe material tag entity are Xk and Yk. This is the material temporallocation onsite, which is tracked daily with the date attribute. The“Material” and “Material Tag” entities have a one-to-many relationship.This relationship was created on the basis that one material or equip-ment is assigned one tag only. The relationship for “Zone” entity and“Reference Tag” entity was designed to be one-to-many. Material andZone entities can either be extracted from project drawings or fromthe 3D BIM of the project.

6. Experimental studies

For validating the proposed method and demonstrating the use ofits components, experiments were conducted in two case studies.Case study I makes use of the construction of the Center for Structuraland Functional Genomics at Concordia University (Fig. 10-a). Otherexperiments were conducted in the Construction Automation Lab, atConcordia University (Fig. 10-b). The RFID hardware componentsused in the two case studies are RFID mobile readers, RFID encapsu-lated tags, RFID label tags and a RFID label tag printer (Fig. 11). TheRFID hardware could collect data in dirty, harsh, and hazardousconditions. For example, the encapsulated RFID tag used, could workin temperatures ranging from −40 °C to 66 °C and could be attachedusing screws, rivets, double-sided adhesive strips or a variety of othermethods. Regarding its memory size, it has a capacity of 512-bit-on-chip. In addition, the RFID mobile readers could work under similarharsh conditions such as temperatures ranging from −15°C to 50 °C,and are protected from dirt, dust, oil, other non-corrosive materials,and splashing water. The readers' connectivity could be Ethernet orWi-Fi and can host applications written in Java, JavaScript, VB.Net orC#.Net. The read range for encapsulated tags is 5 m for and 3 m forlabel tags. The encapsulated RFID tag shown in Fig. 11-b costs approxi-mately $5 per tag. The passive RFID tags used in these experimentswereprinted RFID labels, which cost 2 cents each. The tag labels and theprinter are shown in Fig. 11 (c & d), respectively [31].

Five test beds were setup at different time spans and differentlocations on site. Carrying out the tests at different time spans wasrequired to prove that the proposed methodology is feasible duringconstruction operations. Test bed 6 was setup in a lab environment.Table 1 shows the characteristics of each test bed, including thetotal number of data samples collected, date, location, test bed areaand number of tags used in the test bed. Fig. 12(a & c) shows thesetup and images of test bed 3 where the rectangles refer to the loca-tions of attached reference tags. Fig. 12-b shows a graphical display ofthe error in the calculated location of the mobile worker, which isthe distance in meters between the estimated and actual locations.The triangles represent the worker's previously marked locationswhere he/she stands and starts capturing RFID signals. The circlesand squares represent, respectively, the calculated location usingthe triangulation and proximity methods. A summary of statisticalanalysis for both the triangulation and proximity methods isdisplayed in Fig. 13(a & b). Fig. 13 depicts the accuracy of the

Please cite this article as: A. Montaser, O. Moselhi, RFID indoor location identification for construction projects, Automation in Construction(2013), http://dx.doi.org/10.1016/j.autcon.2013.06.012

image of Fig.�14

image of Fig.�15



developed methodology in identifying locations of the workerobtained from all test beds. Fig. 14 shows a comparison betweenthe two methods. There are five sensed reference tags. Four of themrelated to the user zone and the fifth one is far away from the userzone. Using the triangulation method automatically selects the inter-section area of most circles and due to the fifth reading being awayfrom that intersection, it will not be considered in calculations.However, the proximity method considers all readings and does nothave this mechanism so the results are affected more by noise fromreference tags that far from the user zone.

Fig. 15 shows a tracking material labeled C8 utilizing user locationnumbers 14, 15 and 18. Similarly, a statistical analysis was performedfor material location identification and the results are summarized inFig. 16 (a & b) for both methods (triangulation and proximity). Thedeveloped methodology yields 100% accuracy for zone identificationof the worker and for the tracked material in all test beds. This provesits suitability for zone identification on construction jobsites and/or

4321

Median

Mean

2.001.751.501.251.00


Summary for Material Tr

3.63.02.41.8

32.82.62.42.22.0


Summary for Material

Median

Mean

a

b

Fig. 16. Summary of statistical analysis results for all test


operational built facilities. A Cumulative Distribution Function (CDF)was used to show the localization error and to measure the localiza-tion performance at a given confidence level (see Fig. 17). The CDFalso indicates the error accumulation in material location due to thedependency on worker location. It is clear from Fig. 17 that in caseof the triangulation method the error accumulated and increasedrapidly. However, in case of the proximity method the error accumu-lation is steady.

The results indicate that the triangulationmethod is relatively moreaccurate than the proximitymethod for both user andmaterial localiza-tion. However, the triangulation method suffers from drawbacks suchas the dependency on path loss models (location-environment depen-dent models), which are not robust enough to represent the character-istics of radio waves and their interference in a dynamically changingconstruction environment. In addition, the computational time re-quired for identifying location using the triangulation method is muchmore than that for the proximity method due to the mathematical

5

2.502.25


1.2902 2.5193

0.9051 2.3983

0.9635 1.8856

A-Squared 0.63

P-Value 0.088


Skew ness 0.877538Kurtosis 0.199763N 19

Minimum 0.4279





acking Triangulation

4.2

3.2.0


2.2605 2.9830

1.9756 3.2413

0.5663 1.1083

A-Squared 0.46

P-Value 0.233


Skew ness 0.370128Kurtosis -0.969641N 19

Minimum 1.4822


95% Confidence Interv al for Mean

95% Confidence Interv al for Median

95% Confidence Interv al for StDevs

Tracking Proximity

bed material location a) Triangulation b) Proximity.


image of Fig.�16


543210

1.0

0.8

0.6

0.4

0.2

0.0

Error (m)

Pro

bab

ility

User Location TriangulationUser Location ProximityMaterial Tracking TriangulationMaterial Tracking Proximity

Variable

Empirical CDF of User Location and Material Location

Fig. 17. Cumulative distribution function for estimated error for user andmaterial locationidentification.


complexity of the triangulation method, which gives an advantageto proximity method in near-real-time localization. Further work isneeded to address the impact of metal media proximity to RFID tags,optimum duration for data capturing, number of RFID tags employed,the distance between them and zone configuration to provide guide-lines to the users of RFID technology for localization in buildingconstruction.

The proposed methodology is part of an ongoing research thatintegrates different automated data acquisition technologies for effi-cient tracking and control of constriction operations through continu-ous monitoring of their respective tasks and activities [32,33]. Aproject manager can analyze this data for different purposes such assafety and timing, taking corrective actions as needed. Therefore,the user identified location in step one is crucial for this purposeand needs to be in near-real-time. After finishing the first zone, theuser moves to the next zone for data collection. These steps arerepeated until all zones have been covered. The second step of theproposed algorithm will be used to track material locations and itdoes not need to be in real time. Materials such as electromechanicalequipment can be directly identified and tracked by attaching an RFIDtag directly to the item and utilizing the same methodology. Thesecond step of the algorithm will provide the project manager witha daily layout of the materials per floor.

7. Conclusion

This study presents a detailed methodology on utilizing a low costlocation identification and material tracking for indoor constructionusing a two-step algorithm. The proposed method utilizes UHF passiveRFID technology for capturing spatial data in an indoor environment. Inthis study, the work-active area is divided into exclusive zones, andeach zone is spatially covered with a number of passive RFID tags. Theuser and material locations are estimated using two different RFIDmethods (triangulation and proximity) based on RSSI signal measure-ment. A specially designed relational database was used to storeand organize RFID captured signals. The methodology has beenexperimented on a construction facility in Montreal and a lab environ-ment. The results are presented and compared for 5 different testbeds in different construction time intervals and 1 test bed in a labenvironment. The results shows a mean error of 1.0 m and 1.9 m foruser location identification and material tracking using the triangula-tion method, respectively. The results shows a mean error of 1.9 mand 2.6 m for user location identification and material tracking usingthe proximitymethod, respectively. The proposedmethodology detectsuser location and material zones with 100% accuracy. The resultspresented in this study demonstrate the potential of utilizing short


range RFIDs in location estimation and material tracking with acost-effective manner for indoor construction jobsites. The developedmethod for location identification and material tracking using RFIDtechnology can be used to obtain information required for scalableand near-real-time decision-making, timely tracking of the projectstatus and proactive safety monitoring. The study could be used indifferent types of buildings such as steel structure buildings. However,the tags should be encapsulated tags to reduce the impact of interfer-ence between radio waves and metals. The main limitations of thedeveloped methodology are the need to generate a path-loss modelfor each type of tag used in case of using the triangulation method,the variability associated with deployment of tags, the uncontrolledinfluence of noisy signals and potential interference from equipmentand/or vehicles located between tags and between tags and the mobilereader.

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RFID indoor location identification for construction projects

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