Localization of rfid equipped assets during the operation phase of facilities (bim+rfid)    2012
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Localization of rfid equipped assets during the operation phase of facilities (bim+rfid)    2012 Localization of rfid equipped assets during the operation phase of facilities (bim+rfid) 2012 Document Transcript

  • Localization of RFID-Equipped Assets During the Operation Phase ofFacilitiesAli Motamedi & Mohammad Mostafa SoltaniGraduate Research Assistants, Concordia University, CanadaAmin HammadProfessor, Concordia University, CanadaAbstractIndoor location information has the potential to improve the utilization and maintenance of facilities.RFID technology has been employed for localization in indoor environments in various researchprojects. However, several RFID-base localization systems are inaccurate in indoor environments. Inour previous research, long-range RFID tags are attached to building assets at an early stage of theirlifecycle and the memory on tags is used during the lifecycle. This paper focuses on the localizationopportunities that our proposed RFID-tagged environment can provide. We propose to save currentlocation data (e.g., coordinates) on tags attached to fixed assets and locate them by reading this datafrom a long distance. Additionally, these tags act as reference points for RFID reader localizationtechniques to estimate the position of the user. The paper also evaluates an approach to locatemoveable assets (e.g., tools) using the data saved on fixed assets’ tags and an RSS pattern matchingalgorithm. This localization method incorporates the dynamics of the environments, is device-independent and does not require calibration. As a result, a user equipped with an RFID reader is ableto estimate his/her position, as well as obtaining the location information of target assets, withouthaving access to any central RTLS infrastructure.Keywords: Radio Frequency Identification, Facilities Management, Localization, Building InformationModelling1 IntroductionThe localization problem has received considerable attention in the areas of pervasive computing asmany applications need to know where objects are located. Location information can be used byoccupants unfamiliar with a building to navigate and find their destinations; additionally, facilitiesmanagement (FM) personnel could be provided with locations of assets. Hence, indoor locationinformation is especially valuable as it has the potential to improve the utilization and maintenance offacilities. Furthermore, location information is central to personalized applications in different areasand it is the basis for the delivery of personalized and location-based services (LBS). It is the basis forcontext awareness within the building that involves an automatic recognition of the user’s locationand activity (Papapostolou and Chaouchi, 2011, Li and Becerik-Gerber, 2011). A Radio Frequency Identification (RFID) tag is a memory storage device for storing a certainamount of data that can be read wirelessly. RFID technology does not require line-of-sight and thestored data are dynamic and modifiable. The RFID reader can be a part of other mobile devices suchas cell phones or Personal Digital Assistants (PDAs) (Aimglobal, 2008).
  • RFID technology has been employed for localization in indoor environments in various researchprojects (Li and Becerik-Gerber, 2011). RFID tags or readers can be the target for positioning.However, the main shortcoming of RFID is the interference among its components and somematerials, which makes it sensitive to changes in the environment resulting in inconsistentperformance. In our previous research, a framework has been proposed in which long-range RFID tags areattached to building assets at an early stage of their lifecycle and the memory on tags is used to storevarious types of data during the lifecycle of buildings’ assets. The stored information on tags isbeneficial for several lifecycle processes and is used by various stakeholders. This paper focuses on the localization opportunities that our framework can provide. We proposeto save current location data (e.g., coordinates) on tags attached to fixed assets. Consequently, an FMpersonnel is able to read a tag from a distance and locates the fixed assets on a floor plan. Fixed tagswith known positions act as reference points for RFID reader localization techniques (e.g. trilaterationand proximity). In this scenario, the user estimates his/her position by scanning the surrounding tags.Additionally, information such as floor plans, navigational aid and RF fingerprinting database can bestored in these tags. Furthermore, the paper evaluates an approach to locate moveable assets (e.g.,tools). In this approach, radio signals sensed from fixed tags (attached to fixed assets) help the user toestimate the location of the target tag attached to a movable asset based on received signal strengthindicator (RSSI) pattern matching. This method introduces several improvements to availabletechniques as it incorporates the dynamics of the environments since the target tag and surroundingreference tags are affected by the same environmental effects. Additionally, this approach is device-independent and does not require calibration since it uses RF signal data without converting them intodistance information. As a result, a user equipped with an RFID reader is able to estimate his/herposition, as well as obtaining the location information of target assets, without having access to anycentral Real-Time Location System (RTLS) infrastructure. The objectives of the paper are: (1) toelaborate on a new method to localize fixed assets, (2) to investigate reader localization opportunitiesin buildings with RFID-tagged assets, (3) to investigate techniques to localize movable assets usingfixed tags and, (4) to investigate the applicability of the proposed methods using several case studies.2 Literature review2.1 RFID localization techniquesFuchs et al. (2011) categorized RF-based localization methods into four major groups: (1) Laterationthat uses the distances of the target to at least three points with known positions. The estimation ofdistance based on RF properties are done using the following techniques: Time of Arrival (ToA),Time Difference of Arrival (TDoA), interferometry, signal attenuation and, hop-based. (2) Angulationdetermines the location of an object from the measured angles to at least two fixed points with knownlocations. (3) Fingerprinting uses mapped properties of the environment for position estimation. Inthis method a sensor can be located by measuring its current signal strength pattern and comparing itto a previously surveyed signal map. (4) Connectivity/Proximity uses the analysis of connectivity, i.e.the number of attainable neighbours. It operates by measuring nearness to a known set of points.Various indoor RFID localization techniques are designed based on the above-mentioned methods.Papapostolou and Chaouchi (2011) and Li and Becerik-Gerber (2011) provided thorough surveys andcomparisons between various projects for tag and reader localization.2.2 RFID-assisted lifecycle managementThe use of attached RFID tags for lifecycle management has been proposed in the aerospace industryfor storing unique ID and important lifecycle information on tags attached to aircraft parts for
  • enhancing inspection and repair processes (Harrison, 2007). The framework developed in ourprevious research proposed adding structured information taken from the Building Information Model(BIM) database to RFID tags attached to the building assets (Motamedi and Hammad, 2009). Havingdata related to the assets readily available on the tags provides easy data access for stakeholdersregardless of having real-time connection to a central database. In this framework, every asset is apotential target for tagging. Having tags attached to assets results in a massive tag cloud in thebuilding. The target assets are tagged during or just after manufacturing and are scanned at severalpoints in time during the lifecycle. The scan events are for reading the stored data or modifying thedata based on system requirements and the stage at which the scan is happening. The scanned data aretransferred to different software applications and processed to manage the activities related to theassets (e.g., inspection). Considering the limited memory of the tags, the subset of BIM data has to bechosen and stored on tags based on the requirements of the tasks. This data is used by differentsoftware applications based on designated access levels (Motamedi et al., 2011).3 Proposed approachRTLS technologies are capable of providing real-time location information of assets. However,providing RTLS infrastructure inside the building is costly and imposes tremendous amount oftechnical design and implementation issues such as the scalability of RTLS. In our proposed approach, it is assumed that a subset of assets is equipped with long-range RFIDtags. The memories of these tags contain information taken from a BIM based on our previousframework (Motamedi and Hammad, 2009). This research aims to utilize the available mass of RFIDtags in the environment for localization purposes. In our approach, the user who is searching for assetsis equipped with a handheld RFID reader and is able to read the content of the tags from a distance. Inorder to identify an appropriate location tracking method, the categorization of assets introduced inMotamedi and Hammad (2011) is used (i.e., fixed, semi-fixed, movable and temporary).3.1 Fixed assets localizationReal-time location tracking for fixed assets, that constitute a large portion of available assets, isunnecessary. In our proposed approach, the memory of tags attached to these assets contains the exactlocation data taken from a BIM. Consequently, by accessing the memory of these tags from adistance, the location of the associated asset will be identified. Having the location data together withpreloaded-floor plan, the personnel are able to find the asset even if it is obstructed or hidden withouthaving access to any RTLS infrastructure. In this method, the location data on a tag is manuallyupdated and is not real-time. Hence, this method is not suitable for movable assets. Attaching long-range tags with large memory to all fixed assets may not become financiallyfeasible in the near future. In order to benefit from the proposed method, specific long-range tags canbe attached to selected assets to store location information related to a set of assets in theneighbouring area (location tags). Consequently, when a user tries to locate an asset, the data on thenearest location tag is read from a distance, which contains location data for all assets in that areaincluding the target asset. These location tags can also be equipped with large memory chipsets thatcontain floor plans and navigational aid information. The location information is updated on locationtags when a fixed asset is installed in that specific area, or when a semi-fixed asset is moved to/fromthat specific area. Figure 1 shows the process flowchart for asset localization: (1) The user scans the area to look fortarget RFID tag. (2) The handheld’s reader detects surrounding tags and reads their IDs and data. (3)The software application queries for the ID/property of target asset amongst detected tags. The queriesproperties could be the unique ID of a specific tag or a property of an asset (e.g., condition=requiremaintenance, type=boiler, status=faulty). (4, 5) If the target tag is found, the application reads the
  • location data from the memory, locate the appropriate floor plan and shows the asset on the floor plan.(6) In case the target tag is not found in the scanned area, the application starts an exhaustive searchamong all detected location tags to find the data related to the target tag. (7,8) In case a location tag isdetected, the reader reads the data and queries for the target tag. (9) In case the target tag informationis found on location tags, the application reads the location data and shows the target tag on the floorplan. (10) If the target tag’s data is not found on the location tag, the application prompts that thetarget assets could not be found and advises the user to move and change his location and rescan. Incase the location tags are placed in the building based on planned criteria, the application can providethe user with tips about how to perform the move action. It is proposed to place the location tags based on a predefined guideline known to users to facilitatefinding these tags. The following are recommendations for their placement: (1) Per Room: tags can beplaced at the exterior side of the entrance to each room in a common area (hallway). These tags cancontain information related to the assets that are located inside each associated area. The tags will beplaced at the exterior to provide maximum data accessibility and read range for users who arenavigating in the common areas, (2) Per floor: location tags can be placed at the entrances to the floor(e.g. near elevator, in the lobby). These tags can contain information about the important assets in theassociated floor. Moreover, the tags can contain floor plans and occupants’ data for each floor.Consequently, the user will be able to retrieve the data related to each floor as soon as he/she entersthat floor. Read Read tags Is target asset Y Show on floor Start Scan Area location End description detected? plan 1 2 4 data on tag 5 3 N Y Read Change Location N Any location Y Query for target Is target asset location (Move) tag detected? asset detected? 10 7 data on tag 8 6 9 NFigure 1. Process flowchart for fixed asset localization3.2 User localizationIn addition to locating assets, location data on fixed tags can help users finding their estimatedlocations in the building. Additionally, in scenarios where the user aims to find the location of an assetby retrieving the location information from its RFID tag, as explained in Subsection 3.1, he/she needsto know his/her own location to be able to find the path to the target asset. There are two majorscenarios for the user to estimate his/her location from surrounding tags: (1) Scanning a visible tag:The user scans a visible tag and reads the current location data of the tag. Consequently, the user isprovided with his/her current location on the floor plan. (2) Scanning the area: The user scans the areaand reads the location data of surrounding tags to be used for RFID reader localization technique.Several RFID reader localization techniques are available to locate an RFID reader using referencetags (Li and Becerik-Gerber, 2011). In our proposed framework, tags attached to fixed assets can actas reference points for RFID reader localization techniques. Moreover, RFID tags can also store partof the signal fingerprint database. In this method, tags are not necessarily visible or in a closeproximity.3.3 Moveable assets localizationIn our proposed method, fixed assets are used as reference points to help locating moveable assets.The similarity of received RSSI between target tags and fixed-assets’ tags is used for localization. TheRSSI received from reference tags and from target tags are logged by a handheld RFID reader atseveral locations and the received power for all signals are processed to determine the similaritybetween signal strength patterns. Tags that show similar signal patterns are considered to be spatially
  • adjacent. This similarity of pattern stems from the fact that the propagated radio signals are affectedby similar environmental effects for neighbouring tags. Furthermore, this method does not use RSSIvalues to estimate the distance between the reader and tags due to the unreliability of this conversionin indoor environments. In this method, fixed and target assets are equipped with long range, omni-directional and identicaltags. Tags attached to fixed assets contain their exact location coordinates. Moreover, it is assumedthat the target tags are stationary for the period of localization and the user equipped with a handheldreader is moving within the facility to collect RSSI values and track assets. Figure 2 shows the process flow to locate target tags: (1) The user starts searching for target assetsby scanning the area. (2) If the target tags are not in the range of the RFID reader, the user needs tochange his location to be able to detect the tags. (3) As soon as a target tag is detected by the reader,user starts logging the signal strength from surrounding tags. The user remains stationary during datalogging for the time period of ∆t. (4) The logged data are processed by the data processing module(Localization Engine). The RSSI values received from each tag are filtered for noise elimination.Filtered RSSI values are averaged and used for pattern matching (discussed in Subsection 3.3.2). Thepattern matching algorithm identifies a set of reference tags that their behaviours resemble a target tagand rank them based on calculated similarity indicator values. In order to identify the suitable numberof data logging sessions to localize the target assets (based on the predefined accuracy requirements),the engine calculates the convergence level. Having adequate data for location estimation, the possiblelocations for a target tag are identified. By applying the spatial constraints, some areas from the searchspace are removed to narrow down the results. Finally the engine calculates the accuracy of theestimated location based on the density of reference tags in the target area. (5) After estimating thelocation of the target tags, these areas are shown on the floor plan. (6) If the logged data are notadequate for accurately estimating the location, the localization engine prompts the user to move to anew location and to continue logging data. Is target tag Y Log Data Data Pattern Convergence Location Accuracy Visualization Spatial Start Scan Area Data Level on the floor End detected? Filtering Averaging Matching Calculation Estimation Estimation (RSSI) Constraints 1 2 3 5 plan N 6 Change Move Location (Move) required? Data Processing 4 Y NFigure 2. Process flowchart for movable assets localization3.3.1 Pattern matching algorithmIn order to formulate the data processing part of the proposed method, we assume that there are navailable reference tags in the building and Ri [i∈(1, n)] denotes the ith reference tag. A reference tagis an RFID tag that stores its current coordinates. A target tag is an RFID tag that is the target forlocalization and does not store its coordinates. Tj [j∈(1, p)] denotes the jth target tag. Data logginghappens at m different locations/time instances. A data logging session time series, Ls [s∈(1, m)] is aseries of averaged RSSI values recoded where the reader is stationary for the period of data logging R(∆t). Each Ls is composed of RSS s k (average of RSSI values for the kth reference tag at the sth data Tlogging session) and RSS s u (average of RSSI values for the uth target tag at the sth data logging Rk Tusession). P and P denote the signal patterns received from the kth reference tag and the uth targettag respectively, for m data logging sessions. P Rk = { RSS sRk : s∈(1, m)} , PTu = { RSS sTu : s∈(1, m)} Definition (1) The goal of data processing is to determine which reference tags (Ri) shows similar signal patternto the signal pattern received from the target tags (Tj). Additionally, the acceptable value of m shouldbe identified by the data processing module to stop the process of RSSI logging. The least square
  • difference method is employed to rank the similarity of reference tags to the target tag.  Rk is the u Tdistance indicator value between the k reference tag and the u target tag. β values can be modified th thto show the pattern similarity percentage (α) between each target and reference tags after m datalogging sessions.  n   RSS   m Equation (1)  Ruk  1   Rk /   100 Equation (2) Tu 2 T T Tu   Tu Rk  RSS u Ri   Rk s s s 1 i 1   R   Tj i j 1,.. p ; i 1,..n Definition (2) The matrix of β is constructed using the calculated values from Equation 1. β values in the uth rowof the matrix indicate the distance indicators for each reference tag to the uth target tag. The least βvalue in each row shows the reference tag that is assumably closer to the associated target tag.3.3.2 Development of a simulation environmentA simulation environment is developed in Matlab (MathWorks, 2012) in order to analyze theapplicability of our approach for various possible scenarios and setups. Furthermore, newmathematical and procedural techniques (e.g., data filtering, other pattern matching techniques andlocalization modules) can be added and tested. The simulation platform provides a flexibleenvironment to define and place multiple reference and target tags. Moreover, the floor plans of abuilding can be imported to the simulated environment to help defining realistic routes in a complexbuilding that FM personnel may use in order to find assets. The generation of RSSI values in thesimulation uses Monte Carlo approach based on our field test results (Subsection 4.1).4 Case studies4.1 Testing RFID characteristicsIn order to realize the proposed method for locating moveable assets, the characteristics of anavailable RFID system are analyzed. Several tests are conducted to test the readability range and theeffect of various environment factors on our RFID tags. Long range active tags with the operatingfrequency of 915 MHz have been used for the test. The first test has been performed at ConcordiaStinger Dome to examine the readability range and signal attenuation of tags in an obstacle-freeenvironment. An RFID tag was placed on a tripod and RSSI values were collected at various distancesfrom the tag. Figure 3(a) shows the decrease of RSSI values by increasing the distance. Availabletag’s antenna are omnidirectional (1/4-wave monopole with 2/3 vertical element and 1/3 horizontalelement). it is observed that the gain is higher in front on the same long axis of the tag. Figure 3(b)shows that the standard deviations of RSSI values slightly increase as the distance between the tagand the reader increases. (a) Average values (b) Standard deviationsFigure 3. RSSI vs. distance relationship
  • 4.2 Case study for fixed assets localizationThe case study for estimating the location of fixed assets and location-tags is presented in detail inMotamedi and Hammad (2011). In that case study, active RFID tags with large memory are attachedto fire extinguisher cabinets. The developed mobile application detects tags using the handheld RFIDreader and shows the location of fixed assets on a preloaded floor plan.4.3 Case studies for moveable assets localization4.3.1 Obstacle-free environment (with line-of-sight)This test is performed to test the applicability of the proposed method for tracking moveable assets ina multi-tag indoor environment. The test has been conducted in an obstacle-free environment whereall tags were placed inside one room. The tags have been placed on the ground in a grid of 5 m  7.5m. A target tag is placed randomly in the room with the distance of 70 cm from the closest referencetag (R9, R12) and data were collected using a handheld reader at six data logging points forming a Ushaped route (black line in Figure 4(a)). The calculated β values of the target tag for all reference tagsare presented in Table 1. Figure 4(b) shows the same setup in the simulation environment. The RSSIvalues were generated using our signal propagation model (Subsection 4.1) and are compared with theactual measured data. In the Figure 4, the diameter of red circles around reference tags are inverselyproportional to their β values. The results show that R12 has the least β value in both field test andsimulation environment which demonstrates that our method is feasible. As can be seen in Table 1and Figure 4, the simulated β values are systematically less than those of the test values. This can beexplained by the fact that the environmental factors of the space used in the test are different from theones of the test explained in subsection 4.. (a) Test results (b) Simulated resultsFigure 4. Obstacle-free testTable 1. β and α (β/α) values for field test and one instance of the simulation (obstacle-free environment) R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12Field Test 13.5/93 13.2/ 93 106.4/50 15.4/92 13.7/93 12.17/94 8.7/95 6.2/97 4.3/97 8.1/96 8.3/96 4.1/ 98Simulation 11.1/88 11.3/88 10.5/89 10.2/89 10.3/89 8.6/91 8.2/91 6.1/93 4.1/95 7.6/92 4.4/95 3.8/ 964.3.2 Environment with obstacles (without line-of-sight)In this test, five reference tags and two target tags were placed in different adjacent rooms of thebuilding. Figure 5 shows the setup for the test. The user with an RFID handheld took a route in thecorridors and collected data in eight data logging sessions. The data are collected for 30 seconds (∆t)at each data logging point. The building materials are concrete, metal and wood and the roomscontain several assets. The target tags were placed with the distance of approximately 1.5 meter fromthe closest reference tag in the same room. Table 2 shows the calculated β and α values of the twotarget tags for each of the reference tags. The results show that the proposed approach is capable ofdetecting the closest tag in a cluttered environment where the signal strength cannot be converted to
  • distance using signal propagation formulas. In this scenario, the pattern matching between target andreference tags is used for identifying the closest reference tags to the target tag.Table 2. β/α (%) values forenvironment with obstacle Target 1 Target 2 R1 15.13/89 15.14/78 R2 5.60/96 14.22/79 R3 30.05/79 11.72/83 R4 47.32/67 13.92/79 R5 49.68/66 14.06/79 Figure 5. Test with obstacles (a) Tag 1 (c) Tag 25 Conclusions and future workThis paper investigated several localization methods based on our previously proposed framework. Itdiscussed how various types of assets can be localized in an RFID-equipped building. The paperincluded the different scenarios to assist users (e.g. FM personnel or occupants) estimate theirlocations as well as the location of assets they are looking for. Our proposed method for trackingmovable assets has several advantages over similar techniques. It uses available RFID tags in thebuilding (used for lifecycle management) for the localization purpose. The method does not requireany fixed infrastructure of RFID readers and operates using handheld units. The accuracy of thelocalization is related to the density of reference tags and can be increased by adding tags to moreassets. The main advantage of the proposed system is that it can adapt to the changes in theenvironment. The case studies showed promising results for location estimation of assets.Future work of this research includes: (1) Developing methods to improve the accuracy of movableasset localization; (2) Providing users with navigational aid including floor plans and routes that areextracted from a BIM and saved on tags; (3) Improving the simulation environment to include theeffect of obstacles and noise on signal propagation; and (4) Integrating BIM with the simulation.ReferencesAIMGLOBAL, 2008. What is RFID. Available online: < http://www.aimglobal.org/technologies/rfid/what_is_rfid.asp>.FUCHS, C., ASCHENBRUCK, N., MARTINI, P. and WIENEKE, M., 2011. Indoor tracking for mission critical scenarios: A survey. Pervasive and Mobile Computing, 7 (1), pp.1-15.HARRISON, M., 2007. Guidelines for lifecycle ID & data management. AEROID-CAM-014.LI, N. and BECERIK-GERBER, B., 2011. Performance-based evaluation of RFID-based indoor location sensing solutions for the built environment. Advanced Engineering Informatics, 25(3), pp. 535-546.MATHWORKS, 2012. Matlab, Available online: <http://www.mathworks.com/products/matlab/>.MOTAMEDI, A. and HAMMAD, A., 2009. Lifecycle management of facilities components using radio frequency identification and building information model. Journal of IT in Construction, 14, pp. 238-262.MOTAMEDI, A. and HAMMAD, A., 2011. Location management of RFID-equipped building components. International Symposium on Automation and Robotics in Construction (ISARC), Seoul, Korea.MOTAMEDI, A., SAINI, R., HAMMAD, A. and ZHU, B., 2011. Role-based access to facilities lifecycle information on RFID tags. Advanced Engineering Informatics, 25 (3), pp. 559-568.PAPAPOSTOLOU, A. and CHAOUCHI, H., 2011. RFID-assisted indoor localization and the impact of interference on its performance. Journal of Network and Computer Applications, 34(3), pp. 902-913.