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A proximity-based method for locating RFID tagged objects
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INFORMATICS Advanced Engineering Informatics 21 (2007) 367–376 www.elsevier.com/locate/aei
A proximity-based method for locating RFID tagged objects Jongchul Song a, Carl T. Haas b, Carlos H. Caldas
c,*
a Department of Civil Engineering, University of New Mexico, MSC01 1070, Albuquerque, NM 87131, USA Department of Civil and Environmental Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ont., Canada N2L 3G1 Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin, 1 University Station C1752, Austin, TX 78712, USA b
c
Received 30 August 2006; accepted 6 September 2006
Abstract This paper presents a method intended to extend the use of current radio frequency identification (RFID) technology to tracking the precise location of tagged materials on construction sites. The performance experienced with a commercially available RFID system is compared with the theoretical performance derived from an analytical discrete framework. Also through experimentation, the effects of parameters including RF power, the number of reads, and tag density are assessed, and their performance trade-offs are characterized to suggest guidelines for potential field deployment. 2006 Elsevier Ltd. All rights reserved. Keywords: Location tracking; Construction materials; Radio frequency identification; Global positioning systems; Proximity
1. Introduction Current applications of radio frequency identification (RFID) technology in manufacturing, retailing, transportation and logistics industries rely on its capability to identify tagged objects without requiring physical contact, lineof-sight, or clean environments. The construction industry had also explored its potential applications [1–3], and several pilot tests demonstrated that the technology could be useful in receiving uniquely identified materials at job site laydown yards [4]. The use of RFID technology in construction work processes was also considered for tracking precast concrete components and storing information associated with them through a supply chain [5,6]. Recent field trials indicate that RFID tags installed in pipe spools can be identified simultaneously on moving platforms under realistic field conditions [7]. This capability would eliminate the need to read tags individually or in stationary situations. As prefabricated materials are shipped and received through *
Corresponding author. Tel.: +1 512 471 6014; fax: +1 512 471 3191. E-mail addresses: [email protected] (J. Song), [email protected] (C.T. Haas), [email protected] (C.H. Caldas). 1474-0346/$ - see front matter 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.aei.2006.09.002
portal gates equipped with an RFID reader unit, their delivery and receipt by supply chain parties could be automatically tracked, hence their location in the supply chain, e.g., a fabrication shop, or a constructor’s laydown yard. However, current RFID technology in this ‘‘portal’’ application paradigm does not allow tracking the materials’ location with better positional accuracy than indicating, for example, that a certain item is received at and hence within the constructor’s laydown yard. Each material item must be physically found at the laydown yard before being issued to crew workers who requisition them for installation on site. Accurately tracking the location of materials on a construction site will also facilitate automatic determination of whether a certain material item is in close proximity to a piece of materials handling equipment (and it may be inferred, being handled by the equipment) and thus help to identify the basic construction activity being performed using the equipment. Though tracking the location of tagged objects precisely has become technically more viable with recent advances in automated data collection (ADC) technologies, it is generally considered economically prohibitive. For example, global positioning systems (GPS) provide positional accu-
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racy beyond the portal level, but tagging hundreds of items with simple but expensive GPS receivers would not be economically feasible. Conversely, RFID technology suits identification purposes in tracking individual items, but its current applications do not provide sufficient location accuracy without relying on a fixed communications network. However, a combination of RFID and GPS technologies offers the opportunity to densely deploy low cost RFID tags with a few mobile RFID readers equipped with GPS to form the backbone of a construction materials’ tracking system. In this paper, we demonstrate through experimentation that when combined with GPS, RFID technology is technically feasible in determining the 2D location of tagged materials, inexpensively and without major changes to the regular operations on site. The performance trade-offs among experiment parameters are also characterized to determine optimal configurations for field deployment. 2. Background and literature review 2.1. Principles of localization techniques Before a survey of related work is presented, it is necessary to make a distinction between positioning and location tracking systems in determining the location of tagged objects. In positioning systems like GPS, individual devices tagged to the object being located compute their own position, and no other entity may know where the located object is unless the object specifically takes action to publish that information [8]. In contrast, location tracking systems mandate an external infrastructure to locate tagged objects, relieving smaller, cheaper tagging devices of the computational and power burden. Nevertheless, the infrastructure cost can still be an impediment to a scalable location tracking system, since its coverage area per unit infrastructure is invariably limited. To encompass positioning and location tracking, the terms location sensing and localization are used interchangeably throughout this paper. For any location sensing system implementation to locate objects, people, or both, three principal techniques can be employed individually or in combination: triangulation, scene analysis, and proximity technique [8]. Triangulation involves computing the position of an object by measuring its distance from multiple reference points with known locations. Depending on whether ranges or angles relative to reference points are being inferred, triangulation is divisible into lateration and angulation. Lateration can be further classified into the time of flight (TOF) and received signal strength (RSS) methods, where the ranges to reference points are inferred from time of flight and signal strength of the communication signal (e.g., ultrasound, laser, RF), respectively. The TOF based localization systems like GPS requires clocks with high resolution to deal with the problem known as time synchronization. On the other hand, the RSS technique relies on a particular signal propagation model in the coverage area
which defines the correlation of signal attenuation of the original strength with the range between the signal transmitter and receiver. In environments with many obstructions such as construction sites, signal strength at short ranges (10 m) is subject to unpredictable variation due to fading, multipath, and interference, so does not correlate directly with distance [8,9]. The scene analysis technique infers the location of objects using features of a scene observed, such as visual images, which do not correspond to geometric distances or angles. The useful features of a scene also include electromagnetic signal characteristics that occur when a signal transmitter is at a particular location. Such signal characteristics can serve as ‘‘RF signature’’ unique to a given location, but the major drawback of this technique is the extensive effort needed to generate the signal signature database and reconstruct the predefined database with significant changes in the environment [8,9]. As opposed to fine-grained triangulation and scene analysis methods, the proximity technique does not attempt to actually measure the object’s distance to reference points, but rather determines whether an object is near one or more known locations. The presence of an object within a certain range is usually determined by monitoring physical phenomena with limited range, e.g., physical contact to a magnetic scanner, communication connectivity to access points in a wireless cellular network. 2.2. Related efforts and enabling technologies for comprehensive materials tracking Construction of a facility is a process by which materials are fabricated, assembled and installed on site by workers with the help of equipment, according to designs and specifications. Tracking the location of construction resources on site, i.e., workers, materials and equipment, is critical to automated project performance monitoring that will ultimately enable project management to take corrective actions in a timely manner to control actual performance [10–12]. Navon and Goldschmidt [10] showed that workers’ locations can be automatically collected by GPS and converted into labor hours consumed in execution of a particular activity with reasonable accuracy. Undoubtedly, GPS can be used to precisely track the location of the small number of workers or pieces of equipment over a great range of geographic and geometric scales. However, GPS alone is not a viable option for comprehensive materials tracking. Tagging individual material items with simple GPS receivers costing around US$100 per unit would be prohibitively expensive, and still other means for identification would be required. Alternatively, a state-of-the-art GPS receiver could be used to acquire the precise coordinates for materials’ location without tagging individual material items, as in a recent field test [13]. Though the effectiveness of the technology in locating pipe spools at large laydown yards was demonstrated with immediate payback for a typical industrial project, manual
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acquisition and periodic updates of the GPS coordinates are required in addition to positive identification. On the other hand, RFID technology is suited to identification purposes in tracking hundreds of materials in harsh environments. Though RFID technology presents several advantages over barcoding, its primary use in current applications is still limited to portal based identification purposes. This limitation had driven the commercial development of the Real-Time Location System (RTLS) for indoor asset tracking applications. Unlike conventional RFID systems, the RFID-based RTLS such as RF Technologies’ Pinpoint [14] provides both identification and location of tagged objects by virtue of a pre-configured wired network of fixed RFID readers deployed in the interior of a building. However, the RFID-based RTLS requires the significant infrastructural setup of proprietary networks and has difficulty interoperating with existing IEEE 802.11 wireless networks [15]. Recently, these issues with the RFID-based RTLS have been resolved by leveraging the IEEE standard Wi-Fi networks. Being based on the non-proprietary networks, the Wi-Fi based RTLS successfully overcame the substantial cost barrier to scalable location tracking systems, i.e., the infrastructural setup of separate networks. Good examples include solutions from AeroScout [16] and Ekahau [17]. Nonetheless, the Wi-Fi RTLS using the scene analysis technique requires extensive calibration to map the Wi-Fi signals to locations throughout the building. To circumvent calibration efforts, the Wi-Fi RTLS may employ other localization principles like the RSS triangulation [18], but it still relies on existence of 802.11 access points, which is not guaranteed for a facility being built in the job site. As an alternative to location tracking systems using a network of fixed RFID readers or Wi-Fi access points, positioning systems using mobile readers may be applicable. For example, a reader mounted on a vehicle determines the position of the vehicle using as reference points RFID tags which had been pre-programmed with known location and embedded in the operating environment. However, this approach becomes impractical for comprehensive materials tracking since RFID tags with limited power and computational capability will not be able to pre-determine their own location. Finally, the applicability of RFID technology to the emerging ad hoc localization is discussed. Unlike the cellular network based approach as in the RTLS, ad hoc location sensing under the scenario envisioned by wireless sensor networks does not draw on a preconfigured and calibrated communications infrastructure, thus representing a highly scalable and low-cost approach [15]. The notion of wireless sensor networks envisions that thousands of sensor nodes deeply embedded in physical environments provide sensed data, while self-configured and adaptively coordinated to frequent topology changes [19]. However, current RFID tags have no capability to form wireless sensor networks, much less the ability to discover neighboring peer tags. Only RFID readers can
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discover and communicate with tags within a certain read range, and tags can only respond to readers by sending back the data stored on internal memory. In summary, the existing approaches to accurately tracking materials’ location imply economically prohibitive deployment of ADC technologies. Furthermore, due to its evolving and unpredictable nature, a construction site cannot afford location tracking systems relying on fixed network infrastructure, whether proprietary or not, which should be configured carefully to cover the entire site and calibrated to its RF transmission space. However, current RFID systems can determine the presence of a tag within a range from the reader, providing proximity information of the tag, though not the distance measurements between the tag and the reader. Thus, a proximity localization system composed of a few mobile RFID readers equipped with GPS presents an alternative solution to tracking the location of hundreds of tagged materials on a construction site.
3. A mathematical framework for RFID proximity localization The concept proposed here is a field supervisor or piece of materials handling equipment that is equipped with an RFID reader and a GPS receiver, and serves as a ‘‘rover’’. The supervisor, for example, walks around the site on his or her normal business. The position of the reader at any time is known since the rover is equipped with a GPS receiver, and many reads can be generated by a single rover moving around the site. Suppose that in 2D the reader has a radial read range with the radius r and the rover has generated a read at a known position with Cartesian coordinates (x1, y1), from which an RFID tag fixed at unknown location (x2, y2) is read. Then the RF communications connectivity exists between the reader and the tag, contributing exactly one piece of prox qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 imity information i:e:; ðx1 x2 Þ þ ðy 1 y 2 Þ 6 r to the problem of estimating the tag location. As the rover comes into the range r from the tag time and again, more reads form such convex constraints for the tag. Combining these proximity constraints restricts the feasible set of the unknown position of the tag to the region in which the circles centered at the reads intersect with one another. Estimating the location of the tag then comes down to selecting a point from the intersecting region. Intuitively, such an intersecting region would manifest itself as a point, if the rover generated infinitely many reads and proximity constraints for each tag. Thus, this asymptotic convergence of location estimates suggests a governing trade-off between positional accuracy, time and resources for the approach proposed here. However, to characterize the performance trade-off through experimentation, an analytical framework is needed. Such a framework should also allow one to derive a model of the
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Fig. 1. Modeling the discrete RF communication region.
theoretical performance which can be compared with the performance experienced in the field. A useful mathematical framework has been adapted from a model that Simic and Sastry [20] envisioned with wireless sensor networks in which RFID tags would communicate with neighboring tags. In this framework, a square region Q with sides of length s in which the rover moves around is partitioned into n2 congruent squares called cells of area (s/n)2. Thus, the position of reads as well as tags is represented by a cell with grid coordinates, rather than a point with Cartesian coordinates, and one is only interested in finding the cell(s) that contains each RFID tag. For example, the read shown in Fig. 1 is positioned at the cell with grid coordinates (5, 5). Furthermore, the RF communication region of a read is modeled as a square centered at the read and is characterized by a discrete read range q cells, instead of a disk with the radius r of continuous value. To illustrate, the read shown in Fig. 1 has the discrete range q = 2 cells, and its communication region is the shaded square centered at the cell (5, 5) and containing (2q + 1)2 = 25 cells. This communication region can be expressed by [3, 7] · [3, 7], if we denote by [a, b] · [c, d] the union of all cells with grid coordinates (i, j) such that a 6 i 6 b and c 6 j 6 d, for integers 1 6 a 6 b 6 n, 1 6 c 6 d 6 n. Given that a tag is read from multiple cell locations while the rover moves around the site Q, its location is estimated within a rectangular region in which square communication regions centered at the reads intersect with one another. The number of cells in such a rectangular region is called the size of location estimate for the tag. 4. Experimentation Based on the mathematical framework described above, field experiments were conducted using an off-the-shelf RFID handheld reader and tags. A square region Q with sides of s = 36 m (120 ft) was set up on the grass in an open field and partitioned into n2 = 302 square cells with sides of 1.2 m (4 ft), with the boundary and grids delineated using stake flags and strings. Although the position of the RFID reader at any time would have been known if the experimenter carrying around the reader was equipped with a GPS receiver, experiments dispensed with a GPS receiver.
Instead, the experimenter located himself within one of the 900 cells by referring to the stake flags and physical grid lines in the experiment field – handling of GPS positional error is described later. Experimental parameters included (1) RF power transmitted from an RFID reader, (2) the number of tags placed, (3) pattern of tag placement, and (4) the number of reads generated. The first three parameters were used to vary operating conditions in which the RFID proximity localization may exhibit the performance trade-offs. Each combination of these parameter values characterizes the unique test bed. The number of reads was also parameterized to compare the performance experienced in the field with the theoretical performance derived from the discrete framework, presented later. First, two levels of RF transmission power were associated with a particular value of the discrete read range q, as shown in Table 1. Each of these values was assigned to a different level of RF power upon 200 trials of reading a total of 20 tags placed in the experiment field, rather than derived from the corresponding radial read range r that would have to be determined otherwise. For more details, refer to [21]. In addition, different configurations for the magnitude and density of tags were created by placing a total of 10 or 20 tags in the experiment field so as to form one of seven different patterns. Fig. 2 depicts one such placement pattern, ‘‘Even,’’ which consists of 20 tags and represents an extreme in terms of tag density – for other patterns tried in experiments, see [21]. Particularly, some of the tags were placed within the interior region such that they are distant from the boundary of Q by more than the discrete read range q and hence can be read from as many as (2q + 1)2 different cell locations in the overall region Q. Finally, for each of the 28 test beds (=2 power levels * 7 placement patterns * 2 numbers of tags), the experimenter
Table 1 Assigned discrete read range at different levels of RF power RF power level
Transmission power (dBm)
Assigned discrete read range q (cells)
Corresponding radial read range r (m)
Medium High
20 5
5 7
8.8 (29 ft)–10.1 (33 ft) 12.2 (40 ft)–13.7 (45 ft)
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Fig. 2. The even placement pattern with 20 tags.
attempted only once to read tags in each and every one of a total of 900 cells in the experiment field, thus generating a completely full read array from which virtual rover paths and read rates could be generated. However, to keep the effect of different rover paths from convoluting with that of different read rates, it was necessary to simulate the situations in which the rover samples proximity information at different time intervals while remaining on a single path – implementation of this simulation is reported in [21]. As such, for each test bed, the number Ke (specified later) of reads were randomly selected from the total of 900 reads in 50 iterations to create virtual rover paths. Then for each path, proximity information was sampled at two additional frequencies represented by fractions of Ke reads, 0.2Ke and 0.1Ke. Thus, each of the 28 test beds yielded a total of 150 sets of proximity constraints (=50 random rover paths * 3 read rates), with the overall data set amounting to 4200 sets of proximity information. 5. Performance analysis For each of the 4200 sets of proximity information, we denote by K the total number of attempts made by the rover to read as many as 10 or 20 tags – depending on sampling rate, K = 1.0Ke, 0.2Ke, or 0.1Ke. If one of the K reads acquired an RF signal from a tag and hence provided a proximity constraint for the tag, this read is defined as a successful read for the tag. Expectedly, successful reads for a tag with grid coordinates (x, y) occurred when the tag lay within the discrete read range q, that is, when the rover positioned within the region [x q, x + q] · [y q, y + q]. However, successful reads for the tag also happened when the reader was positioned beyond this region, and are defined to be off-side. In a sense, off-side reads reflect uncertainty in the RF communication region defined by the
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value of q assigned at an RF power level. This uncertainty prompts us to consider the RF power level a candidate factor influencing the performance of RFID proximity localization. In fact, groups of test beds with a different RF power level showed significant differences in distributions (not presented here) of both successful reads and off-side reads, more than did groups with a different pattern or number of tags placed. The high level of RF power yielded more successful reads due to the greater read range that would allow reading the same tag from farther locations. Thus, in 96% of the time when using the high RF power, all the tags were successfully read by at least one of the K reads while reads at the medium power missed some tags in 22% of the time. However, the high RF power also experienced off-side reads more frequently, approximately 20% of successful reads on average, compared with 10% for the medium RF power. One simple way to reduce this ‘‘false alarm’’ rate while remaining at the same power level is discussed later. Interestingly, the proportion of off-side reads to successful reads remained constant regardless of sampling frequency. Thus, as the rover generates more and more reads, the magnitude of off-side reads for each tag will grow (at a greater rate if using the high RF power) and so will the uncertainty in proximity measurements for the tag. However, proximity measurements contributed by offside reads were not eliminated when calculating the location estimate for each tag – practically, whether a successful read for a tag is off-side or not would be unknown unless the true tag location is known. Consequently, the calculated location estimate of a tag was not always valid. A location estimate, represented by the region [a, b] · [c, d], is defined to be valid if the integers a, b, c and d satisfy that 1 6 a 6 b 6 n, 1 6 c 6 d 6 n. An invalid estimate results when combining proximity constraints for a tag leads the feasible region for the tag’s location to a vacuous intersection. As off-side reads were more frequent with the high RF power, fewer tags were localized with a valid estimate when using the high power. In worst cases the location estimate of all tags was invalid, amounting to 13% of the 2100 instances in which the high power was used. This all-invalid case accounted for 7% of a total of 4200 instances, none of which arose when using the medium RF power. Performance in each of the rest 93% of the total 4200 instances may be measured by averaging out the size of valid estimates calculated for individual tags. The size of a valid estimate [a, b] · [c, d] is calculated as (b a + 1) * (d c + 1), and a valid estimate of smaller size is generally considered to be more precise – for an invalid estimate, definition of the size is no longer applicable. However, the size of a valid estimate alone could not be used as an overall measure of positional accuracy experienced in the field because of another consequence of uncertain proximity measurements: that not every valid estimate for a tag contained the true cell location of the tag. Therefore, it was necessary to come up with the smallest region for each
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tag such that it is certain to contain the true tag location, so-called confidence region for the tag. The confidence region of each valid estimate is determined by expanding the valid estimate in both directions along X- and Y-axes by its bias that indicates the extent to which the valid estimate deviated from the true tag location. The bias of a valid estimate is defined as the largest number of cells that the true tag location was distant along the X-axis or Y-axis from the boundary of the region given by the valid estimate. Thus, the bias b of the location estimate [a, b] · [c, d] for a tag with the true location (x, y) is calculated as max{min(jx aj, jx bj), min(jy cj, jy dj)}, while it is given zero if the estimate contained the true tag location, i.e., a 6 x 6 b and c 6 y 6 d. Subsequently, the confidence region of the estimate [a, b] · [c, d] is given as [a b, b + b] · [c b, d + b], and the number of cells contained in the confidence region combines the size and bias of the estimate, representing an overall measure of positional accuracy for the estimate. Fig. 3 summarizes the overall performance experienced in the field when using the high or medium RF power. For a particular level of RF power, a pair of curves shown in Fig. 3 as solid and dotted lines, respectively, represent the frequency with which the size of valid estimates and the confidence regions were on average smaller than or equal to a certain number of cells. Specifically, in 97% of the time when the medium power was in use, on average the valid estimate of a tag was smaller than or equal to 81 cells, which is commensurate with a 9 · 9 cell area, but the true tag location actually deviated within ±4 cells from the center of the valid estimate in 94% of the time. In contrast, performance under the high RF power showed the error in the estimate of true tag location significantly different than if assessed by the size of the valid estimate
alone. This gap can be thought of as the impact of bias encountered at the high power level. Observations thus far of the field performance under different RF power levels indicate a trade-off between coverage and accuracy for RFID proximity localization. The greater read range of the high RF power yielded more successful reads per tag, reducing the odds that tagged items may be left unread and missed. On the other hand, plagued by a higher proportion (and greater magnitude) of off-side reads, localization under the high RF power resulted in the valid estimate for fewer tags than with the medium RF power, but also inferior estimates of true tag location. This trade-off for different RF power levels can be coped with by increasing the total K number of reads and hence the read rate. In addition to a decreased incidence of some tags being missed (Table 2), accuracy in the estimate of true tag location was gradually improved for both power levels (Fig. 4). Nevertheless, the number of tags with a valid estimate still tended to decrease for both power levels (Fig. 5). As more and more reads were generated, off-side reads would only increase no matter what level of RF power in use. Particularly, under the high RF power all tags ended up with an invalid estimate in 40% of the time when using the high read rate, so that in Fig. 4 the corresponding cumulative frequency curve reached only 60%. This suggests the basic Table 2 Frequency of some tags being missed (each out of 700 instances) RF power level
Low read rate
Moderate read rate
High read rate
Medium High
399 88
69 4
0 0
Fig. 3. Overall performance experienced in the field under different RF power levels.
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Fig. 4. Positional accuracy experienced under different combinations of RF power and read rate.
Fig. 5. Distributions of the number of tags with a valid estimate under different combinations of RF power and read rate (each sample size 350 with the total 20 tags).
limits of the positional accuracy attainable without degenerating into invalid estimates. Essential to overcoming these limits is the ability to manage the uncertainty in proximity measurements before fusing them into the localization process, which is the subject of ongoing research. Later in this paper we report on one simple technique in which the discrete read range assigned at an RF power level is expanded while remaining at the same power level. Considering the present limits for the high read rate, the theoretical accuracy under the discrete framework is compared with the field performance in case that the total K number of reads generated was moderately large. Recall that the total K reads sampled at a moderate rate was represented by the fraction 0.2Ke, where Ke denotes the smallest integer value for the K0 reads required to achieve a certain level of positional accuracy. Simic and Sastry [20] showed that the K0 number of reads necessary to localize
a randomly picked interior tag within an (1 + e) cell area satisfies K0 >
ln½8qð2q þ 1Þ þ ln e ; ln 1 2qþ1 n2
ð1Þ
assuming that the position of reads are distributed evenly through the region Q = [1, n] · [1, n]. For the experiment field partitioned into n2 = 302 cells, the smallest integer value of K0 satisfying (1), Ke, is 321 given q = 7 cells at the high RF power and some arbitrary e = 4. Since 0.2Ke given e = 4 is equivalent to Ke given e = 288, 0.2 * 321 64 reads with the high RF power should allow the location of interior tags to be estimated within a (1 + 288) = 17 · 17 cell area, that is, with accuracy of ±8 cells. Similarly, for the medium RF power with q = 5 cells and e fixed at 4, 0.2Ke (=0.2 * 385 = 77) is equivalent to Ke
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given e = 168, for which the true location of an interior tag is expected to be within a 13 · 13 cell area. This positional accuracy predicted for K = 0.2Ke reads was accomplished in the field for both levels of RF power, as shown in Fig. 6. It also suggests that if using the same number of reads at the same RF power level, the overall performance may vary depending on tag density in terms of which focused and even patterns represent extremes. In fact, these extreme tag patterns exhibited a difference in the incidence of some tags being missed, as shown in Table 3. However, this difference between tag patterns became insignificant as the read range (associated with an RF power level) or read rate increased, while the incidence of incomplete coverage decreased for both patterns. As a trade-off, the high RF power level or a greater read rate typically resulted in fewer tags being localized with a valid estimate for both patterns (Table 4), while their difference
became unignorable. As with coverage and integrity, the actual performance on accuracy also degraded when tags were not evenly distributed, but it improved for both tag patterns when the medium RF power was used, or as the read rate increased (Fig. 7). The observations above suggest that while the density of tags has an impact on the overall performance, the tradeoff between coverage, accuracy, and integrity will remain intact due to the interplay between read range and read rate. Since it would be impractical on a construction site to control the density of tagged materials, configurations for field deployment should be determined considering this trade-off to suit specific site needs. For example, if a large construction site or laydown yard is surveyed for complete inventory, the presence and quantity of every item may be the priority over its accurate location so a long read range of high RF power would be advantageous in coverage. If
Fig. 6. Comparing field performance with theoretical positional accuracy.
Table 3 Frequency of some tags being missed under extreme tag patterns (each out of 50 instances) Tag placement pattern
Medium RF power Low read rate
Moderate read rate
High read rate
High RF power Low read rate
Moderate read rate
High read rate
Focused Even
47 23
15 2
0 0
13 6
1 0
0 0
Table 4 Median number of tags with a valid estimate under extreme patterns (each out of 50 instances with a total of 20 tags) Tag placement pattern
Medium RF power Low read rate
Moderate read rate
High read rate
High RF power Low read rate
Moderate read rate
High read rate
Focused Even
17 19
19 18
15 11
12 16
6 11
0 3
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Fig. 8. Positional accuracy for different read ranges at the high RF power level.
Fig. 7. Positional accuracy experienced under extreme tag patterns. (a) Focused pattern. (b) Even pattern.
positional accuracy is of primary concern, low power configurations with a short read range present an alternative. In either case, performance on coverage and accuracy could be further improved by a single rover generating a large number of reads – if the rover potentially moves fast, by using multiple rovers. In the mean time, to keep location estimates from degenerating into an invalid estimate (i.e., to maintain integrity), the uncertainty in proximity measurements contributed by off-side reads should be managed appropriately. Since the communications range at an RF power level is anisotropic, time-varying and dependent on an operating environment, proximity measurements based on the square communication region are inherently uncertain, leading to off-side reads. One obvious way to stifle off-side reads is to dilate the discrete read range by one or two cells while remaining at the same RF power level. Table 5 shows that for both extreme patterns, the median number of tags localized with a valid estimate has increased when expanding the read range by one cell at the high RF power level. Off-side reads of 20 tags placed in the focused pattern
decreased on average from 31% to 22% of successful reads while successful reads averaged about the same as before – for the even pattern, off-side reads reduced from 16% to 10%. Nevertheless, as the read rate increased, the degeneration into invalid estimates was unavoidable so that the number of tags with a valid estimate still tended to decrease. On the other hand, expanding the read range at the same power level had a negative effect on positional accuracy, except for the high read rate under the focused pattern where the all-invalid case prevailed, as shown in Fig. 8. Finally, we consider the impact of mapping the position of the rover to a cell location in reference to physical grids. Unlike in the field experiments, the position of the rover at a cell location may be known with a GPS receiver subject to errors. Thus, to assess the overall error in tag location estimates, the error in estimate of the rover position using GPS should be added in. For example, using the medium RF power, the true tag location was estimated with the error ±3 cells, or ±3.6 m (side of 1 cell = 4 ft 1.2 m), in approximately 68% of the time, as shown in Fig. 3. Assuming that the error in tag location estimates using RFID is normally distributed, the estimate of the tag’s location has the errors with one standard deviation 3.6 m. If the rover were equipped with a GPS receiver whose error in estimate of 2D location has one standard deviation 1.0 m, the tag location estimate would have the overall pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi error with a standard deviation of 3:7 m 1:02 þ 3:62 . 6. Conclusions The combination of RFID and GPS technologies presents the opportunity to densely deploy low cost RFID tags
Table 5 Median number of tags with a valid estimate for different read ranges at the same RF power level (each out of 50 instances with the total 20 tags) Tag placement pattern
High RF power with q = 7 cells Low read rate
Moderate read rate
High read rate
High RF power with q = 8 cells Low read rate
Moderate read rate
High read rate
Focused Even
12 16
6 11
0 3
16 18
11 15
1 7
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with a few mobile RFID readers equipped with GPS to form the backbone of a construction materials’ tracking system. The solution proposed is intended to extend the use of RFID technology to tracking the precise location of materials on a construction site as well as at laydown yards, without relying on a fixed communications network or modifications to current hardware, and potentially at a magnitude less cost than pure GPS or other existing approaches. Field experiments conducted using an off-the-shelf RFID system have demonstrated that using this approach, the approximate 2D location of materials can be determined without adding to the regular operations on a site. Since this approach leverages ambient passive reading without requiring line of sight for positive identification or manual updates of materials’ location, it is potentially much faster than existing approaches for surveying laydown yards and a construction site. In conjunction with its application to tracking the delivery and receipt of prefabricated materials through portal gates, ‘‘roving’’ applications of RFID technology based on this approach can form a unified platform to automate the tracking of materials and components in multiple stages of the project life cycle. Though potential economic feasibility of this approach should be estimated to justify the up-front cost of implementation, the proposed application framework has the potential to make RFID technology economically more attractive and drive implementation of the technology in realizing potential benefits. Finally, several recommendations for field deployment can be made based on performance analysis presented in this paper while field trials in real world settings are encouraged. Though RFID tags with a short read range (10 m) can be localized accurately, especially when tags are evenly distributed, optimal configurations for read range should be determined to suit specific site needs considering the trade-off between coverage, accuracy, and integrity. A slightly larger discrete read range may be assigned to an RF power level since it can reduce off-side reads and help to keep location estimates from degenerating into the invalid. However, to eliminate the false alarms all together would require assigning considerably larger read ranges which will in turn compromise positional accuracy. Therefore, other methods should be developed to manage the uncertainty inherent in proximity measurements to improve both accuracy and integrity. Such methods should handle conflicting information generated by moved or moving tagged objects as well. Further research is also needed to expand the discrete framework to 3D localization and develop methods to determine the position of the rover when GPS may not function.
References [1] E.J. Jaselskis, M.R. Anderson, C.T. Jahren, Y. Rodriguez, S. Njos, Radio-frequency identification applications in construction industry, J. Constr. Eng. Manage. 121 (2) (1995) 189–196. [2] J. Yagi, E. Arai, T. Arai, Parts and packets unification radio frequency identification (RFID) application for construction, Automat. Constr. 14 (4) (2005) 477–490. [3] P.M. Goodrum, M.A. McLaren, A. Durfee, The application of active radio frequency identification technology for tool tracking on construction job sites, Automat. Constr. 15 (3) (2006) 292–302. [4] E.J. Jaselskis, T. El-Misalami, Implementing radio frequency identification in the construction process, J. Constr. Eng. Manage. 129 (6) (2003) 680–688. [5] B. Akinci, M. Patton, E. Ergen, Utilizing radio frequency identification on precast concrete components – supplier’s perspective. In: Proceedings of International Symposium on Automation and Robotics in Construction, Washington, DC, 2002, pp. 381–386. [6] E. Ergen, B. Akinci, R. Sacks, Formalization and automation of effective tracking and locating of precast components in a storage yard. In: Proceedings of Ninth EuropIA International Conference: Eactivities and Intelligent Support in Design and the Built Environment, Istanbul, Turkey, 2003, pp. 31–37. [7] J. Song, C.T. Haas, C. Caldas, E. Ergen, B. Akinci, Automating the task of tracking the delivery and receipt of fabricated pipe spools in industrial projects, Automat. Constr. 15 (2) (2006) 166–177. [8] J. Hightower, G. Borriello, Location sensing techniques, Tech rep UW CSE 01-07-01, University of Washington, Seattle, WA, 2001. [9] N. Bulusu, J. Heidemann, D. Estrin, GPS-less low-cost outdoor localization for very small devices, IEEE Personal Commun. 7 (5) (2000) 28–34. [10] R. Navon, E. Goldschmidt, Can labor inputs be measured and controlled automatically? J. Constr. Eng. Manage. 129 (4) (2003) 437–445. [11] R. Sacks, R. Navon, E. Goldschmidt, Building project model support for automated labor monitoring, J. Comput. Civil Eng. 17 (1) (2003) 19–27. [12] R. Sacks, R. Navon, I. Brodetskaia, A. Shapira, Feasibility of automated monitoring of lifting equipment in support of project control, J. Constr. Eng. Manage. 131 (5) (2005) 604–614. [13] C.H. Caldas, D.G. Torrent, C.T. Haas, Using global positioning system to improve materials-locating processes on industrial projects, J. Constr. Eng. Manage. 132 (7) (2006) 741–749. [14] RF Technologies, Pinpoint. (accessed 28.08.06). [15] J. Hightower, G. Borriello, Location systems for ubiquitous computing, IEEE Comput. 34 (8) (2001) 57–66. [16] AeroScout. (accessed 28.08.06). [17] Ekahau. (accessed 28.08.06). [18] W.C. Stone, A. Lytle, K.M. Furlani, Smart chips in construction, White paper, National Institute of Standards and Technology, Gaithersburg, MD, 2002. [19] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, A survey on sensor networks, IEEE Commun. Mag. 40 (8) (2002) 102–114. [20] S.N. Simic, S. Sastry, Distributed localization in wireless ad hoc networks, Rep UCB/ERL M02/26, University of California, Berkeley, CA, 2002. [21] J. Song, Tracking the location of materials on construction projects. Ph.D. Dissertation, University of Texas at Austin, Austin, Texas, 2005.
INFORMATICS Advanced Engineering Informatics 21 (2007) 367–376 www.elsevier.com/locate/aei
A proximity-based method for locating RFID tagged objects Jongchul Song a, Carl T. Haas b, Carlos H. Caldas
c,*
a Department of Civil Engineering, University of New Mexico, MSC01 1070, Albuquerque, NM 87131, USA Department of Civil and Environmental Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ont., Canada N2L 3G1 Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin, 1 University Station C1752, Austin, TX 78712, USA b
c
Received 30 August 2006; accepted 6 September 2006
Abstract This paper presents a method intended to extend the use of current radio frequency identification (RFID) technology to tracking the precise location of tagged materials on construction sites. The performance experienced with a commercially available RFID system is compared with the theoretical performance derived from an analytical discrete framework. Also through experimentation, the effects of parameters including RF power, the number of reads, and tag density are assessed, and their performance trade-offs are characterized to suggest guidelines for potential field deployment. 2006 Elsevier Ltd. All rights reserved. Keywords: Location tracking; Construction materials; Radio frequency identification; Global positioning systems; Proximity
1. Introduction Current applications of radio frequency identification (RFID) technology in manufacturing, retailing, transportation and logistics industries rely on its capability to identify tagged objects without requiring physical contact, lineof-sight, or clean environments. The construction industry had also explored its potential applications [1–3], and several pilot tests demonstrated that the technology could be useful in receiving uniquely identified materials at job site laydown yards [4]. The use of RFID technology in construction work processes was also considered for tracking precast concrete components and storing information associated with them through a supply chain [5,6]. Recent field trials indicate that RFID tags installed in pipe spools can be identified simultaneously on moving platforms under realistic field conditions [7]. This capability would eliminate the need to read tags individually or in stationary situations. As prefabricated materials are shipped and received through *
Corresponding author. Tel.: +1 512 471 6014; fax: +1 512 471 3191. E-mail addresses: [email protected] (J. Song), [email protected] (C.T. Haas), [email protected] (C.H. Caldas). 1474-0346/$ - see front matter 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.aei.2006.09.002
portal gates equipped with an RFID reader unit, their delivery and receipt by supply chain parties could be automatically tracked, hence their location in the supply chain, e.g., a fabrication shop, or a constructor’s laydown yard. However, current RFID technology in this ‘‘portal’’ application paradigm does not allow tracking the materials’ location with better positional accuracy than indicating, for example, that a certain item is received at and hence within the constructor’s laydown yard. Each material item must be physically found at the laydown yard before being issued to crew workers who requisition them for installation on site. Accurately tracking the location of materials on a construction site will also facilitate automatic determination of whether a certain material item is in close proximity to a piece of materials handling equipment (and it may be inferred, being handled by the equipment) and thus help to identify the basic construction activity being performed using the equipment. Though tracking the location of tagged objects precisely has become technically more viable with recent advances in automated data collection (ADC) technologies, it is generally considered economically prohibitive. For example, global positioning systems (GPS) provide positional accu-
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racy beyond the portal level, but tagging hundreds of items with simple but expensive GPS receivers would not be economically feasible. Conversely, RFID technology suits identification purposes in tracking individual items, but its current applications do not provide sufficient location accuracy without relying on a fixed communications network. However, a combination of RFID and GPS technologies offers the opportunity to densely deploy low cost RFID tags with a few mobile RFID readers equipped with GPS to form the backbone of a construction materials’ tracking system. In this paper, we demonstrate through experimentation that when combined with GPS, RFID technology is technically feasible in determining the 2D location of tagged materials, inexpensively and without major changes to the regular operations on site. The performance trade-offs among experiment parameters are also characterized to determine optimal configurations for field deployment. 2. Background and literature review 2.1. Principles of localization techniques Before a survey of related work is presented, it is necessary to make a distinction between positioning and location tracking systems in determining the location of tagged objects. In positioning systems like GPS, individual devices tagged to the object being located compute their own position, and no other entity may know where the located object is unless the object specifically takes action to publish that information [8]. In contrast, location tracking systems mandate an external infrastructure to locate tagged objects, relieving smaller, cheaper tagging devices of the computational and power burden. Nevertheless, the infrastructure cost can still be an impediment to a scalable location tracking system, since its coverage area per unit infrastructure is invariably limited. To encompass positioning and location tracking, the terms location sensing and localization are used interchangeably throughout this paper. For any location sensing system implementation to locate objects, people, or both, three principal techniques can be employed individually or in combination: triangulation, scene analysis, and proximity technique [8]. Triangulation involves computing the position of an object by measuring its distance from multiple reference points with known locations. Depending on whether ranges or angles relative to reference points are being inferred, triangulation is divisible into lateration and angulation. Lateration can be further classified into the time of flight (TOF) and received signal strength (RSS) methods, where the ranges to reference points are inferred from time of flight and signal strength of the communication signal (e.g., ultrasound, laser, RF), respectively. The TOF based localization systems like GPS requires clocks with high resolution to deal with the problem known as time synchronization. On the other hand, the RSS technique relies on a particular signal propagation model in the coverage area
which defines the correlation of signal attenuation of the original strength with the range between the signal transmitter and receiver. In environments with many obstructions such as construction sites, signal strength at short ranges (10 m) is subject to unpredictable variation due to fading, multipath, and interference, so does not correlate directly with distance [8,9]. The scene analysis technique infers the location of objects using features of a scene observed, such as visual images, which do not correspond to geometric distances or angles. The useful features of a scene also include electromagnetic signal characteristics that occur when a signal transmitter is at a particular location. Such signal characteristics can serve as ‘‘RF signature’’ unique to a given location, but the major drawback of this technique is the extensive effort needed to generate the signal signature database and reconstruct the predefined database with significant changes in the environment [8,9]. As opposed to fine-grained triangulation and scene analysis methods, the proximity technique does not attempt to actually measure the object’s distance to reference points, but rather determines whether an object is near one or more known locations. The presence of an object within a certain range is usually determined by monitoring physical phenomena with limited range, e.g., physical contact to a magnetic scanner, communication connectivity to access points in a wireless cellular network. 2.2. Related efforts and enabling technologies for comprehensive materials tracking Construction of a facility is a process by which materials are fabricated, assembled and installed on site by workers with the help of equipment, according to designs and specifications. Tracking the location of construction resources on site, i.e., workers, materials and equipment, is critical to automated project performance monitoring that will ultimately enable project management to take corrective actions in a timely manner to control actual performance [10–12]. Navon and Goldschmidt [10] showed that workers’ locations can be automatically collected by GPS and converted into labor hours consumed in execution of a particular activity with reasonable accuracy. Undoubtedly, GPS can be used to precisely track the location of the small number of workers or pieces of equipment over a great range of geographic and geometric scales. However, GPS alone is not a viable option for comprehensive materials tracking. Tagging individual material items with simple GPS receivers costing around US$100 per unit would be prohibitively expensive, and still other means for identification would be required. Alternatively, a state-of-the-art GPS receiver could be used to acquire the precise coordinates for materials’ location without tagging individual material items, as in a recent field test [13]. Though the effectiveness of the technology in locating pipe spools at large laydown yards was demonstrated with immediate payback for a typical industrial project, manual
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acquisition and periodic updates of the GPS coordinates are required in addition to positive identification. On the other hand, RFID technology is suited to identification purposes in tracking hundreds of materials in harsh environments. Though RFID technology presents several advantages over barcoding, its primary use in current applications is still limited to portal based identification purposes. This limitation had driven the commercial development of the Real-Time Location System (RTLS) for indoor asset tracking applications. Unlike conventional RFID systems, the RFID-based RTLS such as RF Technologies’ Pinpoint [14] provides both identification and location of tagged objects by virtue of a pre-configured wired network of fixed RFID readers deployed in the interior of a building. However, the RFID-based RTLS requires the significant infrastructural setup of proprietary networks and has difficulty interoperating with existing IEEE 802.11 wireless networks [15]. Recently, these issues with the RFID-based RTLS have been resolved by leveraging the IEEE standard Wi-Fi networks. Being based on the non-proprietary networks, the Wi-Fi based RTLS successfully overcame the substantial cost barrier to scalable location tracking systems, i.e., the infrastructural setup of separate networks. Good examples include solutions from AeroScout [16] and Ekahau [17]. Nonetheless, the Wi-Fi RTLS using the scene analysis technique requires extensive calibration to map the Wi-Fi signals to locations throughout the building. To circumvent calibration efforts, the Wi-Fi RTLS may employ other localization principles like the RSS triangulation [18], but it still relies on existence of 802.11 access points, which is not guaranteed for a facility being built in the job site. As an alternative to location tracking systems using a network of fixed RFID readers or Wi-Fi access points, positioning systems using mobile readers may be applicable. For example, a reader mounted on a vehicle determines the position of the vehicle using as reference points RFID tags which had been pre-programmed with known location and embedded in the operating environment. However, this approach becomes impractical for comprehensive materials tracking since RFID tags with limited power and computational capability will not be able to pre-determine their own location. Finally, the applicability of RFID technology to the emerging ad hoc localization is discussed. Unlike the cellular network based approach as in the RTLS, ad hoc location sensing under the scenario envisioned by wireless sensor networks does not draw on a preconfigured and calibrated communications infrastructure, thus representing a highly scalable and low-cost approach [15]. The notion of wireless sensor networks envisions that thousands of sensor nodes deeply embedded in physical environments provide sensed data, while self-configured and adaptively coordinated to frequent topology changes [19]. However, current RFID tags have no capability to form wireless sensor networks, much less the ability to discover neighboring peer tags. Only RFID readers can
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discover and communicate with tags within a certain read range, and tags can only respond to readers by sending back the data stored on internal memory. In summary, the existing approaches to accurately tracking materials’ location imply economically prohibitive deployment of ADC technologies. Furthermore, due to its evolving and unpredictable nature, a construction site cannot afford location tracking systems relying on fixed network infrastructure, whether proprietary or not, which should be configured carefully to cover the entire site and calibrated to its RF transmission space. However, current RFID systems can determine the presence of a tag within a range from the reader, providing proximity information of the tag, though not the distance measurements between the tag and the reader. Thus, a proximity localization system composed of a few mobile RFID readers equipped with GPS presents an alternative solution to tracking the location of hundreds of tagged materials on a construction site.
3. A mathematical framework for RFID proximity localization The concept proposed here is a field supervisor or piece of materials handling equipment that is equipped with an RFID reader and a GPS receiver, and serves as a ‘‘rover’’. The supervisor, for example, walks around the site on his or her normal business. The position of the reader at any time is known since the rover is equipped with a GPS receiver, and many reads can be generated by a single rover moving around the site. Suppose that in 2D the reader has a radial read range with the radius r and the rover has generated a read at a known position with Cartesian coordinates (x1, y1), from which an RFID tag fixed at unknown location (x2, y2) is read. Then the RF communications connectivity exists between the reader and the tag, contributing exactly one piece of prox qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 imity information i:e:; ðx1 x2 Þ þ ðy 1 y 2 Þ 6 r to the problem of estimating the tag location. As the rover comes into the range r from the tag time and again, more reads form such convex constraints for the tag. Combining these proximity constraints restricts the feasible set of the unknown position of the tag to the region in which the circles centered at the reads intersect with one another. Estimating the location of the tag then comes down to selecting a point from the intersecting region. Intuitively, such an intersecting region would manifest itself as a point, if the rover generated infinitely many reads and proximity constraints for each tag. Thus, this asymptotic convergence of location estimates suggests a governing trade-off between positional accuracy, time and resources for the approach proposed here. However, to characterize the performance trade-off through experimentation, an analytical framework is needed. Such a framework should also allow one to derive a model of the
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Fig. 1. Modeling the discrete RF communication region.
theoretical performance which can be compared with the performance experienced in the field. A useful mathematical framework has been adapted from a model that Simic and Sastry [20] envisioned with wireless sensor networks in which RFID tags would communicate with neighboring tags. In this framework, a square region Q with sides of length s in which the rover moves around is partitioned into n2 congruent squares called cells of area (s/n)2. Thus, the position of reads as well as tags is represented by a cell with grid coordinates, rather than a point with Cartesian coordinates, and one is only interested in finding the cell(s) that contains each RFID tag. For example, the read shown in Fig. 1 is positioned at the cell with grid coordinates (5, 5). Furthermore, the RF communication region of a read is modeled as a square centered at the read and is characterized by a discrete read range q cells, instead of a disk with the radius r of continuous value. To illustrate, the read shown in Fig. 1 has the discrete range q = 2 cells, and its communication region is the shaded square centered at the cell (5, 5) and containing (2q + 1)2 = 25 cells. This communication region can be expressed by [3, 7] · [3, 7], if we denote by [a, b] · [c, d] the union of all cells with grid coordinates (i, j) such that a 6 i 6 b and c 6 j 6 d, for integers 1 6 a 6 b 6 n, 1 6 c 6 d 6 n. Given that a tag is read from multiple cell locations while the rover moves around the site Q, its location is estimated within a rectangular region in which square communication regions centered at the reads intersect with one another. The number of cells in such a rectangular region is called the size of location estimate for the tag. 4. Experimentation Based on the mathematical framework described above, field experiments were conducted using an off-the-shelf RFID handheld reader and tags. A square region Q with sides of s = 36 m (120 ft) was set up on the grass in an open field and partitioned into n2 = 302 square cells with sides of 1.2 m (4 ft), with the boundary and grids delineated using stake flags and strings. Although the position of the RFID reader at any time would have been known if the experimenter carrying around the reader was equipped with a GPS receiver, experiments dispensed with a GPS receiver.
Instead, the experimenter located himself within one of the 900 cells by referring to the stake flags and physical grid lines in the experiment field – handling of GPS positional error is described later. Experimental parameters included (1) RF power transmitted from an RFID reader, (2) the number of tags placed, (3) pattern of tag placement, and (4) the number of reads generated. The first three parameters were used to vary operating conditions in which the RFID proximity localization may exhibit the performance trade-offs. Each combination of these parameter values characterizes the unique test bed. The number of reads was also parameterized to compare the performance experienced in the field with the theoretical performance derived from the discrete framework, presented later. First, two levels of RF transmission power were associated with a particular value of the discrete read range q, as shown in Table 1. Each of these values was assigned to a different level of RF power upon 200 trials of reading a total of 20 tags placed in the experiment field, rather than derived from the corresponding radial read range r that would have to be determined otherwise. For more details, refer to [21]. In addition, different configurations for the magnitude and density of tags were created by placing a total of 10 or 20 tags in the experiment field so as to form one of seven different patterns. Fig. 2 depicts one such placement pattern, ‘‘Even,’’ which consists of 20 tags and represents an extreme in terms of tag density – for other patterns tried in experiments, see [21]. Particularly, some of the tags were placed within the interior region such that they are distant from the boundary of Q by more than the discrete read range q and hence can be read from as many as (2q + 1)2 different cell locations in the overall region Q. Finally, for each of the 28 test beds (=2 power levels * 7 placement patterns * 2 numbers of tags), the experimenter
Table 1 Assigned discrete read range at different levels of RF power RF power level
Transmission power (dBm)
Assigned discrete read range q (cells)
Corresponding radial read range r (m)
Medium High
20 5
5 7
8.8 (29 ft)–10.1 (33 ft) 12.2 (40 ft)–13.7 (45 ft)
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Fig. 2. The even placement pattern with 20 tags.
attempted only once to read tags in each and every one of a total of 900 cells in the experiment field, thus generating a completely full read array from which virtual rover paths and read rates could be generated. However, to keep the effect of different rover paths from convoluting with that of different read rates, it was necessary to simulate the situations in which the rover samples proximity information at different time intervals while remaining on a single path – implementation of this simulation is reported in [21]. As such, for each test bed, the number Ke (specified later) of reads were randomly selected from the total of 900 reads in 50 iterations to create virtual rover paths. Then for each path, proximity information was sampled at two additional frequencies represented by fractions of Ke reads, 0.2Ke and 0.1Ke. Thus, each of the 28 test beds yielded a total of 150 sets of proximity constraints (=50 random rover paths * 3 read rates), with the overall data set amounting to 4200 sets of proximity information. 5. Performance analysis For each of the 4200 sets of proximity information, we denote by K the total number of attempts made by the rover to read as many as 10 or 20 tags – depending on sampling rate, K = 1.0Ke, 0.2Ke, or 0.1Ke. If one of the K reads acquired an RF signal from a tag and hence provided a proximity constraint for the tag, this read is defined as a successful read for the tag. Expectedly, successful reads for a tag with grid coordinates (x, y) occurred when the tag lay within the discrete read range q, that is, when the rover positioned within the region [x q, x + q] · [y q, y + q]. However, successful reads for the tag also happened when the reader was positioned beyond this region, and are defined to be off-side. In a sense, off-side reads reflect uncertainty in the RF communication region defined by the
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value of q assigned at an RF power level. This uncertainty prompts us to consider the RF power level a candidate factor influencing the performance of RFID proximity localization. In fact, groups of test beds with a different RF power level showed significant differences in distributions (not presented here) of both successful reads and off-side reads, more than did groups with a different pattern or number of tags placed. The high level of RF power yielded more successful reads due to the greater read range that would allow reading the same tag from farther locations. Thus, in 96% of the time when using the high RF power, all the tags were successfully read by at least one of the K reads while reads at the medium power missed some tags in 22% of the time. However, the high RF power also experienced off-side reads more frequently, approximately 20% of successful reads on average, compared with 10% for the medium RF power. One simple way to reduce this ‘‘false alarm’’ rate while remaining at the same power level is discussed later. Interestingly, the proportion of off-side reads to successful reads remained constant regardless of sampling frequency. Thus, as the rover generates more and more reads, the magnitude of off-side reads for each tag will grow (at a greater rate if using the high RF power) and so will the uncertainty in proximity measurements for the tag. However, proximity measurements contributed by offside reads were not eliminated when calculating the location estimate for each tag – practically, whether a successful read for a tag is off-side or not would be unknown unless the true tag location is known. Consequently, the calculated location estimate of a tag was not always valid. A location estimate, represented by the region [a, b] · [c, d], is defined to be valid if the integers a, b, c and d satisfy that 1 6 a 6 b 6 n, 1 6 c 6 d 6 n. An invalid estimate results when combining proximity constraints for a tag leads the feasible region for the tag’s location to a vacuous intersection. As off-side reads were more frequent with the high RF power, fewer tags were localized with a valid estimate when using the high power. In worst cases the location estimate of all tags was invalid, amounting to 13% of the 2100 instances in which the high power was used. This all-invalid case accounted for 7% of a total of 4200 instances, none of which arose when using the medium RF power. Performance in each of the rest 93% of the total 4200 instances may be measured by averaging out the size of valid estimates calculated for individual tags. The size of a valid estimate [a, b] · [c, d] is calculated as (b a + 1) * (d c + 1), and a valid estimate of smaller size is generally considered to be more precise – for an invalid estimate, definition of the size is no longer applicable. However, the size of a valid estimate alone could not be used as an overall measure of positional accuracy experienced in the field because of another consequence of uncertain proximity measurements: that not every valid estimate for a tag contained the true cell location of the tag. Therefore, it was necessary to come up with the smallest region for each
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tag such that it is certain to contain the true tag location, so-called confidence region for the tag. The confidence region of each valid estimate is determined by expanding the valid estimate in both directions along X- and Y-axes by its bias that indicates the extent to which the valid estimate deviated from the true tag location. The bias of a valid estimate is defined as the largest number of cells that the true tag location was distant along the X-axis or Y-axis from the boundary of the region given by the valid estimate. Thus, the bias b of the location estimate [a, b] · [c, d] for a tag with the true location (x, y) is calculated as max{min(jx aj, jx bj), min(jy cj, jy dj)}, while it is given zero if the estimate contained the true tag location, i.e., a 6 x 6 b and c 6 y 6 d. Subsequently, the confidence region of the estimate [a, b] · [c, d] is given as [a b, b + b] · [c b, d + b], and the number of cells contained in the confidence region combines the size and bias of the estimate, representing an overall measure of positional accuracy for the estimate. Fig. 3 summarizes the overall performance experienced in the field when using the high or medium RF power. For a particular level of RF power, a pair of curves shown in Fig. 3 as solid and dotted lines, respectively, represent the frequency with which the size of valid estimates and the confidence regions were on average smaller than or equal to a certain number of cells. Specifically, in 97% of the time when the medium power was in use, on average the valid estimate of a tag was smaller than or equal to 81 cells, which is commensurate with a 9 · 9 cell area, but the true tag location actually deviated within ±4 cells from the center of the valid estimate in 94% of the time. In contrast, performance under the high RF power showed the error in the estimate of true tag location significantly different than if assessed by the size of the valid estimate
alone. This gap can be thought of as the impact of bias encountered at the high power level. Observations thus far of the field performance under different RF power levels indicate a trade-off between coverage and accuracy for RFID proximity localization. The greater read range of the high RF power yielded more successful reads per tag, reducing the odds that tagged items may be left unread and missed. On the other hand, plagued by a higher proportion (and greater magnitude) of off-side reads, localization under the high RF power resulted in the valid estimate for fewer tags than with the medium RF power, but also inferior estimates of true tag location. This trade-off for different RF power levels can be coped with by increasing the total K number of reads and hence the read rate. In addition to a decreased incidence of some tags being missed (Table 2), accuracy in the estimate of true tag location was gradually improved for both power levels (Fig. 4). Nevertheless, the number of tags with a valid estimate still tended to decrease for both power levels (Fig. 5). As more and more reads were generated, off-side reads would only increase no matter what level of RF power in use. Particularly, under the high RF power all tags ended up with an invalid estimate in 40% of the time when using the high read rate, so that in Fig. 4 the corresponding cumulative frequency curve reached only 60%. This suggests the basic Table 2 Frequency of some tags being missed (each out of 700 instances) RF power level
Low read rate
Moderate read rate
High read rate
Medium High
399 88
69 4
0 0
Fig. 3. Overall performance experienced in the field under different RF power levels.
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Fig. 4. Positional accuracy experienced under different combinations of RF power and read rate.
Fig. 5. Distributions of the number of tags with a valid estimate under different combinations of RF power and read rate (each sample size 350 with the total 20 tags).
limits of the positional accuracy attainable without degenerating into invalid estimates. Essential to overcoming these limits is the ability to manage the uncertainty in proximity measurements before fusing them into the localization process, which is the subject of ongoing research. Later in this paper we report on one simple technique in which the discrete read range assigned at an RF power level is expanded while remaining at the same power level. Considering the present limits for the high read rate, the theoretical accuracy under the discrete framework is compared with the field performance in case that the total K number of reads generated was moderately large. Recall that the total K reads sampled at a moderate rate was represented by the fraction 0.2Ke, where Ke denotes the smallest integer value for the K0 reads required to achieve a certain level of positional accuracy. Simic and Sastry [20] showed that the K0 number of reads necessary to localize
a randomly picked interior tag within an (1 + e) cell area satisfies K0 >
ln½8qð2q þ 1Þ þ ln e ; ln 1 2qþ1 n2
ð1Þ
assuming that the position of reads are distributed evenly through the region Q = [1, n] · [1, n]. For the experiment field partitioned into n2 = 302 cells, the smallest integer value of K0 satisfying (1), Ke, is 321 given q = 7 cells at the high RF power and some arbitrary e = 4. Since 0.2Ke given e = 4 is equivalent to Ke given e = 288, 0.2 * 321 64 reads with the high RF power should allow the location of interior tags to be estimated within a (1 + 288) = 17 · 17 cell area, that is, with accuracy of ±8 cells. Similarly, for the medium RF power with q = 5 cells and e fixed at 4, 0.2Ke (=0.2 * 385 = 77) is equivalent to Ke
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given e = 168, for which the true location of an interior tag is expected to be within a 13 · 13 cell area. This positional accuracy predicted for K = 0.2Ke reads was accomplished in the field for both levels of RF power, as shown in Fig. 6. It also suggests that if using the same number of reads at the same RF power level, the overall performance may vary depending on tag density in terms of which focused and even patterns represent extremes. In fact, these extreme tag patterns exhibited a difference in the incidence of some tags being missed, as shown in Table 3. However, this difference between tag patterns became insignificant as the read range (associated with an RF power level) or read rate increased, while the incidence of incomplete coverage decreased for both patterns. As a trade-off, the high RF power level or a greater read rate typically resulted in fewer tags being localized with a valid estimate for both patterns (Table 4), while their difference
became unignorable. As with coverage and integrity, the actual performance on accuracy also degraded when tags were not evenly distributed, but it improved for both tag patterns when the medium RF power was used, or as the read rate increased (Fig. 7). The observations above suggest that while the density of tags has an impact on the overall performance, the tradeoff between coverage, accuracy, and integrity will remain intact due to the interplay between read range and read rate. Since it would be impractical on a construction site to control the density of tagged materials, configurations for field deployment should be determined considering this trade-off to suit specific site needs. For example, if a large construction site or laydown yard is surveyed for complete inventory, the presence and quantity of every item may be the priority over its accurate location so a long read range of high RF power would be advantageous in coverage. If
Fig. 6. Comparing field performance with theoretical positional accuracy.
Table 3 Frequency of some tags being missed under extreme tag patterns (each out of 50 instances) Tag placement pattern
Medium RF power Low read rate
Moderate read rate
High read rate
High RF power Low read rate
Moderate read rate
High read rate
Focused Even
47 23
15 2
0 0
13 6
1 0
0 0
Table 4 Median number of tags with a valid estimate under extreme patterns (each out of 50 instances with a total of 20 tags) Tag placement pattern
Medium RF power Low read rate
Moderate read rate
High read rate
High RF power Low read rate
Moderate read rate
High read rate
Focused Even
17 19
19 18
15 11
12 16
6 11
0 3
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375
Fig. 8. Positional accuracy for different read ranges at the high RF power level.
Fig. 7. Positional accuracy experienced under extreme tag patterns. (a) Focused pattern. (b) Even pattern.
positional accuracy is of primary concern, low power configurations with a short read range present an alternative. In either case, performance on coverage and accuracy could be further improved by a single rover generating a large number of reads – if the rover potentially moves fast, by using multiple rovers. In the mean time, to keep location estimates from degenerating into an invalid estimate (i.e., to maintain integrity), the uncertainty in proximity measurements contributed by off-side reads should be managed appropriately. Since the communications range at an RF power level is anisotropic, time-varying and dependent on an operating environment, proximity measurements based on the square communication region are inherently uncertain, leading to off-side reads. One obvious way to stifle off-side reads is to dilate the discrete read range by one or two cells while remaining at the same RF power level. Table 5 shows that for both extreme patterns, the median number of tags localized with a valid estimate has increased when expanding the read range by one cell at the high RF power level. Off-side reads of 20 tags placed in the focused pattern
decreased on average from 31% to 22% of successful reads while successful reads averaged about the same as before – for the even pattern, off-side reads reduced from 16% to 10%. Nevertheless, as the read rate increased, the degeneration into invalid estimates was unavoidable so that the number of tags with a valid estimate still tended to decrease. On the other hand, expanding the read range at the same power level had a negative effect on positional accuracy, except for the high read rate under the focused pattern where the all-invalid case prevailed, as shown in Fig. 8. Finally, we consider the impact of mapping the position of the rover to a cell location in reference to physical grids. Unlike in the field experiments, the position of the rover at a cell location may be known with a GPS receiver subject to errors. Thus, to assess the overall error in tag location estimates, the error in estimate of the rover position using GPS should be added in. For example, using the medium RF power, the true tag location was estimated with the error ±3 cells, or ±3.6 m (side of 1 cell = 4 ft 1.2 m), in approximately 68% of the time, as shown in Fig. 3. Assuming that the error in tag location estimates using RFID is normally distributed, the estimate of the tag’s location has the errors with one standard deviation 3.6 m. If the rover were equipped with a GPS receiver whose error in estimate of 2D location has one standard deviation 1.0 m, the tag location estimate would have the overall pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi error with a standard deviation of 3:7 m 1:02 þ 3:62 . 6. Conclusions The combination of RFID and GPS technologies presents the opportunity to densely deploy low cost RFID tags
Table 5 Median number of tags with a valid estimate for different read ranges at the same RF power level (each out of 50 instances with the total 20 tags) Tag placement pattern
High RF power with q = 7 cells Low read rate
Moderate read rate
High read rate
High RF power with q = 8 cells Low read rate
Moderate read rate
High read rate
Focused Even
12 16
6 11
0 3
16 18
11 15
1 7
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with a few mobile RFID readers equipped with GPS to form the backbone of a construction materials’ tracking system. The solution proposed is intended to extend the use of RFID technology to tracking the precise location of materials on a construction site as well as at laydown yards, without relying on a fixed communications network or modifications to current hardware, and potentially at a magnitude less cost than pure GPS or other existing approaches. Field experiments conducted using an off-the-shelf RFID system have demonstrated that using this approach, the approximate 2D location of materials can be determined without adding to the regular operations on a site. Since this approach leverages ambient passive reading without requiring line of sight for positive identification or manual updates of materials’ location, it is potentially much faster than existing approaches for surveying laydown yards and a construction site. In conjunction with its application to tracking the delivery and receipt of prefabricated materials through portal gates, ‘‘roving’’ applications of RFID technology based on this approach can form a unified platform to automate the tracking of materials and components in multiple stages of the project life cycle. Though potential economic feasibility of this approach should be estimated to justify the up-front cost of implementation, the proposed application framework has the potential to make RFID technology economically more attractive and drive implementation of the technology in realizing potential benefits. Finally, several recommendations for field deployment can be made based on performance analysis presented in this paper while field trials in real world settings are encouraged. Though RFID tags with a short read range (10 m) can be localized accurately, especially when tags are evenly distributed, optimal configurations for read range should be determined to suit specific site needs considering the trade-off between coverage, accuracy, and integrity. A slightly larger discrete read range may be assigned to an RF power level since it can reduce off-side reads and help to keep location estimates from degenerating into the invalid. However, to eliminate the false alarms all together would require assigning considerably larger read ranges which will in turn compromise positional accuracy. Therefore, other methods should be developed to manage the uncertainty inherent in proximity measurements to improve both accuracy and integrity. Such methods should handle conflicting information generated by moved or moving tagged objects as well. Further research is also needed to expand the discrete framework to 3D localization and develop methods to determine the position of the rover when GPS may not function.
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