logical rfid reader using hybrid active–passive solution

CONTRIBUTEDP A P E R

Logical RFID Reader UsingHybrid Active–Passive SolutionA novel logical reader has been implemented by combining tracking information

from passive RFID and position information from active RFID; this provides

low-cost, fine-grain, near real-time tracking.

By Han Chen, Member IEEE, Norman H. Cohen, Sastry S. Duri,

Johnathan M. Reason, Member IEEE, and Paul B. Chou

ABSTRACT | Radio-frequency identification (RFID) has been

applied widely in applications such as supply chain visibility,

pharmaceutical track and trace, etc. The standards body

EPCglobal defines various interfaces in the electronic product

code (EPC) network architecture to facilitate the interoperabil-

ity of different applications. Logical reader is a concept defined

in the application level events (ALE) specification to shield

applications from knowing the physical device infrastructure.

This paper proposes a new approach to the logical reader

abstraction, which is defined using spatial zones and imple-

mented by combining mobile, passive RFID with positioning

technologies, such as active RFID. This hybrid approach ex-

ploits the best benefits of passive and active RFID, while

maintaining compatibility with EPC standards for accessing

logical readers via ALE. An evaluation of competing approaches

is presented. The study shows that this spatial-zone-based de-

sign enables fine grain tracking of assets at lower infrastructure

cost as compared to existing techniques (e.g., using active RFID

only). The study also analyzes the accuracy of the proposed

approach using numerical simulation. The results show that it

outperforms a widely used chokepoint-based solution under

realistic operating conditions.

KEYWORDS | Logical reader; reverse geocoding; radio-

frequency identification (RFID)

I . BACKGROUND

Radio-frequency identification (RFID) technology has

been applied to the tracking of many different kinds of

items, including, pallets and cases for supply chain

management, cars for automatic toll collecting, personnelfor safety and security [1]. RFID technology can be broadly

classified into two categories: passive and active [2]. In

systems based on passive RFID technology, the tags are not

powered. They derive operating power from the radio-

frequency (RF) wave emitted by an RFID interrogator. As a

result, passive RFID tags have to be within the vicinity of

an RFID interrogator to transmit information. In systems

based on active RFID technology, the tags carry their ownpower source and therefore can broadcast information on

their own. This capability gives active RFID tags longer

range and, at the same time, makes them expensive to

operate requiring periodic battery changes.

The cost of deploying RFID technology includes:

1) fixed cost associated with setting up infrastructure and

2) variable cost associated with its operation over its life-

time. For passive RFID technology, the fixed costs areassociated with deploying relatively inexpensive readers

and antennas. But due to passive RFID technology’s li-

mited sensing range, the number of readers required is

typically proportional to the scale of deployment. The

variable cost associated with deploying passive RFID tech-

nology is the tags themselves, which are cheap (tens of

cents). For active RFID technology, the fixed costs are

associated with deploying expensive sensing infrastruc-ture. Due to active RFID technology’s long sensing range,

the sensing infrastructure could cover a wider area with

comparatively fewer sensing elements than passive RFID.

Active tags, which are expensive (tens of dollar) and

usually require additional labor cost for battery replace-

ment, represent the variable cost of an active RFID

solution.

Manuscript received March 15, 2009; revised May 17, 2010; accepted June 10, 2010.

Date of publication July 15, 2010; date of current version August 20, 2010.

H. Chen, S. S. Duri, and J. M. Reason are with IBM Thomas J. Watson Research Center,

NY 10532 USA (e-mail: [email protected]; [email protected];

[email protected]).

N. H. Cohen is with Google, New York, NY 10011 USA (e-mail: [email protected]).

P. B. Chou is with Institute for Information Industry, Taipei 106, Taiwan

(e-mail: [email protected]).

Digital Object Identifier: 10.1109/JPROC.2010.2053331

1636 Proceedings of the IEEE | Vol. 98, No. 9, September 2010 0018-9219/$26.00 �2010 IEEE

The cost/performance characteristics often determinewhich technology best fits a particular application. For

example, for tracking workers in hazardous working envi-

ronments such as an oil refinery, a system based on active

RFID should be used because fine-grain location tracking

is required and the benefit to safety can far outweigh the

infrastructure cost. On the other hand, because of the low

tag cost, a passive RFID solution is best suited for tracking

millions of pallets moving through a supply chain.While active RFID provides direct positional informa-

tion (x-, y-, and z-coordinates in some reference frame) of a

tagged item, passive RFID essentially reports the proximity

and thus association between a tag and an interrogation

device (or antenna), which can be used to infer the posi-

tion of the tagged items indirectly from the known position

of the antennas. Thus, a system based on active RFID

usually provides much higher spatial resolution than asystem based on passive RFID.

A common requirement that occurs in many applica-

tions is knowing the location that tagged items are in, for

example, room 102. Because locations are usually expres-

sed as symbolic names, they offer more business semantics

and can shield client applications from the underlying

tracking technology used and thus allow better solution

reuse. This usage pattern is recognized and supported byEPCglobal’s application level events specification (also

known as ALE) in its abstraction called Blogical reader[ [3].

Logical readers are identified by symbolic names and the

ALE interface presents them as virtual passive RFID

readers and reports the association of tags and the logical

readers.

A number of asset tracking applications require heavy,

tagged items to be detected and monitored as they movethrough different areas. One example is wholesalers track-

ing pallets of merchandises throughout their warehouses.

Another example is shipping companies tracking contain-

ers or pallets in open areas such as an airport tarmac.

When the area of concern is significantly larger than the

effective read zone of an RFID antenna, special techniques

must be employed to reliably detect RFID tags in the area.

Existing solutions are either impractical or expensive forthese requirements. This study proposes a new approach

for implementing such applications by using spatial zones

to represent the logical reader abstraction and combining

mobile, passive RFID with positioning technologies, such

as active RFID. The solution is cost effective, reliable, and

easy to manage.

The rest of the paper is organized as follows. Section II

describes several known techniques for implementinglogical readers using either passive or active RFID.

Section III proposes a new method which integrates mo-

bile passive RFID with positioning technology to support

construction of logical readers based on spatial zones.

Additionally, the cost and the performance of the proposed

method are analyzed in Section IV and compared to

existing solutions. The paper concludes with recommen-

dations on when to apply the different RFID solutionoptions.

II . RELATED WORK

There are several existing methods to implement the log-

ical reader interface defined by the EPCglobal ALE speci-

fication. We will discuss them briefly in this section.

A. Passive RFID Reader/Antennas MultiplexingWith passive RFID, an interrogation device, or reader,

uses RF to detect tags present in the reading fields of the

antennas. The effective reading distance of passive RFID

system is largely governed by the total power emitted by

the reader/antenna. To reduce radio interference and tosecure worker safety, government entities, such as the U.S.

Federal Communications Commission (FCC), regulate the

upper limit on power emission and the operation duty

cycle for RFID readers. Typical commercial passive RFID

readers can reliably detect and collect tag information at a

distance up to 20 ft (6 m) [4]. This number may be reduced

in spaces where the presence of liquid or metallic surfaces

can adversely affect the propagation of radio waves.Typical commercial RFID readers support a number of

antennas (e.g., four) to provide increased coverage. In

situations where even larger logical locations are required,

multiple RFID antennas must be used to provide the

necessary coverage. This can be achieved in two ways.

First, multiple readers may be used to construct a log-

ical reader [5], [6]. Fig. 1 shows such a system. The man-

agement system maintains the mapping between readersand locations. A software agent receives the tag reports

from physical readers and translates the physical reader ID

to the logical location name according to the mapping.

Another agent then aggregates multiple tag report streams

pertaining to the same location into a single stream. This

stream can then be fed into an existing ALE implemen-

tation directly.

Second, hardware-based solutions may be used toincrease the number of antennas that a single reader can

Fig. 1. Implementing logical readers using passive RFID with

reader/antennas multiplexing.

Chen et al. : Logical RFID Reader Using Hybrid Active–Passive Solution

Vol. 98, No. 9, September 2010 | Proceedings of the IEEE 1637

support. A typical solution would use time-division multi-plexing to switch among a number of antennas [7], [8].

This method simplifies the software aspect of logical

reader management and reduces the overall infrastructure

cost, as a single reader can be used with as many antennas

as needed to support a logical location, for example, a large

smart shelf [9] in a retail environment or other Bsmart

box[ applications that simply detect object in-box or out-

of-box [10], [11].

B. Position-Based TrackingIn many situations, logical locations can be defined as

either 2-D shapes or 3-D volumes within a premises. Thus,

if the position of tagged items can be obtained, their

locations can be inferred by performing a polygon or poly-

hedron hit test, a process also known as reverse geocoding.

Positioning technologies, such as global positioning system

(GPS) or active RFID, can be used to determine the pos-

ition of the asset being tracked [12]–[14].

Fig. 2 shows such a system. Active RFID technology isused to provide the precise position information of tagged

items. The logical reader management system consists of a

topology database that maps the symbolic location names

to polygon or polyhedron definitions. The position stream

produced by the active RFID system is fed into a software

agent that translates the position into location. Tags

detected in the same location are aggregated and buffered

to form periodic read cycle reports to the ALE engine.The main drawback of this approach is the cost of

active RFID tags, which are very expensive compared to

passive tags. These tags typically require battery power,

which adds to the labor cost over the solution’s life time.

Therefore, it may not be economically feasible to apply

active tags on every pallet and case. In addition, active tags

are not compatible with the electronic product code (EPC)

standard [15] that is widely used in supply chainmanagement.

An alternative solution to overcome these issues is to

obtain positional data directly from passive RFID tags. The

basic principle of this solution is similar to active RFID.

Triangulation or trilateralization of tags is performed based

on received signal strength indicator (RSSI) or time-of-

flight information from multiple antennas (at least three or

four). The main drawback is the limited range and low

precision due to the lack of battery power on the tags and

the radiation limits on the reader antennas themselves.

C. Chokepoint-Based Software StateIf the access to an area is limited, e.g., a room with a

doorway, passive RFID readers can be placed strategically

at these so-called chokepoints, as shown in Fig. 3. The

current tags that are present in such an area are kept as a

software state. Any movement of tagged items in and out

the area is detected by the reader at the chokepoints, andthe state is modified accordingly. In order for such an

arrangement to function reliably, a mechanism of detect-

ing the direction of item movement is needed. This can be

implemented by using motion sensors or enforced by

business processes.

The main drawback of this solution is that it only works

well in areas with clearly defined chokepoints. In large

open areas such as a load zone in an airport, it may bedifficult to set up such chokepoints.

III . HYBRID ACTIVE–PASSIVELOGICAL READER

Logical reader provides a standardized way of accessing

EPC data regardless of the actual implementation or source

of data. The existing methods discussed before are suf-

ficient for a number of applications. However, there is a

class of applications where these methods are not ade-

quate. They typically involve tracking large volumes of big,

bulky items through an open space.One example is tracking the movement of supplies

throughout an airport. Special zones are marked as logical

reader locations. Some of these zones can even be dynamic

and situational, based on the business and operational

need. It is impractical to instrument the entire tarmac with

passive RFID antennas. On the other hand, tagging all

items with active tags is an expensive proposition, and

Fig. 3. Implementing logical readers using RFID instrumented

chokepoints. Tags inside a logical reader area are inferred using

tag read events and the directionality of item movement.

Fig. 2. Implementing logical readers using active-RFID-based

position tracking.


1638 Proceedings of the IEEE | Vol. 98, No. 9, September 2010

active RFID systems compatible with existing EPC stan-dard are still in development [2].

Another example involves tracking the movement of

goods over a large geographic area, for instance, a courier

tracking packages throughout the country. Some applica-

tions may want to define logical readers at locations such

as truck stops, sorting centers, etc., and use ALE speci-

fication to obtain reports in a standard way.

This paper proposes a new method to implementlogical readers for these applications. It uses mobile pas-

sive RFID readers that are position tracked using a real-

time locationing system (RTLS), such as active RFID or

GPS technology.

A. Device InfrastructureThe following operational assumptions are made for

the proposed system to function properly.

• Items are tagged with passive RFID tags, so that

they can be tracked and traced by existing passive

RFID infrastructure in other parts of the supplychain.

• Tagged items are handled and moved only via

mechanical means, for example, a forklift. This

gives us the opportunity to instrument the forklift.

Fig. 4 shows the overall device infrastructure of the

proposed system. It consists of two main subsystems.

• First, forklifts are instrumented with mobile pas-

sive RFID readers to detect tagged items onboard.The mobile RFID reader’s antennas are installed in

such a way that the reading field covers the entire

loading space of the forklift. These readers have

wireless connectivity either directly or indirectly

via a mobile controller. This allows tag read reports

to be sent to an information system to be processed

in a timely fashion.

• Second, forklifts are position tracked using RTLS,

either active RFID or GPS technology. (Without

loss of generality, this paper limits the discussion

to active RFID only.) The positions of tracked

forklifts are reported by the active RFID infrastruc-

ture to the information system.

A network connects the mobile passive RFID reader

system and the active RFID system to an information sys-tem. The information system consists of a tag stream pro-

cessing system and a logical reader management system,

which are described in Sections III-B and C.

B. Tag Stream ProcessingThe mobile passive reader detects the items onboard a

forklift, while the forklift’s position is tracked. The job of

the tag stream processing system is to correlate these twostreams, deduce the current positions of tagged items, and

translate positions into logical locations. Fig. 5 shows the

component-level design of the tag stream processing

system.

1) Mobile Reader Feed: This component interfaces to the

mobile passive RFID system. It receives tag read reports

from all readers on the forklifts. Each output event is atuple that contains the reader’s ID and a set of tags that are

visible during that report cycle.

2) Position Data Feed: This component interfaces to the

active RFID system. Each output event is a tuple that has

an active tag ID, corresponding to a forklift, and its current

position, in the form of x-, y-, and z-coordinates.

3) Pairing Detection Filter: Even when carefully designed

and installed, the mobile reader on a forklift may still be

able to pick up tags applied on stationary items when the

forklift passes by them. If these tags are not properly

filtered and removed, their positions will be updated by the

downstream processing components. For items that are

near the boundary of a logical reader zone, this may cause

Fig. 4. Device infrastructure of the proposed hybrid active–passive

logical reader system. Tagged items are carried and moved by forklifts

instrumented with onboard RFID reader. The position of a forklift is

tracked using active-RFID-based RTLS technology.

Fig. 5. Component-level design diagram for the tag streaming

processing subsystem. Passive RFID read events and RTLS position

data are filtered and correlated to deduce moving items’ position.

Reverse geocoding determines the zones that tagged items are in.



an erroneous report that an otherwise stationary item hasbeen moved into a new location, resulting in a false positivereading. The pairing detection filter’s function is to try to

remove these Bstray[ tags from its output stream.

We make the following two observations.

• When a forklift is not in motion, stray tags are

harmless, since even if they are included in a tag

report, their positions will not be updated.

• When a forklift is indeed moving, stray tags appliedon stationary items will only stay in the mobile

reader’s reading field for a finite period of time, the

length of which is determined by the dimension of

the reading field ðLÞ and the velocity of the

forklift ðvf Þ. Given a read cycle length of Tr, we can

determine the maximum number of reports a stray

tag can appear in while the forklift is moving.

N ¼ L=vf Tr.To remove these stray tags, the pairing detection filter

uses a finite state machine. The state machine has two

states for each tag, on and off. The initial state is off,

indicating that the tag is not Bpaired[ with the reader. If a

tag is present in the incoming tag report for Non con-

secutive cycles, it is considered to be paired and the state

machine transitions to the on state. While in the on state,

if a tag is missing from the incoming tag reports for Noff

consecutive cycles, it is considered to be a stray tag and is

thus unpaired. The state machine goes back to the off

state. The output tag report is constructed from the set of

tags that are currently in the on state, which represent

the cargo that is onboard the forklift during that read

cycle.

Both Non and Noff are tunable parameters to the sys-

tem. As experiments in Section IV will show, their values,along with other system configurations and characteristics,

have an impact on the overall logical reader accuracy.

4) Position Correlation: This component joins the posi-

tion data from the active RFID system with the filtered tag

reports. The join matches the mobile reader ID from the

tag report against the active tag ID from the forklift

position data so that they correspond to the same forklift.There are two mappings involved in the operation. The

first relates a forklift ID to a mobile reader ID. The second

links a forklift to its active tag ID. The mapping infor-

mation is kept in a relational database, which the position

correlation component accesses.

Because the two incoming streams arrive asynchro-

nously and possibly at different rate, the join operation

applied here is a time-window-based join. The high-levelalgorithm is as follows.

• Maintain in memory a last known position ðx; y; zÞfor each forklift ID.

• Upon receiving a position event tuple (activeTagId,

x; y; z), translate active tag ID to forklift ID and

update the last known position of the forklift ID.

Output nothing.

• Upon receiving a tag report tuple (readerId, tags),translate the reader ID to forklift ID and query the last

known position of the forklift ID from the in-memory

table. Output the tuple (readerId, tags, x; y; z).

The output event of this component is a tuple

containing tags from onboard cargo, the reader ID, and

the forklift’s last known position.

5) Location Mapping: This component performs thereverse geocoding. It translates the position ðx; y; zÞ of a tag

into a symbolic, logical location. Locations are defined as

geometric shapes, either as a 2-D shape or a 3-D volume,

depending on the need of the application. The definitions

are kept in a database table. Any implementation of reverse

geocoding can be used to perform the translation.

The output from this component is a tuple containing

tags from onboard cargo and the logical location they arecurrently in.

6) Logical Reader Mapping: This component is used

when a solution allows a logical reader to be mapped to

multiple locations. The mapping is kept in a database table.

Upon receiving an incoming tuple containing a logical

location, this component translates the location to the

logical reader ID.The output stream presents a client the image of a

virtual, logical reader and can be used directly by appli-

cations. Alternatively, client applications can access the

logical readers through an ALE interface.

7) ALE Engine: This component implements the

EPCglobal ALE interface. It consumes the raw tag reports

from the logical reader mapping component and provides asubscription-based reporting interface to client applica-

tions [3].

C. Logical Reader Life Cycle ManagementBesides the tag stream processing, the information

system also contains a logical reader management subsys-

tem that manages the life cycle of logical readers. At the

center of the management system is a database thatmaintains the definitions of logical readers and logical

locations. Fig. 6 shows a simplified view of the data model.

A web application provides the administrative func-

tions related to the life cycle management of logical

readers. Using a browser-based graphical user interface,

administrators can perform the following tasks:

• register a mobile reader with a forklift;

• register an active tag with a forklift;• create a location;

• modify a location;

• delete a location;

• create a logical reader;

• associate a logical reader with one or more

locations;

• delete a logical reader.



One important feature of this system is that the logical

readers are not tied to any physical infrastructure, there-

fore they can be defined and modified programmatically.

An extension to the ALE interface can be added to exposethe APIs for manipulating the locations and definitions of

logical readers. This enables solutions to create dynamic

logical readers based on application context.

D. ImplementationA prototype of the hybrid active–passive logical reader

system along with an application scenario is implemented

based on an industrial replenishment process where heavy

items must be tracked and identified in various regions

throughout a large space.

The prototype uses business process execution lan-guage (BPEL) to describe the high-level replenishment

process. The process is then deployed on a process engine

(WebSphere Process Server). The process interacts with

the logical reader system via Java Messaging Service (JMS)

bindings over message queues (WebSphere MQ).

The logical reader system is implemented using an

event-driven sensor-and-actuator programming frame-

workVdistributed responsive infrastructure virtualizationenvironment (DRIVE). DRIVE provides developers an

event processing network (EPN) programming model. An

EPN is an interconnected network of event processing

agents (EPAs), which are software components that repre-

sent sensors, actuators, or processing logic. They commu-

nicate with each other asynchronously via input and

output ports. DRIVE provides the tool and runtime envi-

ronment to author individual EPAs and to wire themtogether to form EPNs. DRIVE supports two textual

languages for implementing agents: Java and EventScript.

EventScript is based on regular expression. An EventScript

code specifies the pattern that the incoming events must

form and the corresponding actions to take when such

pattern (at its final or intermediate stages) is detected.

Among many other components, the pairing detection

filter algorithm is implemented using the EventScript

language.

Due to space limit, the full details of the implemen-

tation cannot be described in the paper. For additionaldescription on DRIVE and EventScript, refer to [16].

IV. EVALUATION AND ANALYSIS

This section uses simulation to quantitatively evaluate the

performance of the proposed hybrid active–passive logical

reader. A qualitative analysis of the nonfunctional aspects

of the proposed system, such as cost and ease of

management, is also presented.

A. Reader ModelThe radio power received by an RFID tag determines

whether it will power up and successfully transmit its

encoded content to the reader. There exist full analytical

models based on the physical laws of electromagnetism,

such as Friis’s equation [17]. To simplify the calculation,

this study uses an approximation of the equation. For atypical RFID panel antenna, there is a direction of maxi-

mum radiated power ~M. It is usually the normal of the

antenna surface. Let ~T be the vector from the center of the

antenna to a tag, and � be the angle between ~M and ~T, and

� ¼ j~Tj. We use the nth power of cosð�Þ to approximate

the off-axis falloff. We further use an inverse square func-

tion to approximate the falloff in distance. (This is true for

free space without obstructions.) Thus, the received poweris modeled as

PR ¼ �PTcosnð�Þ�2

where PT is the transmitted power from the reader to the

antenna and � is a constant that captures factors such as

Fig. 6. Data model and the life cycle management subsystem.



the antenna gain, reflection coefficient of the tag, antennapolarization, etc. The 3-dB beam width is then

2 cos�1 10�0:3=n:

Using n ¼ 4 in the model, the 3-dB beam width is 65.4�.This value corresponds to a typical passive RFID panel

antenna, such as the Motorola AN400 antenna, which has

a 3-dB beam width of 60�. Therefore, from now on, n ¼ 4

is used in the simulation.The probability that a tag is read depends on the

incident power PR. According to empirical measurements

carried out in [18], there exists a threshold Pon such that if

PR > Pon, a tag can be reliably read with a high probability

phigh. When PR falls 4 dB below Pon, or about 0:4Pon, a tag

cannot be powered on and thus is never read. For PR

values between these two thresholds, the read probability

falls off from phigh to 0. The simulation model uses apiecewise linear function to approximate this behavior.

B. Simulation Methodology and MetricsRead accuracy is one of the most important perfor-

mance metrics that prospective users care about. Inaccu-racies can be categorized into two types: a false positivehappens when an item is reported to have transitioned

from one reader zone to another while in reality it has not;

a false negative happens when an item has transitioned

from one reader zone to another but is not reported by the

system.

In the proposed hybrid active–passive logical reader

system, they correspond to two distinct scenarios. When astationary item on the ground erroneously pairs with a

passing forklift while it enters a new zone, a false positive

happensVthe item is mistakenly reported to have been

moved. When an item onboard a forklift loses pairing with

the mobile reader for a long period of time while the

forklift is transitioning into a new logical reader zone, a

false negative occursVthe item is not reported as being in

the new zone.

The angle � in the directional antenna model depends

on both the azimuth and the elevation of a tag in a polar

coordinate system. For simplicity, the simulation assumes

that all tags are located in the 0� elevation plane. Thus,

the geometry becomes a 2-D plane. Without loss of gene-rality, two logical reader zones are defined in the simu-

lation, left and right, separated by the x ¼ 0 line, as

shown in Fig. 7.

Fig. 8 shows the detailed configuration of the simulated

forklift. Both axes are in meters. The forklift faces due east

(positive x-axis direction). The shaded rectangle shows the

outline of a commonly used International Organization for

Standardization (ISO) pallet size in North America(4800 � 4000, or roughly 1.22 m � 1.02 m). The curves

are contour lines of equal received powers for RFID tags.

We choose a transmit power PT0and offset the mobile

reader such that the pallet rectangle inscribes in the

PR ¼ Pon contour line (the inner, solid curve in the

figure). This represents an ideal setup where all tags on a

pallet can be reliably read, but without leaking too much

power so that stray tags are picked up. The outer, dottedcontour line corresponds to a power level 4 dB below Pon.

Thus, tags outside the red curve are not read. In the

simulation, the forklift moves from left to right along the

y ¼ 0 line (x-axis).

To calculate false positives, we set up simulated sta-

tionary items in the second quadrant (x G 0, y > Wf=2,

where Wf is the width of the forklift), as shown in Fig. 7.

As the moving forklift passes by, the stationary items maybe paired with the mobile reader and get Bcarried[ into the

right zone, causing false positives. We note that as these

items are stationary, they will eventually lose pairing with

the forklift. Thus, false positive only happens for itemsFig. 7. Simulation environment with two zones, a forklift,

a pallet to be moved, and a number of stationary items.

Fig. 8. Reading characteristics of the simulated mobile forklift

reader. The shaded rectangle represents a 4000 � 4800 ISO pallet.

Tags inside the solid contour line are read at a high probability

reliably. Tags outside the dotted contour line are not read.



that are situated close to the boundary of a logical readerzone.

To calculate false negatives, we set up a simulated ISO

pallet with 100 tags. The pallet’s center is initially located

at ð�D; 0Þ and the tags are uniformly distributed within

the pallet’s 1.2 m � 1.0 m boundary. The forklift picks up

the pallet in Tload seconds, then drives along the x-axis at a

constant speed of vf until the pallet reaches ðD; 0Þ in the

right zone. The forklift drops the pallet off using Tload

seconds and keeps going forward to other destinations. If

the tags on a pallet lose pairing with the mobile reader,

their position may not be correctly updated, resulting in a

false negative.

The positioning error of the active RFID tag on the

forklift is modeled as uniform distributions along both

x- and y-directions. The maximum error in each direc-

tion is �.

C. Performance Simulation ResultsFor all tests, the following simulation parameters are

used.

• Forklift width Wf ¼ 1.4 m, corresponding to a

typical warehouse forklift. (The two horizontal

dotted lines in Fig. 7 mark the width of the

forklift.)

• Forklift speed vf ¼ 1:33 m/s (three miles per

hour).

• Forklift loading/unloading time Tload ¼ 2 s.• Mobile reader’s read cycle 0.5 s, a reasonable

time for a Class 1 Gen 2 reader to pick up around

100 tags.

• Active tag positioning error � ¼ 0.3 m, corre-sponding to a typical commercial grade ultrawide-

band (UWB)-based system.

For each test, 100 simulation runs are performed and the

minimum, maximum, mean, and standard deviation of

error rate are computed.

1) False Positive: Fig. 9(a) shows the false positive

rate for stationary items. The horizontal axis is the

y-coordinate of an item. Each curve represents a different

x-coordinate value for the item. In this figure, the following

parameters are used: pairing threshold Non ¼ Noff ¼ 3,reader output PT¼PT0

(Bideal[ level), and reliable read

rate phigh¼0:95. It shows that the closer an item is to the

forklift and to the boundary of a logical reader, the more

likely it is to be Bdragged[ into a wrong logical reader zone.

For items that are sufficiently far away from these

boundaries, false positive never happens.

2) False Negative: Fig. 9(b) shows the false negative ratefor items being moved. The horizontal axis is the value of

D, a pallet’s distance to the boundary. Each curve repre-

sents a different pairing threshold value. The result shows

that when the pairing delay is long, the closer a pallet is to

the boundary the more likely it is to be missed by the

system. For items that are sufficiently far away from the

boundaries, false negatives can be avoided.

3) Impact of Pairing Threshold: The pairing threshold

value affects the logical reader’s accuracy in two ways. On

the one hand, if the threshold is low, tags are easily paired

Fig. 9. False positive and false negative rates of logical reader systems. (a)–(e) Performance of the proposed hybrid active–passive system.

(f) Performance of a chokepoint-based system.



with the mobile reader, resulting in more false positives.On the other hand, if the threshold is too high, it takes a

longer time for a tag to pair with the reader, resulting in

more false negatives. Therefore, a system designer should

make a tradeoff between them.

Fig. 9(b) already shows the effect the pairing threshold

has on false negatives. Fig. 9(c) shows the effect the

pairing threshold has on false positives. Only the items

closest to the boundary ðy ¼ �0:2Þ are shown in thefigure. Each curve represents a different pairing threshold

value.

These two sets of results show that, under this par-

ticular simulation condition, no value of N can eliminate

both false positives and false negatives. A value of three or

four achieves the best compromise.

4) Impact of Antenna Power: Setting the antenna powerappropriately is important to the overall performance. If

antenna power is too low, onboard tags may be missed,

resulting in false negatives. If antenna power is too high,

stationary tags may be picked up, resulting in false

positives. In the previous simulations, the Bideal[ power

value PT0was used. To study the impact that antenna

power has on performance, the power output is varied in

this simulation, from 50% of PT0(�3 dB) to 200% of PT0

(þ3 dB) in 1.5-dB increments. The pairing threshold is

Non¼Noff¼3 and the reliable read rate phigh ¼ 0:95.

Fig. 9(d) shows the false positive rate for stationary

items located at x ¼ �0:2. It is clear that if the antenna

power is set too high (þ3 dB), a lot of stationary items are

read, resulting in very high false positive rate. Reducing

the power below PT0almost eliminates false positives.

Fig. 9(e) shows the false negative rate for pallets atdifferent locations from the boundary. It shows that if the

antenna power is too low (�3 dB), not all tags on the

forklift can be read reliably, resulting in high false negative

rate. Setting power level to above PT0produces the mini-

mal false negative.

Recall that PT0is selected such that the PR ¼ Pon con-

tour line just barely covers the entire pallet. Intuitively,

this offers the Bideal[ power setting. The simulation re-sults confirm this.

D. Comparison With Other SystemsFrom read accuracy point of view, antenna/reader

multiplexing and position-based tracking represent the

best case scenario; they give close to perfect read accuracy.

Therefore, it is interesting to compare the performance of

the proposed system against that of chokepoint-basedsoftware state. To that end, this paper conducts another

simulation of a typical chokepoint-based system, namely,

an RFID instrumented dock door, which is commonly used

in warehouses and distribution centers.

We consider a typical dock door that is 3 m (about

10 ft) wide. Two RFID antennas are installed on opposite

ends of the dock door opening, facing each other. Fig. 10

depicts the radiated power characteristics of the dock door

readers. The same reader model from previous simulations

is used to construct this simulation. The reader power PT is

set to a value such that PR ¼ Pon contour lines (solid curves

in the figure) are large enough to cover a 3 m � 1 m area,providing reliably read rate, while the �4-dB contour lines

(dotted curves) are small enough so that they do not cross

over into adjacent dock door areas in order to minimize

interference.

In a chokepoint-based system, the association between

tags and logical readers is maintained as software state,

which in turn is manipulated by detection events of tags

by readers installed at the chokepoint. If a tagged item ismoved from one side of the dock door to the other without

being detected by either antenna, a false negative hap-

pens. On the other hand, if stationary items are placed too

close to the dock door, they may be read by the readers

when other items are passing through the dock door,

resulting in false positives. The simulation engine in this

paper uses a deterministic antenna power model along

with a read rate model that generates zero read rate out-side the �4-dB contour line. Hence, it is not meaningful

to simulate the false positive rate, unless stationary items

are intentionally placed inside the �4-dB contours.

Therefore, for this comparative study, only false negatives

are considered.

Using the same parameters as before, a simulated

4000 � 4800 ISO pallet with 100 tagged items is moved by

a forklift in due north direction at 1.33 m/s. Thepallet’s x-position is uniformly distributed across the

entire width of the dock door. Fig. 9(f) shows how the false

negative rate varies in response to changing values of the

reliable read rate phigh. For each value, 100 simulation runs

are conducted.

The result shows that the false negative rate decreases

as phigh increases. For phigh ¼ 0:95, the same value used in

Fig. 10. Reading characteristics of a simulated RFID instrumented

dock door. The dock door’s horizontal opening is 3 m wide. The two

opposing antennas provide reliable read rate for a 3 m � 1 m area.



previous simulations of the proposed system, the choke-point-based approach yields a false negative rate of slightly

under 1%. Fig. 9(b) shows that with N ¼ 4 the proposed

system achieves close to 0 false negative rate for pallets

located at least 3 m away from logical reader boundaries

and less than 0.5% false negative rate for items 0.5 m away

from boundaries. This indicates that, when carefully

planned and configured, the proposed system can outper-

form the chokepoint-based system.

E. Computational ComplexityThe following three components shown in Fig. 5

require nontrivial implementations. Their computational

complexities are discussed below.

The pairing detection algorithm can be implemented

using a deterministic finite state machine. Processing a

single input event takes a constant amount of time.

Therefore, the time complexity of this component is OðnÞ,where n is the number of input tag read events. For each

tag, the number of state is determined by the pairingthreshold, usually a small constant, thus the space com-

plexity is OðNtÞ, where Nt is the total number of tags in the

system.

Position correlation requires keeping one state, its last

position, for each forklift. The correlation itself is a sim-

ple lookup. Therefore, its time complexity is OðnÞ and

space complexity is OðNf Þ, where Nf is the number of

forklifts.Location mapping uses reverse geocoding to deter-

mine the zones that a tag is in. Efficient spatial indexing

algorithms, such as R-tree [19], exist and can be used in

this component. However, detailed discussion is outside

the scope of this paper.

F. Cost and Other Nonfunctional QualitiesWhile performance is an important factor, the total

cost of ownership plays an equally significant role in the

decision of selecting the right solution for implementing

logical reader for a given application. The main contribu-tors to the capital expenditure are the readers and the tags.

To a large extent, installation, management, and mainte-

nance related labor cost determines the operational

expenditure.

This section roughly compares the proposed system

with three existing systems in terms of their hardware cost

and ease of management. For the cost comparison, the

following notations are used (a summary of the cost com-parison can be found in Table 1):

• Ap: the total area of the premises;

• Aactive: the average coverage area per active RFID

sensor;

• Nr: the total number of logical readers;

• Ar: the total area of all logical reader zones;

• Apassive: the average coverage area of a passive

RFID antenna;• Nf : the total number of forklifts;

• Nt: the total number of tagged items.

1) Antenna/Reader Multiplexing: Given that an antenna’s

effective read range is largely limited by the maximum

radiated power allowed by regulation, the number of

reader and/or antennas required in order to provide full

coverage of a space is proportional to the size of all logicalreader zones, that is, Ar=Apassive. The number of passive

tags required is Nt.

The deployment of this solution requires extensive

effort in terms of planning and wiring. The solution pro-

vides limited dynamism in that new locations and logical

readers can only be defined as long as there are antennas

covering the space in those area. Creating logical readers

in new area requires additional capital investment.

2) Position-Based Tracking: The hardware cost is re-

flected in the deployment of sensors, beacons, or access

points for the chosen active RFID technology. The number

of sensors required is usually proportional to the area of

the premises, that is, Ap=Aactive. The number of active tags

requires is Nt.

The management of this solution is relatively easy.Logical readers are defined in software. New locations can

be created and existing locations modified without involv-

ing any additional hardware or field personnel. One dis-

advantage comes from the tags themselves. They usually

require batteries, which need to be maintained and rep-

laced at regular intervals. This adds to the operational

cost.

3) Chokepoint-Based Software State: The number of

readers required is proportional to the number of logical

readers in the system. Assuming that each logical reader

zone requires k readers for all chokepoints, the require-

ment is kNr. The number of passive tags requires is Nt.

Table 1 Hardware Cost Comparison of Different Logical Reader Solutions



The management of logical locations and readers is noteasy with this solution, as it requires careful planning of the

locations of the chokepoints which may have an impact on

existing business operations. It is also relatively expensive

and time consuming to deploy new logical readers.

4) Hybrid Active–Passive: The number of active RFID

sensors required is the same as in position-based tracking,

that is, Ap=Aactive. In addition, Nf mobile passive readersand Nf active tags are required for instrumenting the

forklifts. The number of passive RFID tags required for

tagging items is Nt.

As in the position-based tracking solution, logical readers

are defined in software as geometric shapes. Thus, it offers an

easy way to manage the logical readers and locations. How-

ever, it does require additional effort to manage the regis-

tration of mobile readers and active tags with the forklifts.

G. Solution ApplicabilitySections IV-C–F analyze the performance, cost, and

nonfunctional qualities of the proposed solution against

three existing ones. It is worth pointing out that no one

single solution is the best for all application scenarios. When

choosing a solution, an architect must take into account,

among many other factors, the following considerations.• Is the deployment new or incremental? This deter-

mines how much existing infrastructure can be

reused.

• What is the performance objective for the logical

RFID reader systems?

• How expensive is the human labor cost relative to

hardware? This determines how much the nonfunc-

tional qualities should be factored into the decision.Without being exhaustive, the paper enumerates a few

typical applications for each solutions.

• Antenna/reader multiplexing: smart shelf in retail

environments.

• Position-based tracking: high-value asset tracking,

personnel safety, and security in large spaces.

• Chokepoint-based software state: coarse-grained

tracking of high volume consumer goods in asupply chain.

• Hybrid active–passive: fine-grained and situational/dynamic tracking of bulky items in large spaces.

V. CONCLUSION AND FUTURE WORK

As RFID becomes increasingly deployed in various appli-

cations, it is important to maintain interoperability among

vendors and solution providers. Logical reader along with

an ALE interface is a powerful abstraction to simplify thehigh-level application development. It insulates the busi-

ness logic from the physical device infrastructure and

allows the infrastructure and the business logic to be

managed independently.

This paper surveys existing techniques for implement-

ing logical readers using either passive RFID or RTLS

using active RFID. This paper also proposes a new solution

for implementing logical readers based on spatial zonesusing a hybrid passive and active RFID system. This solu-

tion uses mobile passive RFID readers that are position

tracked by an active RFID system to infer the positions of

tagged items indirectly. Logical locations and readers are

defined as geometric shapes. Based on the correlated posi-

tions of tagged items, reverse geocoding is used to deter-

mine the symbolic locations, and thus the logical reader,

where tagged items are located. This solution enables fine-grained RFID-based tracking solutions on large, open pre-

mises where installing a fixed RFID infrastructure is

impractical or impossible.

This paper performs numerical simulation to quantify

the performance characteristics of the proposed system

and evaluates the tradeoffs of different system configu-

ration parameters. The results show that, when planned

and configured properly, the proposed system performsbetter than a common used chokepoint-based logical

reader solution. This paper also analyzes the proposed

system and compares it with existing approaches vis-a-viscost and other nonfunctional qualities.

This paper also wants to point out that the performance

evaluation is based on models with simplifying assump-

tions. More empirical study should be conducted to

determine the real-world performance of the proposedsolution. h

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ABOUT T HE AUTHO RS

Han Chen (Member, IEEE) received the Ph.D.

degree from the Department of Computer Science,

Princeton University, Princeton, NJ, in 2003.

He is a Research Staff Member at IBM Thomas

J. Watson Research Center, Hawthorne, NY. His

research interests include messaging and event

technology, sensor and actuator solution, distrib-

uted computing systems, scalable display system,

and multimedia.

Norman H. Cohen received the B.A. degree in

mathematics and computer science from Cornell

University, Ithaca, NY, in 1975 and the M.A. and Ph.D.

degrees in applied mathematics and computer

science from Harvard University, Cambridge, MA,

in 1977 and 1980, respectively.

He is a Software Engineer at Google, New York

City, NY. From 1987 to 2009, he was a Research

Staff Member at the IBM Thomas J. Watson Re-

search Center, where he worked on formal

specifications, optimizing compilers, programming-language design,

mobile and sensor-based computing, and context-based computing. He

worked for SofTech, Inc., from 1983 to 1987 and the research group of

Sperry Univac from 1979 to 1983. His work in those positions included

programming-language design, formal verification, and software-

engineering training.

Sastry S. Duri received the M.S. degree in

computer science from the Indian Institute of

Technology, Chennai, India, in 1988 and the Ph.D.

degree in computer science from the University of

Illinois at Chicago, Chicago, in 1995.

He is a Senior Software Engineer at the IBM

T. J. Watson Research Center, Hawthorne, NY.

His professional interests include distributed and

high-performance computing systems, mobile

commerce applications, RFID-based supply

chains, and sensor and actuator applications. He loves to coach teams

for FIRST Robotics competitions. In the past, he represented IBM in the

industry standard group EPCglobal ALE Working Group, a subsidiary of

the Uniform Code Council (UCC), and in OpenLS workgroup.

Johnathan M. Reason (Member, IEEE) received

the Ph.D. degree from the Department of Electrical

Engineering and Computer Sciences, University of

California Berkeley, Berkeley, in 2001.

He is a Research Staff Member at the IBM T. J.

Watson Research Center, Hawthorne, NY. His re-

search interests include wireless sensor networks,

quality-of-service (QoS) networking, mobile com-

puting, and distributed systems. He joined IBM

Research in 2004. During his tenure, he has

conducted research on several topics, including real-time telemetry for

freight trains using wireless sensor networks, programming models for

distributed event systems, and QoS networking for water management and

smart utility grid applications.

Paul B. Chou received the Ph.D. degree from the

Department of Computer Science, University of

Rochester, Rochester, NY, in 1988.

He has over 20 years of experience at IBM in

leading and managing innovative solution and

technology projects. He started as a Research Staff

Member and served as a Research Manager,

contributed to a broad array of research initiatives

leading to new business opportunities and product/

service offerings. His recent interests include inter-

net of things and event-driven information systems for applications in

green ICT and smarter planet. He published in a variety of technology

fields including RFID, sensor networks, event-driven systems, ubiquitous

computing, human–computer interactions, data mining, and computer

vision.



logical rfid reader using hybrid active–passive solution

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