Elementary Petri net inside RFID distributed database (PNRD)

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Elementary Petri net inside RFIDdistributed database (PNRD)José Jean-Paul Zanlucchi De Souza Tavares a & Thiago AugustoSaraiva ba Faculdade de Engenharia Mecânica da Universidade Federalde Uberlândia , Av. João Naves de Ávila, 2121, Santa Mônica,38400-002, Uberlândia-MG, Brazilb Instituto de Matemática e Estatística da USP, Rua do Matão,1010, Cidade Universitária , 05508-900, São Paulo-SP, BrazilPublished online: 18 Mar 2010.

To cite this article: José Jean-Paul Zanlucchi De Souza Tavares & Thiago Augusto Saraiva (2010)Elementary Petri net inside RFID distributed database (PNRD), International Journal of ProductionResearch, 48:9, 2563-2582, DOI: 10.1080/00207540903564934

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International Journal of Production ResearchVol. 48, No. 9, 1 May 2010, 2563–2582

Elementary Petri net inside RFID distributed database (PNRD)

Jose Jean-Paul Zanlucchi De Souza Tavaresa* and Thiago Augusto Saraivab

aFaculdade de Engenharia Mecanica da Universidade Federal de Uberlandia,Av. Joao Naves de Avila, 2121, Santa Monica, 38400-002,

Uberlandia-MG, Brazil; bInstituto de Matematica e Estatıstica da USP,Rua do Matao, 1010, Cidade Universitaria, 05508-900, Sao Paulo-SP, Brazil

(Revision received November 2009)

Usually, a Petri net is applied as an RFID model tool. This paper, otherwise,presents another approach to the Petri net concerning RFID systems. Thisapproach, called elementary Petri net inside an RFID distributed database, orPNRD, is the first step to improve RFID and control systems integration, basedon a formal data structure to identify and update the product state in real-timeprocess execution, allowing automatic discovery of unexpected events during tagdata capture. There are two main features in this approach: to use RFID tags asthe object process expected database and last product state identification; and toapply Petri net analysis to automatically update the last product state registryduring reader data capture. RFID reader data capture can be viewed, in Petrinets, as a direct analysis of locality for a specific transition that holds in a specificworkflow. Following this direction, RFID readers storage Petri net control vectorlist related to each tag id is expected to be perceived. This paper presents PNRDcornerstones and a PNRD implementation example in software called DEMIS –Distributed Environment in Manufacturing Information Systems.

Keywords: Petri net; RFID; tag data structure; elementary PNRD; DEMIS

1. Introduction

Since 2000, there have been huge initiatives to disseminate RFID along supply chains, asan EPC Network� (Anon 2007). A lot of new applications related to RFID have beendeveloped trying to present its benefits and advantages (Zaharudin et al. 2002, Hardgraveet al. 2005, Evdokimov et al. 2008, Grummt and Muller 2008, Kurschner et al. 2008).

As Ngai et al. (2008) presented, RFID has become a hot topic in the fields of supplychain, manufacturing and logistics but its range of application extends far beyond theseareas, for instance enterprise feedback control.

RFID and Petri net are commonly associated with assisting and verifying RFIDsystem modelling. Elementary nets are used to establish robust and reliable communi-cation between the high and low-level sub-holons with a device holon as RFID tag (Bruseyand McFarlane 2005), as well as, to verify ubiquitous RFID healthcare system workflowsimulation (Choi et al. 2005). Temporal Petri nets are used to model an RFID eventprocess system (Xingyi et al. 2008), and coloured Petri nets are applied to model an

*Corresponding author. Email: [email protected]

ISSN 0020–7543 print/ISSN 1366–588X online

� 2010 Taylor & Francis

DOI: 10.1080/00207540903564934

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application of an assembly platform based on tag id group perception (Thomas andChoffel 2006).

This paper presents another approach concerning Petri net application in RFIDsystems, based on an informed systems model which views a system as input, process andoutput and distinguishes perception from action. Figure 1 presents graphically aninformed system model. The first point related to informed systems is the fact that they areintegrated with a process workflow. It can be noticed that information capture is notrestricted only from a process (indirect perception), but also from process input andoutput. Another point is separation of concern related with perception and action. In thisdirection this paper focuses on the perception and contextualisation part of informedsystems.

Kalpakjian and Schmid (2005) affirm that the control of the process is a critical factorin product quality; thus the objective should be to control the process, not products.Process control can be complemented with product information itself, process directoutput. A special requirement in this direction is the need for dynamic information storedin products. RFID tags can be a product information source to generate a lattice ofinformation about product and process with process control systems.

The RFID tag can be useful to store product information dynamically throughout theprocess. Some questions arise here. What and how much information is needed to bestored? How a lot of distributed information inside each tag can be managed?Nevertheless, a prior design of a formal data structure is demanded.

The flow of this information is the key to the effectiveness of RFID system modelling,and the product state is part of the control system baseline. So, a workflow framework ismandatory. This paper proposes elementary Petri nets as this framework. This approach,called elementary Petri net inside an RFID distributed database, or PNRD, is a formaldata structure to identify and update the product state in real-time process execution,allowing automatic discovery of unexpected events during tag data capture; the first stepto improve RFID and control systems integration.

There are two main features in this approach: to use RFID tags as a process expecteddatabase and last product state storage; and to apply Petri net analysis to automatically

Figure 1. Informed system model (Tavares et al. 2006).

2564 J.J.-P.Z.S. Tavares and T.A. Saraiva

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update the last product state registry during reader data capture. RFID reader data

capture can be viewed, in Petri nets, as a direct analysis of locality for a specific transition

that holds in a specific workflow (Tavares 2005, Tavares et al. 2006, 2007). In this regard,

this paper shows how to integrate operationally elementary Petri nets inside RFID

systems.This paper summarises RFID technology in Section 2. Section 3 shows elementary

level Petri net definition and formalisation, including Petri nets applied at workflow

management review, elementary Petri net inside RFID distributed database (PNRD)

concepts, auto-detecting exception state calculus, PNRD example, and PNRD method-

ology. PNRD implementation with DEMIS – Distributed Environment in Manufacturing

Information System – is presented in Section 4 to prove conceptually the PNRD approach,

followed by discussion and conclusion, further work, acknowledgements and references.

2. RFID technology

RFID is composed of tags or labels and reader devices with antennas. Readers can be

connected to a network; and it is expected to be possible to trace and track continuously

physical objects connected by RFID tags. It can also be viewed as a sensor network. A

single overview of a typical RFID system is provided in Figure 2. A reader stimulates

a RFID tag to send its internal information similarly to an actuator and a sensor inside a

control system which controls signal acquisition and observes the sensor response. Reader

tags processing integrates data from physical to information flow and vice-versa. Readers,

besides possessing simple filtering for reducing the amount of collected data, can also carry

out writing of tag data (Chokshi et al. 2003).Nowadays, modern readers are equipment with internal microprocessor and memory,

TCP/IP connection and integrated with a set of antennas, up to four.

Figure 2. Schema of RFID system.

International Journal of Production Research 2565

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There are several RFID frequencies. This paper focuses on UHF. Following UHFClass 1 Generation 2 Standard v.1.1.0 (Anon 2007), tag memory shall be logicallyseparated into four distinct banks. The memory banks are:

. Reserved memory shall contain the kill and/or access passwords, if passwordsare implemented on the tag;

. EPC memory shall contain a cyclic-redundancy check-16 (CRC) following ISO/IEC 13239, protocol-control (PC) bits, and a code (such as an electronic productcode – EPC) that identifies the object to which the tag is or will be;

. TID memory shall contain an 8-bit ISO/IEC 15963 allocation class identifier andoptional features that tag support;

. User memory allows user-specific data storage, optional.

There are some technical issues related with RFID application such as antennapositioning, tag selection, RFID frequency definition, physical barriers analysis, standarddevelopment, and regulations; but system integration is a key point. In this direction, thereis a need to structure RFID data inside a process workflow. The Petri net is a suitable toolto model process workflow. This paper presents an approach in which storage Petri Networkflow is inside user memory bank of RFID tag memory.

3. Elementary Petri net inside an RFID distributed database – PNRD

3.1 Petri net fundamentals

Petri nets (PNs) (Murata 1989) have a quite useful expressive power for modelling(graphically or mathematically) processes, as well as a great capacity to formally verify theproperties of the modelled system (Liu et al. 1994, Santos Filho and Miyagi 1995, Zhou1995, Silva and Miyagi 1996, Rozinat and Van der Aalst 2006).

Figure 3 presents a Petri net representation and its corresponding matrix equation orincident matrix C1. These PN characteristics allow the transformations of such processesin computable routines that assist in the analysis task of the productive processes.

A prescription commonly adopted for the modelling of a productive system using a PNrepresentation can be:

. Identification of the resources, operations or activities in the system;

. Establishment of sequences of the activities in each step;

Figure 3. Graphic and algebraic representation of a process in a Petri net.


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. Representation of the object states, generally for places that are connected by arcsto transitions, which indicates the beginning and ending of activities, belong to theprocess;

. Definition of the resource through places and connection of these to the beginningand ending of each operation represented by transitions;

. Specification of the initial marking.

The structure of a place P having two (or more) output transitions is referred to as aconflict, decision, or choice, depending on applications. In Figure 3, P2 is a place withconflict T2 or T3.

3.1.1 Incident matrix, state equation and coverability tree

Following Murata (1989) for a Petri net N with n transitions and m places, the incidentmatrix A¼ [aij] is an n�m matrix of integers and its typical entry is given by:

aij ¼ aþij � a�ij , ð1Þ

where aþij ¼ wði, j Þ is the weight of the arc from transition i to its output place j, anda�ij ¼ wð j, iÞ is the weight of the arc to transition i from its input place j.

In writing the matrix equation, markingMk is an m� 1 column vector. The jth entry ofMk denotes the number of tokens in place j immediately after the kth firing in some firingsequence. The kth firing or control vector uk is an n� 1 column vector of (n� 1) 0s and onenon-zero entry, a 1 in the ith position indicating that transition i fires at the kth firing.Since the ith row of the incident matrix A denotes the change of the marking as a result offiring transition i, it is possible to write the following state equation for a Petri net:

Mk ¼Mk�1 þ ATuk, k ¼ 1, 2, . . . , n: ð2Þ

Given a Petri net (N,M0), from the initial marking M0, it is possible to obtain as many‘new’ markings as the number of the enabled transitions. From each new marking, it canagain reach more markings. This process results in a tree representation of the markings,called a coverability tree. Nodes represent markings generated from M0 (the root) and itssuccessors, and each arc represents a transition firing, which transforms one marking toanother.

The above tree representation, however, will grow infinitely large if the net isunbounded. To keep the tree finite, a special symbol is introduced w, which can beclassified as ‘infinity’.

3.2 Petri net and workflow management

There is a large amount of research combining workflow management and Petri nets(Van der Aalst 1998, 1999, Van der Aalst and Van Hee 2002, Liu et al. 2007, Rozinat andVan der Aalst 2008). Usually the term of workflow management refers to a domain whichfocuses on the logistics of business processes. Petri net is a tool which can represent andanalyse these processes in a straightforward way (Van der Aalst and Van Hee 2002).

Van der Aalst (1998) discusses and presents how workflow management concepts canbe mapped onto and analysed by Petri nets, and focuses on work items, meaning case andtask relationship mainly. A workflow process definition specifies how the cases are routed


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along the tasks that need to be executed. He identifies four types of routing: sequential,parallel, conditional and iterative ones. Tasks are modelled by transitions, conditions aremodelled by places, and cases are modelled by a set of tokens.

Van der Aalst (1998) formalises sound WorkFlow-net (WF-net) (a Petri net whichmodels a workflow process). Summarising, a WF-net requires the Petri net to have: (i) asingle start place; (ii) a single end place; and (iii) every node must be on some path fromstart to end. The soundness property further requires that: (iv) each task can be potentiallyexecuted; and (v) that process can always terminate properly (i.e., there are no deadlocksand live-locks).

3.2.1 Conformance checking metrics

Rozinat and Van der Aalst (2008) presented conformance checking of a process based onmonitoring real behaviour applying Petri nets to model the way a process should beexecuted and compared it with an event log, a set of event sequences, also referred to astraces, assumed as a process execution record. The most dominant requirement forconformance is fitness. An event log and Petri net ‘fit’ if the Petri net can generate eachtrace in the log.

Unfortunately, a good fitness does not imply conformance, so they introduceappropriateness which tries to capture the idea of ‘Occam’s razor’, i.e., ‘one should notincrease, beyond what is necessary, the number of entities required to explain anything’.There are two types of appropriateness, the structural (if a simple model can explain thelog, why choose a complicated one), and the behavioural (the model should not be toogeneric and allow too much behaviour).

A prerequisite for conformance analysis is that the task in the process model must beassociated with the logged events, which is represented by a label denting the associatedlog event type (if any) for each task in the model. Besides the simple one-to-one mapping,where a task is associated with exactly one type of log event and no other task in the modelis associated with the same type of log event, a mapping may result in duplicate task, ormultiple tasks in the model associated with the same type of log event, and invisible taskswhich are not logged and, therefore, have no log event associated.

A perceived conformance problem can always be viewed from two angles. Firstly, themodel may be assumed to be ‘correct’ because it represents the way the business processshould be carried out, which means a prescriptive model. Secondly, the event log may beassumed to be ‘correct’ because it really happened, and the process model might be eitheroutdated or just not tailored to the needs of task performance. Highlighting these issuesfacilitates the redesign of the model and therefore increases transparency. This secondapproach is called a descriptive model.

3.3 PNRD approach

Elementary PNRD is the evolution of tag extended Petri nets (Tavares and Silva 2008). Inpractical terms, tag extended Petri nets (TEPN) can be viewed as a Petri net model of anindividual tagged object where RFID data is a token, and the RFID reading activity isrelated to a transaction that connects product or object pre and post conditions. As inWF-net tasks are modelled by transitions, TEPN transitions are reader tasks; conditionsare modelled by places as pre- and post-expected object states; and cases are modelled by aset of tokens; in TEPN, this refers to only one token, object actual state.


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Figure 4 represents a TEPN schema of Reader X1 related with Transaction1 and its pre(State1) and post (State2) states concerning EPC Number 1.10.11.01 in State1 (initialmarking).

Elementary PNRD proposes elementary or low level Petri net (LLPN) as the formaldata structure to identify and update the product state inside a specific process duringreader data capture which allows automatic unexpected events identification of eachindividualised tagged object.

From the tag point of view, readings can be modelled by low level Petri nets, whichmeans a sequence of states and transitions from just one token, the own object that this tagrepresents.

From the reader point of view, it is similar with a transition which evaluates severalobjects’ pre and post conditions. It is possible to conclude that readers can be associatedwith a set of transitions imbedded in a Petri net which represents part of a specific process.

3.3.1 Expected perceived transitions

As Rozinat and Van der Aalst (2008) defined duplicate, multiple and invisible tasks, it isnecessary to define two distinct types of expected PN transitions, perceived andnon-perceived.

For instance, a shipment process of 30 ‘free’ units can be modelled as a PN with threedistricts transitions: load, transport and delivery; and four places: free unit, loaded unit,transported unit, and delivered unit. This example is shown in Figure 5.

If only the load transition is perceived with RFID readers, this means only this task hasa physical reader perceiving and generating event logs; transport and delivery transitionsare non-expected perceived ones or invisibles. In this direction, there is a simplifiedperceived PN, which only presents perceived transitions. In this example, Figure 6 presentsthe corresponding simplified perceived PN. The more readers inside the process, the moreperceived transactions it has.

Figure 4. Example of TEPN schema (Tavares and Silva 2008).

Figure 5. Shipment process PN of 30 free units.


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On the other hand, the same reader can be associated with the receiving process. Thismeans it is possible to have a set of tasks related with the same reader. For a shipmentprocess, this perceived transition does not exist. Usually, other transitions are perceivedand checked manually or are not ‘viewed’.

There is another issue associated with reader position labelling. For instance, if onereader portal with four antennas is located at a depot gate entrance, it can be labelled asproduct entrance, or as product acceptance, depending on the process meaning.

Following the descriptive model point of view, when a reader captures a non-expectedperception it can be viewed as an opportunity to refine the PNRD model. In a prescriptivemodel, a non-expected perception needs corrective actions.

3.3.2 PNRD components

From the object point of view, each tag is a singular token inside a PN process model. Thispoint of view deals with elementary PN. This is one of the PNRD cornerstones.

The PNRD approach is based on the fact that each tag can be modelled as an incidentmatrix of an elementary PN and the process is planned in advance. Process planning canbe stored inside each tag as an elementary PN, representing a tag expected process plan.Even a flexible process can be stored, meaning that an object is able to follow differentways during process execution.

The PNRD approach splits elementary PN into RFID components. The PNRDapproach stores the Petri nets incident matrix A and object state Mk inside tag usermemory in order to improve reader computational power usability, which receives inadvance the control vector list {uk}.

It is possible to define PNRD perception as a set of reader perception, tag perception,and timestamp. Reader perception is a set of control vector {uk} depending on tag id, tagactual state Mk and reader antenna identification. Tag perception is composed of tag id,incident matrix A, tag actual state Mk and optional additional data. Formally:

PNRD Perception ¼ ffukðtagId,Mk, antennaId Þg,

ðtagId,A,Mk, additionalDataÞ, timestampg:ð3Þ

Summarising, part of elementary PN is gathered from tag memory after data capture(incident matrix A, actual state Mk). Complementary elementary PN part comes from thereader transaction relationship, this means control vector list {uk} which depends on eachtag id, tag actual state Mk, and, reader antenna or antenna id.

The elementary PNRD approach calculates the tag next state Mkþ1 based onEquation (2). If this calculus has a unitary state vector as a result, it means PN remains

Figure 6. Simplified perceived shipment process PN.


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elementary and Mkþ1 calculus fits expected process workflow and can be stored as a new

actual state inside tag memory, changing the tag state in real-time. If Mkþ1 calculus results

in a non unitary state vector it means that PN is no longer elementary, expected process

workflow was not reached and, as a direct consequence, it is possible to highlight

automatically this exception in real-time.Figure 7(a) shows an example of PNRD distributed along tag and reader represented

before the tag reading activity. There is a possibility to realise Figure 7(a) presents tagObj

as tag class’s object with the following attributes: tag Id 1.2.1.1010, Mk [1,0] (related with

P0 state), AT, and timestamp time1 (as additional data). Reader T1 is reader class’s object

with control vector list uk. [1] is the control vector related with any tag Id and in any state

Figure 7. PNRD representation before and after tag reading.


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(there is no restriction related with tag state inside this control vector list). Reader T1 has

several schemas to Mkþ1 calculus starting with read(Tag#,Mk,AT), followed by

get(uk,Tag#,Mk), time(T ), Mkþ1 calculus, and a schema to write a new tag state.Figure 7(b) represents tagObj data change after tag writing; pointing out new actual

state in tag Mk attribute ([0, 1]) related with P1, and a new timestamp time2. AT remains

the same.Comparing the PNRD approach with the ordinary RFID system, it is possible to

notice that PNRD introduces a low level process aware system, reducing data search in

external databases. Figure 8(a) presents the sequence diagram for the ordinary RFID

system with external application and external database. Figure 8(b) shows the PNRD

sequence diagram, which relies on reader internal application and database (control vector

list). There is an additional tag data capture related with incident matrix A and actual state

Mk in the PNRD sequence diagram. The ordinary RFID sequence diagram, on the other

hand, requires an external database to verify a tag list. Nevertheless, a single reader

antenna can be related with more than one perceived transition, depending on tag id, tag

actual state, and timestamp.

3.4 PNRD example

In order to distinguish the ordinary RFID system from the PNRD approach, this paper

presents a modified example from Thomas and Choffel’s (2006) Scenario 1 of an assembly

platform. The assembly platform comprises conveyors, storage areas, manipulator arms,

work-in-process and RFID systems. It is shown in Figure 9.Conveyors are in white, stocks in light grey, manipulator arms in dark grey and RFID

systems in black. The stock of pallets is S1, the pieces are stocked in S2 and the pastilles in

S3. The arm manipulator M1 permits to assembling (in the middle of conveyor A) and

disassembling (in the end of conveyor F) the products. The arm manipulator referenced

M2 is able to add a pastille to pieces presented on the pallet. The conveyor C corresponds

to a stand-by path which can contain only one piece.In this example, pallets, pieces and pastilles are tagged. Each product is composed of a

pallet and a number of pieces and pastilles. There are two types of product, each one with

a different number of pieces, which mean ‘Type 1’ (with one piece), and ‘Type 2’ (with two

pieces). The colour of the pieces in the ‘Type 2’ products is not taken into account contrary

to ‘Type 1’ products which should be sorted according to the colour of the pieces (blue, tag

Id 0.1.0.X; or green, tag Id 0.1.1.X, where X means EPC serial number).The pallets are manually set at the beginning part of conveyor A. The RFID reader

upstream of the manipulator arm M1 reads the type of pallet (tag Id 0.0.1.X for ‘Type 1’,

0.0.2.X for ‘Type 2’) which is arriving. M1 performs the assembly, and the downstream

RFID reader reads the tag attached to the piece and attributes a localisation of each one.

M1 is adopted to always put the first piece in the first location and the second piece in the

second one. Conveyors A and B transfer the products to the second manipulator arm M2.

M2 adds a pastille (tag Id 0.2.0.X) only on the ‘Type 1’ products which have a green

colour. Then the product is driven to the end of the conveyor E. The ‘Type 1 – green’

products are stored on the conveyor H. The ‘Type 1 – blue’ ones are disassembled at the

end of conveyor F. The pallets join the conveyor G while the pieces (blue ones) join the S2

storage area. As for ‘Type 2’ products, they are stored in G.


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Applying the PNRD approach on this example, there are three different PNs relatedwith pallet, pieces and pastille. Figure 10 presents pallet, piece and pastille elementaryPetri nets and their corresponding incident matrix. Notice that RFID2 readers arenot perceived by pallet and pastille PNRD. Pastille PNRD does not perceive RFID5,as well. As all tags store their own incident matrix and actual state, there is a possibility

Figure 8. (a) Ordinary RFID sequence diagram, and (b) PNRD sequence diagram.


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to have different sizes of incident matrix, depending on the object workflow. After initial

marking, each tag receives a tag id, incident matrix Ai and ‘P1’ as initial marking M0.The control vector list is stored in each reader’s internal memory, and this list can be

viewed in Figure 11. Notice that pieces that have an internal conflict in place P1 are

indicated by the letter ‘C’. If the piece is the first component of a ‘Type 2’ or the only

component of ‘Type 1’ product, then ‘C’ is [0 1 0 0 0 0 0]T; otherwise, it is [0 0 1 0 0 0 0]T.

This conflict can be solved if the RFID2 reader checks which pallet id is in use. Conflict

resolution can be done in several ways by applying decision resolution algorithms. Conflict

resolution algorithms are not focused on in this paper.As presented in this example, PNRD conflicts arise when the same reader/antenna is

related to more than one transition concerning the same tag actual state. In this direction,

it is necessary to apply a decision algorithm to define what transition must be chosen based

on additional data and specific case base reasoning.

Figure 9. Plan of assembly platform (modified from Thomas and Choffel (2006)).

Figure 10. Pallet, piece and pastille elementary PN with corresponding incident matrix A.


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In the control vector list, RFID5 has two distinguished transitions T6 and T7 related topieces Id. In this case there is no conflict because T6 and T7 have different pre-states

(Any and P3 – specifically). It can be noticed that generic rules must be presented first,followed by specific ones.

As a major control vector list identifies ‘Any’ as the actual state, this means that if atag, perceived for a reader/antennas tries to pass by this device and is not in the expected

state, automatically, the next state calculus will find a non unitary vector, identifyingan exception state.

Although PNRD identifies exception states, it does not distinguish the cause from this

effect. From the descriptive model point of view, an exception state can point to amodel refinement, increasing model visibility. From the prescriptive model point of

view, exception states must be integrated with the control system to trigger correctiveactions.

Exception state treatment can be done in several ways. One way identifies whether a

specific goal (end state or an internal state along the event log) will be reached or not. Inthis direction, a coverability tree from the tag incident matrix can be generated to look for

a specific state (goal). If this state is reached, this means that despite any exception statethis tag is able to achieve its target. Otherwise, there is a need to redo process planning or

schedule and write a new tag incident matrix. Exception state treatment is not presentedin this paper.

The control vector list does not include transition for initial marking record, and itonly describes a workflow related with an object id, and this means only a part of the

whole process for a specific object.There is a direct relationship among component states and the manipulator control

system. Each reader data capture, composed of tag id and tag actual state (captured or

Figure 11. Control vector list.


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calculated), can be useful to define manipulator action properly. This relationship is notpresented in this paper.

3.5 PNRD application methodology

PNRD application has some mandatory requirements of: process flow mapping inadvance, including known undesirable events; expected perceptions planning; perceptionpoint definition; reader location with system model perception points integration; andRFID technical issues resolution, as physical barriers analysis, RFID frequency definition,reader antenna positioning and power setting, and tag type and positioning specification.

PNRD application methodology is separated into two parts: design of expected statesdefinition; and execution or analysis of actual states, as follows:

(1) Design (perceived transitions and expected states definition):

(a) Process Mapped in PN, including known expected exceptions;(b) Definition of each specific expected product or object flow as prescriptive or

descriptive model;(c) Labelling of each perceived transaction inside this PN;(d) Definition of each product tag incident matrix, initial marking, additional data

(optional) and reader control vector list;(e) RFID technical issues and information system integration resolution.

(2) Execution (actual states analysis):

(a) RFID hardware and software deployment;(b) Auto-detecting exception based on PNRD approach;(c) Tag data consolidation (event log).

As was informed in the last section, unexpected exception treatment is not yet includedin PNRD methodology.

4. PNRD implementation

Elementary PNRD was implemented in software called DEMIS – DistributedEnvironment in Manufacturing Information Systems – in order to prove, conceptually,the PNRD approach.

4.1 DEMIS

The DEMIS architecture is shown in Figure 12. There are two main parts, DEMIS nucleusand interface. The DEMIS interface has two modules related with device communication(communication module) and data interpretation (interpreter module). These modulesenable DEMIS to connect with several different devices such as readers, PLC (program-mable logical controller), another DEMIS, and other devices.

The DEMIS interface is managed by the DEMIS nucleus, responsible for next statecalculus, conflict resolution and elementary PNRD configuration and contextualisation.The DEMIS nucleus is composed of elementary PNRD engine, an inference machine andthree resource files: Config.xml, which defines the mapped reader inside the process


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workflow; Context.xml, a list of control vectors depending on pre-state, tag id, andtimestamp (optional); and Knowledge.pl, a knowledge database based on Prolog, todistinguish the PNRD engine from conflict resolution algorithms.

DEMIS was implemented in Java and integrated with one M-4 ThinkMagic readerwith four antennas and one M-5 ThinkMagic reader with two antennas.

A portal, associated with one reader M-4 and four antennas, identifies three differentuser states, which means outside user, inside user and cancelled one. Outside users are ableto access a specific location and they must leave this place until the end of a period.Cancelled users are not allowed to access this place. According to the process rules, outsideusers are not cancelled if he or she is identified during user entry by Portal 1 regularly(once a period). If he or she is identified during user entry more than three times in aperiod, he or she becomes a super user. User has absence bonus depending on his or herfrequency. This means that if a user has ‘n’ periods of constant frequency, he or she is ableto be absent during the same period, i.e., up to ‘n’ periods. If an outside user has no

Figure 12. DEMIS architecture.


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absence bonus and he or she is not identified by Portal 1 during the next period, he or shebecomes cancelled permanently. Figure 13 presents the Portal 1 Petri net and its incidentmatrix A. This figure does not present a transition for tag initial marking.

All M4 antennas are associated with Portal 1, which are mapped in T1, T2 and T3transactions. It is possible to note that the outside user state is in conflict regarding theentry and removing transitions.

Figure 14 presents the corresponding Context.xml and Config.xml files. Followingthese files DEMIS was configured to identify M4 reader antennas as portal ‘P’ (theseantennas are triggered as ‘true’), and M5 reader antennas are not part of the DEMISconfiguration (they are triggered as ‘false’). Portal ‘P’ has different transitions dependingon tag state. State ‘100’ means outside user state, and it has transition ‘C’, pointing outstate conflict, associated to any tag id (0x). Similarly, state ‘010’ (cancelled user) is

Figure 14. Config.xml and context.xml files.

Figure 13. Portal 1 Petri net and incident matrix example.


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associated with transition ‘1’ (removing), that generates a non unitary next state during the

PNRD next state calculus; and state ‘001’ (inside user), with transition ‘3’ (departure).DEMIS has an inference machine based on Prolog, called tuProlog (Denti et al. 2005),

imbedded. The Knowledge.pl file stores a business rule model without time restriction,

as described in Figure 15.Every time a tag with state ‘100’ is identified by any antenna from reader M4,

Context.xml associates ‘C’ as transaction for the PNRD engine. PNRD call ‘access(ID)’

from Knowledge.pl. ‘access(ID)’ answer can be ‘true’ or ‘false’. If ‘true’, the PNRD engine

attributes entry (T2) as transition to next state calculus. Otherwise, it attributes cancelled

(T1). Context is able to distinguish an object state, so departure (T3) does not need to be

identified as a conflict; although there is only one set of antennas as a portal.The first call of ‘access(ID)’ generates one prolog object only if it is a non

‘removedId(ID)’. This prolog object is ‘validateId(ID,1,0)’. During other ‘access(ID)’

calls, it verifies whether it is a ‘removedId(ID)’ or not and adds one in the second field of

‘validateId(ID,1,0)’, an access counter.After one period, DEMIS runs ‘runCheck’, which adds or removes user bonus and

removes users without bonus, calling ‘check(ID,X,Y)’ routine for a true

‘validateId(ID,X,Y)’.If somebody leaves without a portal perception caused by a tag bad positioning, the tag

status still remains in inside user state until the next perception. This could be solved by

introducing another routine inside Knowledge.pl which evaluates anyone inside during

the ‘runCheck’ routine.

5. Discussion and conclusion

Despite that PN is usually applied as RFID system modelling this paper presented how to

integrate an operationally elementary PN in RFID systems. This approach was called

elementary Petri net inside an RFID distributed database, or PNRD, and defines what,

how and how much information is needed to be stored in an RFID system to reach RFID

and control system integration baselines.

Figure 15. DEMIS knowledge.pl file based on tuProlog.


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As control system methodologies rely on, for example, ladder diagrams which guidecontrol engineering during control system deployment and implementation, PNRD is thefirst step to fit tag data structure and RFID reader perception points in order to improveRFID and control systems integration. In this direction, this paper follows an informedsystem model and a split control system in perception and decision parts. RFID is relatedto perception and PN is a database formal structure to store process workflow, object state(distributed inside each tag) and control vector list (inside reader memory).

There are a lot of issues to be reached. The informed system must be refined andformally presented, including decision and reaction interfaces:

. An advanced process plan is mandatory and PNRD must be integrated withprocess planning tools and methodologies. A PN coverability tree should be usedto assist process rescheduling and replanning. An appropriated decision resolu-tion engine must be selected to reach rescheduling and replanning requirements.

. If the tag actual state is not updated caused by an RFID system fail, the PNRDapproach must have a contingency and inform all RFID readers inside the tag idprocess to include this missing control vector in the next tag id state calculus.This contingency must be structured appropriately.

. PNRD should have a graphical editor to supervise object tags and exceptionstates during process workflow.

. Conformance checking metrics must be integrated in PNRD approach to reachworkflow management requirements.

. Exception state treatment is an open issue, too.

. DEMIS – Distributed Environment in Manufacturing Information Systems, waspresented with the PNRD engine inside and proved, conceptually, the PNRDapproach. Knowledge base engine must be improved and prolog must be replaced.

Nevertheless, PNRD must be put in practice to be validated, and all issues above mustbe implemented in new versions of DEMIS.

6. Further work

For further work, PNRD is going to be tested in practice in order to refine the PNRDstructure and to integrate with a high level PN or even CPN (Coloured Petri Net) tools.PNRD and a decision and reaction system interface must be developed.

DEMIS is going to be launched. Additional features are going to be implemented inDEMIS such as TEPN graphical interface, automatic initial tag writing, exception statetreatment interface, and a new decision resolution algorithm.

Acknowledgements

Authors specially thank FAPESP, FAPEMIG, UFU, EPUSP and USP.

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Elementary Petri net inside RFID distributed database (PNRD)

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