A Mult-Agent-based RFID Framework for Smart-object Applcatons Ch-Kong Chan, Harry K. H. Chow, Wnson S. H. Su, Hung Lam Ng, Terry H. S. Chu, and Henry C. B. Chan Abstract Coupled wth software agent technology, RFID can transform everyday objects nto smart objects. Currently, n most applcatons, agent defntons are not encoded drectly on the tags due to tag memory lmtatons, and RFID technology s used purely for dentfcaton. Such approaches cannot provde the benefts of flexblty and modularzaton suppled by smart object systems. In ths paper, we present a system called A-FRED for effectvely encodng smart object data and agent defntons onto RFID tags. An XML-based memory-effcent encodng method s mplemented and a behavor-based encodng scheme s adopted. The system provdes an alternatve framework for representng smart objects on passve RFID tags n general, and on memory lmted low-cost UHF tags n partcular. Index Terms RFID, Software Agents, Smart Objects I I. INTRODUCTION t s envsoned that n the future everyday objects rangng from consumer electronc products to company assets wll be tracked and processed automatcally usng RFID tags. Under notons such as the Internet of Thngs (IoT), tagged objects can be automatcally represented, tracked, and quered over a network, partcularly snce the proposal of the EPCglobal archtecture standard [1]. Snce then, numerous RFID-based applcatons have appeared, many of whch were made possble by the wdespread avalablty of low-cost UHF tags, 1 despte ther lmted on-tag memory. A natural approach to dealng wth the large quantty of RFID data s to combne RFID wth software agent technology [2]. Bascally, software agent systems are used to montor tag-readng events generated from RFID readers, and approprate actons are taken accordngly [3-5]. For nstance, Part of ths work s related to the project Enhancng the Compettveness of the Hong Kong Ar Freght Forwardng Industry Usng RFID and Software Agent Technologes, whch s funded by the Innovaton and Technology Fund va the Hong Kong R&D Centre for Logstcs and Supply Chan Management Enablng Technologes. Any opnons, fndngs, conclusons, or recommendatons expressed n ths materal/event (or by members of the project team) do not reflect the vews of the Government of the Hong Kong Specal Admnstratve Regon, the Innovaton and Technology Commsson, or the Panel of Assessors for the Innovaton and Technology Support Programme of the Innovaton and Technology Fund. Ch-Kong Chan, Wnson S. H. Su, Terry H. S. Chu, and Henry C. B. Chan are wth the Department of Computng, The Hong Kong Polytechnc Unversty, Hong Kong (e-mal: {csckchan, csshsu, cshschu, cshchan}@comp.polyu.edu.hk). Harry K. H. Chow was wth the Department of Computng, The Hong Kong Polytechnc Unversty, Hong Kong. He s currently wth the Faculty of Management and Admnstraton, Macau Unversty of Scence and Technology, Macao (e-mal: khchow@must.edu.mo). Hung Lam Ng s wth the Department of Computng, The Hong Kong Polytechnc Unversty, Hong Kong (e-mal: 09658465g@connect. polyu.hk). 1 In ths paper, the term low-cost UHF tags refers to EPCglobal Class 1 Generaton 2 tags wth no more than 120 bts of avalable memory space. an agent may trgger some actons n the local envronment, establsh a connecton wth a remote server [6], or communcate wth other agents n the network to accomplsh a task [7]. Many nnovatve applcatons have been developed. For example, n warehouses, stock volumes can be montored automatcally and exceptonal condtons such as expred products or low stock volumes can be handled wthout delay [8]. In a lbrary, books can be checked out automatcally [9]. So far, many of these agent-based RFID systems utlze a statc (non-moble) system agent approach. That s, the processng agents and RFID modules of these systems are loosely coupled. The agents are not represented n the RFID tags and they functon as an autonomous set of programs for tag processng, perhaps communcatng usng some standardzed software agent languages and protocols. In such systems, one can theoretcally replace the RFID components wth other dentfcaton methods (e.g., barcodes, wreless locaton estmaton, or even manual nputs), and the agent-based modules would requre only small changes n basc desgn. However, there s also a drawback to such statc agent systems. As mentoned, the RFID technology n these approaches functons more or less as automated barcodes supported by elegant agent-based frameworks for data-processng. These frameworks, although promsng n ther own rght, do not fully utlze the combned potental of RFID and software agent technology. For example, there s lttle ntellgence n the tag level, and tags of all types are processed by the same set of statc agents, makng t hard to apply the modular desgn concepts often emphaszed n agent systems. Such potental s explored by smart object applcatons [10][11]. Smart objects are the vrtual representatons of ndvdual everyday objects that are capable of sensng the envronment, makng on-ste autonomous decsons, and communcatng wth humans or other system components. A smart RFID object conssts of two components, namely, object processng logcs and object data. The former s self-explanatory. The object data contan a unque dentfer (for example, but not lmted to, an EPC code), other object-related data such as the objects current processng states, current and past sensor readngs, and other statc object data requred durng processng. An RFID-based smart object requres a substantal amount of memory space to store object logcs and data. Ths can be problematc for applcatons that depend on the more economcal low-cost UHF tags wth lmted memory (typcally 96 bts for the lower-end tags). Thus, we need some flexble and effcent ways to encode the requred agent defntons and correspondng smart object data onto RFID tags. Recently, two possble approaches were dentfed n [12]. In the frst approach, called dentfcaton-centrc RFID systems (IRS), only the dentfer of an object s stored on a tag, whereas the remanng object data and processng logcs
have to be retreved from databases over a network. Ths approach has the advantage of smplcty and compatblty wth the EPCglobal standard. However, the network-based code and data storage leads to delays and lmts ts applcaton to only those locatons wth network access. The second approach s called code-centrc RFID systems (CRS). In ths approach, source codes for defnng agent-processng logcs (.e., agent actons) are stored on the tag usng a compact codng scheme. However, as we shall see, agents encoded usng ths scheme can stll easly exceed the severe memory lmtatons of most low-cost applcatons based on UHF tags. Partally nspred by these prevous works, we propose n ths a paper a behavor-based encodng schema for smart objects on RFID tags. Instead of storng the entre (compact) code on a tag, we specfy the desred behavors that defne an agent. To ths end, we developed an Agent-enabled Flexble RFID Encodng and Decodng (A-FRED) system based on a novel RFID data compacton method for the effcent storage of both object logcs and data. Our objectve s to demonstrate a flexble and effcent approach to autonomous processng usng smart objects. The effcency of our system s demonstrated by experments. An applcaton example n logstcs and a customer-orented RFID applcaton usng low-cost UHF tags are dscussed n ths paper. The remanng sectons of ths paper are organzed as follows. Secton II revews some related works (ncludng the aforementoned code-centrc approach). Secton III descrbes the A-FRED system. Secton IV provdes the results of some experments to evaluate the effcency of our system. Secton V dscusses two demonstraton case study problems. Secton VI compares the applcablty of our system and that of some related approaches n dfferent scenaros. Secton VII gves the concluson. II. RELATED WORKS A. Statc RFID Agent Systems A number of approaches to combnng ntellgent RFID systems wth software agent technology have been proposed. Most of these applcatons employ a statc agent approach, where software agents are employed n some knd of statc envronment to react to some RFID tag readng events. For nstance, n manufacturng, an agent-based manufacturng control and coordnaton system (AMCC) for a manufacturng company n Tawan s reported n [14] and, smlarly, a manufacturng control system usng RFID technology and mult-agent technology s documented n [15] for controllng materal-handlng tasks n packng and assemblng. Examples can also be found n wreless sensor applcatons, for nstance, for montorng pershable tems durng transportaton [16]. For cargo trackng n warehouses, an applcaton example s reported n [17]. Smlar applcatons based on statc agents are also reported n the areas of healthcare [7], supply chan management [8], and lbrary management [9], to name a few. B. Smart Object Agent Systems There have been several recent approaches nvestgatng RFID-based smart objects. Unlke the statc RFID agent approaches mentoned above, each RFID tagged tem n a smart object based system s represented by ts own moble agent. Two major approaches can be dentfed, whch Chen et al. called dentfcaton-centrc RFID Systems (IRS) and code-centrc RFID systems (CRS), respectvely [12]. In the dentfcaton-centrc approach, smart object logcs or defntons are not stored on the RFID tags. Instead, each tme a tag s dentfed by the RFID system, the relevant agent defnton s retreved from a database over the network and the correspondng agent s then created accordngly. Examples of ths approach nclude the work of [11], who studed the use of smart objects for supply chan management, and the smulated IRS system mplemented n [12]. However, the lack of local on-tag storage for smart object logcs and data n ths approach makes t nconsstent wth smart object deology, whch emphaszes dstrbuted ntellgence and moblty, and modularty [13]. To ths end, a code-centrc approach was proposed and studed n [12]. In ths work, compact acton codes were used to represent smart object processng logcs on RFID tags. To make ther code as compact as possble, dentfers, varables, and operators of one to three characters n length were used throughout the language for specfyng agent actons. For nstance, n ther mplemented example, the command l$n nstructs an agent to swtch on an LED lght, whereas the command #3 nstructs the agent to mgrate to another processng node. It was demonstrated n [12] by smulaton experments that the CRS approach s more effcent than the IRS approach n terms of system processng tme. However, there s a drawback to the code-centrc approach. The ssue here s that even wth ts compact codng scheme, the memory lmt on many low-cost UHF tags could stll easly be exceeded. For example, the 31 character-long acton code used n the abovementoned experment would requre over 150 bts to store, exceedng the capacty of many low-end UHF tags. Moreover, the expressve power of ths relatvely smple language also lmts ts applcablty n some applcatons. In the next few sectons, we shall present an alternatve behavor-based encodng approach called A-FRED. Partally nspred by [12], A-FRED utlzes a compact encodng scheme for storng smart object data and the requred behavor on an RFID tag, but wthout the necessty of storng the entre processng code. A. System Overvew III. SYSTEM ARCHITECTURE We developed a system called the Agent-based Flexble RFID Encoder and Decoder (A-FRED), whch we frst descrbe n the followng way (Fg. 1). The RFID-related functonalty of the system s provded by an agent called the FRED-Agent, whch accesses the servces of an Encoder and Decoder module. Ths module provdes generc RFID tag readng and wrtng functons accordng to user-specfed tag schemas, as well as data compacton functonalty for savng tag memory space. The FRED-Agent passes the decoded tag data to a System Agent, whch extracts the smart object data and behavor codes, and uses ths nformaton to create worker agents n the desgnated platforms. The worker agents are the agents that execute the actons specfed by the behavor code. For nstance, a worker agent may access some backend
Fg. 1. A-FRED System Archtecture Defnton (ODD). Note that the schema fle s not stored on the tags. Instead, t s located n an A-FRED Agent Platform where the Encoder and Decoder and the FRED-Agent are also located. The ARL part of the tag-schema fle contans nformaton that deals wth the defnton of the smart object s software agents. Ths ncludes the host platform, where the worker agents are to be created, and a set of Agent Rules. Note that, unlke other felds defned n the tag-schema, the name of the host platform s drectly specfed n the tag-schema nstead of beng stored on the tags. The Agent Rules part of the ARL specfes the actual behavors of the agent to be created. In order to acheve maxmal savngs of memory, each RFID tag only stores a btmap to the set of all possble agent behavors. Each agent behavor conssts of one or more agent actons and s defned n a separate database that resdes on the System Agent platform. The ODD part of the schema defnes the varous felds of the object data part of the tag data. Each feld defnton contans the data type and the length of a feld and, optonally, a vald value range. By requrng the user to specfy the length and/or value range, the system s able to optmze on memory space by usng just the requred number of bts for storng each feld accordng to the provded specfcaton. Fve types of data are currently supported: alphabetc, alphanumerc, nteger, date, and choces. The frst four are self-explanatory. The last type allows the user to specfy an enumerated lst of commonly used values that are defned ether explctly on the tag-schema fle, or at an off-fle locaton. The savng of tag memory space s acheved by storng an ndex to an element on the enumerated lst. Smlarly, data of the other four types are also stored as enumerated felds of all possble values of data n the vald data range. C. Data Tag Compacton Durng the readng and wrtng of tags, correspondng tag-schemas are used to encode the data nto a compacted enumerated format so that the memory space usage of each feld s mnmzed accordng to ts declared type and data range. Addtonal compacton s then acheved by fllng up unused value slots by further combnng all encoded felds nto one enumerated feld. Fg. 2. Example of the A-FRED schema applcatons or databases, or coordnate wth other worker agents to accomplsh certan tasks. B. Tag Encodng and FRED-Agent In order to effectvely utlze the lmted memory on RFID tags (and on low-cost UHF tags n partcular), all tag data n our system are encoded, decoded, and compacted usng a tag encodng and compacton scheme, as dscussed below. The content of each type of tag processed by our system s defned by a correspondng XML-based tag schema fle that s accessble to the Encoder and Decoder module and the FRED-Agent. An example of a tag-schema fle s gven n Fg. 2, whch llustrates the encodng schema of RFID tags n an ar-cargo trackng applcaton. Each schema s comprsed of two parts: the Agent Rule Lst (ARL) and the Object Data D. System Agent The decoded agent rules and object data are sent to a System Agent va an nform-tag-read message. The job of the System Agent s to create the Worker Agents that perform the tasks of the smart objects. In our mplementaton, agent actons are grouped nto a number of agent behavors. When an nform-tag-read message s receved, the System Agent maps the decoded Agent Rules aganst the set of agent behavors defned n an Agent Acton Behavor Database. The requred worker agents are then created n the specfed agent platform usng the retreved agent behavor and the decoded object data. Dependng on the system requrement, the System Agent and the Agent Acton Behavor Database can resde on a dfferent platform or on the same platform as the FRED-Agent.
E. Worker Agent A Worker Agent s the agent for handlng a sngle task on behalf of a sngle smart object. The Worker Agents n A-FRED are moble agents, whch means that t s possble for them to mgrate to dfferent platforms at run tme. For example, a Worker Agent wth an Update Database Behavor can be created at a Man Agent Platform, and then mgrate to a Database Agent Platform to perform the requred updatng actons, and fnally report back to the Man Agent Platform to complete the remanng tasks. Note, however, that the Worker Agents for the same type of tags wll ntally be created on the same platform as that defned on the correspondng tag-schema fle, whch s not necessarly the System Agent Platform. All agent platforms n our system are mplemented usng the Java Agent Development Framework (JADE). All Worker Agents for a gven smart object are created smultaneously so that any coordnaton (or sub-ordnaton) amongst the Worker Agents wll have to be defned explctly n the agent behavors. IV. EXPERIMENT Before we proceed to analyze the advantages of the proposed approach, we frst evaluated the runnng tme effcency of our system n a seres of experments. In these experments, a number of EPCglobal Class 1 Gen 2 UHF RFID tags wth 240 bts of memory were encoded and processed usng A-FRED, and the runnng tme was recorded. The tags were arranged nto batches of varous szes, and n each test case tags n the same batch were processed smultaneously by the system. There were eleven test cases, correspondng to the batch szes of 1, 5, 10, 15,, and 50 tags, respectvely. One agent behavor was defned, whch contaned a sngle acton for loggng down the current system tme when the worker agent was created. Thus, for each batch b and each tag b, we recorded the RFID tag readng tme t 1, whch was logged by the Encodng and Decodng module when a tag was frst read, and the worker agent regstraton tme, t 2, whch was recorded by the (sngle) Worker Agent created for a tag. Each test case was repeated 10 tmes, and for each test case we computed the average tag processng tme ( t t2 t1) b and average batch processng tme tb max( t2) mn( t1). All system components (the tag schema, all agents, and all databases) resded n a hgh-performance computer wth 6Gs b b b Fg. 3. Average processng tme (per tag) Fg. 4. Accumulated processng tme (per batch) of memory. For the RFID equpment, a fxed UHF RFID reader and a sngle crcularly polarzed antenna were used. The tags were placed at a dstance of 1.5 meters from the antenna. Durng the tests, the total processng tme per tag and per batch was recorded. For comparson, we also mplemented a statc agent system and ts average processng tme per RFID tag was also recorded. The results are shown n Fgs. 3 and 4. It can be seen that the per tag processng tme remaned approxmately constant (at between 15 to 20 ms) for all of the batch szes that we tested. In comparson, the processng tme n the statc agent system averaged 4.16 ms, and up to 15 ms n the worst case. Thus, the A-FRED system requres a longer runnng tme than a statc system, although ths s antcpated. The overhead s manly due to the extra work nvolved n creatng the Worker Agents, a step that s not requred n statc systems. However, the results that were obtaned are stll wthn the acceptable range, as over 50 RFID tags were processed per second, a fgure that falls wthn the typcal readng rate of most UHF RFID readers n the majorty of RFID applcatons. V. EXAMPLE APPLICATIONS A-FRED can be employed to support many advanced smart object applcatons. For the purpose of llustraton and to facltate further dscusson, we mplemented two case study applcatons, descrbed as follows. A. A-FRED-Logstcs Fg. 5. RFID-enabled HAWB labels The frst demonstraton applcaton that we mplemented s a busness-orented applcaton called A-FRED-Logstcs. Currently, many consumer products are transported as ar freght to ensure tmely delvery. A-FRED-Logstcs s a system for supportng automated cargo trackng and processng at varous stages of the cargo supply chan. Durng the process, the cargo tems are transported n cartons arranged nto pallets. Each carton s dentfed by an RFID
Fg. 6. Tag schema for A-FRED-Logstcs enabled waybll label (Fg. 5), whch contans an dentty number for the carton (called the HAWB number), as well as other nformaton requred durng the transportaton process, such as the tem s destnaton, place of orgn, and shpment date. Two types of tasks are performed at each checkpont: ) cargo verfcaton, whch means verfcaton that the cartons are beng processed at the correct locaton, and ) status updates, whch refers to updates of the carton s status on system servers. However, some types of cargo tems requre specfc status update strateges. For example, some types of cartons requre the system to connect to the backend system of both the logstcs company and the shppng company for status updates, whle ths step of updatng can be skpped altogether for some other types of cargo. Our mplementaton of A-FRED-Logstcs follows the archtecture as specfed n Fg. 1, and the tag schema s shown n Fg. 6. Three acton behavors are defned for the actons nvolved n verfyng the locaton of a carton, and for the two cargo status updatng strateges, respectvely. For each behavor specfed on the tag, a correspondng worker s created on the System Agent Platform after the tag s read. Overall, the outcome of the system was satsfactory. The object data and agent behavor requred only 61 bts to encode, whch ft well nto the low-cost UHF RFID tags and larger tags alke. Fg. 8. Tag schema for SIAS B. An RFID-Moble-Phone Applcaton for shop customers Another example of an applcaton that we mplemented s called the Shopper Informaton Agent System (SIAS). Ths system s a customer-orented applcaton that allows the user to check n at varous checkponts located n retal stores. Low-cost UHF RFID tags are ssued to the customers, wth each tag storng the followng nformaton: a customer s ID, gender, the customer s surname, and an expry date. Two behavors are defned, namely a check-n behavor, whch records the user s presence at a checkpont, and a moble-message behavor, whch retreves and sends a user-specfc welcome message to a regstered smart phone applcaton to facltate the user s shoppng experence. The archtecture and tag-schema fle of the system are llustrated n Fgs. 7 and 8, respectvely. Note that n ths case, unlke n the prevous example, more than one FRED-Agent (representng multple store locatons) can be connected to an external System Agent. Two types of Worker Agents wll be generated: the Check-n-Worker-Agents and the Moble-Message-Worker-Agents. The Moble-Message- Worker-Agents n turn communcate wth user-agents located on the users moble phone platform, and user-specfc messages are delvered accordngly. The moble phone based user agents are mplemented usng JADE-LEAP. The overall performance of SIAS was also satsfactory. The smart objects behavor and object data requred only 86 bts to encode, whch agan ft well nto low-cost UHF RFID tags. VI. DISCUSSION Fg. 7. System Overvew of the Shopper Informaton Agent System Earler n ths paper, we dscussed three types of RFID-based software agent systems: 1) statc RFID agent systems, whch do not support smart objects, 2) dentfcaton-centrc systems, where no smart object actons or processng logcs are specfed on the tag, and 3) code-centrc systems, whch store a smart object s acton code on a tag usng a compact encodng scheme. In ths paper, we proposed a fourth approach, the behavor-based encodng scheme, whch s utlzed by our current system, A-FRED. Kvat dagrams llustratng varous propertes of the four approaches are presented n Fg. 9. It should be noted that all four approaches have ther respectve advantages and
Fg. 9. Kvat dagrams for the four types of RFID-based software agent systems dsadvantages. For nstance, for supportng system modularty, whch s an mportant concept n software agent desgn, the code-centrc approach and A-FRED are superor due to ther ablty to specfy agent behavors on ndvdual RFID tag levels. Wth regard to runnng tme effcency, the code-centrc approach and the statc agents outperform the dentfcaton-centrc approach and A-FRED, because the latter two need to retreve agent code defntons from the server durng the creaton of agents. By contrast, the relatvely smple acton encodng language of the code-centrc approach has lmted ts applcablty to applcatons wth relatvely smple tasks, and may not be easly appled n low-cost UHF tag based applcatons due to the larger memory sze that t requres. A-FRED, on the other hand, does not have these lmtatons, whch makes t a good canddate for mplementng smart objects on low-cost UHF tags n general. VII. CONCLUSION The concept of smart objects s leadng to many nnovatve applcatons, wth RFID and software agent technologes beng two of the man components. In ths paper, we presented a flexble RFID data encodng and decodng mechansm called A-FRED. By usng an effcent data representaton scheme and a behavor-based encodng scheme, our system can be used to encode not only object nformaton but also the requred agent actons onto RFID tags. The approach that we have presented can complement exstng approaches to RFID-based smart object systems, partcularly for applcatons that are dependent on low-cost UHF tags. REFERENCES [1] EPCglobal Inc., EPC rado frequency dentty protocols Class-1 Generaton-2 UHF RFID protocol for communcatons at 860 MHz 960 MHz Verson 1.2.0, 2007. [2] The Foundaton for Intellgent Physcal Agents( FIPA). http://www.fpa.org [3] H. K. H. Chow, K. L. Choy, and W. B. Lee, A dynamc logstcs process knowledge-based system - An RFID mult-agent approach, Knowledge-Based Systems, vol. 20, pp. 357-372, 2007. [4] A. J. C. Trappey, T. Lu, and L. Fu, Development of an ntellgent agent system for collaboratve mold producton wth RFID technology, Robotcs and Computer-Integrated Manufacturng, vol. 25, pp. 42-56, 2009. [5] M. Tu, Ja-Hong Ln, Ruey-Shun Chen, Ka-Yng Chen, and Jung-Sng Jwo, Agent-Based control framework for mass Customzaton Manufacturng Wth UHF RFID Technology, IEEE Systems Journal, vol. 3, no. 3, pp. 343-359, Sept 2009. [6] D. G. Yun, J. M. Lee, M. J. Yu, S. G. Cho, and C. H. Seo, Agent-based user moblty support mechansm n RFID networkng envronment, IEEE Transactons on Consumer Electroncs, pp. 800-804, 2009. [7] F. Gîză, C. Turcu and C. Turcu, RFID technology and mult-agent approaches n healthcare, Deployng RFID - Challenges, Solutons, and Open Issues, Crstna Turcu (Ed.), ISBN: 978-953-307-380-4, InTech, pp. 127-140, 2011. [8] S. Wang, S. Lu and W. Wang, The smulated mpact of RFID-enabled supply chan on pull-based nventory replenshment n TFT-LCD ndustry, Internatonal Journal of Producton Economcs, vol. 112, pp. 570 586, 2007. [9] T. Mnam, Lbrary servces as mult agent system, Agent and Mult-Agent Systems: Technologes and Applcatons, Lecture Notes n Computer Scence, vol. 4953, pp. 222-231, 2008. [10] G. Kortuem, F. Kawsar, V. Sundramoorthy, and D. Ftton, Smart objects as buldng blocks for the nternet of thngs, IEEE Internet Computng, pp. 44-51, 2010. [11] E. Bajc, A servce-based methodology for RFID-smart object nteractons n supply chan, Internatonal Journal of Multmeda and Ubqutous Engneerng, vol. 4, no. 3, 2009. [12] M. Chen, S. Gonzalez-Valenzuela, Q. Zhang, and V. Leung, Software agent-based ntellgence for code-centrc RFID Systems, IEEE Intellgent Systems, vol. 99, 2010. [13] The European technology platform on smart systems Integraton (EPoSS), Internet of Thngs n 2020: ROADMAP FOR THE FUTURE (2008), Verson 1.1, 2008. http://ec.europa.eu/nformaton_socety/polcy/rfd/documents/otpra gue2009.pdf [14] R. S. Chen and M. Tu, Development of an agent-based system for manufacturng control and coordnaton wth ontology and RFID Technology, Expert Systems wth Applcatons, vol. 36, pp. 7581-7593, 2009. [15] P. Vrba, F. Macurek and V. Mark, Usng Rado-frequency dentfcaton n agent-based control systems for ndustral applcatons, Engneerng Applcatons of Artfcal Intellgence, vol. 21, pp. 331-342, 2008. [16] R. Jedermann, C. Behrens, D. Westphal, and W. Lang, Applyng autonomous sensor systems n logstcs combnng sensor networks, RFIDs and Software Agents, Sensors and Actuators A, vol. 132, pp. 370-375, 2006. [17] H. K. H. Chow, K. L. Choy, and W. B. Lee, A dynamc logstcs process knowledge-based system An RFID Mult-Agent Approach, Knowledge-Based Systems, vol. 20, no. 4, pp. 357-372, 2007.