Deploying RFID Challenges Solutions and Open Issues Part 2 pdf

The Challenges and Issues Facing the Deployment of RFID Technology 17The P2P Collaboration method, proposed by Peng, Ji, Luo, Wong and Tan Peng et al., 2008, is an approach utilising Pee

The Challenges and Issues Facing the Deployment of RFID Technology 17

The P2P Collaboration method, proposed by Peng, Ji, Luo, Wong and Tan (Peng et al., 2008),

is an approach utilising Peer-to-Peer (P2P) networks within the RFID data set to detect andremove inaccurate readings The system works by breaking the readings into detection nodes,which are constantly sending and receiving messages From these transmitted messages, falsenegatives and false positives are able to be detected and corrected resulting in a cleaner dataset

Ziekow and Ivantysynova have presented a method designed to correct RFID anomaliesprobabilistically by employing maximum likelihood operations (Ziekow & Ivantysynova,2008) Their method utilises the position of a tag which may be determined by measuringproperties associated with the Radio Frequency signal

The Cost-Conscious cleaning method is a cleaning algorithm which utilises a BayesianNetwork to judge the likelihood that read tags correctly depict reality when based upon thepreviously read tags (Gonzalez et al., 2007) The Cost-Conscious cleaning approach housesseveral different cleaning algorithms and chooses the least costly algorithm which would offerthe highest precision in correcting the raw data A similar approach has also been proposedthat utilises a Bayesian Network to judge the existence of tags scanned (Floerkemeier, 2004)

It lacks, however, the cost-saving analysis that would increase the speed of the clean

Data Mining Techniques refer to the use of mining past data to detect inaccuracies and possiblesolutions to raw RFID readings A study which has used data mining techniques extensively

to correct the entire data set table is the Deferred Rule Based Approach proposed in (Rao et al.,2006) The architecture of the system is reliant on the user defining rules which are utilised todetermine anomalies in the data set and, possibly, to correct them

Probabilistic Inference refers to a process by which the in-coming data node will be evaluated.This is primarily based upon the weight of its likelihood and the weight of the remainder ofthe readings (Cocci et al., 2007; 2008) The cleaning algorithm utilises several techniques tocorrect that data such as Deduplication, Time conversion, Temporal Smoothing and AnomalyFiltering, and, additionally, uses a graph with probabilistic weights to produce furtherinferences on the data

Probabilistic High Level Event Transformations refers to the process of observing the rawpartial events of RFID data and transforming these into high level probable events It hasbeen primarily used in a program entitled Probabilistic Event EXtractor (PEEX) which hasevolved from several publications In its embryonic phase, Khoussainova, Balazinska andSuciu published a paper detailing the use of an algorithm called StreamClean which employprobabilistic inference to correct incoming data (Khoussainova et al., 2006)

A year after this article, the first papers for PEEX were published This described themethod which enabled high level event extraction based upon probabilistic observations(Khoussainova et al., 2007; Khoussainova, Balazinska & Suciu, 2008) The system architecturedeciphers the raw RFID information searching for evidence which a high level eventtranspired The system uses a Confidence Learner, History Lookup and Event Detector toenhance the reliability of the returned events By transferring these low level readings intohigh level events, PEEX engages in cleaning as the process of probabilistically by categorisingthe results of these events, and in the process, caters for missed and inaccurate readings.Currently, PEEX is being incorporated into a new a system named Cascadia where it will beutilised to help perform high level management of RFID tracking in a building environment(Khoussainova, Welbourne, Balazinska, Borriello, Cole, Letchner, Li, Ré, Suciu & Walke,2008; Welbourne et al., 2008) Bayesian Networks have also been implemented in severalstudies to infer high level behaviour from the raw readings The specific application was first

17The Challenges and Issues Facing the Deployment of RFID Technology

et al., 2007) We then proposed the utilisation of highly intelligent analytical processes coupledwith a Bayesian Network (Darcy et al., 2009b;c), Neural Network (Darcy, Stantic & Sattar,2010a) and Non-Monotonic Reasoning (Darcy et al., 2009a; Darcy, Stantic & Sattar, 2010b)

to correct missing RFID Data Following this, we applied our Non-Monotonic Reasoningapproach to both false-negative and false-positive data anomalies (Darcy, Stantic & Sattar,2010d) We then also introduced a concept to extract high level events from low level readingsusing Non-Monotonic Reasoning (Darcy, Stantic & Sattar, 2010c) Finally, we proposed amethodology that considers and differentiates between a false-positive anomaly and breach

in security using Non-Monotonic Reasoning (Darcy, Stantic, Mitrokotsa & Sattar, 2010)

6 Drawbacks and proposed solutions for current approaches

In this section, we highlight several drawbacks we have found associated with the variousmethodologies currently employed to correct RFID captured data We also supply oursuggested solutions to these problems where possible in an effort to encourage further interest

in this field of research Finally, we conclude with an overall analysis of these methodologiesand their respective drawbacks

6.1 Physical drawbacks and solutions

With regard to Physical Approaches, we have highlighted three main drawbacks and oursuggested solutions to correct these issues where possible:

• Problem: The main problem that we foresee with the utilisation of Physical Approaches is

that it usually only increases the likelihood that the missed objects will be found

Solution: We do not have a solution to the problem of physically correcting wrong

or duplicate anomalies other than suggesting to utilise Middleware and/or Deferredsolutions

• Problem: Physical Approaches generates artificial duplicate anomalies in the event that all

the tags attached are read

Solution: Specific software tailored to the application to automatically account for the

artificially generated duplicate anomalies could be used for correction filtering at the edge

• Problem: Physical Approaches suffer from additional cost to the user or more labour to

purchase extra tags, equipment or time to move the objects

Solution: We do not believe there is a solution to this as Physical Approaches demand

additional labour for the user to correct the mistakes as opposed to Middleware orDeferred Approaches

6.2 Middleware drawbacks and solutions

We found three major drawbacks to the Middleware Approaches that prevent these fromacquiring their maximum integrity These issues include:

• Problem: Correcting incoming data at the edge of the RFID capture process will not

provide the cleaning algorithm with adequate information needed to deal with highly

The Challenges and Issues Facing the Deployment of RFID Technology 19ambiguous and complex anomalies.

Solution: We believe that to correct this drawback, the user must employ a Deferred

methodology in addition to the Middleware Approach to utilise all stored readings Thiswould result in more observational data eliminating highly ambiguous anomalies

• Problem: When utilising probabilistic algorithms such as Bayesian Networks to correct

anomalies, there is a risk of the methodology introducing artificially generated anomalies.This may occur in cases such as the training set not reflecting the reality of the scenarios orthe system probabilistically choosing the incorrect action to take in a situation

Solution: To correct this issue, the user may be able combine various probabilistic

techniques together or to employ a deterministic approach in order to enhance the method

of cleaning the database

• Problem: RFID data streams that are captured by readers can be accumulated quickly

resulting in data collisions Simultaneous transmissions in RFID systems will also lead

to collisions as the readers and tags typically operate on the same channel There arethree types of collisions possible to occur: Reader-Tag collision, Tag-Tag collision, andReader-Reader collision

Solution: It is crucial that the RFID system must employ anti-collision protocols in readers

in order to enhance the integrity of the captured data However, the step of choosing

the right anti-collision protocol is also very important, since we cannot depend solely on

the capability of anti-collision protocol itself, but also on the suitability of each selectedtechnique for the specific scenario The user may employ decision making techniques such

as both the Novel Decision Tree and the Six Thinking Hats strategy for complex selectivetechnique management to determine the optimal anti-collision protocol The novelty ofusing complex selective technique management is that we will get the optimal outcome

of anti-collision method for the specific scenario This will, in turn, improve the quality of

the data collection It will also help over long period of use when these captured data areneeded for transformation, aggregation, and event processing

6.3 Deferred drawbacks and solutions

While reviewing the Deferred Approaches to correct RFID anomalies, we have discoveredthat there are certain shortcomings when attempting to clean captured observational data

• Problem: Similar to the Middleware Approaches which utilise probabilistic calculations,

a major problem in the Deferred Approaches is that due to the nature of probability, falsepositive and negatives may be unintentionally introduced during cleaning

Solution: As stated previously, the inclusion of multiple probabilistic techniques or even

deterministic approaches should increase the intelligence of the methodology to blockartificial anomalies from being generated

• Problem: Specifically with regard to the Data Mining technique, it relies on the order the

rules appear as opposed to using any intelligence to decipher the correct course of action

Solution: It is necessary to increase the intelligence of the order of the rule order by

integrating high level probabilistic or deterministic priority systems

• Problem: With regard to the Cost-Conscious Cleaning method, due to the fact that the

method only utilises immediate previous readings and focuses on finding the least costlyalgorithm, accuracy may be lowered to ensure the most cost-effective action

Solution: In the event that this algorithm is applied at a Deferred stage, it will not require

19The Challenges and Issues Facing the Deployment of RFID Technology

20 Will-be-set-by-IN-TECH

the data to be corrected as fast as possible Therefore in this situation, the emphasis oncost-effectiveness is not relevant as is usually the case and other actions could be examined

to derive the highest accuracy

• Problem: As a general constraint of all Deferred Approaches, it is necessary to apply the

correction algorithm at the end of the capture cycle when the data is stored in the Database.The main problem with this characteristic is that the methodologies will never be able to

be applied as the data is being captured and, therefore, cannot correct in real-time

Solution: As most of the Deferred Approaches, especially the Data Mining and Highly

Intelligent Classifier, requires certain observational data to correct anomalies, we proposethe use of a buffering system that runs as the data is being captured and takes snapshots

of the read data to correct any anomalies present Unfortunately, due to the need that themethodology is run in real-time, it may not be able to include all the complexities of thecurrent Deferred Approaches such as dynamic training of the classifiers

6.4 Drawback analysis

In this research, we evaluated the current state-of-the-art approaches designed to correct thevarious anomalies and issues associated with RFID technology From our findings, we havefound that, while Physical Approaches do increase the chances of a tag being captured, it doesgenerate duplicate anomalies and places cost in both time and labour onto the user that maynot be beneficial With regard to Middleware Approaches, we found that most anomaliesare corrected through these techniques However, due to the limited scope of informationavailable, the more complex procedures such as dealing with highly ambiguous errors ortransforming the raw observations into high-level events is not possible In contrast, DeferredApproaches have an advantage to correct highly ambiguous anomalies and transform events.Its main issue, however, is not being available to process the observational information inreal-time limiting its cleaning to a period after the records have been stored

Overall, we have found from our research that a truly robust RFID system that eliminatesall possible natural and artificial anomalies generated will require the integration of mostapproaches we have recognised For example, various real-time anomalies are best filtered atthe edge while increasingly ambiguous anomalies can only be corrected at a deferred stage ofthe capture cycle Additionally, we found that there is a need to, not only employ probabilistictechniques, but also deterministic where possible as it theoretically should reduce the artificialanomalies produced We, therefore, recommend the inclusion of all methods where possible,

at least one of the Middleware and Deferred categories, and, where applicable, the inclusion

of both deterministic and probabilistic techniques

7 Conclusion

In this study, we have examined RFID technology and its current uses in various applications

We have also examined the three various issues among the integration of the systemsincluding security, privacy and data abnormalities Furthermore, we have examined thedata abnormality issue to find that four problems exist including low-level nature, largeintakes, data anomalies and complex spatial and temporal aspects There have been variousmethodologies proposed in the past to address the various problems in the data abnormalitiescategorised into physical, middleware and deferred solutions Unfortunately, due the variousdrawbacks such as application-specified solutions, lack of analytical information or reliance

The Challenges and Issues Facing the Deployment of RFID Technology 21

on user-specified/probabilistic algorithms, current approaches do not provide the adequatesupport needed in RFID systems to be adopted in commercial sectors

Specifically, we contributed the following to the field of RFID study:

• We provided a detailed survey of RFID technology including how it was developed,its various components and the advantages of integrating its technology into businessoperations

• We highlighted the current usages of RFID categorising it into either “Integrated RFIDApplications” and “Specific RFID Applications”

• We examined the various issues preventing the adoption of RFID technology including theconcerns of security, privacy and characteristics We also focused on the specific Anomaliesgenerated by the capturing hardware including wrong, duplicate and missing errors

• After examining the issues surrounding RFID, we investigated the state-of-the-artapproaches currently employed for correction We categorised these methodologies intoPhysical, Middleware or Deferred Approaches

• Finally, we explored the drawbacks found in currently employed Approaches andsuggested several solutions in the hope of generating interest in this field of study.With regard to future work, we specifically would like to extend our previous studiesdiscussed in Section 5.3 by allowing it to function in real-time We would do this throughthe creation of a buffer system discussed in Section 6.3 by taking snapshots of incoming dataand correcting anomalies where found We also firmly believe that this sincerely is the nextstep of evolution of our approach to allow it to be employed as the observational records areread into the Middleware

8 References

Bai, Y., Wang, F & Liu, P (2006) Efficiently Filtering RFID Data Streams, CleanDB, pp 50–57.

Brusey, J., Floerkemeier, C., Harrison, M & Fletcher, M (2003) Reasoning About Uncertainty

in Location Identification with RFID, Workshop on Reasoning with Uncertainty inRobotics at IJCAI

Carbunar, B., Ramanathan, M K., Koyuturk, M., Hoffmann, C & Grama, A (2005)

Redundant Reader Elimination in RFID Systems, SECON.

Chawathe, S S., Krishnamurthy, V., Ramachandran, S & Sarma, S E (2004) Managing RFID

Data, VLDB, pp 1189–1195.

Chen, W T (2006) An Efficient Anti-Collision Method for Tag Identification in a RFID System,

IEICE Transactions 89-B(12): 3386–3392.

Cocci, R., Diao, Y & Shenoy, P (2007) SPIRE: Scalable Processing of RFID Event Streams, 5th

RFID Academic Convocation.

Cocci, R., Tran, T., Diao, Y & Shenoy, P J (2008) Efficient Data Interpretation and

Compression over RFID Streams, ICDE, IEEE, pp 1445–1447.

Collins, J (2004) Boeing Outlines Tagging Timetable, RFID Journal Available from:<http:

//www.rfidjournal.com/article/view/985/1/1>

Collins, J (2005) DOD Tries Tags That Phone Home, RFID Journal Available from:<http:

//www.rfidjournal.com/article/articleview/1458/1/1/>

Darcy, P., Stantic, B & Derakhshan, R (2007) Correcting Stored RFID Data with

Non-Monotonic Reasoning, Principles and Applications in Information Systems and Technology (PAIST) 1(1): 65–77.

21The Challenges and Issues Facing the Deployment of RFID Technology

22 Will-be-set-by-IN-TECH

Darcy, P., Stantic, B., Mitrokotsa, A & Sattar, A (2010) Detecting Intrusions within RFID

Systems through Non-Monotonic Reasoning Cleaning, Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2010), pp 257–262.

Darcy, P., Stantic, B & Sattar, A (2009a) A Fusion of Data Analysis and Non-Monotonic

Reasoning to Restore Missed RFID Readings, Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2009), pp 313–318.

Darcy, P., Stantic, B & Sattar, A (2009b) Augmenting a Deferred Bayesian Network with a

Genetic Algorithm to Correct Missed RFID Readings, Malaysian Joint Conference on Artificial Intelligence (MJCAI 2009), pp 106–115.

Darcy, P., Stantic, B & Sattar, A (2009c) Improving the Quality of RFID Data by Utilising

a Bayesian Network Cleaning Method, Proceedings of the IASTED International Conference Artificial Intelligence and Applications (AIA 2009), pp 94–99.

Darcy, P., Stantic, B & Sattar, A (2010a) Applying a Neural Network to Recover Missed RFID

Readings, Australasian Computer Science Conference (ACSC 2010), pp 133–142.

Darcy, P., Stantic, B & Sattar, A (2010b) Correcting Missing Data Anomalies with

Clausal Defeasible Logic, Advances in Databases and Information Systems (ADBIS 2010),

pp 149–163

Darcy, P., Stantic, B & Sattar, A (2010c) Intelligent High-Level RFID Event Transformation

Utilising Non-Monotonic Reasoning, Wireless Communications Networking and Mobile Computing (WiCOM), 2010 6th International Conference on, pp 1–4.

Darcy, P., Stantic, B & Sattar, A (2010d) X-CleLo: Intelligent Deterministic RFID Data

Transformer, International Workshop on RFID Technology, pp 59–68.

Decker, C., Kubach, U & Beigl, M (2003) Revealing the Retail Black Box by Interaction

Sensing, ICDCS Workshops, pp 328–333.

Derakhshan, R., Orlowska, M E & Li, X (2007) RFID Data Management: Challenges and

Opportunities, RFID 2007, pp 175 – 182.

Deshpande, A., Guestrin, C., Madden, S., Hellerstein, J M & Hong, W (2004) Model-Driven

Data Acquisition in Sensor Networks, VLDB, pp 588–599.

Deshplande, A., Guestrin, C., Madden, S., Hellerstein, J & Hong, W (2005) Model-based

Approximate Querying in Sensor Networks, VLDB, pp 417–443.

Ding, J & Liu, F (2009) Novel Tag Anti-Collision Algorithm with Adaptive Grouping,

Wireless Sensor Network (WSN) 1(5): 475–481.

Embry, W (2005) Are you ready for RFID? The promise and challenges of implementing

Radio Frequency Identification (RFID) systems across the extended supply chain,

Technical report, SAS.

Emerson & Cuming Microwave Products (2008) Emerson & Cuming Microwave Products

– ECCOPAD [online], Emerson & Cuming Microwave Products Available from:

<http://www.eccosorb.com/europe/english/page/66/eccopad>.Engels, D W (2005) On Ghost Reads in RFID Systems, Technical Report

AUTOIDLABS-WP-SWNET-010, Auto-ID Labs.

EPCGlobal (2005) EPC Radio-Frequency Identity Protocols Class-1 Generation-2 UHF RFID

Protocol for Communications at 860MHz-960MHz Version 1.0.9 Available from:

<http://www.epcglobalinc.org/standards/>

EPCGlobal (2006) EPCGlobal Tag Data Standards Version 1.3: Ratified Specification

Available from:<http://www.epcglobalinc.org/standards/tds/>.EPCGlobal (2008) EPCGlobal Tag Data Standards Version 1.4: Ratified Specification

Available from:<http://www.epcglobalinc.org/standards/tds/>

The Challenges and Issues Facing the Deployment of RFID Technology 23

Ferguson, R B (2005) Logan airport to demonstrate baggage, passenger rfid tracking, eWeek

Available from: <http://www.eweek.com/c/a/Mobile-and-Wireless/Logan-Airport-to-Demonstrate-Baggage-Passenger-RFID-Tracking/>

Floerkemeier, C (2004) A Probabilistic Approach to Address Uncertainty in RFID, Auto-ID

Labs Research Workshop.

Floerkemeier, C (2007) Bayesian Transmission Strategy for Framed ALOHA Based RFID

Protocols, RFID, 2007 IEEE International Conference on RFID Gaylord Texan Resort,

Grapevine, TX, USA, pp 228–235

Floerkemeier, C & Lampe, M (2004) Issues with RFID Usage in Ubiquitous Computing

Applications, in A Ferscha & F Mattern (eds), Pervasive Computing: Second International Conference, PERVASIVE 2004, pp 188–193.

Floerkemeier, C., Lampe, M & Schoch, T (2003) The Smart Box Concept for Ubiquitous

Computing Environments, Proceedings of sOc’2003 (Smart Objects Conference),

Jeffery, S R., Franklin, M J & Garofalakis, M N (2008) An Adaptive RFID Middleware for

Supporting Mtaphysical Data Independence, VLDB Journal 17(2): 265–289.

Jeffery, S R., Garofalakis, M N & Franklin, M J (2006) Adaptive Cleaning for RFID Data

Streams, VLDB, pp 163–174.

Juels, A (2006) RFID Security and Privacy: a Research Survey, Selected Areas in

Communications, IEEE Journal on 24(2): 381–394.

Karygiannis, T., Eydt, B., Barber, G., Bunn, L & Phillips, T (2007) Guidelines for Securing

Radio Frequency Identification (RFID) Systems National Institute of Standards andTechnology

Khoussainova, N., Balazinska, M & Suciu, D (2006) Towards Correcting Input Data Errors

Probabilistically using Integrity Constraints, MobiDE, pp 43–50.

Khoussainova, N., Balazinska, M & Suciu, D (2007) Probabilistic RFID Data Management,

UW CSE Technical Report UW-CSE-07-03-01

Khoussainova, N., Balazinska, M & Suciu, D (2008) PEEX: Extracting Probabilistic Events

from RFID Data, International Conference on Data Engineering (ICDE), pp 1480–1482.

Khoussainova, N., Welbourne, E., Balazinska, M., Borriello, G., Cole, G., Letchner, J., Li, Y.,

Ré, C., Suciu, D & Walke, J (2008) A Demonstration of Cascadia through a Digital

Diary Application, SIGMOD ’08: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp 1319–1322.

Konidala, D M., Kim, Z & Kim, K (2007) A simple and cost-effective RFID tag-reader

mutual authentication scheme, International Conference on RFID Security (RFIDSec)’07,

pp 141–152

Lampe, M & Floerkemeier, C (2004) The Smart Box Application Model, In: Alois Ferscha,

Horst Hoertner, Gabriele Kotsis (Eds.): Advances in Pervasive Computing, Austrian Computer Society (OCG), pp 351–356.

Landt, J (2001) Shrouds of Time The history of RFID, The Association for Automatic

Identification and Data Capture Technologies

Landt, J (2005) The history of RFID, Potentials, IEEE 24(4): 8–11.

23The Challenges and Issues Facing the Deployment of RFID Technology

24 Will-be-set-by-IN-TECH

Langheinrich, M (2009) A Survey of RFID Privacy Approaches, Personal Ubiquitous Comput.

13: 413–421

Lee, J G., Hwang, S J & Kim, S W (2008) Performance Study of Anti-collision

Algorithms for EPC-C1 Gen2 RFID Protocol, Information Networking Towards Ubiquitous Networking and Services, Vol 5200/2008, Springer Berlin/Heidelberg,

Estoril, Portugal, pp 523–532

Lee, S R & Lee, C W (2006) An Enhanced Dynamic Framed Slotted ALOHA Anti-collision

Algorithm , Emerging Directions in Embedded and Ubiquitous Computing, Springer

Berlin/Heidelberg, Seoul, Korea, pp 403–412

Mirowski, L & Hartnett, J (2007) Deckard: A System to Detect Change of RFID Tag

Ownership, International Journal of Computer Science and Network Security 7(7): 89–98.

Mitrokotsa, A., Rieback, S & Tanenbaum, A (2010) Classifying RFID Attacks and Defenses,

Special Issue on Advances in RFID Technology, Information Systems Frontiers pp 491–505.

Myung, J & Lee, W (2006a) Adaptive binary splitting: a RFID tag collision arbitration

protocol for tag identification, Mob Netw Appl 11(5): 711–722.

Myung, J & Lee, W (2006b) Adaptive splitting protocols for RFID tag collision arbitration,

MobiHoc ’06: Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing, ACM, New York, NY, USA, pp 202–213.

Patterson, D J., Liao, L., Fox, D & Kautz, H A (2003) Inferring High-Level Behavior from

Low-Level Sensors, Ubicomp, pp 73–89.

Peng, X., Ji, Z., Luo, Z., Wong, E C & Tan, C J (2008) A P2P Collaborative RFID Data

Cleaning Model, The 3rd International Conference on Grid and Pervasive Computing GPC

2008, pp 304–309.

Philipose, M., Fishkin, K P., Perkawitz, M., Patterson, D J., Fox, D., Kautz, H & Hahnel,

D (2004) Inferring Activities from Interactions with Objects, Pervasive Computing

pp 10–17

Polniak, S (2007) The RFID Case Study Book RFID application stories from around the globe,

Abhisam Software

Potdar, V., Hayati, P & Chang, E (2007) Improving RFID Read Rate Reliability by a

Systematic Error Detection Approach, RFID Eurasia, 2007 1st Annual, pp 1–5.

Preradovic, S., Balbin, I., Karmakar, N & Swiegers, G (2008) A Novel Chipless RFID

System Based on Planar Multiresonators for Barcode Replacement, RFID, 2008 IEEE International Conference on, pp 289–296.

Preradovic, S & Karmakar, N (2009) Design of Short Range Chipless RFID Reader

Prototype, Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2009), pp 307–312.

Pupunwiwat, P & Stantic, B (2009) Unified Q-ary Tree for RFID Tag Anti-Collision

Resolution, in A Bouguettaya & X Lin (eds), Twentieth Australasian Database Conference (ADC 2009), Vol 92 of CRPIT, ACS, Wellington, New Zealand, pp 47–56.

Pupunwiwat, P & Stantic, B (2010a) A RFID Explicit Tag Estimation Scheme for Dynamic

Framed-Slot ALOHA Anti-Collision, Wireless Communications, Networking and Mobile Computing (WiCOM 2010), IEEE, Chengdu, China, pp 1–4.

Pupunwiwat, P & Stantic, B (2010b) Dynamic Framed-Slot ALOHA Anti-Collision using

Precise Tag Estimation Scheme, in H T Shen & A Bouguettaya (eds), Twenty-First Australasian Database Conference (ADC 2010), Vol 104 of CRPIT, ACS, Brisbane,

Australia, pp 19–28

The Challenges and Issues Facing the Deployment of RFID Technology 25

Pupunwiwat, P & Stantic, B (2010c) Joined Q-ary Tree Anti-Collision for Massive Tag

Movement Distribution, in B Mans & M Reynolds (eds), Thirty-Third Australasian Computer Science Conference (ACSC 2010), Vol 102 of CRPIT, ACS, Brisbane, Australia,

pp 99–108

Pupunwiwat, P & Stantic, B (2010d) Resolving RFID Data Stream Collisions using Set-Based

Approach, The Sixth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2010), IEEE, Brisbane, Australia, pp 61–66.

Rahmati, A., Zhong, L., Hiltunen, M & Jana, R (2007) Reliability Techniques for RFID-Based

Object Tracking Applications, DSN ’07: Proceedings of the 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, IEEE Computer Society,

Washington, DC, USA, pp 113–118

Rao, J., Doraiswamy, S., Thakkar, H & Colby, L S (2006) A Deferred Cleansing Method for

RFID Data Analytics, VLDB, pp 175–186.

Raskino, M., Fenn, J & Lenden, A (2005) Extracting Value From the Massively Connected

World of 2015, Technical Report G00125949, Gartner Research.

Ryu, J., Lee, H., Seok, Y., Kwon, T & Choi, Y (2007) A Hybrid Query Tree Protocol for

Tag Collision Arbitration in RFID systems, MobiHoc ’06: Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing, IEEE Computer

Society, Glasgow, UK, pp 5981–5986

Schoute, F C (1983) Dynamic Frame Length ALOHA, IEEE Transactions on Communications

31(4): 565–568

Enterprise Technology News and Reviews Available from:

<http://www.eweek.com/c/a/Enterprise-Applications/>

Shih, D.-H., Sun, P.-L., Yen, D C & Huang, S.-M (2006) Taxonomy and Survey of RFID

Anti-Collision Protocols, Computer Communications 29(11): 2150–2166.

Stockman, H (1948) Communication by Means of Reflected Power, Insistute of Radio Engineers

Thamilarasu, G & Sridhar, R (2008) Intrusion Detection in RFID Systems, Military

Communications Conference, 2008 MILCOM 2008 IEEE, pp 1 –7.

Vogt, H (2002) Efficient Object Identification with Passive RFID Tags, Pervasive ’02:

Proceedings of the First International Conference on Pervasive Computing, pp 98–113.

Wan, D (1999) Magic Medicine Cabinet: A Situated Portal for Consumer Healthcare,

Handheld and Ubiquitous Computing, pp 352–355.

Wang, F & Liu, P (2005) Temporal Management of RFID Data, VLDB, pp 1128–1139.

Wang, F., Liu, S & Liu, P (2010) A temporal RFID data model for querying physical objects,

Pervasive and Mobile Computing 6(3): 382–397.

Want, R., Fishkin, K P., Gujar, A & Harrison, B L (1999) Bridging Physical and Virtual

Worlds with Electronic Tags, Proceedings of the SIGCHI conference on Human factors in computing systems: the CHI is the limit, pp 370–377.

25The Challenges and Issues Facing the Deployment of RFID Technology

26 Will-be-set-by-IN-TECH

Ward, M., van Kranenburg, R & Backhouse, G (2006) RFID: Frequency, standards, adoption and

innovation, JISC Technology and Standards Watch.

Welbourne, E., Khoussainova, N., Letchner, J., Li, Y., Balazinska, M., Borriello, G & Suciu, D

(2008) Cascadia: A System for Specifying, Detecting, and Managing RFID Events,

Mobile systems, applications, and services (MobiSys), pp 281–294.

Ziekow, H & Ivantysynova, L (2008) A Probabilistic Approach for Cleaning RFID Data,

RFDM’08 Workshop in conjunction with ICDE 2008, pp 106–107.

2

RFID Components, Applications and System

Integration with Healthcare Perspective

2 Motivation of RFID technology

Existing research suggests that healthcare organisations are adopting information technology, specifically mobile technology throughout the world including the USA, Europe and UK (Bharadwaj et al., 2001) In the UK, the NHS (NHS-UK, 2009) is keen to adapt mobile technology for better information handling and this argument is supported in this chapter However, real-time techniques and contextual knowledge management concepts for instant care is somehow neglected (Watson, 2006) Healthcare processes are volatile and the context of information changes rapidly New technology has not considered information within their context The context of information is more complex in healthcare in comparison to other industries Although businesses have already started to develop and implement mobile technology for handling contextual information to improve processes but the same approaches cannot be adopted in the healthcare industry due to dominant

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knowledge use rather than just information and substantial human involvement (Connecting for health, 2009) However, the proven technology in business scenarios such as RFID can be adopted for a healthcare situation with the appropriate modelling of its use Managing context for any information is a difficult task but information systems play an important role into it but contextual knowledge is even more difficult and need location, time and duration for information for providing context to any knowledge (Bharadwaj et al., 2001)

If knowledge gets support with context of objects’ location, duration and time then this contextual knowledge can improve various situations for resource optimization and instant better actions RFID technology use is critical to get this knowledge and providing context to

it RFID can also support tacit knowledge on a real-time basis in healthcare situations such

as patients moving between locations to get medical treatment and a change in their medical condition at the same time The utilization of tacit knowledge is crucial but it needs context environmental knowledge for instance actions One of the properties of RFID is to provide instant location information of any object associated to it and this can play a vital role for tacit knowledge support and managing other environmental knowledge Advanced use of RFID technology can integrate patients’ flow processes appropriately and support patients’ treatment processes by deterministic patients’ movement knowledge (location and time etc.) within hospital settings (Connecting for health, 2010)

In a healthcare situation the patients’ movement processes are subject to change due to various reasons including a change in the patients’ medical condition, due to the unavailability of a particular resource at any given time and the unpredictable duration of any medical procedure (DH-UK, 2009) When processes are executed according to a plan and schedule then it consumes healthcare resources in a predicted way, if processes change due to any of the reasons described above then time and resources may be misused or processes become unpredictable These situations consume resources unnecessarily and the instability of one process at one location may affect other processes at another location So, the use of RFID technology is crucial for determining situations through getting time and location of an object within healthcare settings Use of RFID technology is important for better process management including improved decision-making

3 RFID utilisation

RFID works for identification of items/objects (Bohn, 2008) Sometime it only identifies item category or type but it is capable of identify items/objects uniquely RFID also enables data storage for remote items/objects through remotely access items information (Schwieren1 & Vossen, 2009) RFID system consists of RFID tags, RF Antennas, RFID readers and back-end database for storing unique item’s ID In RFID systems, RFID tags use as unique identifier, these tags associate with any items, when system reads these unique tags then information associated with that tags can be retrieved Antennas are first point of contact for tags reading Reader can only work with software resides in reader’s ROM (Glover & Bhatt, 2006) RFID system is based upon tags and reader’s communication and range of communication/reading depends on operating frequency When antennas deduct tags then

an application which is part of reader manipulates tags’ information in readable format for the end user There is a great amount of research being conducted to improve the efficiency

of RFID systems, increasing the accuracy of RFID reader and the feasibility of RFID tags Although RFID accuracy needs more enhancement and efficiency yet to be increase but still RFID system is used in many applications (Bohn, 2008) There are a variety of tags, readers

RFID Components, Applications and System Integration with Healthcare Perspective 29 and antennas types are available Before implementing RFID system, selection among these types needs understanding of these types in relation to their feasibility, capabilities and reliability It is also necessary to understand combinational use of these types for implementing a single feasible RFID system

4 Research approach

Qualitative research methodology is followed for observing the patients’ flow situation within hospital settings It includes observation and open interviews This study tries to find out the pattern within hospital condition, knowledge elements for healthcare processes and priority of each knowledge element for knowledge factor integration with the help of location deduction technology (RFID) Some individual scenarios are considered within patients’ movement processes and understanding is build for integration of RFID integration within these processes In this respect, qualitative methodology is sufficient for including each knowledge element and device a way of handling these elements through location deduction technology This chapter explores RFID technology with its kinds, types and capabilities It is conferred that how RFID technology can be generalised through generalise technical model It is discussed that how component layering approach can be feasible for integrating various healthcare management disciples for providing improved management Healthcare knowledge factors are considered for supporting knowledge elements through RFID technology to improve healthcare situation

5 RFID evaluation

RFID technology continues to evolve in past years in terms of various shapes of tags for increase its feasibility of its use, fast reading rate of reader and range of antennas etc The use of RFID also evolves due to enhancement in its components As the accuracy increases, the use of technology also increases such as baggage handling, goods delivery tracking and courier services RFID system enhancement also evolves automation applications development e.g automatic toll payments, automatic equipment tracking and document management etc (Garfinkel & Rosenberg, 2005) In this connection, the evolution process of RFID with respect to past few decades can be seen in figure 1

6 How RFID system works

The basic unit of RFID system is tags and tags have its own unique identification number system by which it recognizes uniquely These unique identification numbers save in tags’ internal memory and it is not changeable (read-only) However, tags can have other memory which can be either read-only or rewrite able (Application Notes CAENRFID, 2008) Tag memory may also contain other read-only information about that tag such manufactured date RFID reader generates magnetic fields through antennas for getting acknowledgement from tags (Garfinkel & Rosenberg, 2005) The reader generates query (trigger) through electromagnetic high-frequency signals (this frequency could be up to 50 times/second) to establish communication for tags (Srivastava, 2005) This signal field might get large number of tags data which is a significant problem for handling bulk of data together However, this problem can be overcome through filtering these data Actually software performs this filtering and information system is used to supply this data to data

Deploying RFID – Challenges, Solutions, and Open Issues

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Fig 1 RFID evolution: over past the few decades (Srivastava, 2005)

repository or use any other software procedures to control data according to the need and system capability (Srivastava, 2005; Application Notes CAENRFID, 2008) This piece of software works as a middle layer between user application and reader because the reader normally does not have the capability to handle bulk data at once; it has the job to supply

reading data to user application for further process (Frank et al., 2006) This buffering

capability may supply data from reader to information system interface (user interface) directly or may provide and use some routine to save into database for later exploit, it is depend on user requirement

Reader and tags communication can be maintained through several protocols When the reader is switched on then these protocols start the identification process for reading the tags, these important protocols are ISO 15693, ISO 18000-3, ISO 18000-6 and EPC ISO 15693 and ISO 18000-3 protocols are used for high frequency (HF) and, ISO 18000-6 and EPC protocols are used for ultra high frequency (UHF) Frequency bands have been defined for

RFID Components, Applications and System Integration with Healthcare Perspective 31 these protocols and they work within specified range such as HF has 13.56 MHz and UHF between 860 – 915 MHz (Application Notes CAENRFID, 2008) Reader modulates tags

responses within frequency field (Parks et al., 2009)

The reader handles multiple tags reading at once through signal collision detection technique (Srivastava, 2005) This signal collision detection technique uses anti-collision algorithm, the use of this algorithm enables multiple tag handling However, multiple tags handling depend on frequency range and protocol use in conjunction with tag type which can enable up to 200 tags reading at single time Reader protocol is not only use for reading the tag but also perform writing on to tags (Application Notes CAENRFID, 2008)

Fig 2 A typical RFID system (Application Notes CAENRFID, 2008)

The use of the reader within RFIFD system can be seen in figure 2 This figure also define the overall cycle of tag reading by reader through antenna and transforming data into communicate able form to user applications

7 How RFID system works

RFID system deducts tags within antennas’ range and performs various operations onto each tag The RFID system can only work effectively if all RFID components logically connect together and these components need to be compatible with each other Thats’ why understanding of these separate components is necessary Implementation of complete RFID solution is only possible through integration of these components which needs understanding of compatibility for each component, realisation of each components compatibility needs property study for these components (Sandip, 2005) These components are gathered and defined as under Also integration of these components can be understood with figure 3