A Mobile Personalized RFID System Ruay-Shiung Chang, Jih-Sheng Chang, and Jiing-Hsing Ruey Department of Computer Science and Information Engineering National Dong Hwa University, Hualien, TAIWAN {rschang,jschang}@mail.ndhu.edu.tw
[email protected] Abstract RFID is a rapid-developing technology for wireless identification lately. There are many related products and systems for various applications. However, most of them only deal with logistic management or business management without involving users’ concerns for personalized services. Therefore, it motivates us to develop a mobile personalized RFID system with an event processing model focusing on the characterization of identification for personalized services. In this paper, we show how the proposed system prototype works as well as demonstrate a system implementation for movie DVD rentals.
1. Introduction There are many researches or system prototypes for RFID [1, 2, 3, 4] in progress. Most of them only deal with the logistics management or business management without considering users’ concerns, which is especially needed for personalized services. That may not be a winwin situation. For example, as far as a business is concerned, the mode of consumption for users can not be grasped if the business’s RFID platform has nothing to do with users. Instead, from a user’s point of view, if a company fails to push their goods actively for users, the amount of consumption may decrease drastically because users may know nothing at all about goods in which they are interested. The foremost motivation of this research is how the RFID technology can help business especially during the economic depression at present. In this study, we are going to build a prototype of RFID personalized services focusing on what a user really desires. The critical issues that we need to concern about are how to integrate the existing resources and what innovative services we can provide for customers. For this reason, we develop a personalized RFID system with an analytic model for personalized services including consumer’s behaviors and browsing in order to achieve a win-win situation both for users and businesses.
There are two main contributions of this research. One is the seamless integration of personal mobile devices with RFID system for ubiquitous services. That may enhance the competitive advantage of business if customers are satisfied with the convenient services. The other one is the development of analytic model for personalized services. That may stir up a lot of consumption if customers could more easily get information they are interested in. The remainder of this paper is organized as follows. In Section 2, we describe the system prototype. In Section 3, we propose a classification method to be used in a personalized service. An actual example of system is then shown in Section 4. Finally, conclusions are given in the last section.
2. System Prototype In this section, we would like to show how our system works as well as what services we have developed. We use the movie DVD rental business (such as the Blockbuster) as an example. As indicated in figure 1 and figure 2, each member has a RFID membership card to identify oneself while each DVD has an RFID tag. There are two system programs including RFID-DVD rental system and MRFID-DVD Client (Mobile RFID-DVD Client). RFID-DVD rental system is an integrated platform for DVD management. It also provides customers with a function of previews for selected DVD. As long as a customer takes his/her RFID membership card with a select DVD close to the RFID reader, that will enable RFID-DVD rental system to show the preview on a monitor screen. The RFID reader could read multiple RFID items in the meantime due to its anti-collision ability as shown in figure 3. We make good use of the property of anti-collision to recognize which one invokes which browse event. Then a show of preview will be invoked when only one RFID membership card and one DVD item are sensed together at the same time. Meanwhile the browse events or check-out event along with personal ID will be sent back to the middleware automatically to be used as a historical data for the user
such that the personalized service can become smarter as times go on. In addition to the in house service in a DVD store by RFID-DVD rental system, we have also implemented a personal mobile device program called MRFID-DVD Client running on customer’s PDA (Personal Digital Assistant) or smart phone. The PDA in our environment is equipped with a WiFi interface and a SDIO RFID reader as shown in figure 4. Therefore, a customer can use personal PDA to browse through the DVD information or previews by means of MRFID-DVD Client. A customer should login with password and RFID membership card at first before using MRFID-DVD Client. Every browse event together with personal ID will also be sent back to the middleware server. The personalized service is delivered to the user proactively using our personalized analytic model as shown in figure 5. In summary, a customer could either use its RFID membership card through RFID-DVD rental system or manipulate PDA through MRFID-DVD Client to browse the DVD information on demand.
Figure 3. RFID reader with anti-collision ability
Figure 4. PDA with SDIO RFID Reader
Figure 1. System Framework
Figure 5. Personalized Services
3. Preference Classification
Figure 2. RFID membership card and RFID DVD
In watching movies, everyone has his/her likes and dislikes. How does a personalized system know what a client’s favorite movie genre is? In this section, we propose an RFID event analytic model to process raw events for classification. The DVD items are classified
into n classes in advance denoted by Cj where j =1,2,…,n. In addition, each customer has an attribute vector represented by AI ， where AI = (a1, a2,….., an). According to the Bayesian theorem [12, 13, 14], we can obtain eq.(1):
= p(Cj ) p(ai | Cj )ai (1 − p(ai | Cj ))1−ai
(4)
where ai = {0,1}
p (C j | A I ) = p ( C j | a 1, a 2 ,..., a n ) =
model [9, 10] or Gaussian distribution for discrete or continuous attributes respectively as indicated in eq.(4) and eq.(5). For discrete attributes, p(ai | Cj )
p ( C j ) p ( a 1, a 2 , ..., a n | C j ) p ( a 1, a 2 ,..., a n )
(1)
For continuous attributes x = ai, p[ r 1 ≤ x ≤ r 2 ] 2
In eq.(1) p(Cj | AI) stands for the probability of the preference for class Cj. Then
p(Cj ) p(a1, a2, ..., an | Cj )
∫
−( x − μ ) r2 1 2 f ( x ) dx = ∫ e 2σ dx , a i = {0, 1} r 1 σ 2π
( 5)
where
p(Cj ∩ a1 ∩ a2 ∩ ... ∩ an ) p(Cj ) p(Cj ∩ a1) p(Cj ∩ a1 ∩ a2 ∩ ... ∩ an ) = p(Cj ) p(Cj ) p(Cj ∩ a1) = p(Cj ) p(a1 | Cj ) p(a 2, a 3, ..., an | Cj , a1) (multiplicative rule) = p(Cj ) p(a1 | Cj ) p(a2 | Cj , a1) p(a3, a4, ..., an | Cj , a1, a 2 ) = p(Cj ) p(a1 | Cj ) p(a2 | Cj , a1) p(a3 | Cj , a1, a 2 ) p(a4, a5, ..., an | Cj , a1, a 2, a3 ) = p(Cj )
f (x ) =
− 1 e σ 2π
( x − μ )2 2σ 2
,− ∞ < x < ∞
and μis the mean, σis the standard deviation.
= p(Cj ) p(a1 | Cj ) p(a2 | Cj , a1) p(a3 | Cj , a1, a 2 ) … p(an | Cj , a1, a 2 , a3,..., an − 1) (2) According to the Naive Bayesian assumption [5, 6], each attribute is conditionally independent, that is,
p (ai | Cj ∩ ak ) = p (ai | Cj ), ai is conditionally
independent of ak given Cj for all i ≠ j Therefore, we can obtain eq.(3)
p(Cj | AI )
For instance, there is a user Ui logging in with an attribute vector Ai = (aage, apt, abt, ao), where aage, apt, abt, ao mean the attribute of age, purchase times, and the number of browses, and user’s occupation respectively. The system will work out p(Cj | Ai ) proactively for various types of DVD denoted by Cj by means of Eq.(4) or Eq.(5). Taking aage as an example, if it is a boolean with value to be either adult or not, we can apply Eq.(4) to calculate p( aage | Cj ) . Otherwise, if it is a continuous value such as an age range, we can use Eq.(5) to calculate p( aage | Cj ) . Finally, determination of user’s preference for DVD Cj can be determined by using Eq.(3). In summary, we use this proposed model to predict what type of DVD the customer may be interested in with high probability. Thereafter, the selected DVD information will be forwarded to the user’s PDA after accumulating enough training data.
4. System Implementation
= p(Cj ) p(a1, a2, ..., an | Cj ) = p(Cj ) p(a1 | Cj ) p(a2 | Cj , a1) p(a3 | Cj , a1, a2) … p(an | Cj , a1, a2, a3,..., an − 1) = p(Cj ) p(a1 | Cj ) p(a2 | Cj ) p(a3 | Cj )...p(an | Cj ) n
= p(Cj )∏ p(ai | Cj )
=
(3)
i =1
In eq.(3), we call p ( ai | Cj ) as the likelihood probability. We can utilize different probability model to calculate the likelihood probability in terms of various attributes. In this paper, we use multi-variate Bernoulli
Figure 6 shows the overall items in our system including middleware & database server, PDA with SDIO RFID reader, RFID membership card, RFID DVD, and RFID reader. Regarding RFID-DVD system program, a function will not be available till the administrator logins with the RFID card successfully, as shown in figure 7 where user ID is the RFID tag ID. The administrator can activate the function of previews for customers as shown in figure 8. All items with RFID tag will be sensed as long as they are close to the reader. However, the system only accepts one
membership card along with one DVD item at the same time for playing the preview. Simultaneous presences are handled in a first-come-first-served manner to facilitate the admission control. The admission control can also include other functions such as preventing a minor from accessing adult DVD movies. Finally, each browse event or check-out event along with personal ID will be sent back to the middleware automatically in order to accumulate the training data set.
Figure 8. Previews of DVD
Figure 6. Overall system items
Figure 9. One member card and one DVD item are accepted at the same only in our system.
Figure 7. Login As shown in figures 9 and 10, if a customer takes one membership card and one DVD close to the reader, the system will play the previews automatically. The RFID-DVD rental system also provides DVD store with the functions of check-out and DVD return. The customer just takes his/her membership card and all the DVD items he/she selects altogether near the reader, the system will deal with the check-out or return process automatically.
Figure 10. Playing previews of DVD. As for MRFID-DVD Client program, the user needs to login with his/her RFID membership card at first as shown in figures 12 and 13. Then, the customer can take any DVD item close to the SDIO reader as shown in figure 14, the MRFID-DVD Client will sense the item and play the preview automatically on PDA as shown in figures 15 and 16.
Figure 11. Check-out and restoration. After login successfully, the user can receive the personalized information about interesting DVD information from the middleware server as in figure 17. The user also can click on the interesting item to show the previews on PDA. In addition, a user could also request to display his/her rental list from MRFID-DVD Client as indicated in figure 18.
Figure 13. Login successfully
Figure 14. Browse DVD with PDA
Figure 12. Login
5. Conclusions In this research, we build a prototype of RFID personalized services focusing on what a user really desires. We also develop a personalized RFID-DVD rental system with an analytic model for personalized services including consumer’s behaviors and browsing in order to achieve a win-win situation both for users and businesses. In conclusion, a prototype of seamless integration of personal mobile devices with RFID system for ubiquitous services is shown. It may enhance the competitive advantage of business if customers are satisfied with this kind of convenient services.
Figure 15. Sense DVD item
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[6] Figure 16. Play previews on PDA [7] [8] [9]
[10]
[11]
Figure 17. Personalized information [12] [13]
[14]
Figure 18. Rental list
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