In-Network Phased Filtering Mechanism for a Large-Scale RFID Inventory Application

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1 In-Network Phased Filtering Mechanism for a Large-Scale RFID Inventory Application Wonil Choi, Nonmember, and Myong-Soon Park, Member, IEEE ㅔ Abstract--RFID technology is one of automatic identification technologies. In current RFID systems, RFID data are managed and processed by a middleware. In the near future, when RFID technology will be applied to large scale warehouses, airports, or seaports, it is necessary that wireless sensors integrated a RFID reader construct wireless sensor network because of difficulties of building wired network infrastructure. However adjacent RFID readers in wireless sensor network can generate many duplicate data at the same time. These duplicate data transmissions may waste the energy of sensor nodes integrated a RFID reader and cause network congestions. So we propose in-network phased filtering mechanism for a large-scale RFID inventory application. In this paper, we describe our idea to reduce a processing time of filtering and confirm the idea by a simulation. Index Terms--Filtering, RFID, Wireless Sensor Network, Electronic Product Code R I. INTRODUCTION adio Frequency Identification (RFID) technology uses radio-frequency waves to transfer data between readers and movable RFID tagged object. RFID technology has gained significant momentum in the past few years, with several high-profile adoptions (e.g., Walmart). In addition to applications in retail and distribution, RFID technology holds the promise to simplify aircraft maintenance, baggage handling, laboratory procedures, and other tasks. An RFID system includes three components: RFID tags, RFID readers, and a middleware. RFID tags or "transponders" are integrated circuit that is encoded with an identification number that may send and receive information to and from the readers via an antenna. The RFID reader or "transceiver" is composed of a radio frequency module, a control unit, and an antenna that is used to communicate with RFID tags using radio frequency signals. Most readers have external inputs and output connections that are used to interface with the middleware or to send and receive external signals. The reader is often equipped This work was supported by grant No from the Growth Power Technology Development Project, funded by the Ministry of Commerce, Industry and Energy of Korea. This work was supported by the Second Brain Korea 21 Project. Wonil Choi is with the Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea ( wonil22@ilab.korea.ac.kr). Myong-Soon Park is with the Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea ( myongsp@ilab.korea.ac.kr). Myong-Soon Park is a corresponding author. with sophisticated firmware that controls data signals, inputs and outputs through commands in byte-code. The RFID middleware is a server to store and manage the data from RFID readers. And it also requests a query to readers to collect data for various applications. In order to apply the RFID technology to large scale warehouses, seaports or airports, many RFID readers should be required to cover the large area. To extend the radio frequency coverage, the authors of [1] proposed RFID system integrated with a wireless sensor network. In their work, they use wireless sensor nodes with a RFID reader and one host node connected directly to the host computer. However there are several physical characteristics of wireless sensor nodes to support RFID system, such as power consumption, communication range, and the size of RFID readers. If we use many number of readers to extend the radio frequency coverage and cover the area completely, multiple readers should share the coverage area. So, they generate duplicate readings. In that case, duplicate data will be sent to the host computer through the wireless sensor network and it will waste the energy of RFID readers. So we propose the In-Network Phased Filtering Mechanism (INPFM) to have less latency in RFID system integrated with a wireless sensor network. The proposed filtering mechanism has less latency than the existing filtering mechanism because of reflecting the characteristics of RFID data. Additionally our INPFM can increase a performance of middleware of RFID system because redundant data reported to middleware are decreased in network. In this paper, we assume that: - RFID readers can communicate each others wirelessly and have some computability. Building a RFID system for existing facilities, constructing an infrastructure with wired network can be more expensive and difficult than a wireless one. Also embedded system technology makes it possible that a RFID reader has computation ability. - To extend the radio frequency coverage, we use the RFID system integrated with a wireless sensor network [1]. - The radio field of RFID readers can be overlapped by neighbor RFID readers - Applications are not event-driven but query-driven like inventory applications. 401

2 - Sensors : RFID Readers, Objects : RFID tagged objects. We use two words by the same meaning, sensors and RFID readers, objects and RFID tagged objects. - RFID readers have energy limitations because they are powered by a battery. To cover the large area we need many number of RFID readers. If these many readers are run by wired electrical power, the cost of wiring is high. And wireless one is easy to extend an existing system. So we assume that RFID readers are powered by a battery and they have the energy limitation. The assumptions denoted above are justified by the applications of RFID technology. For instance, this assumed RFID system integrated with wireless sensor networks can be applied to the invest management of large scale warehouse system. Fig. 1 shows an example of RFID inventory application for large scale warehouses. In the next section, we explore the related work about the data managements and the large scale RFID applications. And then Section 3 and 4, we describe the RFID data model, define the duplicate data and propose In-Network Phased Filtering Mechanism. In Section 5, we present simulation results and analysis. Finally, we conclude with the contribution of this work and suggest future work in the last section. Host Computer (Middleware) integrated RFID and wireless sensor network. Some issues to consider in the application include RFID system design and placement, networking requirements, limited human intervention, the dynamic movement of tagged objects, and security. Especially, in this application, the authors would like for the size of reader and antenna to be as compact as possible. And a wireless network is more suitable for networking the readers because of a substantial wiring effort and cost to accommodate a number of readers which cover a large area. Because the application of this work is for automated large scale system, it allows only limited human intervention. In [2], the authors concentrated how to manage and model the RFID data effectively. RFID technology has emerged for years and poses new challenges for data management. One of the major challenges for RFID technology is RFID data management [3][4]. Focusing on RFID data management, authors of [2] summarized the characteristics of RFID data, and then they modeled their temporal management of RFID data for tracking applications. III. RFID DATA MODELING In our RFID system, readers send the triplet data to the host computer. The triplet consists of three fields, such as <EPC, Reader_ID, TimeStamp>. Host Computer (Middle ware) RFID Reader Radio Frequency Coverage RFID Tagged Objects Fig. 1. A sample RFID inventory application II. RELATED WORKS As a RFID technology applies to various fields like an asset tracking, an entrance control and an athletic recording, related works of RFID technology also are increasing with activity. Particularly the applications of distribution industry are appeared by the killer application, so researches focus on the efficient management of RFID data used for the management of many products. In [1], the authors suggested the integration of RFID system and wireless sensor network in order to extend the coverage of radio frequency identification. Current RFID system can cover only small area like gates of buildings or one place of conveyer belts, etc. So they focused on this constraint. For an inventory application like distribution warehouses, RFID system can be necessary to cover large areas. That is why the authors of [1] - Electronic product code (EPC) [5] data EPC is an identification scheme for universally identifying physical objects, defined by standard committees [5]. Table. I. describes the EPC bit composition. EPC 96 bit general ID is most popular type in current commercial RFID system for a distributed industry. Table. I. EPC 96 bits General ID Header General Object Serial Number Manager Number Class GID (Binary 68,719,476,735 (Max. decimal 268,435,455 (Max. decimal 16,777,215 (Max. decimal - Reader_ID Reader_ID is a kind of addresses to identify each RFID reader. Reader_IDs are assigned by the relation with a geographic location of readers. It means that the system can decide whether two RFID readers are a neighbor or not by Reader_ID. It can not be only represented as an IP-style address but it can be also represented as a coordinate pair. - Tagging time Tagging time is the time that reader read the EPC value of objects by a query. In our work assumed query driven, tagging time is considered for quick response of multiple 402

3 queries [6]. After readers tag the EPC value of objects, a few moments later, readers tag the same EPC value by another query. But high latency in the wireless network can cause wrong filtering without time consideration. Preventing this situation, we consider the tagging time. Additionally we can get the benefit of compatibility with event-driven applications because event-driven applications should consider the event time, in other words the tagging time. IV. IN-NETWORK PHASED FILTERING MECHANISM In this section, we define the concept of duplicate data for a large-scale RFID inventory application. And we explain the proposed filtering mechanism. A. Definition of Duplicate Data We already mentioned the triplet such as EPC data, Reader_ID and TimeStamp in the previous section. We define the duplicate data if the three listed conditions are satisfied all. If two data, triplet A and B are - EPC_A = = EPC_B - Readers of A and B are neighbors or same. - TimeStamp_A - TimeStamp_B < constant T Satisfying these three conditions, then we define two data, triplet A and B as the duplicate data. It is obvious that EPC values of two data must be equal for defining duplicate data as different EPC values of two data mean different objects. Because objects can be moved by conveying belts, forklifts or carts, we consider the tagging reader s location of data on the first condition. Even though a same object attached RFID tag are observed by several readers, those may be meaningful observations. In order to decide whether two data is duplicate or not, we consider the data reading by neighboring readers. For example, Tag1(t1) can be observed and reported by Reader 1 and Reader 2 in Fig. 2. Some tags, Tag1(t1), Tag3(t3), Tag4(t4) and Tag5(t5) are in the overlapped area, so they can be read by two neighboring readers at the same time [6]. Equivalently, Tag2(t2) also can be read by three readers at the same time. Reader 2 t1 t3 t4 t2 Reader 3 Reader 1 Fig. 2. Overlapped reading ranges with RFID readers and tags t5 Reading range RFID reader Object Tagging time is also considered for quick response of multiple queries [7] and the delay of wireless network. On a same node, if it has many identical data, our filter eliminates the data which has the tagging time within time interval T like Fig. 3. Time interval T is a constant determined by applications specifically. E1 E1 E1 E1 E1 E1 E1 E1 E1 E1 E1 E1 E1 E1 E1 E1 E1 E1 Fig. 3. Time interval condition Meaningful data with a same EPC value Constant T Time Raw Data Filtered Data B. Filtering Method We use the hash function as the comparison method because it is proper to identify numerous data and it is the current most popular method which is used in DBMS for RFID middleware [8]. There are usually so many objects in a large-scale RFID inventory system. If many data are compared with each others by their original values, it will affect significant overhead to RFID readers. Thus we chose the hash method which function process to change the data to hash value. Generally, the hash value has a smaller size than the original data. C. Backward-First Filtering RFID Reader integrated with a sensor node will eliminate duplicate tag data. One of conditions of duplication is the same EPC value. EPC data, as we previously described, consist of four parts - a header, a general manager number, an object class, and a serial number. However similar kinds of objects which have a RFID tag locate closely in a large-scale RFID inventory. So identical EPC values may have only different serial numbers but they have a same object class and general manager number. In this case, a comparison with a whole EPC value is more inefficient than a comparison with a serial number of EPC data. Therefore our proposed filtering is run with two phases. At the first phase, our filter considers only serial numbers of EPC data by the duplicate criteria. When serial numbers are equal, filter becomes the second phase. At the second phase, our filter considers the other values of EPC data. We call this filtering as Backward-First Filtering (BFF). Actually considering the backward matching is not new. Boyer-Moore algorithm was already proposed by the backward matching idea for a fast string search [9]. Inspiring this, we also take advantages of backward matching. For example, following two EPC values mean a same object class and a same manager but different two objects. - 0x35789abcd012345abcdef123-0x35789abcd012345abcdef

4 Table. II. Pseudo code of BFF for INPFM RFID_TAGDATA { EPC, READER_ID, TIMESTAMP } BFF(RFID_TAGDATA) { HASH_KEY HK1 = HASH_FUNCTION(RFID_TAGDATA.EPC.SERIAL_ NUMBER) IF HK1 IS NOT EXISTING IN HASH TABLE THEN CREATE NEW HASH_FIELD OF HK1 INSERT RFID_TAGDATA TO HASH_FIELD OF HK1 ELSE IF HK1 IS ALREADY IN HASH_TABLE THEN IF RFID_TAGDATA.EPC.OTHERS EQUAL HK1.RFID_TAGDATA.EPC.OTHERS IF IS_NEIGHBOR(RFID_TAGDATA.READER_I D, HK1.RFID_TAGDATA.READER_ID) IF RFID_TAGDATA.TIMESTAMP - HK1.RFID_TAGDATA.TIMESTAMP < TIME_INTERVAL THEN DROP RFIDOBJECT AS DUPLICATE DATA BREAK UPDATE RFID_TAGDATA TO HASH_TABLE OF HK1 } If we match by forward-first, we decide that it is different at the last match of the code. In contrast with this, if we match by backward-first, we can decide immediately it is different or not. But comparing with every original data may cause overheads described in Section VI-B, we just use a serial number with hash function. Table. II. describes the concept of BFF. V. SIMULATION AND ANALYSIS We implemented our filter and the simulation code for our study by C++. We set three parameters to change the simulation environment. The First is a number of readers in a cluster, the second is a number of clusters, and the third is a number of generated data count to filter. The generating rate of duplicate data was implemented as uniform random distribution. Above all we simulated whether our BFF worked well or not. So we compared with a normal filter which used the hash function with an entire EPC value. In the first simulation we set the parameters as: - Number of readers count in a cluster: 10 - Number of clusters: 3 - Duplicate data diffusion speed (count/ms): 100 Generating Data(count Total Generated data Hashing Filter Proposed filter Identical data Time(ms) Fig. 4. Filtering rate comparison of the proposed BFF filter In this simulation, we simulated it during 10 ms of simulation time and configured the constant T of condition of duplication to 1 ms. Fig. 4. shows the simulation result. BFF filter distinguished the duplicate data as well as the normal filter. Both two filters eliminated duplicate data from 99.00% to 97.66% of all duplicate data. On 0.5 ms, total number of generated data is 111 counts but the result of filtering counted 12 data - actually identical data is 11. On 10.0 ms, total number of generated data is 1086 counts but the result of filtering counted 198 data - actually identical data is 178. The more simulation time was increased, the less accuracy of the filter. The reason that we guessed is the amount of data for the filtering. However we are not sure of it. We leave it for the future works and we will simulate it later. The numbers of data after the filtering with two filters were exactly same. It is no wonder because we used a same hash function for filtering and same conditions of two filters. Even though we can filter with same accuracy with a normal filter, BFF can reduce the processing time. We measured the processing time of both two filters at the second simulation. In the second simulation we set the parameters as: - Number of readers count in a cluster: 50 - Number of clusters: 10 And we changed the duplicate data diffusion speed for this simulation from 5000 count/ms to count/ms. It means that filters had much overheads. When amount of source data is smaller than 1000 counts, we observed little difference from 404

5 processing time of two filters. So we set the larger number than the first simulation to parameters. Fig. 5 describes the result of the second simulation. The average difference of processing time between two filters is about 7.32 ms. Maximum of differences is 10.7 ms on count/ms and minimum is 6.05 ms on count/ms. Between 5000 count/ms and count/ms, the difference of processing time is less than 7ms and between count/ms and count/ms, the difference of processing time is more than 10 ms. So we roughly say that BFF can work better than the normal filter despite a large amount of data. Processing Time of Filtering (msec) Hashing Filter Proposed Filter Fig. 5. Processing time of two filters a Number of Generated Data VI. CONCLUSION In this paper we proposed the In-Network Phased Filtering Mechanism which is proper to a large-scale RFID inventory application. In order to adapt a filter for characteristics of a large-scale RFID inventory application, we defined the duplicate data, chose the method to use for filtering and proposed detail filtering mechanism in Section IV. [4] S. S. Chawathe, V. Krishnamurthy, S, Ramachandrany and S.Sarma, "Managing RFID Data," In the Proceedings of the 31st Very Large Data Bases Conference (VLDB 2005), pp , August 2005 [5] EPC Tag Data Standards Version 1.1 Technical Reports, EPCGlobal Inc, April [6] Joongheon Kim, Wonjun Lee, Jaewon Jung, Jihoon Choi, Eunkyo Kim and Joonmo Kim, "Weighted Localized Clustering: A Coverage-Aware Reader Collision Arbitration Protocol in RFID Networks," In the Proceedings of the Embedded Software and Systems: Second International Conference, ICESS 2005, pp , December 2005 [7] Sangeetha Seshadri, Vibhore Kumar, and Brian F. Cooper, "Optimizing Multiple Queries in Distributed Data Stream Systems," In the Proceedings of the 22nd International Conference on Data Engineering Workshops (ICDEW'06), April 2006 [8] Taesu Cheong and Youngil Kim, "RFID Data Management and RFID Information Value Chain Support with RFID Middleware Platform Implementation," In the Proceedings of the CoopIS/DOA/ODBASE 2005, LNCS 3760, pp , October 2005 [9] R.S. Boyer and J.S. Moore, "A Fast String Searching Algorithm," CACM, pp , October 1997 The simulation results show us that proposed idea can reduce the processing time to filter duplicate data without decreasing performance. The processing time to filter data can be reduced using by our BFF. REFERENCES [1] Mark L. McKelvin, Jr. Mitchel L. Williams and Nina M. Berry, "Integrated Radio Frequency Identification and Wireless Sensor Network Architecture for automated Inventory Management and Tracking Applications," In the Proceedings of the 2005 conference on Diversity in computing, October 2005 [2] Fusheng Wang and Peiya Liu, "Temporal Management of RFID Data," In the Proceedings of the 31st Very Large Data Bases Conference (VLDB 2005), pp , August 2005 [3] Fusheng Wang, Peiya Liu, Yijian Bai, Bridging Physical and Virtual Worlds: Complex Event Processing for RFID Data Streams Lecture Notes in Computer Science Volume 3896, pp ,