A Smoothing Data Cleaning based on Adaptive Window Sliding for Intelligent RFID Middleware Systems

다국어 초록 (Multilingual Abstract)

Over the past years RFID/SN has been an elementary technology in a diversity of applications for the ubiquitous environments, especially for Internet of Things. However, one of obstacles for widespread deployment of RFID technology is the inherent unreliability of the RFID data streams by tag readers. In particular, the problem of false readings such as lost readings and mistaken readings needs to be treated by RFID middleware systems because false readings ultimately degrade the quality of application services due to the dirty data delivered by middleware systems. As a result, for the higher quality of services, an RFID middleware system is responsible for intelligently dealing with false readings for the delivery of clean data to the applications in accordance with the tag reading environment. One of popular techniques used to compensate false readings is a sliding window filter. In a sliding window scheme, it is evident that determining optimal window size intelligently is a nontrivial important task in RFID middleware systems in order to reduce false readings, especially in mobile environments. In this paper, for the purpose of reducing false readings by intelligent window adaption, we propose a new adaptive RFID data cleaning scheme based on window sliding for a single tag. Unlike previous works based on a binomial sampling model, we introduce the weight averaging. Our insight starts from the need to differentiate the past readings and the current readings, since the more recent readings may indicate the more accurate tag transitions. Owing to weight averaging, our scheme is expected to dynamically adapt the window size in an efficient manner even for non-homogeneous reading patterns in mobile environments. In addition, we analyze reading patterns in the window and effects of decreased window so that a more accurate and efficient decision on window adaption can be made. With our scheme, we can expect to obtain the ultimate goal that RFID middleware systems can provide applications with more clean data so that they can ensure high quality of intended services.

참고문헌 (Reference)

1 이재원, "유비쿼터스 물류를 위한 분산형 RFID 정보서비스 구조" 한국지능정보시스템학회 11 (11): 105-121, 2005

2 Want, R., "The Magic of RFID" 2 (2): 40-48, 2004

3 Lohr, S, "Sampling: Design and Analysis" Duxbury Press 1999

4 김재경, "RFID 기반 이력추적 시스템을 이용한 농축산물 추천방법" 한국지능정보시스템학회 14 (14): 207-222, 2008

5 Derakhshan, R., "RFID Data management: Challenges and Opportunities" 175-182, 2007

6 Ahsan, K., "RFID Applications: An Introductory and Exploratory Study" 7 (7): 1-7, 2010

7 Han, J., "Mining Massive RFID, Trajectory, and Traffic Data sets(tutorial)" 2008

8 Chen, H., "Leveraging Spatio-Temporal Redundancy for RFID Data Cleansing" 51-62, 2010

9 Liao, G., "KLEAP: An Efficient Cleaning Method to Remove Cross-Reads in RFID Data Streams" 2209-2212, 2011

10 Mitrokotsa, A., "In RFID and Sensor Networks" CRC press 511-535, 2009

11 Aggarwal, C. C., "In Managing and Mining Sensor Data" Springer 349-382, 2013

12 Shen, H., "Improved Approximate Detection of Duplicates for Data Streams Over Sliding Windows" 23 (23): 973-987, 2008

13 Bashier, A. K., "Energy Efficient In-network RFID Data Filtering Scheme in Wireless Sensor Networks" 11 (11): 7004-7021, 2011

14 Jin, X., "Efficient Complex Event Processing over RFID Data Stream" 75-81, 2008

15 Wang, L., "Data Cleaning for RFID and WSN Integration" 10 (10): 408-418, 2014

16 Chui, D.-M., "Analysis of the Increase and Decrease Algorithms for Congestion Avoidance in Computer Networks" 17 (17): 1-14, 1989

17 Mahdin, H., "An Approach for Removing Redundant Data from RFID Data Streams" 11 : 9863-9877, 2011

18 Massawe, L. V., "An Adaptive Data Cleaning Scheme for Reducing False Negative Reads in RFID Data Streams" 157-164, 2012

19 Jeffery, S. R., "Adaptive Cleaning for RFID Data Streams" 163-174, 2006

20 Arivarasi, S., "A Detailed Survey on Various Tracking Methods Using RFID" 5 (5): 900-904, 2013