Evaluate the effect of BBS. We design several groups of contrast experiments screening libraries on the data sets include measured data and simulation data. The results show that under all conditions with different missing rates, obviously, the precision of BBS is better than that of sliding-window cleaning.The rest of this paper is organized as follows: we discuss the related work in Section 2. Section 3 defines the Object Movement Detection model and introduces our RFID data cleansing mechanism and arithmetic. An empirical evaluation of our solution is reported in Section 4. Finally, Section 5 concludes the paper.2.?Related WorkRFID technology has posed many challenges to database management systems, such as the requirements of supporting big volume data [9�C11], handing new types of queries [11], event processing and data cleaning [5,12�C16].
Many systems have been developed to manage uncertainty data. RFID data management, is one of the most important applications that drives the recent surge of interest in managing incomplete and uncertain data, which has been studied extensively. Valentine et al. [8] presented an adaptive sliding-window based approach WSTD for reducing false negative reads in RFID data streams. Rao et al. [13] presented a deferred approach for detecting and correcting RFID data anomalies by utilizing declarative sequenced-based rules. Chen et al. [14] proposed a Bayesian inference based approach, which takes full advantage of data redundancy, for cleaning RFID raw data. Gonzalez et al.
[15] proposed a cleaning framework that takes an RFID data set and a collection of cleaning methods, with associated costs, and induces a cleaning plan that optimizes the overall accuracy adjusted cleaning costs by determining the conditions under which inexpensive methods are appropriates, and those when more expensive methods are absolutely necessary.The work in [5,12] is the most relevant research to this paper. Jeffery et al. [5,12] proposed an adaptive smoothing filter SMURF for RFID data cleaning. SMURF focuses on a sliding-window aggregate that interpolates for lost readings. SMURF models the unreliability of RFID readings by taking RFID streams as a statistical sample of physical tags, and exploits techniques in sampling theory to drive its cleaning processes. But it is mainly applied to the circumstances that the movement of tags is infrequent, and is not effective in the case that tags move frequently.
3.?Unreliable RFID Data Anacetrapib Cleaning3.1. A Movement Behavior Detection ModelThe key for a movement behavior-based smoothing filter MEK162 chemical structure lies in how to establish the conversion relationship between read rate sequences and kinematic parameters of tags to assist in RFID data cleaning. To do so, we proposed a movement behavior detection model.The process of tag passing through the reader’s read range follows the laws of kinematics.