Abstract
Radio Frequency Identification (RFID) is widely used to track and trace objects in traceability supply chains. However, massive uncertain data produced by RFID readers are not effective and efficient to be used in RFID application systems. Following the analysis of key features of RFID objects, this paper proposes a new framework for effectively and efficiently processing uncertain RFID data, and supporting a variety of queries for tracking and tracing RFID objects. We adjust different smoothing windows according to different rates of uncertain data, employ different strategies to process uncertain readings, and distinguish ghost, missing, and incomplete data according to their apparent positions. We propose a comprehensive data model which is suitable for different application scenarios. In addition, a path coding scheme is proposed to significantly compress massive data by aggregating the path sequence, the position, and the time intervals. The scheme is suitable for cyclic or long paths. Moreover, we further propose a processing algorithm for group and independent objects. Experimental evaluations show that our approach is effective and efficient in terms of the compression and traceability queries.
Highlights
Radio Frequency Identification (RFID) technology [1] is a flexible and relatively low-cost solution for tagging and wireless identification in a large number of business applications
RFID applications are available for users to track and trace historical trajectories and concrete positions of RFID objects over the Internet according to their related position information denoted as data lineages [2]
We identify several fundamental entities that are directly used in RFID applications as follows [3, 4]
Summary
RFID technology [1] is a flexible and relatively low-cost solution for tagging and wireless identification in a large number of business applications. Path-oriented queries need to obtain all information at all logistic nodes, which may be inefficient over massive objects As a result, these methods are difficult to store and process uncertain data for expressing RFID objects’ movements. According to key features of RFID applications, we present a comprehensive data model for storing uncertain RFID data and employ a strategy to adjust sizes of smoothing windows for capturing suitable rates of different uncertain RFID readings. We further develop inference rules for different types of uncertain data and propose a path coding scheme called path (sequence) for compressing massive data by efficiently aggregating the path sequence, the time interval, and the position. (ii) We propose a comprehensive data model which is suitable for different application scenarios It considers most properties of objects captured by RFID readers.
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