Spatiotemporal Data Cleansing for Indoor RFID Tracking Data

The Radio Frequency Identification (RFID) is increasingly being deployed in indoor tracking systems, e.g., airport baggage monitoring. However, the “dirtiness” in raw RFID readings hinder the progress of applying meaningful high level applications that range from monitoring to analysis. Hence, it is indispensable to cleansing RFID data in such systems. In this paper, we focus on two quality aspects in raw indoor RFID data: temporal redundancy and spatial ambiguity. The former refers to the large number of repeated readings for the same object and the same RFID reader during a period of time. The latter refers to the undetermined whereabouts of an object due to multiple readings by different readers simultaneously. We investigate the spatiotemporal characteristics of indoor spaces as well as RFID reader deployment, and exploit them in designing effective data cleansing techniques. Specifically, we aggregate raw RFID readings to reduce temporal redundancy; we design a distance-aware graph to resolve spatial ambiguity with respect to the indoor topology and the RFID reader deployment captured in the graph. We evaluate the spatiotemporal data cleansing techniques using both real and synthetic datasets. The experimental results demonstrate that the proposed techniques are effective and efficient in cleansing indoor RFID tracking data.