Handling False Negatives in Indoor RFID Data

The Radio-Frequency Identification (RFID) is a useful technology for object tracking and monitoring systems in indoor environments, e.g., Airport baggage tracking. Nevertheless, the data produced by RFID tracking is inherently uncertain and contains errors. In order to support meaningful high-level applications including queries and analyses over RFID data, it is necessary to cleanse raw RFID data. In this paper, we focus on false negatives in raw indoor RFID tracking data. False negatives occur when a moving object passes the detection range of an RFID reader but the reader fails to produce any readings. We investigate the topology of indoor spaces as well as the deployment of RFID readers, and propose the transition probabilities that capture how likely objects move from one RFID reader to another. We organize such probabilities, together with the characteristics of indoor topology and RFID readers, into a probabilistic distance-aware graph. With the aid of this graph, we design algorithms to identify false negatives and recover missing information in indoor RFID tracking data. We evaluate the proposed cleansing approach using both real and synthetic datasets. The experimental results show that the approach is effective, efficient and scalable.