A graph model for false negative handling in indoor RFID tracking data

The Radio Frequency Identification (RFID) emerges to be one of the key technologies to modernize object tracking and monitoring systems in indoor environments, e.g., airport baggage tracking. Although RFID has advantages over alternative identification technologies, the raw RFID data produced is inherently uncertain and contains errors. The dirty nature of raw RFID data hinders the progress of applying meaningful high-level applications that range from querying to analyzing. Therefore, cleansing RFID data is a high necessity. In this paper, we focus on handling one of the main aspects of raw RFID data, namely, false negatives, which occurs 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 model. Further, we evaluate the effectiveness and efficiency of devised graph model in recovering the false negatives using real dataset. The experimental results show that the devised graph model is effective and efficient in handling false negatives in indoor RFID tracking data.