RFID (Radio Frequency Identification)-based object tracking is increasingly deployed and used in indoor environments such as airports, shopping malls, etc. However, the inherent noise in the raw RFID data makes it difficult to support queries and analyses on the data. In this paper, we propose an RFID data cleansing based on regular expressions. We generate the regular expressions in an automaton that captures all possible indoor paths from the spatial and temporal aspects of indoor space and deployed readers. Given the raw data of an object, the proposed matching algorithm finds all the matching paths using the automaton. We evaluate the proposed approach by conducting experimental studies using real dataset. The results demonstrate the effectiveness of the propose approach.

RFID is widely used for object tracking in indoor environments, e.g., airport baggage tracking. Analyzing RFID data offers insight into the underlying tracking systems as well as the associated business processes. However, the inherent uncertainty in RFID data, including noise (cross readings) and incompleteness (missing readings), pose challenges to high-level RFID data querying and analysis. In this paper, we address these challenges by proposing a learning-based data cleansing approach that, unlike existing approaches, requires no detailed prior knowledge about the spatio-temporal properties of the indoor space and the RFID reader deployment. Requiring only minimal information about RFID deployment, the approach learns relevant knowledge from raw RFID data and uses it to cleanse the data. In particular, we model raw RFID readings as time series that are sparse because the indoor space is only partly covered by a limited number of RFID readers.

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.

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.

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.

There has been a huge attention attracted by Wireless Sensor Network (WSN) applications in recent years. One of the significant part of these applications is "Localization". Nodes normally do not have any prior knowledge of their position when deployed. Thus a mechanism is required for nodes to determine their position. In this paper a new Two-Way Ranging (TWR) approach to find out the point-to-point range between the nodes is presented. The two way ranging technique can be prone to errors due to the delays associated with clock drift and low sampling frequency clock, thus may result in poor ranging accuracy. A new technique Two Way Ranging with Multiple Messages (TWR-MM) is introduced to remove such inaccuracies by calibrating the ranging nodes to achieve better ranging accuracy. The proposed solution is evaluated on a real test bed and using simulations. Results show accuracy within centimeters can be achieved, which is much better than simple TWR and Symmetrical Double Sided TWR (SDS-TWR).