Radio Frequency Identification
technology is gaining acceptance in an increasing number of
applications for tracking and monitoring purpose. Despite its promise
to provide unprecedented visibility in various domains, RFID technology
presents numerous challenges:
- Incomplete and noisy data:
- Read rates of RFID readers in actual deployments
are significantly below 100%, due to the intrinsic sensitivity of
radio frequencies (RFs) to environmental factors such as interference
from nearby metal objects and contenttion among tags.
mobile readers have lower read rates than fixed readers becauses they
tend to read objects from arbitrary orientatuons, and certain
orientations can result in poor read rates.
information: Raw RFID readings only containtag identifications and do
not contain additional high-level information such as object locations,
containment and co-location relationships. Such information, however,
is important to object tracking and monitoring, e.g., to ensure that
perishable food is contained in a cooling box, flammable objects are
secured in a fire-proof container, and foods with and without pernuts
are not packaged in the same container.
volume with redundancy: Large deployments could create high volumes of
data, e.g., over terabytes of data in a single day. Such data, however,
may encode significant amounts of redundant information such as an
unchanged object location. Hence, it is crucial that data be filtered
and compressed close to the hardware while preserving all useful
In this project, we
design and develop an efficient inference, compression and query
processing system over RFID data streams. Our system provides the
It provieds accurate interpretation of incomplete and insufficient raw
data; in particular, it infer locations of unobserved objects and
inter-object relationships such as collocation and containment.
To handle high data volumes, SPIRE performs online
interpretation, enabling online compression by identifying and
discarding redundant data close to the hardware..
processing: Our system further provides a query processor that runs
over the infered event stream and returns application-specific high
level information such as complex events or anomalies.
We gratefully acknowledge the funding provided by National