THESIS
2013
xi, 61 pages : illustrations ; 30 cm
Abstract
Radio Frequency Identification (RFID) technology is one of the key technologies to support
automatic identification in many ubiquitous applications. An RFID tag compromises of an
integrated circuit and antenna, which enable to wirelessly communicate with the RFID reader
in a non-line-of-sight way. The RFID tag, especially the passive tag, has constrained
capabilities in computation, communication, and storage, due to the extremely low production
cost. For instance, passive tags can only report their unique IDs to the reader for identification.
On the other hand, modern applications usually require more rich information for better
management. For example, the system requires a timely detection on the abnormal event,
including the improper leaning, rolling, or accidently falling o...[
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Radio Frequency Identification (RFID) technology is one of the key technologies to support
automatic identification in many ubiquitous applications. An RFID tag compromises of an
integrated circuit and antenna, which enable to wirelessly communicate with the RFID reader
in a non-line-of-sight way. The RFID tag, especially the passive tag, has constrained
capabilities in computation, communication, and storage, due to the extremely low production
cost. For instance, passive tags can only report their unique IDs to the reader for identification.
On the other hand, modern applications usually require more rich information for better
management. For example, the system requires a timely detection on the abnormal event,
including the improper leaning, rolling, or accidently falling off the conveyer, etc. Recently,
researchers develop a new technology equipping the tag with sensors and more powerful
computational capacity, termed as Computational RFID (CRFID). CRFID tag is able to sense
the temperature, humidity, and acceleration of objects, etc. Hence, they can provide for
fine-grained monitoring and control over targeted objects. In this thesis, I have focused on
detecting abnormal events of targets based on the multi-dimensional sensing of CRFID tags.
Furthermore, I have developed practical methods to support the fine-grained trajectory
management. A detail description of this technology will be presented in my thesis.
Compared to tags, the RFID reader is very expensive. It would be cost-inefficient, sometimes
impractical, if fully-covering the entire logistic processing flow using readers. To detect the
abnormal events along the whole processing flow, we propose to attach CRFID tags to the
objects and deploy RFID readers only in critical areas. We design a tree-indexed Markov
Chain scheme, which leverages statistical methods to achieve fine-grained abnormal event
detection and dynamic trajectory management. We have implemented a prototype system
onto a passenger luggage handling system and conduct extensive experiments. The result shows that our system can effectively detect the anomalous event with low cost and high
accuracy.
On the other hand, collaboratively sensing multi-dimensional information is important when
processing or transporting the items with server requirements on the environment and
condition. For example, chemical shipment has a rigorous constrain on both the temperature
and vibration. Thus, multi-dimensional information, such as the temperature and vibration,
should be collaboratively sensed for comprehensive analysis and detection on the abnormal
event.
We propose a multi-dimensional information based surveillance scheme to timely detect the
abnormal event that occurs in cold chain logistics. Cold chain is a supply-chain which
maintains the important parameters, e.g., the temperature or vibration, in a given range. We
adopt the minimum entropy of accelerometer readings and classification algorithms to
distinguish various statuses of items. Through multi-dimensional information processing, the
scheme can gain a guaranteed accuracy with low false alarm rate and false detection rate. We
also demonstrate the effectiveness of the framework by performing a preliminary
implementation and trace-driven simulations.
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