Small logo of ETH main building ETH Zurich : Computer Science : Pervasive Computing : Distributed Systems : Education : Student Projects : Abstract

Addressing Uncertainty in RFID using Dynamic Bayesian Networks (D)

Status: Abgeschlossen

The popularity of Radio Frequency Identification (RFID) has increased in recent years. Although RFID has many benefits over other identification technologies, there are certain situations that can lead to uncertainty about the true location of the tagged object. In this diploma thesis we will incorporate the knowledge about the operation of an RFID system and its various failure modes, and evidence from the RFID reader such as low-level diagnostic information in a single probabilistic model. The probabilistic model will allow us to quantify and reduce the uncertainty associated with RFID operation, since we can provide the application with a single probability value that expresses the belief that a certain tagged object is present. The probabilistic framework will be demonstrated with the help of a typical UBICOMP application.

Student/Bearbeitet von: Patrick Leutholdt
Contact/Ansprechpartner: Christian Floerkemeier

ETH ZurichDistributed Systems Group
Last updated May 7 2012 07:19:02 PM MET cfl