Train Composition Using Motion as a Common Context.
Master's thesis, University of Twente.
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The research we conducted and describe in this thesis involves the autonomous discovery of the
composition of a train. Using wireless sensor nodes equipped with 3D accelerometers, we aim
to use motion as the common context for the correlation of the wagons behind a train.
The draft of the European Rail Track Management System (ERTMS) level 3 specifies a train
should be able to be aware of the composition of the train. Since freight trains do not have any
form of electrical connection between the freight wagons, either electrical connections should
be made or a wireless solution should be developed to be able to detect the train composition.
The goal of our research is the development of a wireless system capable of sensing and
reporting the train composition.
The lack of electrical connections introduces one of our challenges: energy consumption. Since
freight trains are scheduled for maintenance every six to twelve months and there is a lack of a
continuous power supply, the train composition system should be energy efficient. An energy
efficient system implies using a minimal amount of computational power. Our research is based
on building a system using energy efficient wireless sensor nodes.
In the first part of our research, we establish the means we are able to use for identifying two
wagons behind the same train. Based on previous research, we use correlation of the filtered
data from an accelerometer. We show it is possible to use the Pearson product-moment
correlation coefficient, but besides that, we show the use of an optimized version of this
For our algorithm, we implemented two methods. Our first solution uses the Pearson
correlation coefficient over a growing correlation window. This approach enables very fast
response times at the expense of computational power. Our second solution implements the
optimized version of the correlation coefficient. Using the optimized version, less computational
power is required per node, but the response time has a lower bound of 5 seconds.
Simulation results show that both approaches are applicable; the wireless sensor nodes are
able to perform the necessary calculations and determine the train composition within a given
time window of 15 seconds. Our fast approach is able to deliver the train composition after just
two seconds, given trains with not near identical acceleration characteristics.
Our simulation results also show that the bandwidth of the radio chip of the wireless sensor
nodes is capable of handling the necessary communication for our algorithm. The LogNormal
Shadowing model used in the network layer of our simulator shows that heavy shadowing does
not interfere with the correct operation of our algorithm.
|Item Type:||Master's Thesis|
|Research Group:||EWI-PS: Pervasive Systems|
|Deposited On:||16 August 2010|
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