Train Composition: Using Motion as a Common Context

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 correlation coefficient. 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.