Continuous and Energy-Efficient Transportation Behavior Monitoring

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dc.contributor Helsingin yliopisto, Matemaattis-luonnontieteellinen tiedekunta, Tietojenkäsittelytieteen laitos fi
dc.contributor University of Helsinki, Faculty of Science, Department of Computer Science en
dc.contributor Helsingfors universitet, Matematisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap sv Hemminki, Samuli 2012
dc.identifier.uri URN:NBN:fi-fe2017112251839
dc.description.abstract In this thesis we present and evaluate a novel approach for energy-efficient and continuous transportation behavior monitoring for smartphones. Our work builds on a novel adaptive hierarchical sensor management scheme (HASMET), which decomposes the classification task into smaller subtasks. In comparison to previous work, our approach improves the task of transportation behavior monitoring on three aspects. First, by employing only the minimal set of necessary sensors for each subtask, we are able to significantly reduce power consumption of the detection task. Second, using the hierarchical decomposition, we are able to tailor features and classifiers for each subtask, improving the accuracy and robustness of the detection task. Third, we are able to extend the detectable motorised modalities to cover most common public transportation vehicles. All of these attributes are highly desirable for real-time transportation behavior monitoring and serve as important steps toward implementing the first truly practical transportation behavior monitoring on mobile phones. In the course of the research, we have developed an Android application for sensor data collection and utilized it to collect over 200 hours of transportation data, along with 2.5 hours of energy consumption data of the sensors. We apply our method on the data to demonstrate that compared to current state-of-art, our method offers higher detection accuracy, provides more robust transportation behavior monitoring and achieves significant reduction in power consumption. For evaluating results with respect to the continuous nature of the transportation behavior monitoring, we use event and frame-based metrics presented by Ward et al. en
dc.language.iso en
dc.publisher Helsingfors universitet sv
dc.publisher University of Helsinki en
dc.publisher Helsingin yliopisto fi
dc.title Continuous and Energy-Efficient Transportation Behavior Monitoring en
dc.type.ontasot pro gradu-avhandlingar sv
dc.type.ontasot pro gradu -tutkielmat fi
dc.type.ontasot master's thesis en
dc.subject.discipline Computer science en
dc.subject.discipline Tietojenkäsittelytiede fi
dc.subject.discipline Datavetenskap sv
dct.identifier.urn URN:NBN:fi-fe2017112251839

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