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- W3118249780 abstract "Describing an accurate simulation model of the driving behavior of real-world vehicles is a laborious or even impossible task, because a driver reacts to a dynamically changing environment. As multiple external factors determine driving behavior, it is usually difficult to obtain an accurate model, owing to a lack of sensors or inability to collect data. In this paper, we propose a novel technique to combine driving behavior in vehicular Internet of Things (IoT) big data with a multi-agent system. This enables correct and scalable simulation without modeling the behavior of vehicular IoT devices or the environment. We develop an extensible simulation framework, called FlowSim, that demonstrates the application of our technique for a simulation of camera-image data collection from connected cars." @default.
- W3118249780 created "2021-01-18" @default.
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- W3118249780 date "2020-01-01" @default.
- W3118249780 modified "2023-09-27" @default.
- W3118249780 title "Simulating Vehicular IoT Applications by Combining a Multi-agent System and Big Data" @default.
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- W3118249780 doi "https://doi.org/10.1007/978-3-030-66534-0_8" @default.
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