Machine learning for optimizing disposition and planning of vehicles with near real-time IoT events at scale

Daemi-Ahwazi, AnuschRost, DanielReussner, Ralf H.Koziolek, AnneHeinrich, Robert2021-01-272021-01-272021978-3-88579-701-2https://dl.gi.de/handle/20.500.12116/34778Cargo vehicles today are equipped with power saving IoT devices measuring various aspects of the vehicle and cargo itself. The real-time stream of IoT events from the vehicles are sending large amounts of data each day, which needs to be correlated with each other and existing data sources to generate business value. The algorithmic challenges for discussion are the handling of noisy data and fast correlation of the sensor data as well as software engineering challenges to ensure the system(s) are highly performant and maintainable over the next decades.enMachine LearningGraph TheoryIoTSoftware EngineeringMachine learning for optimizing disposition and planning of vehicles with near real-time IoT events at scale10.18420/inf2020_671617-5468