52 citations · 152 across the 17 of their papers we have counts for
5 papers · 1 filter
Boosting Vehicle-to-cloud Communication by Machine Learning-enabled Context Prediction
Benjamin Sliwa, Robert Falkenberg, Thomas Liebig +2
The exploitation of vehicles as mobile sensors acts as a catalyst for novel crowdsensing-based applications such as intelligent traffic control and distributed weather forecast. Ho…
System-of-Systems Modeling, Analysis and Optimization of Hybrid Vehicular Traffic
Benjamin Sliwa, Thomas Liebig, Tim Vranken +2
While the development of fully autonomous vehicles is one of the major research fields in the Intelligent Transportation Systems (ITSs) domain, the upcoming longterm transition per…
Machine learning based context-predictive car-to-cloud communication using multi-layer connectivity maps for upcoming 5G networks
Benjamin Sliwa, Thomas Liebig, Robert Falkenberg +2
While cars were only considered as means of personal transportation for a long time, they are currently transcending to mobile sensor nodes that gather highly up-to-date informatio…
Resource-efficient Transmission of Vehicular Sensor Data Using Context-aware Communication
Benjamin Sliwa, Thomas Liebig, Robert Falkenberg +2
Upcoming Intelligent Traffic Control Systems (ITSCs) will base their optimization processes on crowdsensing data obtained for cars that are used as mobile sensor nodes. In conclusi…
Efficient Machine-type Communication using Multi-metric Context-awareness for Cars used as Mobile Sensors in Upcoming 5G Networks
Benjamin Sliwa, Thomas Liebig, Robert Falkenberg +2
Upcoming 5G-based communication networks will be confronted with huge increases in the amount of transmitted sensor data related to massive deployments of static and mobile Interne…