1 code implementation • 26 Jan 2020 • Benjamin Sliwa, Robert Falkenberg, Christian Wietfeld
Machine learning-based data rate prediction is one of the key drivers for anticipatory mobile networking with applications such as dynamic Radio Access Technology (RAT) selection, opportunistic data transfer, and predictive caching.
Networking and Internet Architecture Signal Processing
1 code implementation • 23 Jul 2019 • Robert Falkenberg, Christian Wietfeld
Network data analysis is the fundamental basis for the development of methods to increase service quality in mobile networks.
Networking and Internet Architecture
no code implementations • 23 Apr 2019 • Benjamin Sliwa, Robert Falkenberg, Thomas Liebig, Nico Piatkowski, Christian Wietfeld
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.
Networking and Internet Architecture
1 code implementation • 18 Jun 2018 • Robert Falkenberg, Benjamin Sliwa, Nico Piatkowski, Christian Wietfeld
Energy-aware system design is an important optimization task for static and mobile Internet of Things (IoT)-based sensor nodes, especially for highly resource-constrained vehicles such as mobile robotic systems.
Networking and Internet Architecture
no code implementations • 17 May 2018 • Benjamin Sliwa, Thomas Liebig, Robert Falkenberg, Johannes Pillmann, Christian Wietfeld
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 information for crowdsensing-enabled big data services in a smart city context.
Networking and Internet Architecture
1 code implementation • 10 Jan 2018 • Benjamin Sliwa, Thomas Liebig, Robert Falkenberg, Johannes Pillmann, Christian Wietfeld
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 Internet of Things (IoT) systems.
Networking and Internet Architecture
1 code implementation • 18 Nov 2017 • Robert Falkenberg, Karsten Heimann, Christian Wietfeld
Based on this information and in conjunction with existing indicators like Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ), a neural network is trained to perform a data rate prediction for the current LTE link.
Networking and Internet Architecture
1 code implementation • 12 Jan 2017 • Robert Falkenberg, Christoph Ide, Christian Wietfeld
Advanced Cyber-Physical Systems aim for the balancing of restricted local resources of deeply embedded systems with cloud-based resources depending on the availability of network connectivity: in case of excellent connectivity, the offloading of large amounts of data can be more efficient than the local processing on a resource-constraint platform, while this latter solution is preferred in case of limited connectivity.
Networking and Internet Architecture
