LIMITS: Lightweight machine learning for IoT systems with resource limitations

B Sliwa, N Piatkowski, C Wietfeld - ICC 2020-2020 IEEE …, 2020 - ieeexplore.ieee.org
Exploiting big data knowledge on small devices will pave the way for building truly cognitive
Internet of Things (IoT) systems. Although machine learning has led to great advancements …

Client-based intelligence for resource efficient vehicular big data transfer in future 6G networks

B Sliwa, R Adam, C Wietfeld - IEEE Transactions on Vehicular …, 2021 - ieeexplore.ieee.org
Vehicular big data is anticipated to become the “new oil” of the automotive industry which
fuels the development of novel crowdsensing-enabled services. However, the tremendous …

Comparison of machine learning techniques applied to traffic prediction of real wireless network

D Alekseeva, N Stepanov, A Veprev… - IEEE …, 2021 - ieeexplore.ieee.org
Today, the traffic amount is growing inexorably due to the increase in the number of devices
on the network. Researchers analyze traffic by identifying sophisticated dependencies …

Boosting vehicle-to-cloud communication by machine learning-enabled context prediction

B Sliwa, R Falkenberg, T Liebig… - IEEE Transactions …, 2019 - ieeexplore.ieee.org
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 …

Machine learning based uplink transmission power prediction for LTE and upcoming 5G networks using passive downlink indicators

R Falkenberg, B Sliwa, N Piatkowski… - 2018 IEEE 88th …, 2018 - ieeexplore.ieee.org
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 …

AI4Mobile: Use cases and challenges of AI-based QoS prediction for high-mobility scenarios

DF Külzer, M Kasparick, A Palaios… - 2021 IEEE 93rd …, 2021 - ieeexplore.ieee.org
The integration of functions into future communication systems that predict crucial Quality of
Service (QoS) parameters is expected to enable many new or enhanced use cases, for …

Data-driven network simulation for performance analysis of anticipatory vehicular communication systems

B Sliwa, C Wietfeld - IEEE Access, 2019 - ieeexplore.ieee.org
The provision of reliable connectivity is envisioned as a key enabler for future autonomous
driving. Anticipatory communication techniques have been proposed for proactively …

Machine learning based context-predictive car-to-cloud communication using multi-layer connectivity maps for upcoming 5G networks

B Sliwa, R Falkenberg, T Liebig… - 2018 IEEE 88th …, 2018 - ieeexplore.ieee.org
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 …

Lightweight simulation of hybrid aerial-and ground-based vehicular communication networks

B Sliwa, M Patchou, C Wietfeld - 2019 IEEE 90th Vehicular …, 2019 - ieeexplore.ieee.org
Cooperating small-scale Unmanned Aerial Vehicles (UAVs) will open up new application
fields within next-generation Intelligent Transportation Sytems (ITSs), eg, airborne near field …

Empirical analysis of client-based network quality prediction in vehicular multi-MNO networks

B Sliwa, C Wietfeld - 2019 IEEE 90th Vehicular Technology …, 2019 - ieeexplore.ieee.org
Multi-Mobile Network Operator (MNO) networking is a promising method to exploit the joint
force of multiple available cellular data connections within vehicular networks. By applying …