7 research outputs found
Machine learning based context-predictive car-to-cloud communication using multi-layer connectivity maps for upcoming 5G networks
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. Consequently, upcoming 5G communication networks will be
confronted with massive increases in Machine-type Communication (MTC) and
require resource-efficient transmission methods in order to optimize the
overall system performance and provide interference-free coexistence with human
data traffic that is using the same public cellular network. In this paper, we
bring together mobility prediction and machine learning based channel quality
estimation in order to improve the resource-efficiency of car-to-cloud data
transfer by scheduling the transmission time of the sensor data with respect to
the anticipated behavior of the communication context. In a comprehensive field
evaluation campaign, we evaluate the proposed context-predictive approach in a
public cellular network scenario where it is able to increase the average data
rate by up to 194% while simultaneously reducing the mean uplink power
consumption by up to 54%
Towards Data-driven Simulation of End-to-end Network Performance Indicators
Novel vehicular communication methods are mostly analyzed simulatively or
analytically as real world performance tests are highly time-consuming and
cost-intense. Moreover, the high number of uncontrollable effects makes it
practically impossible to reevaluate different approaches under the exact same
conditions. However, as these methods massively simplify the effects of the
radio environment and various cross-layer interdependencies, the results of
end-to-end indicators (e.g., the resulting data rate) often differ
significantly from real world measurements. In this paper, we present a
data-driven approach that exploits a combination of multiple machine learning
methods for modeling the end-to-end behavior of network performance indicators
within vehicular networks. The proposed approach can be exploited for fast and
close to reality evaluation and optimization of new methods in a controllable
environment as it implicitly considers cross-layer dependencies between
measurable features. Within an example case study for opportunistic vehicular
data transfer, the proposed approach is validated against real world
measurements and a classical system-level network simulation setup. Although
the proposed method does only require a fraction of the computation time of the
latter, it achieves a significantly better match with the real world
evaluations
Lightweight Simulation of Hybrid Aerial- and Ground-based Vehicular Communication Networks
Cooperating small-scale Unmanned Aerial Vehicles (UAVs) will open up new
application fields within next-generation Intelligent Transportation Sytems
(ITSs), e.g., airborne near field delivery. In order to allow the exploitation
of the potentials of hybrid vehicular scenarios, reliable and efficient
bidirectional communication has to be guaranteed in highly dynamic
environments. For addressing these novel challenges, we present a lightweight
framework for integrated simulation of aerial and ground-based vehicular
networks. Mobility and communication are natively brought together using a
shared codebase coupling approach, which catalyzes the development of novel
context-aware optimization methods that exploit interdependencies between both
domains. In a proof-of-concept evaluation, we analyze the exploitation of UAVs
as local aerial sensors as well as aerial base stations. In addition, we
compare the performance of Long Term Evolution (LTE) and Cellular
Vehicle-to-Everything (C-V2X) for connecting the ground- and air-based
vehicles