554 research outputs found
Efficient Machine-type Communication using Multi-metric Context-awareness for Cars used as Mobile Sensors in Upcoming 5G Networks
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. Cars acting
as mobile sensors will become important data sources for cloud-based
applications like predictive maintenance and dynamic traffic forecast. Due to
the limitation of available communication resources, it is expected that the
grows in Machine-Type Communication (MTC) will cause severe interference with
Human-to-human (H2H) communication. Consequently, more efficient transmission
methods are highly required. In this paper, we present a probabilistic scheme
for efficient transmission of vehicular sensor data which leverages favorable
channel conditions and avoids transmissions when they are expected to be highly
resource-consuming. Multiple variants of the proposed scheme are evaluated in
comprehensive realworld experiments. Through machine learning based combination
of multiple context metrics, the proposed scheme is able to achieve up to 164%
higher average data rate values for sensor applications with soft deadline
requirements compared to regular periodic transmission.Comment: Best Student Paper Awar
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%
Exploiting Map Topology Knowledge for Context-predictive Multi-interface Car-to-cloud Communication
While the automotive industry is currently facing a contest among different
communication technologies and paradigms about predominance in the connected
vehicles sector, the diversity of the various application requirements makes it
unlikely that a single technology will be able to fulfill all given demands.
Instead, the joint usage of multiple communication technologies seems to be a
promising candidate that allows benefiting from characteristical strengths
(e.g., using low latency direct communication for safety-related messaging).
Consequently, dynamic network interface selection has become a field of
scientific interest. In this paper, we present a cross-layer approach for
context-aware transmission of vehicular sensor data that exploits mobility
control knowledge for scheduling the transmission time with respect to the
anticipated channel conditions for the corresponding communication technology.
The proposed multi-interface transmission scheme is evaluated in a
comprehensive simulation study, where it is able to achieve significant
improvements in data rate and reliability
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
Towards Tactile Internet in Beyond 5G Era: Recent Advances, Current Issues and Future Directions
Tactile Internet (TI) is envisioned to create a paradigm shift from the content-oriented
communications to steer/control-based communications by enabling real-time transmission of haptic information (i.e., touch, actuation, motion, vibration, surface texture) over Internet in addition to the conventional audiovisual and data traffics. This emerging TI technology, also considered as the next evolution phase of Internet of Things (IoT), is expected to create numerous opportunities for technology markets in a wide variety of applications ranging from teleoperation systems and Augmented/Virtual Reality (AR/VR) to automotive safety and eHealthcare towards addressing the complex problems of human society. However, the realization of TI over wireless media in the upcoming Fifth Generation (5G) and beyond networks creates various non-conventional communication challenges and stringent requirements
in terms of ultra-low latency, ultra-high reliability, high data-rate connectivity, resource allocation, multiple access and quality-latency-rate tradeoff. To this end, this paper aims to provide a holistic view on wireless TI along with a thorough review of the existing state-of-the-art, to identify and analyze the involved technical issues, to highlight potential solutions and to propose future research directions. First, starting with the vision of TI and recent advances and a review of related survey/overview articles, we present a generalized framework for wireless TI in the Beyond 5G Era including a TI architecture, the main technical requirements, the key application areas and potential enabling technologies. Subsequently, we provide a comprehensive review of the existing TI works by broadly categorizing them into three main paradigms; namely, haptic communications, wireless AR/VR, and autonomous, intelligent and cooperative mobility systems. Next, potential enabling technologies across physical/Medium Access Control (MAC) and network layers are identified and discussed in detail. Also, security and privacy issues of TI applications are discussed
along with some promising enablers. Finally, we present some open research challenges and recommend promising future research directions
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