9 research outputs found
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
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
Car-to-Cloud Communication Traffic Analysis Based on the Common Vehicle Information Model
Although connectivity services have been introduced already today in many of
the most recent car models, the potential of vehicles serving as highly mobile
sensor platform in the Internet of Things (IoT) has not been sufficiently
exploited yet. The European AutoMat project has therefore defined an open
Common Vehicle Information Model (CVIM) in combination with a cross-industry,
cloud-based big data marketplace. Thereby, vehicle sensor data can be leveraged
for the design of entirely new services even beyond traffic-related
applications (such as localized weather forecasts). This paper focuses on the
prediction of the achievable data rate making use of an analytical model based
on empirical measurements. For an in-depth analysis, the CVIM has been
integrated in a vehicle traffic simulator to produce CVIM-complaint data
streams as a result of the individual behavior of each vehicle (speed, brake
activity, steering activity, etc.). In a next step, a simulation of vehicle
traffic in a realistically modeled, large-area street network has been used in
combination with a cellular Long Term Evolution (LTE) network to determine the
cumulated amount of data produced within each network cell. As a result, a new
car-to-cloud communication traffic model has been derived, which quantifies the
data rate of aggregated car-to-cloud data producible by vehicles depending on
the current traffic situations (free flow and traffic jam). The results provide
a reference for network planning and resource scheduling for car-to-cloud type
services in the context of smart cities
The AutoMat CVIM - A Scalable Data Model for Automotive Big Data Marketplaces
In the past years, connectivity has been introduced in automotive production
series, enabling vehicles as highly mobile Internet of Things sensors and
participants. The Horizon 2020 research project AutoMat addressed this scenario
by building a vehicle big data marketplace in order to leverage and exploit
crowd-sourced sensor data, a so far unexcavated treasure. As part of this
project the Common Vehicle Information Model (CVIM) as harmonized data model
has been developed. The CVIM allows a common understanding and generic
representation, brand-independent throughout the whole data value and
processing chain. The demonstrator consists of CVIM vehicle sensor data, which
runs through the different components of the whole AutoMat vehicle big data
processing pipeline. Finally, at the example of a traffic measurement service
the data of a whole vehicle fleet is aggregated and evaluated
Empirical evaluation of predictive channel-aware transmission for resource efficient car-to-cloud communication
Nowadays vehicles are by default equipped with communication hardware. This
enables new possibilities of connected services, like vehicles serving as
highly mobile sensor platforms in the Internet of Things (IoT) context. Hereby,
cars need to upload and transfer their data via a mobile communication network
into the cloud for further evaluation. As wireless resources are limited and
shared by all users, data transfers need to be conducted efficiently. Within
the scope of this work three car-to-cloud data transmission algorithms
Channel-Aware Transmission (CAT), predictive CAT (pCAT) and a periodic scheme
are evaluated in an empirical setup. CAT leverages channel quality measurements
to start data uploads preferably when the channel quality is good. CAT's
extension pCAT uses past measurements in addition to estimate future channel
conditions. For the empirical evaluation, a research vehicle was equipped with
a measurement platform. On test drives along a reference route vehicle sensor
data was collected and subsequently uploaded to a cloud server via a Long Term
Evolution (LTE) network
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%
Novel Common Vehicle Information Model (CVIM) for Future Automotive Vehicle Big Data Marketplaces
Even though connectivity services have been introduced in many of the most
recent car models, access to vehicle data is currently limited due to its
proprietary nature. The European project AutoMat has therefore developed an
open Marketplace providing a single point of access for brand-independent
vehicle data. Thereby, vehicle sensor data can be leveraged for the design and
implementation of entirely new services even beyond trafficrelated applications
(such as hyper-local traffic forecasts). This paper presents the architecture
for a Vehicle Big Data Marketplace as enabler of cross-sectorial and innovative
vehicle data services. Therefore, the novel Common Vehicle Information Model
(CVIM) is defined as an open and harmonized data model, allowing the
aggregation of brand-independent and generic data sets. Within this work the
realization of a prototype CVIM and Marketplace implementation is presented.
The two use-cases of local weather prediction and road quality measurements are
introduced to show the applicability of the AutoMat concept and prototype to
non-automotive applicatio
Resource-Efficient Transmission of Vehicular Sensor Data Using Context-Aware Communication
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 conclusion, public cellular networks will be confronted with massive increases in Machine-Type Communication (MTC) and will require efficient communication schemes to minimize the interference of Internet of Things (IoT) data traffic with human communication. In this demonstration, we present an Open Source framework for context-aware transmission of vehicular sensor data that exploits knowledge about the characteristics of the transmission channel for leveraging connectivity hotspots, where data transmissions can be performed with a high grade if resource efficiency. At the conference, we will present the measurement application for acquisition and live-visualization of the required network quality indicators and show how the transmission scheme performs in real-world vehicular scenarios based on measurement data obtained from field experiments