Simulation Platform for Connected Heterogeneous Vehicles
Tobias Meuser, Daniel Bischoff, Ralf Steinmetz and Bj
¨
orn Richerzhagen
Multimedia Communications Lab (KOM), Technische Universit
¨
at Darmstadt, Darmstadt, Germany
Keywords:
Vehicular Networks, Simulation, Rapid Prototyping.
Abstract:
The increasing number of connectivity features in current vehicles poses additional challenges for large-scale
vehicular communication systems. Already deployed systems rely on the cellular network infrastructure,
while the Wifi-based 802.11p standard will likely be implemented on a large scale in the next years. As real-
world tests are costly, simulations are used to develop mechanisms for efficient short-range communication via
802.11p. However, efficient long-range communication between vehicles is pivotal for non-safety related in-
formation sharing. Current simulators often focus on short-range communication exchange, while approaches
for efficient long-range communication are barely considered in automotive scenarios.
To enable rapid development of new approaches, we propose a scalable simulation environment for automotive
applications. Our contributions are (i) the realistic modeling of heterogeneous vehicles including sensors and
network interfaces, (ii) the automated generation of road properties like accidents and jams, and (iii) a config-
urable back-end infrastructure distributing events to the vehicles. All of the above contributions enable rapid
prototyping and evaluation of automotive applications in various environments. We showcase two exemplary
use cases to demonstrate the versatility of our simulation framework: an efficient road-based dissemination
approach for long-range information exchange and a distributed information validation approach.
1 INTRODUCTION
In recent years, increasingly complex Advanced
Driver Assistance Systems (ADASs) were developed,
which require vast amounts of sensor information
(Tigadi et al., 2016). Vehicles exchange information
with each other to improve their perception range and
quality. As practical tests are often unsuitable for
the vehicular scenario, network simulators are used
to model this information exchange. These simula-
tors commonly model the information exchange via
802.11p, the cellular network, or a combination.
Local Inter-Vehicle Communication (IVC) via
802.11p is required for research performed on col-
laborative perception using Cooperative Awareness
Messages (CAMs) (ETSI 102 637-2 V1.2.1, 2011)
or Decentralized Environment Notification Messages
(DENs) (ETSI 102 637-3 V1.1.1, 2010), and in-
formation exchange in Vehicular Ad-hoc Networks
(VANETs) in general (Riebl et al., 2015; G
¨
unther
et al., 2015). This type of communication is used for
both safety-related applications like accident preven-
tion and non-safety-related applications like offload-
ing of the cellular connection.
The cellular connection is mainly used for the ex-
change of non-safety related information like traffic
information and road properties. If a vehicle receives
information, it can detour to prevent a jam or decel-
erate in-time to prevent emergency braking. The ef-
ficient distribution of these messages is a challenging
topic in research, as the context of the vehicle and
the message need to be considered in the dissemina-
tion. For many non-safety related applications, an ex-
act model of the channel like in (Virdis et al., 2015)
is often not required, as a message drop can often be
compensated on the higher layers.
However, the heterogeneity of the vehicles in
terms of their available network interfaces, computa-
tional resources, and sensors is an essential aspect for
all vehicular applications. Today’s network simula-
tors often focus only on one dimension of heterogene-
ity, which limits the possible simulation scenarios.
Based on this gap, we design our simulation frame-
work for the vehicular scenario which simulates het-
erogeneous vehicles. Additionally, we enable rapid
prototyping of vehicular applications, as we provide
networking, processing and sensing components to
limit the additional implementation effort when eval-
uating new approaches. Our simulation framework
is based on the Simonstrator framework (Richerzha-
gen et al., 2015) and the vehicular traffic simulator
Simulation of Urban Mobility (SUMO) (Lopez et al.,
412
Meuser, T., Bischoff, D., Steinmetz, R. and Richerzhagen, B.
Simulation Platform for Connected Heterogeneous Vehicles.
DOI: 10.5220/0007713004120419
In Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2019), pages 412-419
ISBN: 978-989-758-374-2
Copyright
c
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
2018). SUMO provides realistic movement of the ve-
hicles, while the Simonstrator focuses on rapid proto-
typing of networking applications of various domains.
Our contributions for the simulation of vehicular
scenarios are (i) the setup of the road network in-
cluding the realistic movement of the vehicles, (ii)
the generation events on the roads which can be de-
tected by the vehicles, (iii) the configuration of each
vehicle in terms of available sensors and network in-
terfaces and (iv) the connectivity services utilized by
the vehicles. For the movement of the vehicles, we
couple the Simonstrator and SUMO bidirectionally
using the Traffic Control Interface (TraCI) (Wegener
et al., 2008), i. e., the Simonstrator can influence the
vehicle movement. Based on the vehicle information
gathered from SUMO, we configure each vehicle in-
dividually. This configuration includes different stor-
age and computation capabilities, sensors, and net-
work interfaces. Additionally, we provide different
connectivity solutions, including a central back-end
and a pure-forwarding based broker.
All of the above contributions enable rapid proto-
typing and evaluation of automotive applications un-
der varying environmental conditions. The variety of
available communication paradigms and methods in
the Simonstrator like the Publish/Subscribe (Pub/Sub)
paradigm (Richerzhagen et al., 2015), and different
ad-hoc communication schemes and mechanisms for
cellular offloading (Richerzhagen et al., 2016) further
support rapid prototyping of automotive applications.
Especially the Pub/Sub pattern is pivotal for the au-
tomotive scenario, as inter-vehicle communication is
often information-centered (Amadeo et al., 2016).
The rest of this work structures as follows: In
Section 2, we provide an overview of the existing
simulators for the vehicular scenario. Next, we de-
scribe our first contribution, the modeling of the en-
vironment based on the Simonstrator in Section 3.
This modeling includes the road network, the vehi-
cles, and the generation of road events. In Section 4,
we describe the different connectivity configurations
we provide in our automotive simulation framework
in detail. Next, we show two exemplary use cases in
the vehicular scenario in Section 5. Finally, we con-
clude our paper in Section 6.
2 RELATED WORK
Several network simulators like NS-3 (Riley and Hen-
derson, 2010) and OMNeT++ (Varga and Hornig,
2008) aim to simulate vehicular communication. To
simulate inter-vehicle communication, these simu-
lators model the vehicles’ movement and network
communication. For the vehicle movement, SUMO
(Lopez et al., 2018) simulates the vehicle movement
for all these simulators. For the network simula-
tion, the above simulators differ significantly regard-
ing available network models and vehicle modeling.
NS-3 with the RACE extension (Jomrich et al., 2017)
can precisely model the cellular channel including
X2-handover but focuses on the pure networking as-
pects of the vehicular scenario. Thus, the develop-
ment of new applications is complex.
In that regard, OMNeT++ is much more versatile
through the high number of available extensions. The
extension Veins (Sommer et al., 2011) couples OM-
NeT++ with SUMO bidirectionally, such that OM-
NeT++ applications can influence the movement of
the vehicles. While Veins initially provided only ad-
hoc communication between vehicles, it was later
extended to support the exchange of messages via
Long Term Evolution (LTE) (Hagenauer et al., 2014).
To follow current trends of the automotive industry,
Artery (Riebl et al., 2015) added the ITS-G5 stack to
Veins, which enable the test of novel VANET appli-
cations. In this approach, the vehicle’s onboard sen-
sors are modeled in an extendable manner. However,
Artery did not consider the heterogeneity of sensors
regarding sensor quality. Artery was further extended
by local perception sensors and the LTE communica-
tion stack (G
¨
unther et al., 2017), which increased the
possible vehicular applications further.
While all of the above approaches are suitable for
their intended use cases, they lack at least one of
the following properties which are essential for long-
range communication in vehicular networks: the het-
erogeneity of the vehicles in case of the NS-3 exten-
sion or the lack of different cellular communication
paradigms in case of OMNeT++. This lack increases
the implementation time of vehicular applications and
slows down the development of new features.
Thus, we base our automotive evaluation setup for
non-safety-related applications on the event-based Si-
monstrator (Richerzhagen et al., 2015), which sup-
ports rapid prototyping in different scenarios. While
the Simonstrator only supports a limited amount of
channel models, it provides the possibility to use dif-
ferent communication paradigms and node sensors.
Consequently, we focus more on applications and the
improvement of message dissemination and filtering.
To this end, we model the vehicle including vehicle
sensors, network interfaces, computational resources,
and different back-end communication strategies like
pull-based and push-based communication.
Different back-end communication strategies are
especially valuable for the evaluation of vehicular ap-
plications. Currently, these applications rely mostly
Simulation Platform for Connected Heterogeneous Vehicles
413
Figure 1: Overview of the required components for the
modeling of vehicles.
on a central server entity, which is continuously
queried for new information (Turcanu et al., 2016;
Rayeni et al., 2018). However, the monetary costs for
server maintenance might hinder their practical appli-
cability. These approaches might be enhanced fur-
ther by utilizing highly-scalable low-resource com-
munication paradigms like Pub/Sub (Gascon-Samson
et al., 2015; Majumder et al., 2009), but due to the re-
strictions of the existing simulators, these approaches
cannot be evaluated. Our automotive evaluation setup
resolves this issue and enables the variation of differ-
ent back-end communication strategies.
3 ENVIRONMENTAL MODELING
Figure 1 displays the required components for a net-
work simulator in the vehicular scenario. This sce-
nario consists of vehicles, connectivity features, and
the environment. Each vehicle is equipped with sev-
eral sensors and networking capabilities. A central
back-end may coordinate the message dissemination
and offer additional back-end services. As the sensors
measure the environment, we need to model the envi-
ronment first. Section 3.1 describes the modeling of
the environment including the efficient accessibility
of the road network in the network simulator and the
extensible generation of measurable road properties.
Subsequent, Section 3.2 describes the modeling of
the vehicle including the vehicle movement, available
sensors, network interfaces, and storage capabilities.
Using the components from these two sections, Sec-
tion 4 describes the connectivity, which includes dif-
ferent communication paradigms, ad-hoc and cellular
dissemination approaches, and offloading schemes.
3.1 Environment
For the vehicular scenario, information is commonly
geocasted to a particular area. Geocasting often relies
solely on the location of each vehicle. Thus, no infor-
mation about the road network is required. In recent
years, pure location-based geocasting has proven to
be inefficient, as vehicle follow predictable routes. If
information about the road network is used in the dis-
semination process, the cellular traffic decreases dras-
tically (Meuser et al., 2018b). To this end, the road
network needs to be accessible to develop approaches
that improve communication quality. We retrieve the
road network from SUMO using TraCI.
The road network class is accessible from every
class in the Simonstrator and manages all roads in the
scenario. A road consists of a unique id, the max-
imum allowed speed, its length, the corresponding
lanes, incoming and outgoing roads, and active prop-
erties. The incoming and outgoing roads are required
to enable rapid path search. Additionally, the active
properties describe the current state of the road.
3.1.1 Road Property Modeling
We model a road property if it supports the driver in
his driving decisions and, thus, is shared between ve-
hicles. It provides the creation date of the property,
the value of the property (if necessary), and the de-
fault value of the property, e. g., no jam in case of a
jam property. Currently available properties are fog,
bump, hazard, jam, rain, and traffic signs. However,
new properties can be added easily due to the exten-
sible design of our environment properties.
Some properties like jams influence the behavior
of the vehicles directly. Thus, these properties change
the representation of the road in SUMO. In that case,
the road is manipulated using TraCI to set the cor-
responding properties like lowering the allowed road
speed. If only start and destination are provided, this
leads to issues in SUMO if shortest-path routing is
utilized. Due to the changes of the road speed, the ve-
hicle’s route might be adjusted. However, this route
change is based on global knowledge, which would
not be available in real-world scenarios. Thus, only
scenarios like the TAPAS Cologne scenario shall be
used currently, as the routes are predefined there.
In our vehicular scenario, the location of road
events has a significant influence on the system per-
formance. As the properties are generated artificially,
the generation is essential for the simulation results.
3.1.2 Road Property Generation
At the start of the simulation, no road has an active
property, i. e., no roads are jammed or otherwise af-
fected. To this end, the vehicles do not need to com-
municate. After a configurable time, the generation of
road properties starts. Once a property is created, the
location, duration, type, and the value of this prop-
erty need to be specified. As these parameters influ-
ence the simulation results, our property generator is
VEHITS 2019 - 5th International Conference on Vehicle Technology and Intelligent Transport Systems
414
Figure 2: Connection of the Simonstrator and SUMO.
extensible by multiple plugins. Each plugin is respon-
sible for the generation of a particular road property.
These plugins choose the location of the property ran-
domly. This randomness can be configured to either
induce a specified load to the network or reproduce
real-world behavior based on statistical observations.
Regarding the lifetime of a property, we offer dif-
ferent possible lifetime distributions to simulate dif-
ferent environmental conditions. We designed three
lifetime distributions for our scenario, (i) an exponen-
tial distribution, (ii) a Gaussian distribution, and (iii)
a custom distribution to reflect real-world measure-
ments. Each distribution can be configured according
to the type of the property that is simulated. Once the
event lifetime is reached, the property value changes.
Depending on the chosen generator and the event
type, the behavior of the property value might be ei-
ther continuous or discrete. An example of a con-
tinuous variable is the driving speed in a jam, while
a hazard is a discrete variable with the states haz-
ard and no hazard. For discrete variables, we use a
Markov chain to determine the transitions between
the states of the variable. For continuous variables,
we also divide them into states of similar size, with
a transition probability between the states. Between
the state changes, the variable value is linearly inter-
polated between the two states. To simulate different
types of events, we have one property generator plu-
gin per event type. Thus, it is possible to plugin cus-
tom property generators. That way, new road proper-
ties can easily be added to our simulation.
3.2 Vehicle
Vehicles can only sense road properties using their
equipped sensors. As the range of these sensors is
limited, the movement of the vehicles impacts the
measurable road properties. Thus, realistic vehicle
movement is required for reliable simulation results.
3.2.1 Vehicle Movement
Map-based movement is a common characteristic of
vehicle movement. For map-based movement, the Si-
monstrator targets only human mobility (Richerzha-
gen et al., 2017), which does not satisfy the require-
ments of realistic vehicle movement. Thus, we con-
nect the Simonstrator to the traffic simulator SUMO
to achieve realistic vehicle movement. Figure 2 dis-
plays the technical implementation of the connection.
The vehicle movement class utilizes TraCI to access
the information available at SUMO. Based on this in-
formation, the vehicle movement moves the vehicles
in the scenario. An issue regarding the connection of
the Simonstrator and SUMO is the fluctuating num-
ber of active vehicles in SUMO. Due to simulator
restraints, we cannot generate new hosts at runtime.
To resolve this issue, we initialize a number of of-
fline hosts which are initialized at the start of the sim-
ulation. This number depends on the scenario cho-
sen in SUMO. Once a new vehicle spawns in SUMO,
the vehicle movement selects one host from the set of
available hosts and turns its network interfaces online.
Similarly, the network interfaces are turned offline af-
ter the vehicle went inactive in SUMO. The vehicle
movement binds the selected host to the respective
vehicle in SUMO. In our simulation framework, ev-
ery application can read information from SUMO and
manipulate the vehicle movement in SUMO. Exam-
ples for information are the future route of the vehi-
cle and the current vehicle speed. To dynamically ex-
change the scenario through different simulation runs,
we mirrored the essential parts of the configuration of
SUMO in the Simonstrator. As simulating the whole
network might not be necessary for huge scenarios,
we provide the possibility to reduce the size of the
simulated area independently of SUMO. Only if a ve-
hicle enters the described area of the scenario, it is
modeled in the Simonstrator, and its network inter-
faces and sensors are activated.
3.2.2 Sensors
For sensing the environment, we model multiple sen-
sors for the vehicular use case. Compared to previous
sensor modeling approaches like in (G
¨
unther et al.,
2017), we explicitly model sensor inaccuracy in our
system. This inaccuracy is important for the vehicu-
lar use case, as perfect information will not be avail-
able. Thus, every sensor has an accuracy value, which
states the probability that the sensor measures the cor-
rect value. We added a configurable random deviation
to the sensor quality to reflect the sensor quality dif-
ference of vehicles in real-world conditions.
In this work, we can simulate both continuous and
discrete events. As each continuous variable can be
approximated with a discrete variable, we perform
most of our evaluations on discrete variables to re-
duce complexity. To this end, we divide the values
of the variable into n possible states. For continuous
variables, n should be chosen such that a required ac-
curacy level is reached.
Simulation Platform for Connected Heterogeneous Vehicles
415
There are different sensor results available: (i) a
single value that can be measured for both continu-
ous and discrete variables, and (ii) a probability vec-
tor that is only available for discrete variables. This
probability vector states the probability that the vari-
able is in a particular state. The advantage of the prob-
ability vector is the provided meta-information about
the inaccuracy of the sensor. This meta-information
is important in the aggregation process, as the impact
of low-quality sensors is reduced.
When a sensor measures the environment, it needs
to determine the measured state. This state depends
on the final accuracy of the sensor, i. e., the accu-
racy after the random deviation was added. To reflect
different types of environmental variables, we mod-
eled the sensing process using different distributions.
However, for most use cases, the Gaussian distribu-
tion is considered to be most appropriate. Compared
to the work of (Meuser et al., 2018c), we revised the
design of the sensors, which simplifies the configura-
tion of sensors for different use-cases. Additionally,
new sensors can easily be added through our modular
sensor design. Similarly, new road properties can be
added easily through our extensible design. To pro-
vide a basic set of properties, we implemented sensors
for fog, jam, accident, bump, and traffic signs.
Every simulated automotive application can query
the current observations from the available sensors.
The observations of all plugins are bundled into an
Environment object. Depending on the application
requirements, only some sensor plugins may be re-
quired. Thus, the deployed plugins can be configured
either on startup or during the request.
At the simulation start, each vehicle is provided
with a configurable set of sensors with configurable
quality. This configurability is pivotal for vehicu-
lar networks, as the heterogeneity of the sensor net-
work is a common topic for vehicular applications.
While the heterogeneity of vehicular sensors is one
important aspect, vehicles are additionally heteroge-
neous regarding their available computation capabili-
ties, storage, and network resources.
3.2.3 Network Resources and Computational
Resources
Vehicles have the possibility to exchange informa-
tion via two network types, decentralized and central-
ized communication. In our simulation, we assume
that the decentralized communication is performed
Wifi-based and the centralized communication is per-
formed based on the mobile network. While Wifi-
based communication is commonly used for CAMs,
it can also offload the cellular connection or carry
messages over large distances. However, mobile com-
munication is often more suitable for information ex-
change over large distances. As both communication
types are viable in the vehicular scenario, we provide
the possibility to use both decentralized and central-
ized communication in the vehicles.
We wrapped the road properties from above into
the transmittable RoadInformation class which con-
tains a RoadProperty and additional attributes like ex-
pected event lifetime. The RoadProperty contains the
respective road id, the measurement location, the date
of the measurement, and the value of the property.
With the help of this class, communication is inde-
pendent of the concrete road property implementa-
tion. Dependent on the road property, often one of the
two communication technologies is preferable. For
jam information, LTE is preferable, as the jam in-
formation required early to potentially detour. Con-
trary, information about a bump can be disseminated
via 802.11p, as the driver only needs to slow down.
However, not all vehicles can communicate via both
of these technologies. Thus, a single technology is
often not sufficient for vehicular communication. To
design applications solving this issue, we configure
the network interfaces for each vehicle individually.
Depending on the available communication technolo-
gies, the Simonstrator provides different communica-
tion patterns. These include the Pub/Sub communica-
tion paradigm (Richerzhagen et al., 2015), ad hoc dis-
semination mechanisms and gateway selection strate-
gies (Richerzhagen et al., 2016). With the availability
of these patterns, new applications can easily be eval-
uated without consideration of the underlying com-
munication paradigm. The possibilities for communi-
cation are discussed in Section 4.
Every time vehicles exchange measurements, they
either use the received measurements immediately for
decision-making or store them locally. While the im-
mediate use of measurements requires only a few re-
sources, the storing of information requires storage
capabilities of the vehicles. While the limitation of
storage seems to be negligible in this scenario, the
huge number of roads with possibly important infor-
mation might lead to storage issues. Thus, we mod-
eled the cache of the vehicle with different sizes and
invalidation strategies. As measurements might be in-
consistent, the vehicles need to validate the received
information to use it in their decision-making. We en-
capsulate the validation of information such that it is
independent of the road property and uses only com-
monly available meta-information like location, de-
tection date, and Time to Live (TTL). Based on this
information validation, further investigation of opti-
mal vehicular decision-making is possible.
VEHITS 2019 - 5th International Conference on Vehicle Technology and Intelligent Transport Systems
416
4 COMMUNICATION
COMPONENTS
Connectivity is an essential feature for future vehic-
ular applications. While today’s network simulators
focus on the exact modeling of the data transmis-
sion, the overlay network is mostly ignored. An over-
lay network is on top of the underlay network (LTE,
802.11p) and provides different addressing schemes,
e. g., content-based addressing. Especially, content-
based information dissemination matches the require-
ments of vehicular applications well (Amadeo et al.,
2016). To this end, our goal is to provide different
types of overlay networks to kick-start the develop-
ment and evaluation of vehicular applications.
4.1 Addressing Schemes
For the inter-vehicle communication, overlay net-
works with different addressing schemes are avail-
able. All these schemes consider the location as a
pivotal property for the dissemination process. We
implement existing schemes for addressing based on
the exact vehicle location, geohash
1
, street segments,
and an information-dependent relevance score.
In the addressing based on the exact vehicle lo-
cation, each vehicle receives all events in a particular
area around its current location. While this addressing
scheme is commonly used for Mobile Ad-hoc Net-
works (MANETs), it is unsuitable for vehicular net-
works due to the high node mobility. This forces
frequent location updates and, thus, induces unnec-
essary load to the network. Based on this considera-
tion, we implemented the geohash concept in the Si-
monstrator. The geohash differs from the addressing
scheme based on exact location, as each vehicle only
subscribes to the grid cell it is currently in. The size
of the subscription area can be adjusted with increas-
ing vehicle speed, which further reduces the number
of location updates. However, even this addressing
scheme induces a considerable overhead: Informa-
tion is often not relevant in the whole grid cell, but
only for a small set of roads in that cell. To alleviate
for this issue, we implemented a road-based address-
ing scheme. In this addressing scheme, vehicles sub-
scribe to street segments and receive the information
of these segments. The subscriptions to street seg-
ments reduce the overhead compared to the address-
ing based on exact location. However, the overhead
is higher compared to the geohash approach, which
is justified by the more efficient distribution of pay-
load messages. For road-based addressing, each ap-
plication must specify the set of roads, for which the
1
http://geohash.org/
information is relevant. In addition to this, we pro-
vide a relevance-aware addressing scheme which dis-
seminates measurements depending on the context of
both the measurement and the vehicle. The relevance-
aware addressing scheme simplifies the configuration
of the dissemination strategy drastically, as the appli-
cation only needs to specify a threshold for the rel-
evance between 0 and 1. The availability of these
addressing schemes decreases the development time
of new vehicular applications and the evaluation time
of new addressing schemes drastically. Moreover,
these strategies are applicable to every communica-
tion strategy, which are presented in the following.
4.2 Communication Strategies
There are two communication strategies, pull-based
communication, and push-based communication.
Both communication strategies are suitable for local
and cellular communication and have their specific
challenges and advantages. Pull-based communica-
tion relies on periodic pulling of information, which
induces additional network load for the requests and,
thus, reduces the available bandwidth for payload
messages. Push-based communication omits these
requests, but might share information that is not re-
quired by the receiver.
The normally used strategy differs for the under-
lying communication technology. Local communica-
tion via 802.11p is commonly push-based, as the cur-
rently available messages like CAM and Decentral-
ized Environment Notification (DEN) are sent peri-
odically or event-based. Thus, pull-based communi-
cation would increase bandwidth usage without bene-
fiting the network performance. Cellular communica-
tion is often performed pull-based, as current vehicu-
lar applications require only a few data, which leads to
few pull requests. A central server manages all avail-
able information and distributes them to the vehicles.
Future vehicular applications will require an increas-
ing amount of information. Thus, the load on this
central server and the cellular network will increase.
Push-based communication generally decreases the
load on the server, as less server storage and compu-
tational power is required. This load decrease is due
to the local processing of information in the vehicles.
Additionally, no requests like in pull-based communi-
cation are required, which might decrease the overall
consumed bandwidth. To model both current and fu-
ture communication scenarios, we implemented both
communication strategies, including strategies com-
bining push-based and pull-based communication. To
further reduce the load on the cellular network and
compensate for dead spots, we implement strategies
Simulation Platform for Connected Heterogeneous Vehicles
417
for offloading the cellular connection based on Wifi.
The required server configuration is described in the
following section.
4.3 Backend Configuration
The required amount of server resources depends
on the used communication strategy. While pull-
based communication generally requires computa-
tional power and storage, push-based communication
only requires information about the vehicle’s posi-
tions. Thus, the required server resources for push-
based communication are generally lower compared
to pull-based communication, as vehicles manage and
aggregate provided information.
For pull-based communication, the server often
aggregates information centrally and distributes the
aggregates to the vehicles. However, relying only
on a central server limits the scalability and contra-
dicts the edge computing trend for mobile networks.
If the server resources are not sufficient or the server
costs should be minimal, push-based communication
is generally preferable. A push-based server with-
out storage provides information of lower quality to
the vehicles, which the vehicles aggregate locally us-
ing their computational resources. Additionally, a hy-
brid strategy is available, in which information is dis-
tributed pull-based or push-based dependent on dif-
ferent information properties. As the performance of
these approaches depend on the available server re-
sources, we provide the possibility to configure the
server regarding available storage.
5 EXEMPLARY USE CASES
In this section, we provide an overview of our pre-
vious work, which used our simulation platform
(Meuser et al., 2018b; Meuser et al., 2018a). Due
to space constraints, the reader is referred to the re-
spective papers for an in-depth description of the eval-
uation results. The difference in application scenar-
ios shows the versatility of our simulation framework
which is freely available
2
. To support the evaluation
of developed approaches, we designed different met-
rics assessing the behavior of developed approaches
from different perspectives. Examples of available
metrics are the quality of information at the vehicles,
the produced network traffic, and the computational
effort of the approaches.
In (Meuser et al., 2018b), we evaluated our ap-
proach with a focus on information validation for ve-
hicular applications. Thus, we used vehicles with a
2
dev.kom.e-technik.tu-darmstadt.de/simonstrator/
2000 m
2000 m
0.0 0.2 0.4 0.6 0.8
Information Dissemination
(a) Exemplary informa-
tion dissemination range
for a threshold of 1% in
(Meuser et al., 2018b).
(b) Required process-
ing for the information
validation approaches in
(Meuser et al., 2018a).
Figure 3: Additional insights to our approaches of our pre-
vious work.
default communication interface to speed the devel-
opment process of their information validation ap-
proach and focused on information quality metrics.
Figure 3(b) shows the computational overhead of the
different approaches, which can be useful to evaluate
the practical applicability of the developed informa-
tion validation approaches.
In (Meuser et al., 2018a), we focused on a dis-
semination strategy, which aims to distribute informa-
tion efficiently while preserving communication qual-
ity. Thus, we used the available information valida-
tion modules to concentrate on the networking as-
pect of the investigated problem and used network
traffic metrics. Figure 3(a) displays the communi-
cation range of one specific message. This visual-
ization can be very useful to analyze the impact of
different environmental conditions on the dissemina-
tion and support the development of new dissemina-
tion approaches.
6 CONCLUSION
In this work, we present a platform for the rapid de-
velopment and evaluation of automotive networking
applications. Compared to existing simulators, our
platform focuses on the heterogeneity of the vehicles
under varying environmental conditions. For the ve-
hicles, this includes different sensors, storage capa-
bilities, and networking components. For the envi-
ronment, we simulate different types of measurable
events. Additionally, we provide different server con-
figurations to evaluate varying networking conditions.
We demonstrate the versatility of our platform by
evaluating different types of automotive applications.
While the focus of both applications differs, our plat-
form can be used to evaluate both applications. Addi-
tionally, our platform is used by many researchers and
continuously extended. Currently, we are extending
VEHITS 2019 - 5th International Conference on Vehicle Technology and Intelligent Transport Systems
418
our platform with strategies for offloading the cellular
connection and efficient event dissemination.
ACKNOWLEDGEMENTS
This work has been funded by the DFG within the
CRC 1053 - MAKI (B1, C2).
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