Time-controlled Neighborhood-driven Policy-based Network Selection

Algorithm for Message Dissemination in Hybrid Vehicular Networks

Oleg Oleinichenko, Yagmur Sevilmis, Karsten Roscher and Josef Jiru

Fraunhofer Institute for Embedded Systems and Communication Technologies ESK, Munich, Germany

Keywords:

Hybrid Communication, Network Selection, Adaptive Heterogeneous Networking, VANET, LTE, ITS-G5.

Abstract:

In vehicular ad hoc networks (VANETs), successful delivery of GeoUnicast and GeoBroadcast packets de-

pends on scenario-speciﬁc aspects like vehicle density, distribution of vehicles on the road and type of the

environment (e.g., urban, rural). These aspects can signiﬁcantly inﬂuence the reliability of the connection

between communication parties making traditional ITS-G5 based ad hoc networks unreliable. The absence of

communication partners in range, long transmission distances, non-line-of-sight (NLOS) conditions are just

a few examples that could hinder ITS-G5 transmissions. In this paper, we propose a Hybrid Policy-based

Network Selection Algorithm that uses LTE to strengthen and complement ITS-G5 under critical conditions

in which successful transmission over the ad hoc network is highly unlikely. The main objective is to use as

less LTE transmissions as possible whilst maintaining high Packet Delivery Ratio (PDR) within deﬁned delay

constraints. The results, which are derived from extensive simulation campaigns, show a clear advantage of

using the hybrid scheme over solely ITS-G5 or LTE.

1 INTRODUCTION

Information timeliness and reliability are key for V2X

message distribution, however catering for different

Intelligent Transport System (ITS) application classes

and their diverse communication requirements is not

possible using a single wireless access technology.

Strengths and shortcomings of the enabling technolo-

gies namely ETSI ITS-G5 and LTE for hybrid net-

works used in the scope of this work, were ana-

lyzed extensively in literature (5G-PPP, 2015) (Aran-

iti et al., 2013). ITS-G5 (ETSI, 2013) which is the

de facto standard for V2X communication in Europe

allows low-latency communication in quickly chang-

ing network topologies, but it has some drawbacks

due to its shared medium characteristics, transmission

range limitations and network fragmentation issues

that could arise when the penetration rate of ITS-G5

equipped vehicles is under a certain threshold (5G-

PPP, 2015). LTE on the other hand provides wide cov-

erage thanks to its high market penetration rates so it

can be used to bridge communication gaps in low den-

sity scenarios or enable communication in challeng-

ing ones e.g., intersections where NLOS conditions

exist however, it has some shortcomings related to

message transmission latency. Necessity of all trans-

mitted messages to pass through the infrastructure,

random access procedure and mode switching from

idle to connected could create considerable delays

(Araniti et al., 2013). Moreover, it can suffer from

packet loss due to fading for fast moving vehicles. As

a result, provisioning of concepts for intelligent radio

access technology selection in a multi-networking en-

vironment by combining the strengths of ad hoc and

infrastructure-based networks becomes necessary in

order to better fulﬁll application Quality of Service

(QoS) requirements. Therefore for ITS stations capa-

ble of hybrid communication, it is signiﬁcant to se-

lect the most appropriate technology/technologies for

message dissemination.

In this work, we propose a policy-based network

selection algorithm that aims to determine the opti-

mal communication technology on a per-packet ba-

sis based on identiﬁed context indicators and pre-

deﬁned policies. The main design goal and philos-

ophy behind the algorithm is to enable the cooper-

ation of both ad hoc and cellular radio technologies

with the aim of providing application transparent geo-

localized message dissemination over ITS-G5 and

LTE so that improved QoS will be provided to appli-

cations while keeping the LTE footprint low. Limiting

LTE usage is important since in typical V2X scenar-

ios where a large number of small packets are being

exchanged with high frequencies, the excessive us-

Oleinichenko, O., Sevilmis, Y., Roscher, K. and Jiru, J.

Time-controlled Neighborhood-driven Policy-based Network Selection Algorithm for Message Dissemination in Hybrid Vehicular Networks.

DOI: 10.5220/0006705901410155

In Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2018), pages 141-155

ISBN: 978-989-758-293-6

141

age of this technology might become costly and lead

to cellular network congestion (Araniti et al., 2013).

We focus on geographical (position-based) network-

ing since ETSI ITS GeoNetworking is the network

layer protocol standardized for ITS in Europe. Fur-

thermore, addressing of nodes based on their location

(geo-broadcast) is beneﬁcial for many ITS use cases

(ETSI, 2010) e.g., informing the following vehicles

about a sudden braking maneuver.

The algorithm operates on the network layer (cor-

responding to the ISO/OSI layer 3) of the ETSI

ITS protocol stack. Delegating this task to the net-

work layer enables coordinating multiple applica-

tions. Moreover, direct access to the link layer to

retrieve real-time performance indicators of the un-

derlying access networks becomes possible. The al-

gorithm will gather requirement indicators from the

applications, performance metrics from the link layer

and exploit this information combining it with its

view of the network conditions that are directly avail-

able in the network layer. Using such an approach

abstracts applications from the network selection pro-

cess and provide them an always best informed ap-

proach transparently. The downside is that the appli-

cations lose direct control over technology selection

and message dissemination.

The algorithm was evaluated using a high-ﬁdelity

simulation environment comprising of multiple sim-

ulators coupled together. Orchestration of multiple

simulators for realistic evaluation of vehicular com-

munication protocols is a widely used approach (Ku-

mar et al., 2009) (Sommer et al., 2011b). Two car-

dinally different scenarios (i.e., Manhattan grid and

open highway) were used throughout the simulation

campaigns in order to assess the algorithm’s perfor-

mance under different communication environment

conditions from a delay restricted packet delivery per-

spective with an indication on LTE resources usage.

Taking into consideration the results of our work,

main advantages of the proposed policy-based adap-

tive hybrid networking algorithm are:

• Improved QoS to applications

• An increase in path resilience and fault tolerance

with low LTE footprint

• Efﬁcient use of available resources (i.e., both

intra- and inter-technology optimizations are pos-

sible, limitations imposed by a single technology

are reduced)

• Higher performance without requiring any modi-

ﬁcations at the application level (i.e. application-

agnostic)

• Ease of implementation and integration into the

ITS environment

• Performance improvements with respect to the

identiﬁed Key Performance Indicators (KPIs)

over individual technologies in all scenarios

• Facilitation in the market introduction for cooper-

ative ITS services (intermittent connectivity due

to network fragmentation in the initial deployment

phase of ITS-G5 can be overcome by LTE)

• Possibility of extension with future technologies

and new services

This article is structured as follows: Section 2

summarizes related work regarding hybrid communi-

cation and network selection in the context of vehicu-

lar networks. Section 3 describes the hybrid network

architecture providing some background information

necessary to understand the context in which the pro-

posed algorithm operates. In section 4 we present the

policy-based network selection algorithm in detail in-

cluding the design considerations made, parameters

used for decision-making and its control ﬂow. Section

5 describes the simulation toolchain, selected scenar-

ios and network conﬁguration parameters used in per-

formance evaluation. Section 6 presents the evalua-

tion results. Section 7 summarizes the ﬁndings and

gives an outlook.

2 RELATED WORK

Network selection in hybrid communication environ-

ments is a widely researched problem. (Charilas and

Panagopoulous, 2010) provides a methodological ap-

proach to address this problem by decomposing it into

four steps, namely selection of the parameters that

should be considered in the process, collection of the

values for the selected criteria, estimation of impor-

tance of each parameter by weight assignment and de-

termination of the optimal choice by ranking the alter-

natives. Various methods that can be used in each step

are also provided and analyzed in terms of their pros

and cons. (Wang and Kuo, 2013) gives an overview

of mathematical theories applied in the modeling of

the network selection problem. A broad classiﬁcation

of the set of attributes that might be considered during

the network selection process is given in (Charilas and

Panagopoulous, 2010) (Kumaran and Shaji, 2014). It

is important to stress that most of the research on net-

work selection is based either on direct feedback or

on continuous measurement of QoS parameters as in

the case of multimedia streaming none of which are

applicable to the vehicular networking case.

On the ITS side, one reference architecture of a

standardized component responsible of coordinating

multiple access technologies is the ETSI communi-

VEHITS 2018 - 4th International Conference on Vehicle Technology and Intelligent Transport Systems

142

cation interface (CI) management component which

aims to provide application friendly communication

services to a diverse range of applications over multi-

ple media (Bouali and Senouci, 2015). Although the

speciﬁcation of the component is at a block diagram

level and no speciﬁc methods for network selection

are mentioned, the component architecture stresses

the value of collecting and combining different cri-

teria.

In literature, several policy-based network selec-

tion mechanisms were proposed. Policy-based net-

work selection systems are composed of identiﬁed

rules and policies that are used in the technology se-

lection process. Optimum communication medium is

selected by evaluating the current network selection

parameters based on the policies stored. In (Brickley

et al., 2007), estimated quality of the wireless chan-

nel derived from achievable throughput and allow-

able load is used to determine the selection between

UMTS and WLAN. In (Meneguette et al., 2012), a

multi-network packet scheduling technique based on

the mapping of ITS application classes to one or more

channels is proposed. If two application classes com-

pete for the same channel, the order of access is deter-

mined by the priorities associated with each applica-

tion class (e.g., safety applications take priority over

infotainment). In (Olivera et al., 2009), all commu-

nication is done by default over the V2V ad hoc net-

work and complementary technologies are only uti-

lized in cases where it is not possible to communicate

via V2V (e.g., no neighbors in the communication

range). Despite providing improved performance, ap-

proaches based on a few criteria/attributes often result

in suboptimal decisions as they do not follow a holis-

tic approach and therefore cannot adapt to dynamic

environments.

More holistic approaches in which the selection

is done by monitoring the current context of the ve-

hicle and capturing context indicators is suggested in

(Bouali and Senouci, 2015) (Gopinath et al., 2016b)

(Gopinath et al., 2016a). These context indicators can

be provided by in-vehicle sensors, the underlying ac-

cess layer technology or the network layer. The pro-

posed approaches are based on estimating the QoS

that can be offered by the ad hoc and cellular networks

subject to the context indicators and making a selec-

tion based on how well the QoS they offer matches

the requirement indicators (gathered from the appli-

cations) using fuzzy inference systems (Bouali and

Senouci, 2015) (Abbas and Saade, 2015) (Ndashimye

et al., 2016). The drawback of using fuzzy inference

systems is that they do not scale well when the num-

ber of input parameters increase.

The approach we propose strikes a delicate bal-

ance between the tradeoffs mentioned in this section

i.e., it uses a holistic approach taking a multitude of

parameters and most importantly application require-

ments (maximum tolerable latency, required reliabil-

ity in terms of PDR) into account and still remains

viable to be used for ITS applications that have strict

latency requirements.

3 HYBRID NETWORK

ARCHITECTURE

Hybrid communication systems (HCS) are systems

that are able to leverage multiple communication

technologies for improved QoS. Although a wide va-

riety of technologies can be utilized in order to realize

such a system, in the context of vehicular network-

ing use of cellular networks with Dedicated short-

range communications (DSRC) is regarded as a po-

tential solution for meeting ITS application require-

ments (Zheng et al., 2015). Therefore we focused

on enabling hybrid communications in a distributed

manner using ITS-G5 and in a centralized manner via

LTE since these technologies are already available.

Some background information about the hybrid net-

work architecture we utilized is given below so as to

ease understanding of the network selection algorithm

that will follow.

3.1 ITS-G5

The distributed approach with ITS-G5 is based on

short range wireless radio and position based ad hoc

networking. Using this approach, data packets can

be distributed to the speciﬁed geographical position

or geographical area potentially in a multi-hop fash-

ion via direct communication among the vehicles and

possibly Roadside Units (RSUs) without requiring

back-end infrastructure support (Le et al., 2011). Dis-

tribution of packets in geographical areas is possi-

ble through a set of network layer functionalities also

known as GeoNetworking (ETSI, 2017). GeoNet-

working supports multiple addressing schemes mak-

ing it possible to address an individual node by its

position or multiple nodes within a geographical re-

gion. Each node maintains a location table which

holds information about neighboring nodes and their

locations along with other features that might become

relevant for the protocol operation. Packets received

by a node are locally forwarded towards their geo-

graphic destination based on the nodes view of the

network topology and the geographical address con-

tained in the packet.

Time-controlled Neighborhood-driven Policy-based Network Selection Algorithm for Message Dissemination in Hybrid Vehicular Networks

143

3.2 LTE and GeoMessaging Server

The infrastructure assisted approach is based on LTE.

The aim of this approach is to emulate the function-

ality of the ETSI ITS-G5 network using the cellular

network i.e. to support ad hoc like communication

between ITS stations equipped with LTE. In order to

realize this approach, we introduced a new network

component called the GeoMessaging Server (GMS).

GMS manages the distribution of ETSI ITS GeoNet-

working data packets over cellular networks. It op-

erates on the network layer (ISO/OSI layer 3) of the

protocol stack thus is unaware of the information and

contents of messages pertaining to higher protocol

layers making it application agnostic. It maintains the

context of the vehicular network through periodic lo-

cation updates sent from nodes which are LTE capa-

ble. Since GMS is aware of the mapping between IP

addresses and geographical positions, it can distribute

messages to vehicles in a geographical target area via

cellular links (Le et al., 2011). Figure 1 shows the

possible communication ﬂows in a hybrid V2X net-

work.

eNodeBeNodeB

GeoServer

RSU

Relevance

Area

Figure 1: Possible communication ﬂows in a hybrid V2X

network.

Hybrid ITS stations were enabled to communi-

cate with the GMS with what can be seen as a vir-

tual access layer device. This device makes UDP/IP

tunneling of GeoNetworking packets over LTE pos-

sible. Moreover, it has the added beneﬁt of being

completely coherent with the ETSI common refer-

ence architecture as no modiﬁcations to the network

layer and above are necessary in order to use it. Un-

like wireless ad hoc communications which relies on

a shared medium with broadcast characteristics via

direct communication among the ITS stations, com-

munication is based on a client-server design with in-

dividual connections for each user and traverses the

infrastructure of the cellular network. When a vehicle

for instance detects an event and determines the geo-

graphical target area where this event should be dis-

seminated, it sends this message uplink (UL) to the

base station towards the GMS. The GMS then evalu-

ates the headers of the GeoNetworking protocol as-

sociated with this message to determine the packet

transport type and destination geographical area. It

then distributes the message downlink (DL) accord-

ingly over the cellular infrastructure. In order to

receive messages from the GMS, at least one loca-

tion update should have been sent by the ITS station

(through a time-based beaconing scheme) otherwise

the GMS would be unaware of the ITS station and

have no means to deliver the message to it.

4 POLICY-BASED NETWORK

SELECTION ALGORITHM

The design goal of the hybrid algorithm is to exploit

ITS-G5 as the primary (default) technology for trans-

mission and complement it with a limited and se-

lective support via LTE under critical conditions in

which successful transmission over the ad hoc net-

work is highly unlikely and while doing so keeping

the LTE footprint low. The algorithm takes into con-

sideration a multitude of parameters. Selection is per-

formed by each node i.e., both source and forward-

ing nodes along a packet’s path towards its destination

by monitoring application, mobility and QoS related

context indicators. As it relies on simple policies with

basic rules and parameters for network selection can

be collected without additional communication over-

head, it is characterized by its ease of implementation

and integration into the ITS environment.

In the following design considerations and main

building blocks of the algorithm will be described

followed by a detailed explanation of the parame-

ters used in the decision-making process along with

their optimal thresholds derived from conducted anal-

ysis and/or literature review. After explaining its con-

stituent components i.e. two pillars on which it rests

namely the time control mechanism and classiﬁca-

tion of neighbors into reliable and unreliable ones, the

overall control ﬂow of the algorithm will be provided.

4.1 Design Considerations and Features

GeoNetworking requires forwarding of a packet to-

wards the destination. This process is called line for-

warding. GeoUnicast uses line forwarding, GeoAny-

cast and GeoBroadcast use it as well if the sender

is not located within the destination area (Cam-

polo et al., 2015). There are two approaches for

line forwarding namely Contention-Based Forward-

ing (CBF) and greedy forwarding. CBF has shown to

provide much better performance in terms of reliabil-

ity than the greedy approach as it leverages multiple

forwarding candidates (Roscher et al., 2016) and is

therefore the basis of our algorithm.

VEHITS 2018 - 4th International Conference on Vehicle Technology and Intelligent Transport Systems

144

Using CBF every node that is closer to the desti-

nation than the previous forwarder is considered a vi-

able forwarding candidate and sends the packet with

a delay inversely proportional to its distance progress.

This means that the candidate that is closest to the

destination will try to forward the packet ﬁrst. A

forwarding candidate refrains from transmitting the

packet if it received it the second time while its CBF

contention timer is running since it means the packet

was already forwarded by another node (Campolo

et al., 2015). This suppression mechanism is an in-

tegral part of CBF.

In light of this information, it is important to ana-

lyze what would happen if the forwarding nodes were

allowed to perform network selection. In such a case,

multiple forwarding candidates might prefer to trans-

mit the packet via LTE and send the packet imme-

diately towards the GMS. As a result, the destina-

tion would receive the same packet over LTE multiple

times resulting in a very redundant usage of cellular

network resources and high congestion assuming that

under ideal network conditions, it is enough to use

LTE only once along a transmission path. The suc-

cessive forwarding over ITS-G5 only aggravates the

problem. Addressing this problem necessitates hav-

ing to implement a suppression mechanism for LTE

message transmissions by introducing additional mes-

sages or extending standardized messages for piggy-

backing this information.

In order to avoid introducing proprietary mech-

anisms and instead utilize the existing ones, LTE

transmission mechanism is integrated with CBF. This

means that whenever LTE is selected, the packet is

queued in the CBF buffer and is sent to the GMS only

when its CBF contention timer expires. It should be

noted that forwarders are not allowed to select only

LTE and should use ITS-G5 in parallel whenever LTE

is selected so as to make use of the CBF suppression

mechanism. This ensures that when the forwarder

with the most distance progress forwards the packet

ﬁrst, possible LTE transmissions of other forward-

ing candidates which overhear this transmission over

ITS-G5 will be suppressed. The downside of utilizing

the CBF mechanism for LTE transmissions is that the

packets will incur queuing delays in the CBF buffer.

Also there might be some redundant transmissions in

the UL channel due to some nodes not overhearing

the ITS-G5 transmissions (NLOS conditions) how-

ever, these redundant transmissions are detected by

the duplicate packet detection performed by the GMS

based on packet IDs and sequence numbers and do not

translate into DL congestion. This logic which elim-

inates duplicates in the DL on the GMS side is also

regarded as an integral part of the algorithm’s func-

tionality.

Another important design feature of the algorithm

related to LTE transmissions is what we termed as the

LTE suppression ﬂag which inﬂuences the selection

logic at the forwarding nodes. For this ﬂag, we used

the reserved ﬁeld of the GeoNetworking basic header

(ETSI, 2017). This ﬂag can assume two values: 0 or

1. Whenever a forwarder receives a packet, it ﬁrst in-

spects this ﬂag. If the ﬂag is set to 0 the forwarder

refrains from selecting LTE, if it is set to 1 the for-

warder is allowed to select LTE. This ﬂag is set at the

decision making nodes whenever LTE is selected ac-

cording to one of the following 3 strategies and which

strategy to use is a conﬁguration option that applies to

all nodes:

• Always set the ﬂag to 0 i.e., indicate to other

nodes that LTE should not be selected again: This

option will henceforth be referred to as the “Flag

0“ policy. Using this conﬁguration results in other

forwarders which will overhear this transmission

via ITS-G5 to refrain from using LTE. The ad-

vantage of this approach is that it decreases traf-

ﬁc in the UL however, if the LTE transmission of

the node that set the ﬂag fails due to some reason

(e.g., no coverage in the destination area), the pos-

sibility of using LTE at a later stage is eliminated

as the ﬂag gets propagated among the forwarders.

This in turn decreases reliability.

• Always set the ﬂag to 1 i.e. indicate to other

nodes that LTE could be reselected: This option

will henceforth be referred to as the “Flag 1“ pol-

icy. Using this approach the forwarders in the

next hop are allowed to reselect LTE. This ap-

proach increases UL channel congestion, how-

ever improves reliability. For instance if the LTE

transmission of the previous node has failed, next

hops along the transmission path are allowed to

try again and might be successful in delivering the

message.

• Set the ﬂag dynamically: This option will hence-

forth be referred to as the “Flag dynamic“ pol-

icy. It uses the current UL Reference Signal Re-

ceived Power (RSRP) value as an indication of the

network propagation conditions and determines

whether to set the ﬂag based on RSRP to delay

and RSRP to PDR mappings. It constitutes a mid-

dle ground in terms of LTE footprint and relia-

bility between “Flag 0“ and “Flag 1“ policies. If

the estimated delay via LTE is over the remain-

ing lifetime of the packet or the RSRP to message

delivery success probability is below the thresh-

old required by the application, the ﬂag is set to 1

as the LTE transmission is deemed unreliable and

likely to fail.

Time-controlled Neighborhood-driven Policy-based Network Selection Algorithm for Message Dissemination in Hybrid Vehicular Networks

145

4.2 Time Control Mechanism

One of the key parameters that steers the technology

selection logic is the remaining packet lifetime. This

can be classiﬁed as an application-related network se-

lection attribute. Depending on its ITS application

class, each application can indicate the maximum tol-

erable latency for a packet. This value is set at the

source and decremented at each hop as the packet is

being forwarded.

Time control mechanism employed by the algo-

rithm is similar for source and forwarders, the only

difference being a minor extension for the source

nodes. It should also be noted that the algorithm takes

into account the CBF buffer queuing time when calcu-

lating the remaining packet lifetime at the forwarding

nodes.

4.2.1 Source Node Speciﬁc Functionality

Communication sessions originate at source nodes

thus the source nodes represent unique points in the

decision making process. Since the originated packet

has never been forwarded before and only the source

is aware of its existence at the time of the technol-

ogy selection process, source nodes are allowed to

select only LTE unlike the forwarders. Using only

LTE at the source nodes for long range transmissions

can reduce ITS-G5 channel congestion and decrease

the LTE footprint compared to its use in parallel with

ITS-G5 when “Flag 1“ or “Flag dynamic“ policy is in

effect. But sticking with the design philosophy of us-

ing ITS-G5 by default and supporting it via LTE only

when it is necessary, we limited using only LTE at

the source to one situation: only when the estimated

optimistic delay of ITS-G5 transmission based on dis-

tance to the destination (derived using dense highway

scenarios) is larger than the packet lifetime i.e., trans-

mission via ITS-G5 is not feasible and estimated to

fail.

4.2.2 Common Functionality

The main philosophy behind the time mechanisms ex-

plained in this subsection is to support the transmis-

sions via LTE whenever it is determined or estimated

that using LTE would not be possible in the next hop

due to remaining packet lifetime constraints. Mech-

anisms described in this chapter make use of the two

thresholds deﬁned below:

• LTE Lower Delay Threshold: This is the mini-

mum possible LTE transmission latency. If the

packet lifetime drops below this value, it is not

feasible to use LTE anymore. In our simulated

LTE network this threshold was 35ms.

• LTE Upper Delay Threshold: This value is set

such that the average LTE transmission latency is

in the middle of the LTE Lower Delay Thresh-

old and this value. In our simulated LTE network,

the average LTE transmission latency was 50ms

therefore this threshold was set to 65ms.

Using these two thresholds, time mechanism func-

tions as follows:

• If the remaining packet lifetime is below the min-

imum LTE transmission latency, it is not possible

to transmit via LTE anymore therefore select only

ITS-G5. If this condition holds, LTE suppression

ﬂag is set to 0 regardless of the chosen ﬂag policy

so as to inform forwarders that selecting LTE is

not feasible anymore.

• If the remaining packet lifetime is between the

lower and upper thresholds i.e., it is close to the

average LTE time and destination is not among

forwarder’s reliable neighbors, support the trans-

mission via LTE in parallel to ITS-G5 otherwise

it might be too late to use it again.

• If the remaining packet lifetime is above the up-

per LTE threshold, calculate the CBF delay that

would be incurred by the next forwarder (reliable

neighbor with the most distance progress). If the

remaining packet lifetime drops below the lower

threshold by the taking into account the delay that

would be incurred in the next hop and destination

is not among forwarder’s reliable neighbors, sup-

port the transmission via LTE in parallel to ITS-

G5 otherwise it will be too late to use it again.

4.3 Reliable Neighbors

Success of multi-hop message dissemination in

VANETs depends on the presence of neighboring

nodes with stable links. Therefore a major part of the

algorithms selection logic is based on the current set

of neighbors and their classiﬁcation into reliable and

unreliable ones. The algorithm classiﬁes a communi-

cation partner as a reliable neighbor if it satisﬁes all

of the conditions listed below:

• It is a direct neighbor (accessible via 1 hop).

• Its distance progress is positive meaning that it is

closer to the destination than the current node.

• It is a fresh neighbor meaning that time since the

last direct message received from it, is less than

or equal 1 second. Last update time is the most

informative value for neighborhood classiﬁcation

and its optimal value is taken from (Roscher et al.,

2017).

VEHITS 2018 - 4th International Conference on Vehicle Technology and Intelligent Transport Systems

146

• Received signal strength of the last message from

it, is above the conﬁgured threshold. This thresh-

old is determined to be -80 dBm based on our sim-

ulations to eliminate the nodes that are close to the

maximum transmission range.

At the end of this classiﬁcation process if the num-

ber of reliable neighbors is below a certain threshold,

communication is supported via LTE in parallel to

ITS-G5. This threshold was determined to be 1 mean-

ing that whenever a node does not have at least one

reliable neighbor, the algorithm secures the transmis-

sion by selecting LTE in addition to ITS-G5. It should

be noted that setting this threshold above 1 resulted in

considerable increases in LTE footprint without sig-

niﬁcant improvements in PDR.

4.4 Algorithm Flowchart

Control ﬂow of the algorithm is provided in a top-

down fashion in ﬁgure 2 with “Time-to-live“ (TTL)

denoting the remaining packet lifetime.

Source Node

Time Mechanism

Source Specific Part

Estimated ITS-

G5 delay > TTL

trueLTE

Time Mechanism Common Part

Forwarder

LTE Suppression

flag is 0

ITS-G5true

TTL < Lower LTE

Threshold

ITS-G5 true

ITS-G5 & LTE

true

TTL within

[Lower, Upper]

LTE Threshold

TTL - Delay

incurred by the next

hop < Lower LTE

Threshold

ITS-G5 & LTE true

Reliable Neighbors

# of reliable

neighbors < 1

ITS-G5 & LTEtrueITS-G5 false

false

Figure 2: Algorithm ﬂowchart.

5 SIMULATION ENVIRONMENT

AND SCENARIOS

In this section the constituent components of the sim-

ulation environment are described. Moreover, the sce-

narios used to evaluate the performance of the al-

gorithm are detailed in terms of their road topology

and network design summarizing all the relevant pa-

rameters used and design considerations/assumptions

made.

5.1 Simulation Environment

Simulation environment comprises of the Network

Simulator 3 (ns-3) discrete event simulator (ns-3,

2017), Simulation of Urban Mobility (SUMO) traf-

ﬁc simulator (D. Krajzewicz and Bieker, 2012) and

ezCar2X framework - ETSI ITS-compliant commu-

nication stack with added support for hybrid commu-

nications (Roscher et al., 2014). Figure 3 illustrates

the block diagram of the simulation toolchain.

Figure 3: Block diagram of the simulation environment.

ezCar2X is a modular software framework for rapid

prototyping of cooperative ITS applications and novel

communication protocols. It consists of an ETSI stan-

dard compliant communication stack, which was ex-

tended with an adaptive GeoNetworking layer sup-

porting application transparent geo-localized message

dissemination via ITS-G5 and LTE. ezCar2X pro-

vides basic set of protocols and abstractions, enabling

implementation of new communication algorithms

and concepts. Moreover, it allows using a single im-

plementation for evaluation both within a simulation

environment and on real world prototypes. The net-

work simulator of choice is ns-3.25. It provides out of

the box device models for IEEE 802.11-family as well

as a variety of wireless channel models. On the LTE

side, it includes the LTE/Evolved packet core (EPC)

network simulator (LENA) (LENA, 2017), which was

used for the LTE network design of the scenarios.

Trafﬁc simulator of choice is SUMO. SUMO is cou-

pled with ns-3 using its well documented Trafﬁc Con-

Time-controlled Neighborhood-driven Policy-based Network Selection Algorithm for Message Dissemination in Hybrid Vehicular Networks

147

trol Interface (TraCI) (Wegener et al., 2008) API with

ns-3 nodes mapped to vehicles in SUMO i.e., vehicle

movements in SUMO are communicated to the mo-

bility model of the assigned ns-3 node.

5.2 Simulation Scenarios

Performance evaluation was done utilizing 2 different

scenarios.

The ﬁrst scenario is the Manhattan grid scenario

with four blocks, one lane roads and one intersec-

tion. A relatively small grid size (720 x 200 m) was

preferred to ensure straightforward results interpre-

tation. This scenario was chosen to simulate urban-

like mobility with very limited line-of-sight (LOS)

conditions. Utilizing a topology with one lane roads

makes it possible to create trafﬁc congestion with

lower number of vehicles particularly at the central in-

tersection as the vehicles cannot drive with their max-

imum target speed as in the case of a highway sce-

nario. Moreover, it helps create a non-uniform distri-

bution of vehicles where inter-vehicle gaps are more

pronounced allowing simulation of challenging con-

ditions for ITS-G5, where ad hoc communication can

be disrupted due to network fragmentation.

The second scenario is the highway - a 10 km long

road with 3 lanes in each direction. This scenario was

chosen to evaluate the algorithm with highly mobile

users in LOS conditions. Contrary to the Manhat-

tan scenario, vehicles do not face any obstacles in the

form of buildings that might disrupt their communi-

cation via ITS-G5 however, due to their high mobility

patterns; they experience higher frequency dispersion

as a result of Doppler shift (fast fading) which reduces

LTE transmission reliability.

5.2.1 Trafﬁc Parameters

Vehicle and packet trafﬁc can be varied using the fol-

lowing parameters:

• Vehicle injection rate: Determines the trafﬁc den-

sity by controlling how many cars depart every

second from each lane.

• Penetration rate: Determines the proportion of ve-

hicles equipped with communication capabilities

i.e, vehicles that are ITS-G5 and LTE capable.

• Packet generation rate: Determines the number of

packets generated within a predeﬁned time frame.

Summary of trafﬁc related parameters for each

scenario is presented in Table 1. It is worth mention-

ing that the values for these parameters were chosen

so as to create low road trafﬁc saturation. This al-

lowed us to test our algorithm under unfavorable ITS-

G5 conditions to gauge the beneﬁts of using a hybrid

Table 1: Summary of trafﬁc related parameters for each sce-

nario.

Scenario Manhattan Highway

Vehicles saturation level

40 180

Penetration rate 0.1, 0.3, 0.6, 1 0.1, 0.3, 0.6, 1

Hybrid capable vehicles (avg.)

4, 12, 24, 40 18, 54, 108, 180

Packet generation rate [Hz]

10 10

Vehicle injection rate [Hz]

0.5 0.1

Average speed [kmph] 30 100

Simulation time [s] 400 500

approach, but also to better understand the inﬂuence

of simulation parameters on deﬁned KPIs.

5.2.2 Communication Parameters

In our simulations at every packet generation instant,

the framework picks randomly the source and desti-

nation nodes and creates a corresponding communi-

cation session. These sessions are created uniformly

with respect to distance and correspond to a valid

range of [0,720] m for the Manhattan scenario and

[0,5000] m for the Highway scenario. All created ses-

sions are unicast ones, meaning that each packet is

designated for only one receiving node. Table 2 pro-

vides a summary of ITS-G5 communication parame-

ters.

Table 2: Summary of ETSI ITS-G5 parameters.

Parameter Value

Tx Power 23 dBm

Propagation Model Nakagami with obstacles

Uni-cast Packet size 300 Bytes

Neighbor timeout 20 s

Default Beaconing rate 0.3 Hz

For ITS-G5 communication we used a realis-

tic, computationally inexpensive simulation model

for IEEE 802.11p radio shadowing in urban environ-

ments that allowed us to model a city scenario with

obstacles. It is comprised of the Nakagami fading

channel described in (Taliwal et al., 2004) with the

obstacle shadowing model in (Sommer et al., 2011a).

From the LTE design point of view, the network

is planned to provide good coverage conditions, with-

out severe fading/shadowing effects that would result

in intermittent connectivity. The detailed overview of

LTE design parameters is presented in Table 3. The

Figures 4 and 5 depict the Manhattan and the High-

way scenarios under the coverage of the designed LTE

network respectively.

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148

Table 3: Summary of relevant LTE design parameters.

Parameter Manhattan Highway

Number of sites (cells)

1 (3) 3 (6)

UL EARFCN 18100

DL EARFCN 100

Frequency Reuse 1

Bandwidth per Cell 25 PRB

Propagation Model Log Distance

Pathloss Exponent 3.9 3.1

Reference Loss 38.57 dB

UE max. Tx Power 23 dBm

eNodeB max. Tx Power

43 dBm

Antenna Model Cosine

UE Antenna Gain 0 dBi

eNodeB Antenna Gain

18 dBi 11 dBi

eNodeB Antenna HPBW

65 degrees 120 degrees

UE NF 7 dB

eNodeB NF 2 dB

Target Edge Throughput

0.5 Mbps

Target min. SINR

0.2 dB

Cell Radius 578 m 1773 m

HO type PBGT

HO Hysteresis 3 dB

HO TTT 256 ms

Average Speed for Channel Fading

30 km/h 100 km/h

Fading Model Time Resolution

1 ms

Figure 4: Schematic coverage area for the Manhattan sce-

nario.

Figure 5: Schematic coverage area for the Highway sce-

nario.

6 PERFORMANCE EVALUATION

The evaluation considers different scenarios and pen-

etration rates as well as the distance to destination

and application time requirements to present impor-

tant KPIs, such as PDR and LTE Packet load, in com-

parison to reference simulations, namely transmission

using only ITS-G5, only LTE and both technologies.

6.1 Methodology

Since for the Manhattan scenario the generated ses-

sions are in the close range of [0,720] m, the re-

sults were not fractioned into different distance bins

and are presented as an average over all transmis-

sions. In the Highway scenario the results are pre-

sented for the following ranges: close [0,500] m,

medium [500,2500] m and far [2500,5000] m. Time

requirement, which is termed as “Application delay

requirement”, is used to assess the performance of the

algorithm under two application-imposed time con-

straints (100ms and 500ms) deﬁned in (Karagiannis

et al., 2011) as plausible thresholds for foreseen ITS

services. Additionally, the algorithm is evaluated un-

der time-unlimited conditions to obtain an insight on

the performance of its Neighborhood-driven part. The

KPIs used, namely PDR and Packet load, are deﬁned

as follows:

PDR

dest

u,r

source

u,g

, (1)

where PDR

dest

is PDR of unique packets measured

at the destination, n

dest

u,r

is the number of correctly re-

ceived unique packets at the destination and n

source

u,g

the total number of unique packets generated at the

source.

gms

u,r

source

u,g

, (2)

where L

gms

is the Packet load due to the unique pack-

ets measured at the GMS, n

gms

u,r

is the number of

uniquely received packets at the GMS and n

source

u,g

the total number of unique packets generated at source

nodes.

gms

r,r

source

u,g

, (3)

where L

gms

is the Packet load due to redundant pack-

ets measured at the GMS, n

gms

r,r

is the number of re-

dundantly received packets at the GMS and n

source

u,g

the total number of unique packets generated at source

nodes.

gms

tot

= L

gms

+ L

gms

, (4)

where L

gms

tot

is the total Packet load measured at the

GMS. To illustrate the gains of the algorithm over

a single technology, the results were depicted for

the same scenarios and conﬁguration using a single

technology at each node i.e., only ITS-G5 or only

LTE. Moreover, to show the maximum achievable

PDR (ceiling values) using the hybrid ITS-G5/LTE

approach, the same simulations were run using both

technologies simultaneously at each node, without

any DL redundancy suppression techniques at the

GMS/vehicle side. All results are presented with a

conﬁdence level of 95%.

Time-controlled Neighborhood-driven Policy-based Network Selection Algorithm for Message Dissemination in Hybrid Vehicular Networks

149

6.2 Simulation Results

In the following the graphs for the Manhattan sce-

nario are shown. Figures 6, 7 and 8 present the results

for 100ms, 500ms and unlimited Application delay

requirements respectively. Figure 9 shows the total

Packet load when both technologies are used.

0.1 (4) 0.3 (12) 0.6 (24) 1.0 (40)

Penetration rate (avg. number of vehicles)

0.2

0.4

0.6

0.8

PDR

100ms application delay requirement

Flag 0

Flag dynamic

Flag 1

only ITS-G5

only LTE

BOTH technologies

0.1 (4) 0.3 (12) 0.6 (24) 1.0 (40)

Penetration rate (avg. number of vehicles)

0.5

1.5

total Packet load

100ms application delay requirement

Flag 0 (Unique packets)

Flag dynamic (Unique packets)

Flag 1 (Unique packets)

Redundant packets

Figure 6: Upper graph: PDR; Lower graph: Total Packet

load ratio for Application delay requirement 100ms (Man-

hattan scenario).

0.1 (4) 0.3 (12) 0.6 (24) 1.0 (40)

Penetration rate (avg. number of vehicles)

0.2

0.4

0.6

0.8

PDR

500ms application delay requirement

Flag 0

Flag dynamic

Flag 1

only ITS-G5

only LTE

BOTH technologies

0.1 (4) 0.3 (12) 0.6 (24) 1.0 (40)

Penetration rate (avg. number of vehicles)

0.5

1.5

total Packet load

500ms application delay requirement

Flag 0 (Unique packets)

Flag dynamic (Unique packets)

Flag 1 (Unique packets)

Redundant packets

Figure 7: Upper graph: PDR; Lower graph: Total Packet

load ratio for Application delay requirement 500ms (Man-

hattan scenario).

0.1 (4) 0.3 (12) 0.6 (24) 1.0 (40)

Penetration rate (avg. number of vehicles)

0.2

0.4

0.6

0.8

PDR

unlimited application delay requirement

Flag 0

Flag dynamic

Flag 1

only ITS-G5

only LTE

BOTH technologies

0.1 (4) 0.3 (12) 0.6 (24) 1.0 (40)

Penetration rate (avg. number of vehicles)

0.5

1.5

total Packet load

unlimited application delay requirement

Flag 0 (Unique packets)

Flag dynamic (Unique packets)

Flag 1 (Unique packets)

Redundant packets

Figure 8: Upper graph: PDR; Lower graph: Total Packet

load ratio for unlimited Application delay requirement

(Manhattan scenario).

0.1 (4) 0.3 (12) 0.6 (24) 1.0 (40)

Penetration rate (avg. number of vehicles)

total Packet load

100ms (Unique packets)

500ms (Unique packets)

Unlimited (Unique packets)

Redundant packets

Figure 9: Total Packet load using both technologies (Man-

hattan scenario).

From the Manhattan scenario graphs, we observed

a poor ITS-G5 performance - only up to 50% PDR for

time critical transmissions (100ms) and at very low

Penetration rates (0.1) - only up to 25% for any time

requirement. Whereas LTE ensures average to decent

communication support resulting in average at 60%

to 80% PDR depending on the time requirement. In

light of such results, our algorithm demonstrated very

high PDR, above ITS-G5 and LTE for any conﬁgura-

tion, resulting in average at 70% to 90% and getting

very close to the value achieved using both technolo-

gies whilst keeping Packet load stable at around 75%.

It is important to note that using both technologies,

the Packet load is drastically higher than for the algo-

rithm and results in up to 17-fold increase (conﬁdence

intervals are provided for the total Packet load only).

Since in this scenario mostly the neighborhood-driven

logic of the algorithm is triggered due to lack of re-

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150

0.1 (18) 0.3 (54) 0.6 (108) 1.0 (180)

Penetration rate (avg. number of vehicles)

0.2

0.4

0.6

0.8

PDR

100ms application delay requirement

Flag 0

Flag dynamic

Flag 1

only ITS-G5

only LTE

BOTH technologies

0.1 (18) 0.3 (54) 0.6 (108) 1.0 (180)

Penetration rate (avg. number of vehicles)

0.5

1.5

total Packet load

100ms application delay requirement

Flag 0 (Unique packets)

Flag dynamic (Unique packets)

Flag 1 (Unique packets)

Redundant packets

Figure 10: Upper graph: PDR; Lower graph: Total Packet

load ratio for Application delay requirement 100ms (High-

way scenario, close range).

0.1 (18) 0.3 (54) 0.6 (108) 1.0 (180)

Penetration rate (avg. number of vehicles)

0.2

0.4

0.6

0.8

PDR

500ms application delay requirement

Flag 0

Flag dynamic

Flag 1

only ITS-G5

only LTE

BOTH technologies

0.1 (18) 0.3 (54) 0.6 (108) 1.0 (180)

Penetration rate (avg. number of vehicles)

0.5

1.5

total Packet load

500ms application delay requirement

Flag 0 (Unique packets)

Flag dynamic (Unique packets)

Flag 1 (Unique packets)

Redundant packets

Figure 11: Upper graph: PDR; Lower graph: Total Packet

load ratio for Application delay requirement 500ms (High-

way scenario, close range).

liable neighbors, we did not observe any signiﬁcant

difference in using one of the three LTE suppression

techniques as they get pronounced when the time con-

trol mechanism is used to trigger LTE transmission.

Next, we present the graphs for the Highway sce-

nario. Figures 10, 11 and 12 illustrate the results for

100ms, 500ms and unlimited Application delay re-

quirements respectively for close range transmissions

0.1 (18) 0.3 (54) 0.6 (108) 1.0 (180)

Penetration rate (avg. number of vehicles)

0.2

0.4

0.6

0.8

PDR

unlimited application delay requirement

Flag 0

Flag dynamic

Flag 1

only ITS-G5

only LTE

BOTH technologies

0.1 (18) 0.3 (54) 0.6 (108) 1.0 (180)

Penetration rate (avg. number of vehicles)

0.5

1.5

total Packet load

unlimited application delay requirement

Flag 0 (Unique packets)

Flag dynamic (Unique packets)

Flag 1 (Unique packets)

Redundant packets

Figure 12: Upper graph: PDR; Lower graph: Total Packet

load ratio for unlimited Application delay requirement

(Highway scenario, close range).

0.1 (18) 0.3 (54) 0.6 (108) 1.0 (180)

Penetration rate (avg. number of vehicles)

total Packet load

100ms (Unique packets)

500ms (Unique packets)

Unlimited (Unique packets)

Redundant packets

Figure 13: Total Packet load using both technologies (High-

way scenario, close range).

up to 500m. Figures 14, 15 and 16 depict the results

for medium range transmissions and Figures 18, 19

and 20 for the far range. Figure 13, 17 and 21 show

the total Packet load for both technologies at close,

medium and far ranges respectively.

From the Highway scenario graphs for closed

range transmissions, we observed that ITS-G5 pro-

vides a very high PDR - on average 95% for all con-

ﬁgurations, whereas LTE due to fast fading achieved

at maximum 60%. In this respect, our algorithm had

shown its good adaptability and achieved the same

PDR as ITS-G5 while keeping the total Packet load at

maximum 30% - only in time critical case of 100ms

which proves the proper functioning of its time con-

trol mechanism. Usage of both technologies resulted

in enormous total Packet load, especially in the time

unlimited case where the number reached 3000%.

Time-controlled Neighborhood-driven Policy-based Network Selection Algorithm for Message Dissemination in Hybrid Vehicular Networks

151

0.1 (18) 0.3 (54) 0.6 (108) 1.0 (180)

Penetration rate (avg. number of vehicles)

0.2

0.4

0.6

0.8

PDR

100ms application delay requirement

Flag 0

Flag dynamic

Flag 1

only ITS-G5

only LTE

BOTH technologies

0.1 (18) 0.3 (54) 0.6 (108) 1.0 (180)

Penetration rate (avg. number of vehicles)

0.5

1.5

total Packet load

100ms application delay requirement

Flag 0 (Unique packets)

Flag dynamic (Unique packets)

Flag 1 (Unique packets)

Redundant packets

Figure 14: Upper graph: PDR; Lower graph: Total Packet

load ratio for Application delay requirement 100ms (High-

way scenario, medium range).

0.1 (18) 0.3 (54) 0.6 (108) 1.0 (180)

Penetration rate (avg. number of vehicles)

0.2

0.4

0.6

0.8

PDR

500ms application delay requirement

Flag 0

Flag dynamic

Flag 1

only ITS-G5

only LTE

BOTH technologies

0.1 (18) 0.3 (54) 0.6 (108) 1.0 (180)

Penetration rate (avg. number of vehicles)

0.5

1.5

total Packet load

500ms application delay requirement

Flag 0 (Unique packets)

Flag dynamic (Unique packets)

Flag 1 (Unique packets)

Redundant packets

Figure 15: Upper graph: PDR; Lower graph: Total Packet

load ratio for Application delay requirement 500ms (High-

way scenario, medium range).

Again, we did not observe any signiﬁcant difference

between the three LTE suppression techniques as the

transmission distance is very short.

From the Highway scenario graphs for medium

range transmissions, we observed that ITS-G5 has

very low PDR - 20% for the Penetration rate of 0.1

for all delay requirements and performs mediocre for

all other considered penetration rates in the time crit-

0.1 (18) 0.3 (54) 0.6 (108) 1.0 (180)

Penetration rate (avg. number of vehicles)

0.2

0.4

0.6

0.8

PDR

unlimited application delay requirement

Flag 0

Flag dynamic

Flag 1

only ITS-G5

only LTE

BOTH technologies

0.1 (18) 0.3 (54) 0.6 (108) 1.0 (180)

Penetration rate (avg. number of vehicles)

0.5

1.5

total Packet load

unlimited application delay requirement

Flag 0 (Unique packets)

Flag dynamic (Unique packets)

Flag 1 (Unique packets)

Redundant packets

Figure 16: Upper graph: PDR; Lower graph: Total Packet

load ratio for unlimited Application delay requirement

(Highway scenario, medium range).

0.1 (18) 0.3 (54) 0.6 (108) 1.0 (180)

Penetration rate (avg. number of vehicles)

total Packet load

100ms (Unique packets)

500ms (Unique packets)

Unlimited (Unique packets)

Redundant packets

Figure 17: Total Packet load using both technologies (High-

way scenario, medium range).

ical case of 100ms. LTE showed in average 50% to

80% of PDR depending on the time requirement. The

selection algorithm conﬁrmed its efﬁciency and over-

performed all technologies in all cases for all conﬁg-

urations. Importantly, for the time limited scenario

of 100ms, it showed a 10% to 20% increase in PDR

over the best technology. Total Packet load in most of

the cases was below 100%. The usage of two tech-

nologies is highly inexpedient in this scenario and re-

sulted in up to 4500% of total Packet load providing

only minor increases in terms of PDR in the 500ms

and time unlimited cases. For the medium range, us-

ing the dynamic LTE suppression technique i.e., “Flag

dynamic“ policy can save up to 12% of total Packet

load while guaranteeing the same PDR as the unre-

stricted “Flag 1“ policy.

From the Highway scenario graphs for far range

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152

0.1 (18) 0.3 (54) 0.6 (108) 1.0 (180)

Penetration rate (avg. number of vehicles)

0.2

0.4

0.6

0.8

PDR

100ms application delay requirement

Flag 0

Flag dynamic

Flag 1

only ITS-G5

only LTE

BOTH technologies

0.1 (18) 0.3 (54) 0.6 (108) 1.0 (180)

Penetration rate (avg. number of vehicles)

0.5

1.5

total Packet load

100ms application delay requirement

Flag 0 (Unique packets)

Flag dynamic (Unique packets)

Flag 1 (Unique packets)

Redundant packets

Figure 18: Upper graph: PDR; Lower graph: Total Packet

load ratio for Application delay requirement 100ms (High-

way scenario, far range).

0.1 (18) 0.3 (54) 0.6 (108) 1.0 (180)

Penetration rate (avg. number of vehicles)

0.2

0.4

0.6

0.8

PDR

500ms application delay requirement

Flag 0

Flag dynamic

Flag 1

only ITS-G5

only LTE

BOTH technologies

0.1 (18) 0.3 (54) 0.6 (108) 1.0 (180)

Penetration rate (avg. number of vehicles)

0.5

1.5

total Packet load

500ms application delay requirement

Flag 0 (Unique packets)

Flag dynamic (Unique packets)

Flag 1 (Unique packets)

Redundant packets

Figure 19: Upper graph: PDR; Lower graph: Total Packet

load ratio for Application delay requirement 500ms (High-

way scenario, far range).

transmissions, we observed that cumulative ITS-G5

delay (due to CBF buffer waiting time) excludes the

possibility of successful packet transmission for the

time critical case of 100ms or for the very low pen-

etration rate of 0.1. The only technology that can be

used under these circumstances is LTE, that shows on

average 50% to 80% PDR which our algorithm per-

0.1 (18) 0.3 (54) 0.6 (108) 1.0 (180)

Penetration rate (avg. number of vehicles)

0.2

0.4

0.6

0.8

PDR

unlimited application delay requirement

Flag 0

Flag dynamic

Flag 1

only ITS-G5

only LTE

BOTH technologies

0.1 (18) 0.3 (54) 0.6 (108) 1.0 (180)

Penetration rate (avg. number of vehicles)

0.5

1.5

total Packet load

unlimited application delay requirement

Flag 0 (Unique packets)

Flag dynamic (Unique packets)

Flag 1 (Unique packets)

Redundant packets

Figure 20: Upper graph: PDR; Lower graph: Total Packet

load ratio for unlimited Application delay requirement

(Highway scenario, far range).

0.1 (18) 0.3 (54) 0.6 (108) 1.0 (180)

Penetration rate (avg. number of vehicles)

total Packet load

100ms (Unique packets)

500ms (Unique packets)

Unlimited (Unique packets)

Redundant packets

Figure 21: Total Packet load using both technologies (High-

way scenario, far range).

fectly matches in every tested case. Once ITS-G5

PDR rises above zero in the 500ms delay requirement

case, the selection algorithm starts to over-perform

the best technology resulting in a clear improvement

over a single technology approach. In general, if the

time requirement is not too strict (500ms or higher)

and penetration rate is above 0.1, we observed that the

transmission over far distances using our algorithm is

possible with decent PDR values of 80% and above.

Since the communication range is long and average

number of forwarders is larger in comparison to pre-

vious ranges, usage of two technologies redundantly

results in extremely high total Packet load values of

up to 6500%. Using the proposed algorithm and the

“Flag dynamic“ policy in this scenario, one can save

up to 40% of total Packet load while ensuring the

same PDR as the unrestricted “Flag 1“ policy.

Time-controlled Neighborhood-driven Policy-based Network Selection Algorithm for Message Dissemination in Hybrid Vehicular Networks

153

6.3 Evaluation Summary

Abstracting from the absolute numbers, we can con-

clude that the results of our experiments proved the

capability of the proposed algorithm to provide sub-

stantial performance improvements (measured in tens

of percent) over a single technology approach when-

ever using ITS-G5 and LTE is physically possible (i.e.

their respective PDRs are greater than zero). When it

is infeasible or risky to use ITS-G5, the hybrid algo-

rithm will immediately switch to LTE, for instance in

medium/far range transmissions in time limited cases,

which conﬁrms the effectiveness of its time control

mechanism, or under the absence of forwarding vehi-

cles in the vicinity, which conﬁrms the effectiveness

of its neighborhood assessment logic. The ultimate

PDR gain of the policy-based algorithm is not only

achieved by using dozens of times less LTE resources

than the both technologies approach, but it also relies

on the proposed GMS logic that entirely eliminates

LTE DL redundancy.

7 CONCLUSION AND OUTLOOK

In this work we tried to remedy the cases when ITS-

G5 is physically unable to satisfy the QoS require-

ments demanded by applications particularly pertain-

ing to reliable and time critical data transfers. This

occurs usually at low trafﬁc densities with low num-

ber of forwarders, at long communication ranges or in

presence of obstacles. In order to better fulﬁll appli-

cation QoS requirements we proposed a policy-based

network selection algorithm that enables combining

the strengths of ad hoc and infrastructure-based net-

works. The algorithm was designed to take into ac-

count the most relevant parameters for ITS-G5 to ad-

dress critical cases for vehicular ad hoc connectivity.

It relies on a multi-parameter neighborhood estimate,

being on the other hand secured with a time control

mechanism that facilitates in-time delivery of mes-

sages.

Evaluation showed that the proposed algorithm

signiﬁcantly improved the overall reliability of ve-

hicular data transmissions in all considered scenarios

and distance ranges, over-performed traditional ITS-

G5, provided a necessary support in neighborhood-

constrained or time critical scenarios and demon-

strated an ability to select the optimal technology to

achieve PDR values always equal or higher than the

best technology. LTE load rarely exceeded the num-

ber of uniquely generated packets leading to a limited

LTE usage which was one of the main design goals of

the algorithm. Performance improvements over ap-

proaches using a single technology is the result of

proper and selective simultaneous usage of ITS-G5

and LTE. It is worth noting that the algorithm not only

achieved higher PDR efﬁciency than the best technol-

ogy, it also entirely eliminated the DL redundancy,

since the duplicate packets received on the UL are not

forwarded in the DL thanks to the mechanisms imple-

mented on the GMS side.

In the future it is worth evaluating the proposed

algorithm for GeoBroadcast message dissemination

under diverse LTE scenario realizations. Also both

GeoUnicast and GeoBroadcast cases have to be veri-

ﬁed in real ﬁeld-tests to obtain better insights on the

algorithm’s performance. Additionally, it is foreseen

that the current GMS logic will be improved to take

advantage of UL redundancy by enabling DL success

estimation via incorporation of RSRP values in the

location update beacons sent to the GMS. Moreover,

a scheme utilizing the nodes with negative distance

progress will be considered in order to increase LTE

transmission success in cases exhibiting spotty cover-

age.

ACKNOWLEDGEMENTS

The research leading to these results has received

funding from the Bavarian Ministry of Economic Af-

fairs and Media, Energy and Technology and the Eu-

ropean Union in the Horizon 2020 project TIMON,

Grant Agreement No. 636220.

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