Int. J. Mobile Network Design and Innovation, Vol. X, No. Y, 200X
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Impact of vehicle movement models on VDTN
routing strategies for rural connectivity
Vasco N.G.J. Soares
Instituto de Telecomunicações,
University of Beira Interior,
Rua Marquês D’Ávila e Bolama,
6201-001 Covilhã, Portugal
and
Superior School of Technology,
Polytechnic Institute of Castelo Branco,
Av. do Empresário, 6000-767 C. Branco, Portugal
E-mail: vasco.g.soares@ieee.org
Farid Farahmand
Department of Engineering Science,
Sonoma State University,
1801 East Cotati Ave.,
Rohnert Park, CA 94928, USA
E-mail: farid.farahmand@sonoma.edu
Joel José P.C. Rodrigues*
Instituto de Telecomunicações,
University of Beira Interior,
Rua Marquês D’Ávila e Bolama,
6201-001 Covilhã, Portugal
E-mail: joeljr@ieee.org
*Corresponding author
Abstract: Vehicular delay-tolerant networks (VDTNs) appear as an alternative to provide low
cost asynchronous internet access on developing countries or isolated regions, enabling non-real
time services, such as e-mail, web access, telemedicine, environmental monitoring and other data
collection applications. VDTNs are based on the delay-tolerant network (DTN) concept applied
to vehicular networks, where vehicles mobility is used for connectivity.
This paper considers a rural connectivity scenario and investigates how different mobility
patterns and vehicle densities influence the performance of DTN routing protocols applied to
VDTN networks. Moreover, routing protocols parameters are also changed in the present study.
We analyse their effect on the performance of VDTNs through the bundle delivery ratio and the
bundle average delay.
We expect that this contribution will provide a deep understanding about implications of
movement models on the performance of VDTNs applied to rural scenarios, leading to insights
for future routing algorithm theoretic study and protocol design.
Keywords: vehicular delay-tolerant networks; VDTNs; delay/disruption-tolerant networks; rural
connectivity; movement models; node density; performance assessment.
Reference to this paper should be made as follows: Soares, V.N.G.J., Farahmand, F. and
Rodrigues, J.J.P.C. (xxxx) ‘Impact of vehicle movement models on VDTN routing strategies for
rural connectivity’, Int. J. Mobile Network Design and Innovation, Vol. X, No. Y, pp.000–000.
Biographical notes: Vasco N.G.J. Soares is a PhD student on Informatics Engineering at the
University of Beira Interior, and Instituto de Telecomunicações, Portugal. He received his
five-year BS (Licentiate) in 2002 in Informatics Engineering from University of Coimbra,
Portugal. He teaches in the Informatics Engineering Department at the Superior School of
Technology of the Polytechnic Institute of Castelo Branco, Portugal. His current research areas
include vehicular delay-tolerant networks, delay-tolerant networks, and vehicular networks. He
authors or co-authors more than 12 international conference papers, participates on several
Technical Program Committees, and also has a journal publication and a book chapter
publication.
Copyright © 200X Inderscience Enterprises Ltd.
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V.N.G.J. Soares et al.
Farid Farahmand received his PhD in 2005 and is currently an Assistant Professor in the
Department of Engineering Science at Sonoma State University. He is also the Director of
Advanced Internet Technology in the Interests of Society Laboratory. Prior to this position, he
worked as a Research Scientist at Alcatel-Lucent Corporate Research and was involved in
development of terabit optical routers. He holds multiple international patents, numerous
reference conference articles and journal publications, and several book chapters, on the subject
of wireless communications, optical networking, and delay-tolerant networks. He is a member of
IEEE, ASEE, and Engineers Without Borders-USA.
Joel J.P.C. Rodrigues is a Professor at the University of Beira Interior, Covilhã, Portugal, and
Researcher at the Instituto de Telecomunicações, Portugal, leading NetGNA Group. His research
interests include delay-tolerant networks, sensor networks, high-speed networks, and mobile and
ubiquitous computing. He is the EiC of IJEHMC journal (IGI-Global). He was the general Chair
of many conferences, member of many international TPCs, and several editorial review boards.
He has co-authored over 100 papers in refereed international journals and conferences, and a
book. He is a Licensed Professional Engineer and member of ACM, Internet Society, IARIA
Fellow, and IEEE Senior Member.
1
Introduction
Over the last years, the problem of providing data
communications to undeveloped remote areas has been
addressed by several projects with approaches that focus on
asynchronous (disconnected) messaging, in order to reduce
the cost of connectivity. Several approaches were proposed
and examples of these projects are described hereby.
The DakNet project aimed to provide low cost internet
connectivity to rural villages in India (Pentland et al.,
2004). In this project, mobile access points (MAPs) are
mounted on vehicles and, when they are in contact with
kiosks located at villages, data is exchanged between
them. Afterwards, MAPs can use an access point to
download/upload information from/to the internet. The
Saami Network Connectivity (SNC) project focuses on
providing internet connectivity to the Saami population of
the reindeer herders, who live in Lapland and move from
their villages through the year, following the migration of
reindeers (Doria et al., 2002). The Wizzy Digital Courier
service was designed to provide internet access to schools
located in remote villages of South Africa (Wizzy Digital
Courier, 2008). This system is based on a courier using a
motorbike, equipped with a USB storage device, which
travels from a village school to a large city with broadband
internet access. The Message Ferry project aimed to develop
a data delivery system in disconnected areas (Zhao et al.,
2004). In this system, mobile nodes called message ferries
(e.g., cars, buses, boats, etc.) move around the network and
collect messages from source nodes. The Networking for
Communications Challenged Communities (N4C) is another
example of a recent project that aims to create an
opportunistic networking architecture to allow internet
access on remote regions without network connectivity
(N4C and eINCLUSION, 2009).
All the above-mentioned projects are based on the
concept of the delay-tolerant networking (DTN) that
addresses the challenges created in these scenarios,
by limited/episodic connectivity, large interconnectivity
intervals, limited network capacity, limited network
resources and energy constraints. In this work, we present
vehicular delay-tolerant networks (VDTNs), a novel
proposal for a DTN-based architecture (Soares et al., 2009).
VDTNs create a communication infrastructure composed
of vehicular nodes and fixed nodes, offering a low cost
connectivity solution in challenging scenarios where a
telecommunications infrastructure is unreliable or not
available due to disconnected areas, natural disaster or
emergency situations. This paper considers a scenario
where a VDTN is used to provide connectivity on a sparse
rural area with 2,500 square kilometres. We are interested
in evaluating how different vehicle mobility models and
vehicle densities affect the performance of well-known
DTN routing strategies applied to VDTNs.
The remainder of this paper is organised as follows.
Section 2 provides a brief overview of DTNs, focusing on
its architecture, application scenarios and routing protocols.
Section 3 introduces the VDTN architecture and the vehicle
movement models evaluated on this study, identifying our
contributions. Section 4 studies the performance assessment
of DTN routing protocols applied to the VDTN network
scenario, while Section 5 concludes the paper and provides
suggestions for future works.
2
Delay-tolerant networks
Internet reliable transport protocols (highly interactive
application protocols) and routing protocols are not suitable
for scenarios that involve paths with intermittent links
or over extremely long propagation delays, such as the
above-described on Section 1 (Burleigh et al., 2003).
These problems lead to the introduction of the delayand disruption-tolerant networking (DTN) approach.
DTNs address challenging connectivity issues enabling
communication on scenarios with sparse and intermittent
connectivity, long or variable delay, asymmetric data rate,
high error rates and even with no guarantee of end-to-end
connectivity (Fall, 2003).
Impact of vehicle movement models on VDTN routing strategies for rural connectivity
2.1 DTN architecture and application scenarios
The work on interplanetary internet architecture, later
generalised to DTN architecture, began in the late 1990s
(Burleigh et al., 2003). This architecture implements a
store-and-forward paradigm by overlaying a protocol layer,
called bundle layer, as may be seen in Figure 1. This new
layer is meant to provide internetworking on heterogeneous
networks operating on different transmission media (Cerf
et al., 2007).
Figure 1
DTN overlay network architecture (see online version
for colours)
3
designed to support wildlife tracking for biology research
(Juang et al., 2002). Vehicular networks are another
example for the use of the DTN concept, with several
potential application scenarios, such as traffic condition
monitoring, emergency message dissemination, free parking
spots information, advertisements (Leontiadis and Mascolo,
2007), cooperative vehicle collision avoidance (Tatchikou
et al., 2005) and to gather information collected by vehicles
such as road pavement defects (Franck and Gil-Castineira,
2007). Vehicular networks have also been proposed to
implement the transient networks to benefit developing
communities and disaster recovery networks (Farahmand
et al., 2008).
2.2 Routing protocols
In this type of networks, a source node originates a bundle
and stores it while a contact is not available. The bundle
will be forwarded when the source node is in contact
with an intermediate node thought to be more close to the
destination node. Afterwards, the intermediate node stores
the bundle and carries it while a new contact is not
available. This process is repeated and the bundle will be
relayed hop by hop until reaching its destination (storecarry-forward paradigm). Bundles have a finite time to live
(TTL) and can be dropped because of buffer overflow.
DTN architecture defines different types of contacts
(between network nodes) that can be classified as
opportunistic, scheduled and predicted. In opportunistic
contacts, communication opportunities appear opportunistic,
end-to-end connectivity may not exist, and intermittent
connectivity is common (Chen et al., 2007). In scheduled
contacts, connectivity with other portions of the network
is scheduled based on resources within the area (Leguay
et al., 2005). Predicted contacts are not scheduled (Fall,
2004). They require analysing previous observations (use of
routing tables) to ‘predict’ the next time when the contact
with a portion of the network will be available.
The concept of DTN has been widely applied on several
scenarios. For example, the interplanetary networking
is used to establish communication between planets
(Burleigh et al., 2003). Data MULEs are used for data
retrieval in the context of sensor network applications (Jain
et al., 2006). Underwater networks enable applications for
the oceanographic data collection, pollution monitoring,
exploration, disaster prevention, assisted navigation and
tactical surveillance applications (Partan et al., 2006).
Wildlife tracking sensor networks like ZebraNet are
DTN routing protocols depend on node mobility for bundle
delivery. According to Zhang (2006), routing protocols can
be classified as deterministic or stochastic. Deterministic
routing assumes the network topology is deterministic
and previously known, therefore, future movements of
nodes and connection are known ahead of time. In
stochastic routing, the network behaviour is random (nondeterministic) and not known. In the context of this paper,
the traffic matrix is not provided in advance and there is no
any knowledge about the transfer opportunities. Therefore,
stochastic routing is applied and the following four wellknown multicopy DTN routing protocols are considered:
Epidemic (Vahdat and Becker, 2000), MaxProp (Burgess
et al., 2006), PRoPHET (Lindgren and Doria, 2008) and
Spray and Wait (Spyropoulos et al., 2005). In addition,
PRoPHET and Spray and Wait parameters were changed in
order to study their influence on the performance of routing
algorithms.
Epidemic is a flooding-based routing protocol where
nodes exchange the bundles they do not have. In an
environment with infinite buffer space and bandwidth, this
protocol performs better than the other ones in terms of
bundle delivery ratio and latency, providing an optimal
solution. MaxProp prioritises the schedule of bundles
transmitted to other nodes and also the bundles scheduling
to be dropped. Historical data of path probabilities to nodes,
acknowledgments, head-start mechanism and lists of
previous intermediaries are used to calculate the priorities.
PRoPHET is a probabilistic routing protocol that
considers a history of encounters and transitivity. It
considers that nodes move in a non-random pattern and
applies ‘probabilistic routing’. This routing algorithm
uses, as parameters, the following values in the calculation
of the delivery predictability metric: P_encounter (delivery
predictability), beta (transitive property) and gamma
(delivery predictabilities age). We are interested on studying
the effect of the transitive property since this parameter
adjusts the importance given to the information about
destinations received from encountered nodes. We change
the value of beta between 0, 0.25 and 0.5 [0.25 is the
recommended value in Lindgren and Doria (2008)].
The Spray and Wait protocol does not use any network
information. It creates a number of copies (N) to be
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V.N.G.J. Soares et al.
transmitted (‘sprayed’) per bundle. In its normal mode, a
source node A forwards the N copies to the first M different
nodes encountered. In binary mode, any node A that has
more than one bundle copies and encounters any other node
B that does not have a copy, forwards to B N/2 bundle
copies and keeps the rest of the bundles. A node with
one copy left, only forwards it to the final destination. We
evaluate both the normal and binary modes, with six, 12 and
18 bundle copies.
3
another. Relay nodes are store-and-forward stationary
devices located at road intersections. They allow mobile
nodes that pass by to collect and leave data on them.
Thus, they contribute to increase the frequency of contact
opportunities in sparse networks, improving the network
performance in terms of bundle delivery ratio and bundle
delivery delay (Farahmand et al., 2009, 2008; Zhao et al.,
2006).
Figure 2
Example of a VDTN in a rural scenario (see online
version for colours)
Figure 3
Mobile nodes carrying data between terminal nodes
(see online version for colours)
Vehicular delay-tolerant networks
VDTN (Soares et al., 2009) proposes a new network
architecture based on the DTN paradigm, using out-of-band
signalling (decoupling control plane and data plane
functions) and in which the bundle layer is positioned under
the network layer. Bundles are defined as the protocol data
unit at the VDTN bundle layer and represent aggregates of
internet protocol (IP) packets with common characteristics,
such as the same destination node. At a contact opportunity,
signalling data is exchanged through the use of a control
channel, enabling out-of-band signalling. This information
is used to set up a data plane connection to transmit data
bundles.
In VDTNs, vehicle mobility and the store-carryand-forward paradigm are explored to extend the range of
the network, allowing data paths to exist over time in
networks that suffer from long periods of disconnection.
Vehicles act as the communication infrastructure for the
network, being opportunistically explored to collect, carry
and disseminate data.
Data transmission on vehicular DTNs presents complex
challenges. Network resources (e.g., buffer, bandwidth)
are not only limited, but also limited transmission ranges,
physical obstacles and interferences, which contribute to
intermittent connectivity. When vehicles are driven at high
velocities, it also causes short contact durations and frequent
topology changes. In addition, their mobility pattern also
has an impact on the network performance, as it influences
the connectivity of the VDTN.
In the context of this work, the use of a VDTN is
considered to provide low cost asynchronous internet access
on a rural region sparsely populated, without a network
infrastructure. The large distances involved in such scenario
pose additional challenges. Node density is very low in
such environments. Therefore, network nodes are rarely in
communication range with one another, which results in
few transmission opportunities and high and unpredictable
delays.
Figure 2 illustrates a rural connectivity scenario with the
following three VDTN node types: terminal nodes, mobile
nodes and relay nodes. Terminal nodes are located in
isolated regions (villages) and provide network connection
to end-users. At least one of the terminal nodes may have
internet access. Mobile nodes (e.g., vehicles) physically
carry data between terminal nodes (Figure 3). They can
move along the roads ‘randomly’ (e.g., cars) or following
predefined routes (e.g., buses) and exchange data with one
This work studies the influence of mobile nodes density
and their mobility pattern on the bundle delivery ratio and
the bundle delivery delay observed in a VDTN applied to a
rural connectivity scenario. Three movement models are
considered to model the mobile nodes movement, across
the roads of a map scenario. The first movement model
considers that mobile nodes move between random map
locations. For the second movement model, we introduce
additional map data containing two groups of points of
interest (POIs). A group includes the terminal nodes that
are the traffic sources, whereas the other one contains the
terminal node connected to the internet that represents
the traffic sink. This movement model uses information
about the probabilities configured to each POI groups, to
determine which POI will be the next destination for a
Impact of vehicle movement models on VDTN routing strategies for rural connectivity
mobile node. The third movement model is the map route
movement, where mobile nodes follow predefined routes
(e.g., buses) moving from terminal node to terminal node.
5
the movement models considered in each scenario, together
with the corresponding performance analysis.
4.1 General network settings
4
Performance assessment
In order to analyse the impact of the mobile node density
and mobile node movement models on a VDTN rural
network, we present a simulation-based study. We created a
real world map-based model representation of the Serra da
Estrela Region, a Portuguese mountain region that covers
an area with large dimensions (about 2,500 km2). This mapbased model, shown on Figure 4, is used to represent a rural
dispersed region and it was created using Google™ Maps
(Google, 2009) and OpenJUMP Geographic Information
System (GIS) (Open JUMP, 2009).
The study was conducted by simulation using the
opportunistic network environment (ONE) simulator
(Keränen et al., 2009). Three simulation scenarios are
evaluated. Each scenario assumes the changing of the
mobile nodes density and considers different groups of
mobile nodes moving in accordance to the above-described
movement models.
The performance of the above-described four DTN
routing protocols is evaluated per each scenario.
Performance metrics considered in this study are the overall
bundle delivery ratio (measured as the relation of the
number of unique delivered bundles to the number of
bundles sent) and the bundle delivery delay (measured as
the time between bundle creation and delivery).
Figure 4
Area of the Serra da Estrela Region with the locations
of the terminal nodes and the relay nodes and the buses
routes (see online version for colours)
To simulate a rural connectivity scenario such as the above
described, we select 24 real world village locations to place
the terminal nodes that act as traffic sources (Figure 4).
Each of the terminal nodes has a 125 Mbytes (first-in firstout) FIFO buffer and generates bundles using an interbundle
creation interval in the range (15, 30) minutes of uniformly
distributed random values. Each bundle has a size in the
range (500 KB, 2 MB) of uniformly distributed random
values. It is assumed that all the bundles exchanged in
the simulations have an infinite TTL. Bundles destination
address is the terminal node connected to the internet that
acts as the traffic sink (Figure 4).
We deploy six relay nodes with 500 Mbytes FIFO
buffer, placing them at the selected crossroads presented on
Figure 4. Depending on the scenario, two types of mobile
nodes can move in the map roads, cars and buses. Cars
have a 125 Mbytes FIFO buffer, whereas buses have a
250 Mbytes FIFO buffer. All the network nodes connect to
each other using the standard IEEE 802.11b with a data rate
of 6 Mbit/s [the 802.11b approximate throughput according
to Cisco Systems, Inc. (2005)] and a transmission range of
350 metres using omni-directional antennas. We consider
IEEE 802.11b as the link layer because of its wide
availability. Terminal nodes and relay nodes exchange data
only with mobile nodes. In addition, mobile nodes can
communicate between themselves.
When mobile nodes are in contact with the traffic sink,
they try to deliver the bundles stored on their buffers. Each
bundle successfully delivered is removed from the buffer,
thus, freeing essential storage space. We simulate the
creation and bundles exchanging for a period of 12 hours
(e.g., from 8:00 to 20:00). It is assumed that the traffic
matrix is not provided in advance and there is not any
knowledge about the transfer opportunities.
4.2 Scenario 1
The next subsection describes the common network
parameters used to study all the considered scenarios. The
following subsections present specific parameters related to
For the first simulation scenario, we have a group of cars
moving on roads between random map locations. Once a car
reaches a destination, it randomly waits 15 to 30 minutes.
Then, it selects a new random map location and a random
speed between 30 and 80 km/h. The car moves to the new
destination using the shortest path (road) available. This
process is repeated till the end of the simulation. We are
interested to study how the routing protocols perform when
five or eight cars follow this movement model.
Figure 5 shows that no bundles were successfully
delivered in the case where only five cars were moving
on the scenario. These results seem surprising at first, but
remember that cars were moving between random map
locations. The terminal node that acts as a traffic sink is
located in a remote map position, which decreases the
probability for cars passing there. They will only pass there
if the road segment where the traffic sink is located is used
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V.N.G.J. Soares et al.
in the shortest path to a previous calculated destination.
Then, even if they pass nearby the traffic sink, they will
only stop there if its exact map location was select as the
destination, which is highly improbable. All this conditions
contribute to the very low delivery probabilities registered
even in the case where eight cars were deployed. Intuitively,
placing the traffic sink at a central map point would increase
the delivery probability.
Figure 5
Figure 7
Number of contacts per hour between all network
nodes with five and eight cars
Bundle delivery probability for Epidemic, MaxProp,
PRoPHET and Spray and Wait routing protocols, with
five and eight cars
4.3 Scenario 2
Figure 5 also shows that Epidemic (E) and MaxProp (M)
are the routing protocols that perform better in respect to
bundle delivery probability. Spray and Wait binary mode
(SWB) registers better values than its normal mode (SW).
In SWB, bundle delivery probability increases when we
augment the number of bundle copies from six (SWB 6) to
12 (SWB 12), and to 18 (SWB 18), and approximates from
the values of E and M. SW registers the same delivery
probabilities for the cases with six (SW 6), 12 (SW 12) or
18 (SW 18) bundle copies. PRoPHET did not successfully
deliver any bundle for any of the variations of the beta
parameter: zero (P 0), 0.25 (P 0.25) and 0.5 (P 0.5).
In terms of bundle average delay (shown in Figure 6),
all protocols register similar values. As expected, the
analysis of Figure 7 shows that deploying eight cars
increases the contact opportunities, therefore, this suggests
that more bundles are collected at the terminal nodes and
exchanged between cars and relay nodes.
Figure 6
Bundle average delay for Epidemic, MaxProp,
PRoPHET and Spray and Wait routing protocols, with
five and eight cars
In the second scenario, we have a group of cars moving
on roads between terminal nodes. When a car reaches a
terminal node, it randomly waits 15 to 30 minutes. Then,
instead of selecting any random location for the next
destination, our movement model is configured to give a
new destination in accordance to a probability. Afterwards,
a random speed between 30 and 80 km/h is selected and the
mobile node moves there using the shortest path. Our map
data contains two groups of POIs. One of the POI groups
contains the terminal nodes that are the traffic sources and
the other one contains the terminal node that is the traffic
sink. For this simulation scenario, we associate a 15%
selection probability for the traffic sink POI group and 85%
selection probability for the traffic sources POIs group.
Hence, there is an 85% probability for the movement model
to select a random village (traffic source) as the next
destination for the mobile node. We are interested in
evaluating how the routing protocols perform when five or
eight cars follow this movement model.
As expected, this movement model registers much better
delivery probabilities than ones presented in Scenario 1.
This result is due to cars moving only between random
traffic sources and the traffic sink. Increasing the number
of cars (mobile nodes density), the number of contacts
per hour also increases (Figure 8), improving the delivery
probability as well (Figure 9).
Figure 8
Number of contacts per hour between all network
nodes with five and eight cars
Impact of vehicle movement models on VDTN routing strategies for rural connectivity
Figure 9
Bundle delivery probability for Epidemic, MaxProp,
PRoPHET and Spray and Wait routing protocols, with
five and eight cars
Figure 9 also shows that, for five cars, binary Spray and
Wait with six copies performs better, followed by MaxProp
protocol. For eight cars, Spray and Wait protocol also
performs better than the other protocols. Its binary variant
registers the best delivery probabilities in the cases of six
and 12 bundle copies. For SWB 18, delivery probability
drops. That suggests that 18 bundle copies lead to a poor
utilisation of the nodes buffers. The same behaviour is
observed in its normal variant. Epidemic does not register
a big improvement because of its poor utilisation of the
network resources.
The analysis of PRoPHET confirms the importance of
the beta parameter. Augmenting its value to 0.25 increases
the bundle delivery probability. This happens because if
beta is set to zero only direct encounters will be used in the
calculation of the delivery predictability (used by the
routing algorithm). In this type of scenario (dispersed region
with a low number of vehicles), the transitive property is
very important, since the information about destinations
received from encountered nodes should be taken into
account. Another interesting finding shown in Figure 10
is that increasing the number of cars to eight, decreases the
average delay in all routing protocols. This is interesting
since minimising average delay reduces the time that
bundles spend in the network, reducing the contention for
resources in the network (e.g., buffer).
7
4.4 Scenario 3
In this last scenario, we combine the configuration of the
other two studied scenarios. Therefore, at the same time, we
will have a group of eight cars moving between random
map locations and a group of eight cars moving in
accordance to the movement model based on POI group
selection probabilities. Additionally, we introduce one or
two buses that will follow the predefined circular routes
shown on Figure 4. Buses move from terminal node to
terminal node. Each time they arrive at a terminal node,
they stop for a period of 15 minutes, then they select a
random speed between 30 and 50 km/h and follow their
route to the next terminal node.
As expected, bundle delivery probability increases
highly in this scenario (Figure 11) when comparing to the
previous ones. This is mainly due to the buses movement,
since they follow predefined circular routes collecting
bundles generated on some terminal nodes/traffic sources
and delivering them to the traffic sink. Cars moving
randomly over the map also contribute to disseminate data
to other cars, buses and relay nodes. Therefore, they also
have an important role to improve the overall performance
of the network. Figure 11 shows that delivery probability
increases for all routing protocols when two buses are
deployed instead of one. This was expected because the
number of contacts per hour also increases. The poor
utilisation of the network resources by Epidemic protocol
is more explicit in this scenario. It performs worse than
the other routing protocols. MaxProp has the best delivery
probability when a single bus is used. When two buses are
deployed, there is just a small increase on this performance
metric.
Figure 11 Bundle delivery probability for Epidemic, MaxProp,
PRoPHET and Spray and Wait routing protocols, with
one and two buses
Figure 10 Bundle average delay for Epidemic, MaxProp,
PRoPHET and Spray and Wait routing protocols, with
five and eight cars
Spray and Wait binary variant increases the delivery
probability when the number of bundle copies is augmented.
SWB 18 is the routing protocol variant with the best overall
delivery probability, when two buses are deployed. Spray
and Wait normal variant decreases the delivery probability
when the number of bundle copies is increased. This
obervation suggests that, for SW, increasing the number
of copies congests nodes buffers. Setting PRoPHET beta
parameter to 0.25 produces the best results in this scenario.
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V.N.G.J. Soares et al.
Figure 12 shows that having two buses decrease the
average delay in all routing protocols except for SWB 12
and SWB 18. For that specific cases, having one or two
buses results in similar average delays. Even though buses
move in a circular route, stopping in predefined places for
static periods of time, cars did not have an access to that
information. That would allow the schedule of meetings
between vehicles and would further increase the overall
performance of the VDTN.
Figure 12 Bundle average delay for Epidemic, MaxProp,
PRoPHET and Spray and Wait routing protocols, with
one and two buses
and geographical routing (Leontiadis and Mascolo, 2007)
may also be considered.
Acknowledgements
Part of this work has been supported by the Instituto de
Telecomunicações, Next Generation Networks and
Applications Group, Portugal, in the framework of the
Project VDTN@Lab, and by the Euro-NF Network of
Excellence from the Seventh Framework Programme of EU.
References
5
Conclusions and future work
This paper investigated the influence of mobile node density
and mobility models on the performance of VDTNs, applied
to a rural connectivity scenario. First, a brief overview
of the concept of DTN was presented, followed by the
presentation of the four multicopy DTN routing strategies
considered in this work. DTN paradigm serves as a basis for
the VDTN architecture proposal, presented in Section 3,
together with the discussion of the different mobility models
studied.
The study analysed the impact of the node density and
the movement models, on the performance of DTN routing
protocols, applied to VDTNs in a rural scenario. In addition,
routing protocols parameters were changed in order to
study their effect on the bundle delivery ratio and the bundle
delivery delay. Simulation scenarios assumed a cooperative
opportunistic environment without knowledge of the traffic
matrix and contact opportunities. As expected, the obtained
results shown the number of vehicles and their mobility
patterns influence the number of opportunistic contacts and
the intercontact times, observed in the remote and sparsely
populated region studied in this work. Therefore, they have
a significant impact on the performance of routing protocols
applied to VDTNs.
Future research directions on VDTNs may include
the study of bundle assembly and bundle fragmentation
mechanisms, enabling congestion control and introducing
support for traffic differentiation and ‘quality of service’
routing capabilities. Moreover, the adaptation of DTN node
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