Int. J. Mobile Network Design and Innovation, Vol. X, No. Y, 200X 1

Copyright © 200X Inderscience Enterprises Ltd.

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.

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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 delay- and 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 3

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)

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 (store-carry-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

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

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 (non-deterministic) 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 well-known 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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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 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-carry- and-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

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)

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 5

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.

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 map-based 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)

The next subsection describes the common network parameters used to study all the considered scenarios. The following subsections present specific parameters related to

the movement models considered in each scenario, together with the corresponding performance analysis.

4.1 General network settings

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 first-out) 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

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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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 Bundle delivery probability for Epidemic, MaxProp, PRoPHET and Spray and Wait routing protocols, with five and eight cars

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

Figure 7 Number of contacts per hour between all network nodes with five and eight cars

4.3 Scenario 2

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 7

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).

Figure 10 Bundle average delay for Epidemic, MaxProp, PRoPHET and Spray and Wait routing protocols, with five and eight cars

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

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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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

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 cooperation concepts (Panagakis et al., 2007), content storage and retrieval mechanisms (Ott and Pitkanen, 2007)

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.

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