I. INTRODUCTION. Keywords-disruption tolerant networks; custody transfer; route discovery; message ferry

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1 Performance Comparison of Unicast Routing Schemes in DTNs Mooi Choo Chuah, Peng Yang, Brian D. Davison, Liang Cheng {chuah, pey204, davison, Lehigh University Abstract Delay and disruption tolerant network(dtn)s have been proposed to address data communication challenges in network scenarios where an instantaneous end-to-end path between a source and destination may not exist, and the links between nodes may be opportunistic, predictably connectable, or periodically-(dis)connected. In this paper, we focus on comparing the performance of different proposed unicast routing schemes. In particular, we conduct performance studies for different DTN scenarios, e.g., DTNs with different node densities, DTNs with different mobility models, networks with different percentage of nodes supporting DTN functionality, etc. Our results indicate that (a) the store-and-forward and custody transfer concepts have significantly improved the delivery ratio in a sparsely connected network, (b) a high delivery ratio can be maintained even with only 50% of the nodes supporting DTN functionality, (c) multihop routing approach achieves higher delivery ratio at lower message delivery latency than the twohop routing approach but incurs higher transmission overhead, and, (d) some routing schemes are optimized to perform well in certain mobility scenarios and thus a hybrid scheme will work best in all scenarios. Keywords-disruption tolerant networks; custody transfer; route discovery; message ferry I. INTRODUCTION Packet-switched network communication has been studied for decades. Most packet-switched applications are built on top of TCP/IP protocol suite whose service model rely on a few key assumptions e.g. an end-to-end path exists between a source and a destination pair, the end-to-end packet drop probability is small and the maximum round trip time between any node pair is not excessive. However, there are many scenarios in which an end-to-end connection is not guaranteed or even possible, and so an intermediary is needed, perhaps to translate between protocols or to provide temporary storage (e.g., in mail servers). In these cases, without such intermediaries, communication would fail. In other cases, communication may fail not because of a lack of instantaneous connection, but because the connection properties fall beyond the expected bounds (excessive round-trip-time or high packet loss probability). Solutions have been proposed to deal with some specific situations, e.g., using link layer retransmissions to deal with high packet loss probability in wireless environments [4] or using performance enhancing proxies [28]. Such proxies typically modify the end-to-end data stream, to try to fool TCP/IP based end stations into operating more efficiently over paths with poor or unusual performance. However, these solutions still do not work in situations where there are no end-to-end paths. In contrast, DakNet [3] deploys physical transport devices, e.g., buses and motorcycles, to carry mobile access points between village kiosks and hubs with Internet connectivity so that the data carried by the physical transport devices can be automatically uploaded and/or downloaded when the physical transport devices are in the wireless communication range of a kiosk or a hub. Similar techniques are proposed in [1],[2]. In the past two years, a considerable amount of research focusing on delay/disruption-tolerant networking and communications has been published (e.g., [13],[14],[15],[27]). DieselNet [14],[27] is a disruption tolerant network where connections between nodes are short-lived and occasional. A common approach used to address delays and disruptions is via the use of a store-and-forward mechanism similar to electronic mail [11]. This makes communication possible, even when an instantaneous end-to-end path does not exist. Message ferrying schemes [14],[27] are proposed where special mobile nodes called message ferries are used to facilitate connectivity between nodes. The message ferries visit the nodes in the network and deliver data among them. Sensor Network DTN router DTN Gateways Internet Satellite Network Ad-hoc Network

2 Figure 1: Proposed DTN architecture [5] In [5], Fall describes an architecture for delay tolerant networking that implements much of what we have described. It proposed the idea of topological regions connected by gateways, which were responsible for storing messages in non-volatile storage to provide for reliable delivery. End-point addressing in his scenario consisted of a region name used for inter-region routing and a locally-resolvable name for intra-region delivery. As shown in Figure 1, a sensor or adhoc network can each form a DTN region. DTN gateways interconnect regions running potentially dissimilar protocol stacks. In addition, a custody transfer feature is defined [13],[17] for DTN message forwarding. A node accepting a message with custody transfer means it promises not to delete it until it can reliably deliver the message to another node providing custody transfer or the message arrives at the destination. More recently, we have proposed an enhanced disruption-tolerant network architecture called EDIFY (Enhanced Disruption and Fault Tolerant Bundle Delivery) [6]. Our approach builds on many ideas from Fall [5], but adds support for multiple, overlapping name spaces and node and group mobility. For example, in Figure 2, we show four groups of DTN nodes, three of which belongs to US-DOD and one is a NATO squad team made up of army personnel from different countries. The NATO squad team can be connected to the other three groups via non-dtn networks like Spain or North Korea but prefers the Spain route. A platoon member (UserHost-1093) is currently visiting at US- DOD.Navy.Battalion5 and hence is given a visiting identifier like US-DoD.Navy.Battalion5.Visitor5. This platoon member has an original identifier US- DoD.Army.Platoon44.User-Host1093. Information is kept at both US-DoD.Army.Platoon44 and US- DoD.Navy.Battlion5 gateways so that any message sent to DoD.Army.Platoon44 will reach that visiting platoon member. In addition, a temporary group identifier will be given to the squad team. Whenever a squad team member wishes to communicate with any squad team members, he can use his squad team identifier e.g. Nato.SquadTeam5.member5. If a squad team member intends to communicate with any team members in his original group, he will use his original identifier e.g. US- DoD.Army.Platoon44.User-Host1044. US-DoD.Army.Platoon44 US-DoD.Army.Platoon36 North Korea Nato.Squad.Platoon1 US-DoD.Navy.Battalion5 US-DoD.Army.Platoon44,UserHost-1093 Visiting Identifier=US-DoD.Navy.Battalion5,visitor5 Spain TGID PID Nato.Squad.Platoon1,UserHost-1044 GID PID US-DoD.Army.Platoon44,User-Host-1044 Figure 2: Enhanced DTN architecture [6] Several DTN unicast routing schemes have been proposed [19],[20],[21], [22] [23]. These DTN unicast routing schemes can be classified into three categories. In one category, special nodes referred to as message ferries or data mules are used to connect nodes that are far apart geographically. These message ferries visit the nodes using fixed or dynamic ferry routes. The second category of routing schemes keep track of contact histories and may use of such history information to derive deliverable probabilities. Each node will pass the message to another node that it comes across if the other node has a higher deliverable probability to the destination node. In the third category, the nodes use two-hop forwarding schemes [19]. The source node distribute K copies of a message to the first K contacts it comes across. The nodes that receive these messages will store these messages and deliver them only if they happen to come into contact with the destination. Erasure coding approach may be used to improve the efficiency of such schemes e.g. a message may be coded into n data blocks and the destination only needs to receive k (where k<n) data blocks for message reconstruction. In this paper, we focus on comparing the performance of a multihop routing with custody transfer (MRCT) scheme with a two hop with erasure coding (THEC) scheme [19]. Specifically, our contributions in this paper are: (a) we study the effectiveness of the custody transfer feature with on-demand routing protocols in

3 DTNs. It is important to know when the custody transfer feature needs to be turned on since the deployment of the custody transfer feature incurs extra overhead. (b) we study the impact of mobility models on the performance of on-demand routing protocols in DTNs. We consider both the random waypoint model and the Zebranet-like model. Zebranet-like mobility model is a mobility model derived from the traces obtained from the sensor nodes attached to zebras in the ZebraNet project described in [19]. (c) we compare the performance of two-hop [19], and multihop routing schemes in DTNs. We believe this is the first paper that compares the performance difference between the two-hop and multihop routing schemes in DTNs. The rest of our paper is organized as follows: we first give an overview of the three routing approaches that have been proposed for forwarding unicast messages in DTNs in Section II, and discuss their advantages and disadvantages. In Section III, we describe our simulation model. In Section IV, we study the performance of the MRCT scheme by studying how its message delivery performance is impacted by node densities, mobility models and percentage of nodes that support the custody transfer feature. In addition, we also compare the performance of the MRCT scheme with the THEC scheme. Our results indicate that the MRCT scheme can provide higher delivery ratio and lower packet delivery latency. We give some concluding remarks in Section V. II. UNICAST ROUTING SCHEMES AND CUSTODY TRANSFER FEATURE FOR DTNS In this section, we first describe the various categories of the unicast routing schemes that have been proposed for DTNs. Then, we describe the custody transfer feature proposed for DTNs. A. Unicast Routing Schemes Three categories of forwarding schemes have been proposed for DTNs. The first category uses special nodes to connect partitioned networks. The second category uses multihop routing approach where contact history information to determine the next hop node to pass a message. The third category uses two-hop routing approach where the intermediate nodes that receive messages from any source has to store the messages until deliver them when they come into contact with the destination. We elaborate on each category in subsequent paragraphs. In the first category [12],[15],[20], [29], special nodes called message ferries or data mules are used to collect data from stationary or moving sources and deliver them to their destinations. The message ferries move around the deployment area and are responsible for carrying data between nodes. The design of ferry routes will have significant impact on network performance. However, in these existing researches, regular nodes cannot become message ferries. Some preliminary work on turning moving nodes into message ferries in areas with sparse connectivity has been explored in [31]. In the second category [22], [24],[27], history-based routing is proposed in which each node maintains a utility value for every other node in the network, based on a timer indicating the time elapsed since the two nodes last encountered each other. These utility values, which carry indirect information about relative node locations, get diffused through nodes mobility. Nodes forward message copies only to those nodes with a higher utility for the message s destination. For example in [22], the authors propose a probabilistic metric called delivery predictability at every node A for each known destination B. This metric indicates how likely it is that node A will be able to deliver a message to that destination. The delivery predictability ages with time and also has a transitive property, i.e., a node A that encounters node B which encounters node C allows node A to update its delivery predictability to node C based on its (A s) delivery predictability to node B and node B s delivery predictability to node C. In [22], a node will forward a message to another node it encounters if that node has higher delivery predictability to the destination than itself. Such a scheme was shown to produce superior performance than epidemic routing [24]. The MaxProp approach in [27] is similar to [22], MaxProp differs from the approach in [22] only in how they maintain the contact probabilities. MaxProp renormalizes the contact probabilities a node maintains for all its neighbors but the probabilistic routing approach in [22] uses different equations to update the contact probability a node maintains for its neighbors. The performance of the history-based routing approach depends on how

4 frequent the various history-based schemes perform neighbor discovery. Doing it more often improves the delivery ratio but incurs more control overhead. In the third category [19], [23], [32], a twohop relay forwarding scheme is proposed in which the source sends either single or multiple copies (e.g., different erasure coding blocks) to different relaying nodes and the relaying nodes deliver the copies they have to the destination node when they encounter the destination node. The Binary Spray and Wait scheme proposed in [32] assumes that each source node starts with L copies of the same message, and sends half of its remaining copies to a new contact until it has a single copy left. Then, the source node switches to direct transmission (which means it will only forward to the destination node). The authors in [19], [23] use a similar approach except that they optimize transmission overhead by using erasure coding. Thus, in erasure-coding based two hop routing approach [19],[23], a message is coded and divided into n blocks and send to n contacts but the destination node only needs k (where k<n) copies to reconstruct the original message. With the same amount of overhead, the erasure-coding based approach allows the source to send copies of the same message to more contacts and hence increase the chances of having the destination node receives enough copies to reconstruct the original message. We anticipate that a two-hop routing strategy can achieve small transmission overhead but may not enjoy high delivery ratio especially if the messages have delivery deadlines. B. Custody Transfer Feature As indicated earlier, custody transfer feature has been proposed for DTNs [13],[17]. This custody transfer feature [13],[17] provides reliable communications in an intermittently connected network. When a node accepts a message with custody transfer, it means the node promise not to delete the message until it can be reliably delivered to another node providing custody transfer or the message arrives at the destination. Nodes holding a message with custody are called custodians. Normally, a message has a single custodian (referred to as sole custody) but in some circumstances, more than one custodian owns a message or message fragment (referred to as joint custody). Applications can optionally request the custody transfer feature on a per-message basis and they will receive a custody acknowledgement when their host system can find one or more nodes that are willing to take custody of the message. A node may agree to accept custody for messages initially and refuse to do so when its local node resources, e.g., buffers, become substantially consumed. Potential problems that may occur with custody transfer are discussed in [17]. One of the problems discussed in [17] is the head of line blocking problem. At a node R, messages destined to node A may be sitting behind a message destined to node B and there is no communication link between node R and node B but the communication link between node R and node A is available. Node R needs to be able to transmit those messages destined to node B. III. SIMULATION MODEL We use NS-2 simulator [29] for our extensive simulation studies. Below we describe the new modules that we add to the NS2-simulator. A. Implementation of DTN Routing Schemes We are interested in understanding whether a multihop routing combined with custody transfer feature can perform as well as the two-hop routing approach that has been proposed for DTNs. Hence, we implement two DTN unicast routing schemes, namely the multihop routing with custody transfer (MRCT) scheme and the two-hop with erasurecoding (THEC) scheme [19]. For the MRCT scheme, we assume the custodian of each message will try to find a route to the destination node by issuing route request messages as in DSR [18]. The difference between the MRCT scheme and the DSR scheme is that the message custodians instead of just the source node will perform route discovery and route repair. In addition, the custody transfer feature is turned on by nodes that support DTN functionalities. The two-hop with erasure coding (THEC) scheme [19] is implemented as follows: the nodes exchange hello messages with one another periodically. This allows them to build a neighbor table. Each message is encoded and divided into eight blocks at the source node. Upon meeting a new 1-hop contact, the source node sends a data block to that new neighbor. Thus, all eight blocks will be disseminated (to eight contacts). The contacts carry the block until they meet the destination. If that happens, a contact will deliver the message block. As long as the destination

5 receives four out of the eight blocks, that message is considered successfully delivered to the destination. B. Implementation of Custody Transfer feature We implement the custody transfer feature in our simulator as follows: when a DTN node has a message to send for which it holds custodianship, it checks its cache to see if it has a route to the destination node. If it finds more than one routes, it picks the one with the lowest cost (e.g., using hop count, delivery latency etc., as metrics). When a route is selected, it checks the DTN nodes included in this selected route to see which node is the best candidate for custody transfer, e.g., the closest DTN node that has buffer space available. Then, it sends a custodian request to that downstream DTN node. If the DTN node can accept the custodianship, it will respond with a custody acknowledgement. Otherwise, it sends a negative reply. If the sending DTN node cannot find a route to the destination of the message, it will send a custody request to its 1-hop DTN neighbors to see if any one of them has a route to this destination. If there is a custodian accept reply from any 1-hop DTN neighbors, then, this sending DTN node will send the bundle to that replying node. If there is no reply (after a wait-for-reply timer expires), then this sending DTN node will trigger its underlying ad hoc network layer to look for a route or neighboring nodes that are closer to the destination than itself. At the ad hoc network routing layer, all DTN nodes that receive a route reply message with the DTN option flag set will set a bit in the appropriate position (according to its hop distance from the sending node of the route request) to indicate buffer availability before relaying the route reply message. Thus, our dual-layer (at ad hoc network routing and DTN layers) approach allows a node to identify downstream nodes to which we can forward the messages. Once a custodian node is selected, the sender transmits a message to it and waits for an acknowledgement. If the sender does not receive custodian acknowledgement from the new custodian node, it will retransmit up to a certain maximum number of times. If the sender still fails to receive acknowledgement after multiple attempts, the sender can select another node to be the custodian. Our custody transfer implementation avoids the head of line blocking problem described in [17] by allowing the DTN node to search through the queued messages until it finds a message that can be sent to the next hop node. C. Mobility Models Since we intend to compare the impacts of different mobility models on the delivery performance of various routing schemes, we implement a scaled Zebranet mobility model. As in [19], we create a semi-synthetic Zebranet mobility model as follows: we synthesize node speed and turn angle distributions from the observed data and create other node movements using the same distribution. We scale the grid size to 1500mx1500m and use a transmission range of 250 m (as compared to a grid size of 6Kmx6Km and a transmission range of 1000 m used in [19]). In the original Zebranet trace [19], the inter-sample interval is 8 minutes but we scale this interval to 2 minutes in the experiment we conducted. This allows us to keep the node speed to be similar to what is reported in the Zebranet trace. D. Traffic Models For traffic models, we typically use UDPbased Constant Bit Rate traffic model. In addition, we also implement bidirectional flows. Bidirectional flows are implemented as follows: a source sends a message to a destination and the destination will respond with a message of the same size back to the source before the source generates further messages. IV. PERFORMANCE STUDY OF UNICAST ROUTING SCHEMES IN DTNS In this section, we perform extensive simulation studies to understand how different factors e.g. node densities, mobility model affect the performance of the MRCT scheme and also compare the MRCT scheme with the THEC scheme. The performance metrics we use are: (a) transmission overhead [19] which is defined as the number of transmitted bytes over the number of generated bytes. Note that in this case, the transmitted bytes include the routing overhead. Each routing message and each custody transfer request/acknowledgement message is assumed to be 35 bytes long. (b) total number of control messages sent (including custody transfer acknowledgements) (c) the average end-to-end delivery latency (denoted as Avg Delay in the tables or plots), and (d) the packet delivery ratio (PDR) For each scenario, we conduct 10 simulation runs and report the average of the metrics obtained from these 10 runs. A warm up period of 1000

6 seconds is used for each simulation run and the simulation is run for 10,000 seconds. We use IEEE radio in our simulator with a transmission range of 250m and a peak data rate of 2 Mbps. Unless otherwise stated, we use a network scenario with 40 nodes distributed randomly in the following areas: (a) 1000x1000 m 2, (b) 1500x1500 m 2, (c) 2000x2000 m 2, and (d) 3000x3000 m 2. A. Impact of Node Densities in a DTN with Custody Transfer In this section, we investigate how the DTN node density impacts the delivery performance of the MRCT scheme. We conducted two sets of experiments. The nodes are moving according to the random waypoint mobility model with a maximum speed of 5 m/s. First, we run some experiments assuming that the nodes do not support custody transfer, i.e., they are just regular adhoc network nodes. Then, we run the same experiments assuming all nodes turn on the custody transfer feature. 10 source/destination pairs are used in this set of experiments. The source/destination pairs are randomly picked among the 40 nodes. Each source generates one packet every 4 seconds. Thus, the traffic model used is the Constant Bit Rate traffic model. The packet size is 512 bytes. Figures 3,4, and 5 plot the delivery ratio, the average delay, and the transmission overhead for the case with and without custody transfer feature. From Figure 3, we see that the packet delivery ratio starts to drop significantly (by 19%) when the node density changes from 4x10-5 node/m 2 to 1.8x10-5 node/m 2. The drop in PDR is 30% as the node density decreases from 1.8x10-5 node/m 2 to 1.0x10-5 node/m 2 without the custody transfer feature. The custody transfer feature significantly improves the packet delivery ratio for those scenarios where the node density is below 1.8x10-5 /m 2. In the 1x10-5 node/m 2 (the 40 nodes over 2000x2000m 2 ) case, we see that the delivery ratio has dropped to 48% without custody transfer. However, the packet delivery ratio increases to 98.6% when the custody transfer feature is turned on. The additional price to pay for this improvement is an increase of transmission overhead by almost 217%(=( )/3.27) for the case with a node density of 1x10-5 node/m 2. We expect the delivery ratio to continue to drop significantly as node density continues to drop below 0.5x10-5 node/m 2, and that the custody transfer feature alone will not be enough to allow the sparsely connected nodes to communicate with one another. For such cases, we propose to use message ferries to connect partitioned nodes [33]. Figure 3: Delivery ratio with and without custody transfer in the MRCT scheme. Figure 4: Average Delay with and without custody transfer in the MRCT scheme. Figure 5: Transmission Overhead with and without custody transfer in the MRCT scheme.

7 B. Impact of different mobility models Next, we conduct experiments to understand the impact of mobility models on the message delivery performance. We use the same network scenario. The nodes move either according to random waypoint (RWP) model or according to Zebranet movement [19]. For the random waypoint movement, the nodes have a maximum speed of 5m/s. Again, ten CBR flows with the source and destination nodes randomly picked among the 40 nodes, are used in this set of experiments. The packet generation rate is 0.25 packet/sec. Figures 6, 7, and 8 plot the delivery ratio, the average delay, and the transmission overhead for both mobility models. Figure 6 shows that the delivery ratios achieved using both mobility models are comparable but the average packet delivery latency (shown in Figure 7) is higher with the Zebranet mobility model. This may be caused by the faster and more chaotic node movements in the Zebranet mobility model. The transmission overhead (shown in Figure 8) is also higher using the Zebranet mobility model as compared to that obtained using random waypoint mobility model. Figure 7: Average Delay with RWP and Zebranet mobility models Figure 8: Transmission Overhead with RWP and Zebranet mobility models. Figure 6: Delivery Ratio with RWP and Zebranet mobility models The delay distributions obtained using the random waypoint model and using the Zebranet model for the case with a geographical area of 1500x1500 m 2 are plotted in Figure 9. Here, we see that the Zebranet mobility model results in packet delivery latency that has a larger tail. The 90 percentile delay is 100 seconds using the random waypoint model but it is 260 seconds using the Zebranet mode. Figure 9: Delay Distribution using different mobility models.

8 C. Impact of DTN percentage & bidirectional flows In this section, we investigate how the percentage of DTN nodes impacts the delivery ratio. We also measure the end-to-end delay for both oneway and bidirectional flows. In this set of experiments, we simulate 40 nodes randomly distributed in an area of 1000x1000 m 2. The nodes move according to the random waypoint mobility model. In addition, we select 10 source/destination pairs that require many hops for packet delivery. Then, we evaluate the delivery ratio, the one-way end-to-end delay, the two-way end-to-end delay as well as the transmission overhead. The results with 50% and 100% of the nodes supporting DTN functionalities are tabulated in Table 1. The delay distributions with one-way and bidirectional flows are plotted in Figures 10 & 11 respectively. 100% 50% DTN DTN Delivery Ratio 98.7% 96.5 Avg One-way Dly (sec) Avg Bidirectional Dly(sec) Transmission Overhead Table 1: Impact of DTN percentage From the results, we see that even with 50% DTN nodes, the delivery ratio is as high as 96.5% and the one way and bidirectional delay is reasonably low. The 95% one-way delay is about 1150(927) seconds and the 95% bidirectional delay is about 1650 (1250) seconds for the 100% (50%) DTN nodes case respectively. Those packets that are not delivered in the 50% DTN nodes case but are delivered in the 100% DTN nodes incur larger delay. Thus, we observe larger 95% bidirectional delay for the 100% DTN nodes case when compared to that achieved in the 50% DTN nodes case. D. Impact with varying link bandwidths The DieselNet [27] trace does not have accurate GPS information. Thus, we cannot use the trace data to provide yet another mobility model. However, the trace provides information about varying link bandwidths between two nodes. Figure 12 shows the CDF of the link bandwidths information we extracted from the trace. We fit this data to a Pareto distribution and use this distribution in our simulator. The CDF of the fitted Pareto distribution is as shown in Eq 1 below. 3 5 F( x) = 1 ( ) --- (1) x Figure 10: Delay distribution with 100% DTN nodes. Figure 11: Delay distribution with 50% DTN nodes. In all experiments in this subsection, we use 40 nodes distributed over an area of 2000x2000m 2. When a node needs to send a packet to another node, we assume that the available link bandwidth is obtained from this distribution. We vary the traffic load from 0.25 pkt/sec to 1 pkt/sec. The buffer size is set to 1200 messages since it is observed that such buffer size is sufficient to maintain delivery ratio at 97-99% at all traffic load (up to 1 pkt/sec). In the

9 first set of experiments, we let the nodes move according to random waypoint mobility model with a maximum speed of 5 m/s. In the second set of experiments, we let the nodes move according to RWP with a maximum speed of 20 m/s since the buses in [27] move rather fast. Figures 13, 14 and 15 plots the delivery ratio, the average delay and the overall control overhead for the cases with fixed bandwidth (labeled as RWP) and variable bandwidth (labeled as UMass trace) and different maximum node speeds. The plot in Figure 13 indicates that delivery ratio improves with increasing maximum speed. The delivery ratios achieved with fixed and varying bandwidths are comparable. However, the varying link bandwidth (varies between 0 to 2 Mbps) increases the average packet delay as can be seen from Figure 14. The average delay with a higher maximum speed has increased since the links get broken more easily at higher speed and hence the packets incur additional route repair and route re-discovery delays. The difference in the achieved delay with varying link bandwidth and with fixed bandwidth is more significant in the fast moving scenario (with a maximum speed of 20 m/s) than the slow moving scenario (with a maximum speed of 5 m/s). The bigger difference can be attributed to the fact that more messages need to be retransmitted with more frequently broken links which translate to higher delay difference between the varying and fixed link bandwidth cases. The control overhead increases with increasing maximum node speed. Figure 13: Delivery Ratio with fixed/varying bandwidths and different maximum node speed. Figure 14: Average Delay with fixed/varying bandwidths and different maximum node speed. Figure 12: CDF of the link bandwidth from UMass Trace Figure 15: Overall Control Overhead with fixed/varying bandwidths and different maximum node speed. E. Comparison between two hop and multihop approaches In this section, we present the simulation results we obtained by comparing the MRCT scheme with

10 the THEC scheme. Again, we use 40 nodes distributed over an area of (a) 1000x1000 m 2, (b) 1500x1500 m 2, (c) 2000x2000 m 2, and (d) 3000x3000 m 2. Ten CBR flows with sources and destinations randomly picked among the 40 nodes are used. The packet generation rate is 0.25 pkt/sec. Figures 16, 17 & 18 plot the delivery ratio, the average delay and the overall control overhead of the two-hop and the multihop relay approaches with the random waypoint and Zebranet mobility models. The THEC scheme is marked as the two-hop approach while the MRCT scheme is marked as the multihop approach in the plots. The results in Figure 16 indicate that the delivery ratios using both routing schemes are comparable using Zebranet mobility models. The delivery ratios using both routing schemes are maintained at high values (90%) even when the network is sparse. However, the delivery ratios for both schemes using the random waypoint mobility model drop drastically when the network becomes sparse (especially for node density below 1.8x10-5 node/m 2 ). The drop is more significant for the THEC scheme than the MRCT scheme. In addition, from Figure 17, we see that the average delay achieved by the multihop approach is significantly smaller than the two-hop approach especially with the random waypoint mobility model. The delay difference is bigger with the random waypoint mobility model than with the Zebranet model. The improvement in the delivery ratio and packet delivery latency using the multihop routing approach comes at the price of larger transmission overhead when compared to the two-hop approach. It is interesting to note from Figure 18 that the transmission overhead using the MRCT scheme for both mobility models initially increases with decreasing node density since more retransmissions need to be done. Beyond a certain node density, the transmission overhead drops since fewer messages are being delivered. The same trend is observed for the transmission overhead using the THEC scheme and the Zebranet model. Figure 16: Delivery Ratio vs Node Density using THEC and MRCT schemes with RWP and ZebraNet mobility models. Figure 17: Average Delay vs Node Density using THEC and MRCT schemes with RWP and Zebranet mobility models. Figure 18: Transmission Overhead vs Node Density using THEC and MRCT schemes with both mobility models.

11 When different mobility models are used, the THEC scheme seems to perform better with the Zebranet mobility model. It seems like this protocol [19] has been optimized for Zebranet mobility model. The multihop approach still performs slightly better than the two-hop approach using the Zebranet mobility model. With the Zebranet mobility model, the MRCT scheme has slightly higher delivery ratio and significantly better 95% delay when compared to the two-hop approach. In addition, the MRCT scheme performs much better than the THEC scheme with random waypoint model. This again comes at the price of incurring larger transmission overhead. V. CONCLUDING REMARKS In this paper, we describe the various approaches for delivering unicast messages in disruption tolerant networks. Via extensive simulation studies, we specifically look into how mobility models, node densities affect the delivery performance of a multihop routing with custody transfer (MRCT) scheme. Our results show the usefulness of the custody transfer feature in improving delivery ratio of the messages with node density larger than 0.5x10-5 node/m 2. With a very sparse network (smaller than 0.5x10-5 node/m 2 using a transmission range of 250m), the custody transfer feature alone does not provide sufficiently high delivery ratio for communications. Instead, message ferry needs to be used to achieve high delivery ratio [30], [33]. In addition, we investigated how the percentage of nodes supporting DTN functionality affects performance. Our results indicate that the delivery ratio achieved with only 50% nodes supporting DTN functionality can be as high as 96.5% (only 2.2% lower than that achieved with all nodes supporting DTN functionality) using the scenarios we have simulated. Our results also suggest that the two-way delay experienced in bidirectional flow is only about 10% more than the one-way delay with unidirectional flow. We also provide some results that compare the performance difference between two categories of routing protocols, namely the multihop approach and the two-hop approach. Specifically, we compare the performance of the MRCT scheme with a two-hop relay with erasure coding (THEC) scheme used in the Zebranet project [19]. Our results indicate that the MRCT scheme achieves higher delivery ratio than the THEC scheme but incurs higher transmission overhead. Using the traces provided by the UMass DieselNet group, we also investigate how the varying link bandwidth impacts the performance. Our simulation results indicate that the varying link bandwidth increases the average packet delay by about 24% compared to that experienced with fixed link bandwidth. Acknowledgments This work is sponsored by Defense Advanced Research Projects Agency (DARPA). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of DARPA. This document is approved for public release, unlimited distribution. We also thank E. Krohne from Colarado School of Mines for his comments on an earlier version of our paper that help us to improve our presentation. REFERENCES [1] Y. Gitman, Magic Bike: Wireless Internet, [2] L. Arent, G4techTV, Tech-Live, Wi-Fi on Two Wheels, /WiFi_on_Two_Wheels.html, May 2004 [3] A. Pentland, R. Fletcher, and A. Hasson, DakNet: Rethinking connectivity in developing nations, IEEE Computer, 37(1):78-83, January [4] H. Balakrishnan, V. N. Padmanabhan, S. Seshan, and R. H. Katz, A comparison of mechanisms for improving TCP performance over wireless links, IEEE/ACM Transactions on Networking, 5(6): , December [5] K. Fall, A delay-tolerant network architecture for challenged Internets, Proceedings of SIGCOMM 03, August [6] M. Chuah, L. Cheng, and B. D. Davison, Enhanced Disruption and Fault Tolerant Network Architecture for Bundle Delivery (EDIFY), in Proceedings of IEEE Globecom, Nov [7] C. E. Perkins and E. M. Royer, Ad-hoc On- Demand Distance Vector Routing, Proceedings of IEEE Workshop on Mobile Computing Systems and Applications, pp , Feb [8] UCB/LBNL/VINT, The Network Simulator ns- 2, Online at

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