Independent DTNs Message Deletion Mechanism for
Multi-copy Routing Scheme
Sathita Kaveevivitchai

Hiroshi Esaki

The University of Tokyo
7-3-1 Hongo Bunkyo-ku
Tokyo 113-8656 JAPANThe University of Tokyo
7-3-1 Hongo Bunkyo-ku
Tokyo 113-8656 JAPAN

wan@hongo.wide.ad.jp

hiroshi@wide.ad.jp

ABSTRACTIn this paper we discuss the independent message deletionmechanism for Delay Tolerant Networks (DTNs) multi-copy
routing schemes, and the enhancement of this method withthe knowledge of human mobility patterns. This mechanism can optimize the resource utilization and improvemessage delivery performance. In DTNs, communicationis achieved by the movement of mobile devices and the ability to store-carry-and-forward the messages. Since contacts
in this network occur opportunistically, multi-copy routing scheme suggests messages to be replicated, carried andforwarded by several devices to enhance the delivery ratio
though it consumes greater resources compared to singlecopy routing scheme. We propose the method to optimize
the resource utilization in multi-copy routing scheme by eciently deleting the copy of delivered message. This method
reduces storage consumption, and avoids devices from wasting their bandwidth, and energy, hence, improves the performance of DTNs message delivery in multi-copy routing
scheme. We have taken the intrinsic characteristic of human movements, known as, Human Gathering Points" toenhance our proposed method. We evaluate the proposed
method in the simulations with real human movement data
trace on three major DTNs multi-copy routing schemes, Epidemic, Spray and Wait, and ProPHET. The result shows
signicant improvement in message delivery (10-50%) while
reduces the resource consumption (overhead) by 30-70%. Its
independent characteristic makes it possible to be appliedwith any multi-copy routing protocol though the degree of
impact is varied based on the underlying routing scheme.

Categories and Subject Descriptors

C.2.1 [Network Architecture and Design]: Wireless Communication; C.2.2 [Network Protocols]: Resource Management
Protocolperformance measures

General Terms

Algorithms, Performance, Theory

Keywords

Delay Tolerant Networks, Human Mobility Patterns, Message Deletion Mechanism

1. INTRODUCTION

As we move from the Information era to Attention era,
where information can be generated and shared anytime,
anywhere and by anyone, there is a need for individuals tocreate and consume information freely and instantly. Therefore it demands the constant communication on mobile personal devices. Through the movement of these devices, theconnection and message delivery become possible in sparsely
connected networks known as Delay Tolerant Networks (DTNs)[1].
The concept of DTNs has addressed the network in challenging environments. The movement of mobile devices create
frequent topology changes, disruptions, and network partitions. Moreover, the limited storage and energy of thesedevices also cause the intermittent connectivity and the absence of end-to-end connectivity. To overcome these challenges, one needs to provide the protocols that can tolerate
the dynamic network environment with abrupt changes andhigh delay while stay tune to the optimization of resources
utilization.
To achieve messages delivery upon above requirements, many
DTNs routing algorithms had been proposed in recent years,
which can be categorized into two main classes: single-copy
routing scheme[2] and multi-copy routing scheme[3, 4, 5].
While the multi-copy routing scheme promises higher delivery ratio, there are more resources to be consumed. The
method to achieve high delivery performance at the minimal cost is still to be devised. One way to enhance theperformance of DTN message delivery is through message
management scheme such as buer management and message deletion mechanism. We need to optimize the resources
usage (storage, bandwidth and energy) while maintaining
high delivery ratio as in multi-copy routing scheme.
Generally, the storage managements in DTNs are often integrated with the routing algorithm such as Hop-based TTL
in Spray and Wait[4], or passive-cure in Potential-based Entropy Adaptive Routing (PEAR)[17]. So far, there are several works regarding buer management policies which canwork independently. Yet our method diers from those since
it works as preventive measure for resources exhaustion andstorage overow problems rather than counter measure asin [20, 21]. The contribution of this paper is the proposal ofindependent message deletion mechanism for DTNs multicopy routing scheme. And the proposal of its enhancement with the Human Gathering Points", the locations
where people naturally gather together, detailed explanation in Section 2.2. The key design of our protocol is to
cut-o unnecessary resource usage through the deletion of
the copies/replicas of delivered messages. It will prevent
nodes from the storage overow problems, reduces storageand energy consumption, avoid unnecessary transfer of useless message replicas, therefore it can enhance the performance of DTNs message delivery. We evaluate our proposed
method by simulation with the real human movement data
traces[12], on three major DTNs routing algorithms: Epidemic[3], Spray and Wait[4], and ProPHET[5].
The results show that our method improves the performanceof multi-copy routing algorithms though the degree of impact is dierent based on the underlying routing algorithm.
The remainder of this paper is organized as follows. Section
2 explains our proposed deletion mechanism, mobility patterns and Human Gathering Points". Section 3 describes
the simulation environments and presents the results. Related work is presented in Section 4. Finally we conclude in
Section 5 and present our future work.

2.

DELETION MECHANISM AND
MOBILITY PATTERNS

Oftentimes, the countless replica in multi-copy routing scheme,
overwhelm the limited network resources. When the messages had reached their destinations, their replicas become
useless though they are still occupying storage space andbeing forwarded to other nodes. Without the proper resource management scheme, these useless replicas can cause
the degradation of the DTNs delivery performance. We propose a message deletion scheme, which will be able to apply
independently from the underlying routing protocols.
Since the devices in DTNs environment are generally carriedby human, their movements are closely correlated to thehuman movements. The key element in our design is thestudy and application of the heuristic knowledge of actual
human mobility. This method is proven to be ecient interms of: (i) Ability to remove copies of delivered messages
in timely manner; (ii) improvement in resources utilization(bandwidth, storage).
We use the acknowledgment from the destination node to
indicate that the message has already been received andits replicas can be deleted from the intermediate nodes. We
separate our design into two parts: (i) methods for distributing acknowledgment messages (ACK); (ii) The use of Helper
nodes at Human Gathering Points to relay ACK messages.

2.1

ACK Distribution Method to
Delete Replicas

The ACK messages can be distributed in two dierent ways,
either passive or active policies, as it is illustrated in Figure. 1. In passive distribution policy, ACK of any message
m will not be expressed to any encountering node (B) unlessthe encountering node makes an attempt to send a copy of

Figure 1: Passive and Active ACK Distribution Policies
message m to the node that currently holds ACK of m (A).
In this case, ACK message will be slowly distributed andcopies of m will be slowly discarded from the system. The
down point of this policy is the timeliness in distributing
ACK message. This is crucial in multi-copy routing scheme
since the number of message is growing with time. Whilethe useless message has not been deleted, its replicas will
be further generated and forwarded around, consumed more
network resources. In active distribution policy, a node holding ACK of any message m (A) will express its knowledge
to its encountering node without any condition on the peer
node (B). The extreme case of this active distribution is toexpress its knowledge, ACK, to every encounter.
There are several reasons why we have chosen an active distribution policy for our design. First, since the communications in DTNs occur opportunistically and often experience disruption, we would like to expediently utilize theseconnection opportunities by using only one way acknowledgment (Active ACK Distribution) and giving the rest for message transmission. Second, active ACK distribution method
would enable us to swiftly discard the copies of delivered
messages and prevent them from being regenerated and forwarded to other intermediate nodes. In our design, any nodein the system is required to hold a list of known delivered
messages ID. However, the size of this list is very small compared to the size of a message. When the contact occurs,
encountering nodes will exchange the information in the list.
If a message in their buer matches the ID of delivered message in the list, they will delete corresponding message fromtheir buers.
To enhance our deletion mechanism, we integrate the knowledge of human mobility patterns into this design.

2.2 Mobility Patterns and Helper nodes

As previously mentioned, communications in Delay Tolerant
Networks are often facilitated by the mobility of devices.
Therefore understanding this mobility patterns can benet
in designing new mechanisms for DTNs. We have integrated
the knowledge of human mobility patterns into our deletionmechanism as we introduce the static helper nodes (static
H-nodes) based on the idea of Human Gathering Points".
These static H-nodes will play the role in expediting the
distribution of ACK messages
Human Gathering Points: The hotspot model is one of thecommonly found characteristics of human mobility in various studies [8, 9, 10, 11]. This model suggests that there arelocations where many people naturally meet in space. Thisis an essential knowledge in designing the mobile network
structure, since it represents strategic locations where a lotof contact opportunities will occur as well as the exchange ofFigure 2: Map of Simulation Area and Location of static
H-nodesmessages. We assumed that the popularity of these points
will be useful in the distribution of acknowledgment andour deletion mechanism. In the experiment, we deployed
the stationary nodes (static H-node) at these locations andstudy their impacts on our proposed method.
Dataset: The dataset used in our study is the collected GPS
traces from Crawdad database [12]. There are 39 GPS traces
collected from 10 volunteers in the area of 12 square kilometers in Midtown Manhattan (from 14th Street to 59th Street
and from Hudson River to East River). Some of the volunteers are living in Manhattan's vicinity, so their traces arelikely to contain long distance travel across from other areas. Their daily means of commuting include subway trains,
buses, and walking. We have plotted the frequency of thevisits or passes by on each map point (relative x,y coordinates), shown in Figure. 2. From the graph we can noticethree frequently visited locations. These points are selected
as the locations to deploy our static H-nodes. Based on spatial structure of the city, we can also explain the reasons
behind these locations' popularity using Space Syntax theory [13]. These three locations include, at the center Herald
Square (HC: Hotspot Central), on the east (HE: Hotspot
East) and west (HW: Hotspot West) are the entrance/exit
to Long Island Expressway and to Lincoln Tunnel respectively. HE and HW have similar characteristics; they serveas port of entry/exit to other areas, which make them popular to a group of nodes that travel across from other areas.
While at the center, HC, is a major intersection where total
accumulated ow is high, which also infers high integration
values in Space Syntax Theory.

3.

PERFORMANCE EVALUATION

3.1

Simulation Setup

In this section we will explain the evaluation for our proposed method. We rst show the simulation environment
and then present the results.
In order to evaluate our proposed mechanism and the impact of stationary nodes deployed at human gathering points
on this mechanism we have simulated our work in the Opportunistic Networking Environment (ONE) simulator [14],which is designed to support DTN nodes characteristics. In
addition we also used the OpenJUMP software [15] to tailor
our nodes movements according to real collected data tracesfrom the Crawdad dataset. The dataset is the collectedtraces in Manhattan area, New York City as discussed in
Section 2.2. With these tools we were able to simulate ourmechanism in more realistic environment.

We simulated three dierent node-density scenarios on thesame terrain, 10-nodes, 20-nodes, and 100-nodes. Table 1
summarizes the simulation parameters. We would like toobserve the impact of our method and the eect of humangathering points in the sparse network environments (10 and
20-nodes scenarios) as well as in the dense network environment (100-nodes scenario). Since the contact opportunity
are varied by the density of node. In sparse network thecontact opportunities are rare due to the small number ofnodes while in dense network, there are frequent contact
opportunities. In all scenarios, messages are generated by
uniformly selected random sources, towards random destinations. The size of message is also randomly chosen between 500KB-1MB equivalent to the size of large text leor medium size image le. In our simulations, the messages
had innite Time-To-Live (TTL value). Our nodes are allidentical, exhibit Bluetooth properties as shown in Table
1. The nodes movement speed is correspondent to humanwalking speed, 0.5-1.5 m/s.
To evaluate the impact of stationary nodes deployed at human gathering points, we added one stationary node at eachpoint in respective order according to their frequencies of being visited by distinct nodes. The order and locations areshown in Table 2.

3.2 Simulation Results

We use the following performance metrics, namely, delivery
probability, overhead, average latency, and average bueroccupancy, to evaluate the performance of our deletion mechanism and the impact of static H-node at Human Gathering
Point. The delivery probability metric indicates the successful rate in delivery the message. While overhead and bueroccupancy metrics indicates the resources consumed in delivering the message. Average latency shows the averagetime required in order to complete the delivering process.
These metrics can denote the performance of DTNs message delivery. We implement our algorithm onto three routing protocols which are the representatives of routing protocols in DTNs, Epidemic, Spray and Wait, and ProPHET.
Epidemic[3] represents the extreme ooding case, where all
messages will be copied to all nodes without any consideration. In contrast, Spray and Wait[4] routing protocol
represents the case where every messages are bounded only
to limited number of copies, or in another word, controlledooding. ProPHET[5] routing protocol represents the probabilistic routing protocols, which the number of copies or
the nodes carrying the copies, are selected based on history
of node encounters and transitivity. This protocol is considered to be adaptive compare to the previous two protocols.
The results on each performance metrics are described below.

3.2.1 Delivery Probability

Delivery Probability (DP) is the percentage of delivered
messages over total unique generated messages.
DP =DeliveredM essages
U niqueGeneratedM essages(1)

The delivery probability graphs are shown in Figure. 3, therst column on x-axis (noACK) in all graph represents thedefault scenario where there is no acknowledgment in theTable 1: Scenarios Setting
Characteristics
Scenario 1 Scenario 2
Number of Nodes
1020
Speed (m/s)
0.5-1.5
0.5-1.5Buer Size (MB)20
20
Bandwidth (KB/s)
250
250
Transmission Range (m)1010Number of Static H-node
1-3
1-3
Messages Generated Interval (s) 25-35
25-35

Scenario 330
0.5-1.5
20
250
10
1-3
25-35

Table 2: Static H-node Setting
250Number of Static H-nodeLocation
1
2
3

HC
HW
HE

system. The second column (ACK) is where the destination node generates the ACK message and distribute to
all node without any help from static H-node, our proposed deletion mechanism. Then the rest of the cases involve 1 to 3 static H-nodes at Human Gathering Points(ACK+1hs to ACK+3hs). For each routing protocol, Epidemic, ProPHET, Spray and Wait (from left to right), we
perform three simulations with dierent node density as previously described in Section 3 A (10,20,100-nodes).
From Figure.3, we can see that our deletion mechanism can
improve the delivery ratio by 10% in 10-nodes scenarios,
in Epidemic and ProPHET routing protocols. The delivery probabilities also improve by 20% in 20-nodes scenario
and 50% in 100-nodes scenario. The deployment of static
H-node at HC (Herald Square) can further improve the delivery performance by 5-15% in sparse network settings for
all routing algorithms. Though, with Spray and Wait routing protocol, our deletion mechanism has smaller improvement. Since Spray and Wait is a ooding-control routing
algorithm, it is less likely to be devastated by the message
replicas as in other routing protocols. In dense network environment, static H-nodes do not have signicant impact due
to the high frequency of contact opportunity among moving
nodes.

3.2.2 Overhead

Overhead is the average resource (storage, bandwidth andenergy) consumed per one delivered message. This is thenumber of replicas that had been relayed in order to achieve
one successful delivered message. The layout of Figure. 4,is the same as in Figure 3 described in previous paragraph.
In Figure 4, we enlarge the bottom part (below the black
horizontal line) of the graphs in order to show the resultsfrom sparse network scenarios.
Our proposed mechanism, (ACK, ACK+1hs, ACK+2hs,
ACK+3hs), shows that it can reduce the resource consumption signicantly in Epidemic and ProPHET routing protocols. Since it helps deleting the unnecessary replicas andalso preventing nodes from further distributing the replicas of delivered messages. In second column (ACK), where
no static H-node is deployed, overhead can be reduced 30-

Herald Square
Entrance/Exit to Lincoln Tunnel
Entrance/Exit to Long Island Expressway
50% in sparse-network scenarios of Epidemic and ProPHET
routing protocols. In dense environment it can reduce up to
70% approximately. However, in Spray and Wait the overhead remains approximately equal to the predened maximum copies value. Spray and Wait routing protocol allows
only M copies (predened maximum copies) to be replicatedper one message. The static H-nodes at Human Gathering
Points causes the overhead to become slightly higher sincemore replicas are generated to these nodes, however, thiscan also contribute to higher message delivery probability
at the mean time. Single stationary node accounts for 10%
higher in overhead for Epidemic. While in Spray and Wait
and ProPHET, one H-node only accounts 3% higher in overhead.

3.2.3 Average Latency

Average Latency is the time interval between sending message by source and receiving its copy by destination.
AverageLatency = T received − T sent

(2)

Where Treceived is time when any copy of message m reachedat the destination. Tsent is time when message m is generated at the source.
In Figure 5, we can see that the deletion mechanism increased the average latency in message delivery. However,by deploying the static H-nodes at Human Gathering Points
can reduce the average latency in sparse network scenarios
for all routing protocols. Epidemic routing protocol case
(Figure 5-left), in dense environment, applying our proposeddeletion mechanism can vastly reduce the average latency by
35% compare to the default case where no deletion mechanism is presented. Our mechanism serves as the countermeasure for resource exhaustion, which is likely to occur
when using Epidemic routing protocol in dense environment.
It allows the network to use the resources more eectively
thus contribute to the reduction of average latency in this
scenario.

3.2.4 Average Buffer OccupancyBuer Occupancy (BO) is the percentage of buer spacethat occupied by the messages and messages replicas. We

Figure 3: Delivery Probability

Figure 4: Overhead
have plot the average buer occupancy (AverageBO) at eachsimulation time step shown in Figure 6, 7, 8.
nP

AverageBO =buf f eroccupancy

i=0

n

(3)

Where n equals to total number of nodes in the system.
When the node's buer becomes full, the rst incoming message in the buer queue will be dropped by node, in orderto make space available for the incoming message. This isdone in First-In-First-Out(FIFO) manner. From our perspective, the lower buer occupancy will prevent the messages from being dropped while it is being forwarded to thedestination. Figure 6, shows the average buer occupancy in
10-nodes scenario for Epidemic, ProPHET, Spray and Wait
protocols. Figure 7 and Figure 8 show average buer occupancy in 20-nodes and 100- nodes scenarios respectively.
The graphs illustrate that our proposed mechanism can reduce the buer occupancy in all scenarios regardless of applied routing protocols. The default scenario is shown in
topmost line which always nearly 100% occupied and variations of our proposed methods in other lower lines. The
impact in dense-environment (100-nodes scenario) is greaterthan in sparse environment, since ACK messages can spreadout more rapidly due to the frequent contact opportunities among nodes. In dense environment we can reduce thebuer occupancy approximately 30-33% in ProPHET and
Spray and Wait. While in sparse network the reduction isabout 10-25%.
The static H-nodes deployed at Human Gathering Points
can enhance the opportunities of ACK messages distribution
as well as opportunities in forwarding messages in sparse

network environments. One static H-node at HC can reduces the buer occupancy by 10% in Epidemic and Sprayand Wait routing protocols, while reduces 20% in ProPHET
routing protocol. The introduction of static H-nodes at
HE and HW decreases the buer occupancy 10% more in
ProPHET. Since ProPHET is adaptive in nature, where forwarding decision depends on delivery predictability value,
so it is able to benet more from the proposed routing
algorithm. It is also likely that our mechanism will have
similar impact on other adaptive routing protocols such as
potential-based routing protocols.

4. RELATED WORKSAs previously mentioned, message and storage managementin DTNs was generally integrated with some routing algorithms such as hop-based time-to-live (TTL) in Spray andWait[4], passive-cure in potential-based entropy adaptive
routing (PEAR)[17]. Other works are focusing on buermanagement policies[20, 21].
In practical scenario, the selection of routing scheme maybounded under many requirements such as application scenarios. However, some of the routing schemes may not already provide message management function. Therefore theindependent message deletion mechanism would be essential
in optimizing the performance of these routing scheme. Realizing that network resources, storage, energy, bandwidth
are limited, we can foresee many problems. To cope with the
problem of buer overow/limited storage, one has to consider both preventive measure and procedure to handle thesituation once occurs: (i) Preventive measure is the mechanism which is designed to control the buer space usage by
only allowing message with valid status, valid time-to-liveFigure 5: Average Latency

Figure 6: Average Buer Occupancy in 10-nodes Scenario(TTL) or has not yet been delivered, in the buer otherwise it will delete the message. This includes global TTL,
local TTL, hop-based TTL, and time-based TTL[18]. Yet,
it is dicult to determine an appropriate TTL value. Another method is called anti-packet or passive cure[19], where
destination creates the ACK message once it has received
the message. Our proposed method falls into this category.
However, the method in relaying ACK messages are dierent
from previously proposed algorithm. The previously proposed passive-cure, ACK messages are forwarded in passive
approach, as described in Section 2.1. Moreover we takethe advantage of nodes' movements and human gathering
points to distribute ACK messages more eciently and effectively. None of the previous works had integrated thehuman mobility patterns in their studies. The novelty ofthe design is how to distribute ACK messages to all relevant nodes with minimum resource usage in timely manner.
Further optimization of resource use in deleting messages
will be considered in our future work.
For (ii) Countermeasure or so called buer management
policies[20, 21] will only take action when node's buer become full. These policies instruct node in making decision onwhich packet to be dropped in order to make space available
for other messages. For eectiveness of message management, the combination of these methods may be considered
as well.

5.

CONCLUSION

In this paper, we present the independent message deletion mechanism for DTNs multi-copy routing schemes, theenhancement of this method through knowledge of human
movements and introduction of static H-node. We evaluate

our mechanism by simulations and present the simulation
results.
The contributions of this work is two folds; rst, the outcomes of eective message management, and second, thebenets of human gathering points on our message deletionmechanism. We have presented the design of an independent
message deletion mechanism for DTNs multi-copy routing
scheme. By eciently deleting copies of delivered messages,
we can reduce storage consumption and avoid unnecessary
bandwidth and energy usage, which in turn enhance theoverall performance of DTNs message delivery.
The heuristic knowledge of human movement characteristics has been taken in consideration. We study the impactofHuman Gathering Point", strategic location where people naturally swarm, on our proposed method. The resultshows positive impacts in increasing delivery probability andreducing overhead especially in sparse network and for adaptive and potential-based routing scheme. This paper showsthat the integration of the knowledge of human movements
would be benecial to future research.
For future works, we would like to extend our proposed
method into larger scale. Subsequently, we would consider
the corresponding scheme to manage the list of delivered
message ID and alternative method for distributing ACK
messages such as multicasting scheme. Moreover, we would
like to investigate other characteristics of human mobility
which might be useful for our design as well as other network protocols. The knowledge of human movements is also
provide us the proper simulation environment which similar to the real-world settings. It will be benet for futureFigure 7: Average Buer Occupancy in 20-nodes Scenario

Figure 8: Average Buer Occupancy in 100-nodes Scenario
deployment.

6.

REFERENCES[1] K. Fall, A Delay-Tolerant Network Architecture for
Challenged Internets, Proc. ACM SIGCOMM 2003.
[2] S. Jain, K. Fall and R. Patra, Routing in a Delay
Tolerant Network, Proc. ACM SIGCOMM 2004.
[3] A. Vahdat and D. Becker, Epidemic routing forpartially-connected ad hoc networks, Technical Report
CS-200006, Duke University.
[4] T. Spyropoulos, K. Psounis, and C.S. Raghavendra,
Spray and Wait: An Ecient Routing Scheme forIntermittently Connected Mobile Networks, Proc. ACM
SIGCOMM Workshop on Delay-Tolerant Networking(WDTN) 2005.
[5] A. Lindgren, A. Doria, and O. Schelen, Probabilistic
routing in intermittently connected networks, ACM
SIGMOBILE Mobile Computing and Communications
Review, 7(3): July. 2003.
[6] R.J. D'Souza and J. Jose, Routing Approaches in Delay
Tolerant Networks: A Survey, International Journal ofComputer Applications (0975-8887) Vol.1, No.17, 2010.
[7] K. Lee, S. Hong, S.J. Kim, I. Rhee, and S. Chong,
SLAW: A Mobility Model for Human Walks, Proc.
IEEE INFOCOM, 2009.
[8] K. Lee, S. Hong, S.J. Kim, I. Rhee, and S. Chong,
Demystifying Levy Walk Patterns in Human Walks,Technical Report, NCSU, http://netsrv.csc.ncsu.
edu/export/Demystifying_Levy_Walk_Patterns.pdf,
2008.
[9] V. Borrel, M.D. Amorim, and S. Fdida, A Preferential
Attachment Gathering Mobility Model, IEEE

Communications Letters, Vol.9, No.10, October. 2005.
[10] M. Kim, D. Kotz, and S. Kim, Extracting a mobility
model from real user traces, Proc. IEEE INFOCOM,
2006.
[11] S. Lim, C. Yu, and C.R. Das, Clustered mobility
model for scale-free wireless networks, Proc. IEEE
Local Computer Networks (LCN), 2006.
[12] CRAWDAD trace ncsu/mobilitymodels (v.
2009-07-23), Downloaded fromhttp://crawdad.cs.dartmouth.edu/meta.php?name=ncsu/mobilitymodels, December.
2009.
[13] B. Hillier, and L. Vaughan, The city as one thing,
Progress in Planning, 67(3). pp.205-230. ISSN
03059006, 2007.
[14] A. Keranan, J. Ott, and T. Karkkainen, The ONE
Simulator for DTN Protocol Evaluation, SIMUTOOL,
2009.
[15] OpenJUMP - The free Java based and open source
Geographic Information System for the World,
http://openjump.org, 2007.
[16] W. Zhao, Y. Chen, M. Ammar, M. Corner, B. Levine,
E. Zegura, Capacity Enhancement using Throwboxes in
DTNs, IEEE International Conference on Mobile
Adhoc and Sensor Systems (MASS), 2006.
[17] H. Ochiai, and H. Esaki, Mobility Entropy andMessage Routing in Community-Structured Delay
Tolerant Networks AINTEC, November. 2008.
[18] W.H. Yuen, and H. Schulzrinne, Message Replication
and Deletion in Delay Tolerant Networks underHop-based and Time-based TTL Schemes, Columbia
University.

[19] K.A. Harras, and K.C. Almeroth, Transport Layer
Issues in Delay Tolerant Mobile Networks, Proc. IFIP
Networking, 2006.
[20] D. Passos, H. Bueno, E. Oliveira, and C. Albuquerque,
Packet Scheduling and Discard Policies for Diusion
Control in Delay and Disruption Tolerant Networks,
Journal of Communication and Information Systems,
Vol.23, No.1, pp.12-21, 2008.
[21] A. Krifa, C. Barakat, and T. Spyropoulos, OptimalBuer Management Policies for Delay Tolerant
Networks, Proc. IEEE SECON, 2008.