Evolving Model of Opportunistic Routing in DelayTolerant Networks

Xiaofeng Lu

School of Computer Science

Beijing University of Posts and Telecommunications

Beijing 100876, China

luxf@bupt.edu.cn

Pan Hui

Deutsche Telekom Laboratories and TU-Berlin

Berlin, Germany

Pan.hui@telekom.de

Pietro Lio

Computer Laboratory

University of Cambridge

Cambridge CB3 0FD, UK

pietro.lio@cl.cam.ac.uk

Abstract—In Delay Tolerant Networks (DTN), as disconnec-tions between nodes are frequent, establishing the routing pathsfrom the source node to the destination node may not bepossible. Messages are forwarded in DTN similarly to an infectivedisease spreading among humans. This paper sets up an evolvingmodel of messages delivery. Also, in this paper, we proposethe ’crash’ issue. Because large part of persons will turn offtheir mobile phones and discard the messages received, the’crash’ could happen in DTNs. The ’crash’ in DTN means largepart of the copies of messages are discarded within a shorttime duration. The evolving model is validated by a messageforwarding simulation among mobile nodes. In the simulation,we study the influence of ’crashes’ on the delivery ratio anddelay of Epidemic routing, SprayAndWait and PRoPHET routingprotocols.

I. INTRODUCTION

The increasing popularity of wireless mobile communica-

tion devices (e.g., Bluetooth and BlueSky) have made wireless

networks to be the most convenient solution for interconnec-

tion in many scenarios. When these mobile devices are carried

by persons or mobile agents, the connections between devices

are not stable and disconnections between nodes are frequent.

A large number of routing protocols for wireless ad hoc

networks have been proposed in the past. However, traditional

ad hoc routing protocols are not appropriate for networks

with high mobility. The common assumption behind existing

ad hoc routing techniques is that there is always at least a

connected path from the source to the destination. However,

if nodes move unpredictably with high speed, the end-to-end

path between any node-pair may not be always possible, thus,

source-based techniques are expected to be inappropriate since

the calculated path will most likely be invalid before it is

used [1] [2]. Such a kind of mobile wireless network is referred

to as a Delay or Disruption Tolerant Network (DTN) [1].

In DTNs, messages are delivered from the source to the

destination through a totally different way from the ad hoc

networks. Like infectious diseases spreading in human being

when people meet each other, mobile nodes in DTNs can

forward packets in the same way. Nodes send messages to

encounter nodes and then these encounter nodes relay this

packet to other encounter nodes. Such packet delivery is

analogous to the spread of infectious diseases [3] [4] [5].

If a copy of the packet reaches the destination node, the

transmission is successful.

Many other routing protocols have been developed for DTN

[6] [7] [8] . For example, by epidemic routing protocol,

when a relay meets another node, it sends its messages to

the encounter. Thus, a node may send a packet to different

encounters lots of times. Since the mobile devices are in

general powered by batteries and are not able to charge

batteries expediently, energy is a major constraint in some

mobile scenarios [9] [10]. If a device sends each packet many

times, it will use up its battery energy quickly and can not

relay other packets for other nodes. Thus, the overall network

capacity is not high.

Just as the delivery of a message is like the spreading

of a infectious disease, we believe the process of spreading

a message in DTN can be considered as the evolving of a

infectious disease. Secondly, when we study the evolving of

a species, we can also find the crash phenomenon, which

means the number of a species decrease rapidly in a short

time. In fact, crashes in the evolving of species are not rare

[11], the most famous crash in the hisotry is the depopulation

of dinosaurs.

In computer networks, a router stores and relays packets,

and then all the packets stored in its buffer will be cleaned

up after the router is turned off. Usually, when a mobile

device,such as a mobile phone works for the DTN, it runs

a DTN engine which is a software to receive messages and

relay them. When a user turn off the software, the user can

determine whether clean up all the messages it receives or not.

A DTN software user can turn off the software at anytime

during a day. Also, we assume larger part of all users will

2011 Seventh International Conference on Mobile Ad-hoc and Sensor Networks

978-0-7695-4610-0/11 $26.00 © 2011 IEEE

DOI 10.1109/MSN.2011.35

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2011 Seventh International Conference on Mobile Ad-hoc and Sensor Networks

978-0-7695-4610-0/11 $26.00 © 2011 IEEE

DOI 10.1109/MSN.2011.35

276

2011 Seventh International Conference on Mobile Ad-hoc and Sensor Networks

978-0-7695-4610-0/11 $26.00 © 2011 IEEE

DOI 10.1109/MSN.2011.35

276

turn off their mobile devices (Laptop or PDA) before sleeping.

Then lots of the DTN engine will be turned off because the

users will turn off their mobile devices at night. Thus, large

part of the mobile devices will clean up all the messages

before it is turned off. We define this event as a ’crash’ in

the delivering of a message. Therefore, the number of devices

that received a message is not increasing always.

This paper is organized as follows: We review the related

work in Section 2 and introduce the problems in current

DTN routings in Section 3. Section 4 is the introduction of

the proposed evolving model. In section 5, we introduce the

proposed Large Opportunity routing and establish the evolving

model. Then, we validate the evolving model and evaluate the

delivery performance of Large Opportunity routing in section

6. Section 7 is the conclusion of the whole paper.

II. RELATED WORK

There are lots of work on DTN routing algorithms. One of

the simplest approaches is to let the source or a moving relay

node carry the message all the way to the destination [13].

Although this scheme performs only one transmission, it is

very slow.

An faster way to perform routing in DTN is Epidemic rout-

ing. The basic idea of Epidemic routing is to select all nodes

in the network as relays, say Epidemic routing being flooding-

based in nature, as nodes continuously replicate and transmit

messages to newly discovered contacts that do not already

possess a copy of the message [3]. However, as messages are

flooded to all other nodes, it is extremely wasteful of resources,

such as batteries energy. More sophisticated techniques can be

used to limit the number of message transfers.

Some later work studied relay selection strategies to reduce

the overhead and improve the performance of epidemic routing

[7] [8] [12].

SprayAndWait is a routing protocol that attempts to gain

the delivery ratio benefits of replication-based routing as well

as the low resource utilization benefits of forwarding-based

routing [14]. SprayAndWait routing protocol consists of two

phases: spray phase and wait phase. In the spray phase, the

source of a message initially starts with L copies; any node

A that has n > 1 message copies (source or relay), and

encounters another node B (with no copies), hands over to B

�n/2� and keeps �n/2� for itself; when it is left with only one

copy, it switches to direct transmission. If the destination is

not found in the spraying phase, each of the L nodes carrying

a message copy performs direct transmission.

PRoPHET is a Probabilistic Routing Protocol using a His-

tory of Encounters and Transitivity [15]. PRoPHET uses an

algorithm that attempts to exploit the non-randomness of real-

world encounters by maintaining a set of probabilities for

successful delivery to known destinations in the DTN (delivery

predictabilities) and replicating messages during opportunistic

encounters only if the node that does not have the message

appears to have a better chance of delivering it.

MaxProp is flooding-based in nature, in that if a contact is

discovered, all messages not held by the contact will attempt to

be replicated and transferred [16]. The intelligence of MaxProp

comes in determining which messages should be transmitted

first and which messages should be dropped first. Some routing

schemes select some nodes with desirable mobility patterns [7]

[8]. Another strategy is social-based forwarding scheme which

exploit sociological centrality metrics for relay selections [17]

[18]. The BUBBLE scheme improves the forwarding effi-

ciency significantly compared to PRoPHET algorithm based

on the users’ social activity [18].

III. REALITY IN DTN ROUTING

Routing in DTN is totally different from routing in ad hoc

networks because the disconnections are frequent in DTN.

When we design a routing protocol for DTN, we must face

the following realities.

A. Energy Constraint

Goldsmith et al. pointed out that energy constraints is not

inherent in all ad hoc networks, as some nodes may charge

their batteries timely. However, many if not most, mobile

nodes will be powered with batteries and their lifetime are

limited. Forman et al. believed limited power to be the greatest

constraints in designing algorithms for mobile devices [20].

Part of current DTN routing protocols do not consider the

energy constraint, such as Epidemic routing protocol. The

problem of Epidemic routing is that if a device sends each

packet many times, it will use up its battery energy quickly and

can not relay other packets for other nodes. Thus, the overall

network capacity is not high. Many DTN routing protocols

have been designed to decrease the overhead of Epidemic

routing as well. For example, SprayAndWait routing protocol

is a routing protocol that limits the number of replication of

a message to reduce the overhead of delivering a message.

B. Human Behavior Habit

Current DTN routing protocols study does not consider the

human behavior habit impact on the spreading of messages. In

fact, most mobile phone users shutdown their mobile phones

before sleeping. Before these mobile phone users turn off their

phones, the DTN engine that is a software runs on the mobile

phones will indicate whether to discard all the messages whose

destinations are not itself. So, the number of the replications of

a message toboggans because most of the message are deleted

after mobile devices are turned off. We call it to be a ’crash’.

Researches showed that SprayAndWait routing protocol

can get high delivery ratio with low overhead. However,

SprayAndWait routing protocol does not consider the crash

issue. How is the delivery performance of SprayAndWait

routing protocol after a crash?

C. Opportunism and Privacy

In fixed infrastructure networks, link failures are relatively

rare, but the rate of link failure is the primary obstacle to rout-

ing in mobile ad hoc networks due to node mobility. In DTN,

user may move freely and organize themselves arbitrarily, thus

the network’s topology may change rapidly and unpredictably.

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It means that any topology information may become stale

after a short period of time and is ineffective for use in

forwarding decisions. As a result, most DTN routing protocols

are opportunistic, which means a sender sends messages if it

gets a chance to send messages.

To improve the probability of delivering a message to its

destination successfully, many DTN routing protocols make

use of nodes’ history knowledge to select available relays.

However, the history knowledge of a node is the privacy of

the node. Not all nodes want to provide their routing messages.

If there are un-trusted nodes in the network, a node does

not share its information with others for the security reason

[21] [22]. In this assumption, lots of routing protocols, such

as PRoPHET and BUBBLE protocols, cannot work because

they need encounter nodes to tell the sender their history

knowledge.

IV. EVOLVING IN DTN

A. Evolving Model

We study the messages spreading based on the disease

spreading [23] study. We divides all nodes into three groups:

Susceptible (S), Infective (I) and Received (R). We indicate

the number of nodes in each group at time t as S(t), I(t) and

R(t), respectively. The total number of S(t) and I(t) is N .

In our evolving model of a message, we consider a message

as an infective disease.

Assumption 1: When a device is turned off, its user can

determine whether to discard its messages.

Assumption 2: Before sleeping, a large part of users turn

off their mobile devices.

Definition 1: Susceptible nodes are nodes that have not

received the message.

Definition 2: Infective nodes are nodes that receives a

message and the node’s DTN routing engine works.

Definition 3: Received nodes of a message are nodes that

have received the message.

If a node has received a message, it is the infective node

of this message. If this node discards this message, then this

node is a received node but not a susceptible node.

Definition 4: Crash means that a large part of users turn off

their mobile devices within a short time duration simultane-

ously, and all the messages that they received are discarded.

Assume the number of the infective nodes at time t is I(t),and let the probability of a node catching the infective disease

be p. If an user is in the S state at time t, and one of its

neighbors is in the I state, then it moves into the I state with

the probability p and stays in the S state with probability 1−p.

In addition to this, each infective agent can move into the S

state with probability μ. If we do not consider crashes in the

evolving of infective disease, the evolving model of delivering

a infective disease is equation 1.

⎧⎨⎩

S(t + 1) = S(t) − pS(t) + μI(t)I(t + 1) = I(t) + pS(t) − μI(t)R(t + 1) = R(t) + p(S(t) − μI(t)

S(t) )(1)

0 200 400 600 800 1000 1200 1400 1600 1800 20000

20

40

60

80

100

120

140

160

180

time

I(t)

Fig. 1. The variation of I(t)

TABLE INOTATION AND MEANING.

Notations MeaningsN 300p 0.0025μ 0.002t time

I(t) the number of infective nodesS(t) the number of susceptible nodesR(t) nodes that receive a message

where p is the probability of a susceptible node becomes a

infective nodet and μ is the probability of a infective node

becomes a susceptible node.

We assume one node is in the infective group at t = 0,

and all other nodes are in the susceptible group at t = 0.

Figure 1 shows the I(t) function of time. I(t) increases in the

beginning phase and reaches the largest value soon, and then,

I(t) keeps the largest value until the message’s TTL equals

zero. We divide the total experiment time into two phases:

growing phase and balance phase. In the growing phase, the

number of receivers of a message increases and reaches the

maximum. In the balance phase, the number of receivers of a

message keeps the same.

Figure 1 shows the variation of the number of receivers of a

message. However, in DTNs, there are not only one message.

Here, we suppose that a new message will be generated

periodically and a message is considered to be delivered to its

destination when I(t) of a message is larger than a threshold,

such as 40% of all nodes. Figure 2 shows the variation of

the number of delivered messages. It is easy to find that the

number of all delivered messages increases almost linearly.

The values the parameters in Equa. (1) and Equa. (2) are listed

in table 1.

B. Evolving Model With ’Crashes’

In the evolving of infective disease, ’crashes’ happen some-

times. Suppose part of users turn off their devices at night and

279278278

0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000

20

40

60

80

100

120

140

160

180

200

time

deliv

erd

mes

sage

s

Fig. 2. The variation of delivered messages

0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000

20

40

60

80

100

120

140

160

180

time

I(t)

crash happens when time=200crash happens when time=1200

Fig. 3. I(t) function of time with crashes happening at different time.

determine to discard all its messages, a ’crash’ happens. Let

the proportion of the users who will turn off their devices and

discard the messages is μ1.

⎧⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎩

S(t + 1) = S(t) − pS(t) + μI(t)I(t + 1) = I(t) + pS(t) − μI(t)R(t + 1) = R(t) + p(S(t) − μI(t)

S(t) )S(t + 1) = S(t) − pS(t) + μ1I(t), t0 < t + 1 < t1I(t + 1) = I(t) + pS(t) − μ1I(t), t0 < t + 1 < t1R(t + 1) = R(t) + p(S(t) − μ1I(t)

S(t) ), t0 < t + 1 < t1(2)

In above evolving model, we did not add energy constraint

for the simplification reason. Figure 3 shows the development

of I(t) when crashes happen at different times without energy

constraint. It indicates that I(t) increases again after a crash.

When a crash happens, large part of infective nodes discard

TABLE IITHE VALUES OF PARAMETERS.

Parameters ValuesEndtime 5000

UpdateInterval 1movement model ShortestPathMapBasedMovement

bufferSize 50 mtransmitSpeed 2500kBps

waittime 0,120transmitRange 100

msgTTL 2000number of nodes 180

message size 500k, 1Mmessage hosts 0,180Events1.size 500k, 1M

the messages they received. However, there are still some users

who do not turn off their mobile devices at night. So, these

mobile devices can spread their message continue after the

crash. On the other hand, those mobile devices who have

discarded all their messages have large free buffer to receive

the messages that may be a new message or an old message

they have discarded. So, the number of the susceptible nodes

increases after a crash, and the number of the infective nodes

increases again after the crash and can reaches the ’balanced’

I(t) value again.

V. EVALUATION

A. Simulation Setup

To validate the evolving model, we evaluated different DTN

routing protocols. We do a simulation by THEONE. THEONE

is a popular DTN simulator and has been used in DTN study

widely. Table 2 lists all the values of the parameters used in

THEONE. The mobile nodes’ mobility model in THEONE

is Shortest Path Map-Based Movement because it shows the

trait of mobile agents’ movement in a city. We compare

the message delivery ratio, delivered messages and delay

of Epidemic routing, SprayAndWait and PRoPHET. Delivery

ratio is the proportion of delivered messages over all created

messages.

B. Delivered Messages

A delivered message means the message’s destination has

received this message. Obviously, the number of delivered

messages will not decrease theoretically. Figure 4 shows

the increasing of delivered messages. We can find that the

function of delivered messages of Epidemic routing reported

by THEONE increases almost linearly, which is very similar

to the Figure 2. This indicates that the evolving model we

proposed is reasonable.

The increasing of delivered messages of Epidemic routing

is faster than SprayAndWait and PRoPHET as SprayAndWait

routing protocol limits the number of the copies of a message

and PRoPHET routing protocol selects the relay nodes strictly.

We can find that the total number of the delivered messages of

PRoPHET is larger than that of Epidemic routing. We believe

the reason is that by Epidemic routing a node will generate

more copies of a message so that it’s buffer saves lots of copies

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0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000

20

40

60

80

100

120

time

deliv

ered

mes

sage

s

Epidemic routingSprayandWaitProphet

Fig. 4. Delivered messages of Epidemic, SprayAndWait and PRoPHET.

Fig. 5. Delivery ratio of Epidemic, SprayAndWait and PRoPHET.

of different messages. When a node’s buffer is not enough to

receive a new message, it deletes some oldest messages to get

free buffer space no matter whether the deleted messages have

been delivered to the destinations or not. Thus, after a long

time, most nodes’ buffer are full and they have to discard many

messages to receive a new message so that lots of messages

that have not been delivered to their destinations are discarded.

C. Delivery Ratio

Figure 5 shows the comparison of delivery ratio of Epi-

demic, SprayAndWait and PRoPHET when there is no crash-

ing and there is a crash. We find that the delivery ratio of

Epidemic routing and PRoPHET increases when a crash hap-

pened at time=2500 compared with there is no crash. We think

the reason is that when a crash happens, about 50% messages

copies stored by all nodes are dropped so as to they can

have free buffer to receive and relay new messages. In DTNs,

when a destination receives a message, it will not response

Fig. 6. Delay of Epidemic, SprayAndWait and PRoPHET.

to the source node or other nodes, so actually most copies of

messages are useless after some time. This is the first time that

we find a crash can help nodes to get free buffer space and

increases the delivery ratio of the DTN routing protocol that

have no copies limitation. On the contrary, the delivery ratio

of SprayAndWait decreases when a crash happened. As the

number of the copies of a message is fixed in SprayAndWait

protocol, the buffer constrain is not a pivotal factor of the

routing performance. The number of the copies of a message

is the pivotal factor of the routing performance. So, when a

crash happens, about 50% messages copies are discarded and

the routing performance of SprayAndWait decreases heavily.

D. Delay

Figure 6 shows that the delay of all the three DTN rout-

ing protocols decrease. If there is no crash, the delay of

SprayAndWait is largely higher than that of Epidemic and

PRoPHET. This is reasonable because the copies of a message

is limited in SprayAndWait. So, with SprayAndWait, each

copies of a message has to wait for longer time to reach

its destination. If there was no crash, 111 messages were

delivered to the destinations. But when a crash happens at

time=2500, large part of messages copies are droppedn and

only 80 messages are delivered to the destinations. The reason

of the decreasing of the delay is that the crash divided the

simulation into two time parts. As the crash happened at half

of the whole simulation time, the simulation time of each part

was just half of the total simulation time so that the lifetime

of some messages shortened. therefore, the average delay of

all messages shortened in result.

VI. CONCLUSION

We consider the delivering a message according to op-

portunistic routing protocols in DTN as the spreading of an

infectious disease. In this paper, we study the development

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of the receivers of a message from the disease evolving

viewpoint. Firstly, we propose the crash issue in the delivering

of a message in DTNs.

We validated the evolving model by simulating messages

being delivered among mobile nodes in THEONE. The ex-

periment results validate the correctness of the proposed

evolving model. Also, we compared the delivered messages,

delivery ratio and delay of Epidemic routing, SprayAndWait

and PRoPHET. The simulation results indicate that the delivery

ratio of Epidemic routing and PRoPHET increase if there was

a crash. But the delivery ratio of SprayAndWait decreases

when there was a crash. Also, the delay of the three routing

protocols decrease when a crash happens.

VII. ACKNOWLEDGMENT

This work was supported by National Natural Science

Foundation of China (Grant No. 61100208), Natural Science

Foundation of JiangSu (Grant No. BK2011169) and Beijing.

Pietro lio is supported by the EU FP7 project RECOGNITION:

Relevance and Cognition for Self-Awareness in a Content-

Centric Internet.

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