Heuristic Congestion Control for Message
Deletion in Delay Tolerant Network
Lertluck Leela-amornsin, Hiroshi Esaki
Graduate School of Information Science and Technology,
Information and Communication Engineering, The University of Tokyo,
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
lertlove@hongo.wide.ad.jp,hiroshi@wide.ad.jp
http://www.hongo.wide.ad.jp

Abstract. Delay tolerant network (DTN) challenges on large delay ofcommunication, opportunistic and intermittent connectivity so that data
can be transferred over vulnerable network. Most routing protocols in
DTN basing on replicating routing work well over infinite buffer assumption but the performance drops when using with finite buffer due to thecongestion problem. However, it is still lack of work on congestion control
in DTN. Since simple congestion control policies can cause uselessly looping problem and cannot maintain high performance when nodes buffer
become overflow, a new buffer management policy for DTN should beconsidered. In this paper, we identify the looping problem caused by using simple buffer management policies and propose the Effective Looping
Control mechanism to solve such a problem. We also propose the Creditbased Congestion Control as for heuristically deleting messages when thebuffer congested to retain high delivery rate with low number of replicas.
Key words: Buffer Management, Credit Dropping, Looping Control

1

Introduction

Delay tolerant network (DTN) is a network where communications may experience large delay, and the connectivity is often opportunistic and intermittent.
DTN’s characteristics are different from existing Internet leading to newly challenging issues as described in [3]. Many works have been studied, used anddeployed [1], [5], [6], [7], [12], [22] following the DTN architecture proposed in
RFC4838 [2] by overlaying the network with bundle layer to provide delay tolerant service. To construct a high performance system, most of the works [8], [9],[10], [11], [12], [13] route messages in replicating way, and apply custody transfer[4] that each DTN host needs to take action in carrying many of messages in the
non-volatile buffer [17] for a long period. These approaches can achieve high delivery rate but trade off with number of replicating messages. The performanceis high under infinite buffer and infinite bandwidth assumption, however, in realsituations, the performance is worse when the buffer and bandwidth are limited
due to the congestion control problem.

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Lertluck Leela-amornsin, Hiroshi Esaki, The University of Tokyo

Although modern storage devices have large capacity in scale of gigabytes,
it may limit a part of memory for storing and forwarding DTN messages as described in [21]. Some applications apply DTN to personal mobile devices, which
may contain any kinds of contents such as songs and videos. In cooperative
aspects, people normally share their resources greedily as few as they can. Moreover, some applications may generate large size messages, while other generatesmaller size but more frequently. All of these reasons can cause a diffusion ofcongested traffic over the system.
Regarding replicating-based routing in DTN, buffer management policies forcongestion control in DTN attempts to drop insignificant messages subjecting
to improving the performance essentially to reduce the number of replicas in
the network while maintaining high delivery probability. Conventional policies
cannot maintain high performance, and some of them can cause uselessly looping
problem because of dropping some messages before they are expired. Therefore,a buffer management mechanism for controlling message deletion is required tomaintain high performance for the system. We believe that the study of thisissue will give a great contribution to DTNs.
In this paper, we analyze the effect of various environments to buffer occupancy of DTN nodes. After that, we study the buffer management policies in theprospect of how we should delete the messages, and classify them into two types:the proactive policy, and the reactive policy. The proactive scheme deletes themessages depending on the time-to-live (TTL) or feedback acknowledgment as
for definitely deleting. On the other hand, the reactive policy deletes some messages due to congestion in nodes’ buffer. Our work scopes in the reactive policy
to deal with congestion control yearning for high-performance message deletionmechanism in DTN. Along our study, we identify the looping problem, whichis caused by using popular reactive policy, as a result of degradation in routing
performance. After that, we propose the Effective Looping Control mechanism toprevent such a problem so that routing can achieve better performance in mostenvironments. Moreover, we propose a heuristic congestion control policy called
Credit-based Congestion Control (CCC), which pursues the low number of replication messages but still keep high delivery ratio, without using history-based
information. Finally, we validate our CCC policy comparing to other policies inrelated works by performing extensive of the ONE simulation [19], which is the
popular DTN simulation.
The rest of this paper is organized as follows. We surveyed the related workin section 2. We identify looping problem and propose Effective Looping Control to relieve such a problem in section 3. In section 4, we propose the newcongestion control scheme, Credit-based Congestion Control, which can improve
performance comparing to the existing policies. We verify our proposed policies
in section 5 with extensive simulation. Finally, we summarize this paper andpresent our future work in section 6.

Heurictic Congestion Control in Delay Tolerant Network

2

3

Related Works

In recent years, DTN has been researched in various aspects to counter withmany newly challenging issues as described in [3]. However, the main issue in
DTN is to develop the routing strategy [18] to achieve high delivery probability
which is the ratio of delivered messages over the total generated messages. Since
legacy-forwarding scheme achieves very low delivery rate, many researchers developed their routing protocols in replicating-based approach. Epidemic [8] isthe common and simple replicating-based routing protocol. When two nodes get
contact, they exchange the information of messages that they are carrying, theneach node clones all of the messages another node does not have, and transfers.
Although epidemic can achieve high delivery ratio, the replicas are too many resulting in consuming much bandwidth. Thus, other works attempted to reduce
the number of replicas, for instance, the Probabilistic ROuting Protocol using
History of Encounters and Transitivity, or PROPHET [10] reduces replicas byusing knowledge based of probability that a certain message can reach the destination. Spray and Wait [9] controls the maximum number of messages that canbe replicated to other nodes. In addition, other routing protocols such as RAPID[12], PEAR [11], Bubble RAP [13] are using other knowledge based metrics toachieve better performance.
Many routing algorithms work well under assumption that buffer is unlimited, but still in doubt if they do work under buffer constraint that may leadto congestion in certain environments. To prevent degrading in performance weneed to concern deeply when/which/where and how we should drop the messages to maintain higher performance for any routing protocol. There are many
buffer management policies had been proposed in [14], [15], [16], [23], and we
can classify into two groups, 1)proactive, and 2) reactive policy.
2.1

Proactive Policy

When DTN hosts update their status (generally in a time unit, or every activating cycle), they delete messages depending on
1. Time-Based TTL [14]: Time-based TTL initiates the time-to-live (TTL)
value and the value decreases a unit periodically every a certain amountof time. DTN hosts will delete the messages whose TTLs are less than zero.
2. Hop-Based TTL [14]: Like time-based TTL, original message is implemented
with the maximum number of hops in two tuples, breadth and depth. Depth
value decreases by one per hop, and the relay node will no further forward
the message and keep carrying it for directly transmitting to the destination
if the depth value becomes zero. Each node can replicate the message only
b copies no more than breadth value, and deletes the message that has been
forwarded for b times.
3. Feedback Policy: The destination node broadcasts an acknowledge message
as a feedback policy, then, each relay node drops the carrying message corresponding to the acknowledge message.

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2.2

Lertluck Leela-amornsin, Hiroshi Esaki, The University of Tokyo

Reactive Policy

DTN hosts delete the message when their buffers are congested. Many reactive
dropping schemes have been proposed in [15], [16], and [23].
1. Last-In First-Drop (LIFD): is a simple dropping scheme but very low performance. A node just rejects any new coming message, if its buffer overflows.
2. First-In First-Drop (FIFD): drops the message stored in the buffer first.
3. Oldest-Drop or Least Remaining Life First-Drop: concerns about the amountof remaining TTL to decide which message should be dropped. For Oldestdrop, the message that has the least remaining TTL will be dropped first.
4. Youngest-Drop or Most Remaining Life First-Drop: on the other hand, YoungestDrop drops the message that has the most remaining TTL.
5. Most-Forwarded First-Drop (MOFD): the message that has been forwarded
the largest number of times is the first to be dropped, thus giving messages
forwarded less times produce more chances of getting forward.
6. Least-Forwarded First-Drop (LEFD): the message that has low delivery
probability and has not been forwarded for the fewest number of times isthe first to be dropped. This is because it has a little chance to reach thedestination.
In addition, in [16] also proposed the history-based policy, which requires
history information to estimate an optimum utility for two popular metrics:- maximizing the average delivery rate, and minimizing the average delivery
delay. However, it needs the beacon-updating message to learn the status of the
network, and needs much time for computation. Besides that, in [23] proposed
the mechanism to migrate the custody from congested node to other nodes wherestill have some vacant rooms. However, this algorithm cannot work well if thenodes congest at the same time, and if there is no encounter node at the timesuch a node congests.

3

Effective Looping Control Mechanism

To delete messages in an effective way, we usually implement proactive policy
like global time-to-live as a final control to guarantee that the messages will bedeleted from the system eventually. In parallel, we use the reactive policy to control message deletion when the DTN buffer becomes congested. However, all ofreactive policies (except LIFD) generally drop the carrying messages before they
are expired, as a result that the same messages in other nodes can be looped backto the DTN node that has just deleted such messages as illustrated in figure 1.
It may meet greater impact, if the DTN router inherits the simple-based routing
like Epidemic [8]. And even the DTN router uses knowledge-based routing suchas PROPHET [10], PEAR [11] and others, which may have less effect, but looping problem may still cause heavy damage in some situations where certain pairnodes may uselessly loop some messages back-and-forth repeatedly. Although

Heurictic Congestion Control in Delay Tolerant Network

5

global TTL can eventually delete the message, there is no mechanism to estimate TTL accurately for DTN’s application whose TTL is not fixed. Therefore,
we generally implement a large TTL value in term of days, weeks or longer
resulting in wasting amount of bandwidth if such useless looping occurs manytimes and repeatedly. Hence, in this section, we propose the Effective Looping
Control, as shown in figure 2, to relieve the degradation in performance becauseof looping problem.

Fig. 1. Looping problem due to congestion control policy leads to wasting of bandwidth.

The key feature of this method is that, instead of keeping track of every
whole message, we save space by caching only header of the deleted message inthe entry named junk entry. And we denote clock down time in junk, as for, eachcaching entry can stay in the junk within a time limit. A node that is receiving
any new message will check the junk entry list and ignore the message, if it islisted in the junk. However, after the time in junk period, the node allows themessage to come into its buffer again. This soft policy is to relax in some incidents
that a message that has a better chance to reach the destination (depending on
routing scheme) should have a permission to be looped and replicated again. The
Effective Looping Control mechanism can be described in the following steps andillustrated in figure 2.
1) Once any node has deleted the messages, it tracks the header of thosemessages that can distinguish the messages (usually it is constructed by sequencenumber of message and the node id the message originally generated from).
2) Each entry of deleted message will be listed in the junk until the time injunk expired.
3) If a message loops back to any node that still has the entry of such a
message in the junk, that node will ignore and not request that message.
Although we can eventually delete the message via TTL, in DTN we usually
set TTL to very large value because no one knows the appropriate value of it.
This Effective Looping Control mechanism not only prevents the useless looping,but also gives an option for the network to adjust whether it will allow the looping

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Lertluck Leela-amornsin, Hiroshi Esaki, The University of Tokyo

Fig. 2. The Effective Looping Control.

again or not by setting the time in junk parameter. For instance, we can justset the time in junk greater than the TTL of the message or infinite value, if we
desire loop-free network.

4

Credit-based Congestion Control (CCC) for Message
Deletion

As mentioned in section 2, the simple LIFD cannot achieve high performance, so[15] has presented other congestion control policies to obtain better performance,
essentially to reduce the number of replicas. Although the history-based policyin [16] has been studied, it focused on minimizing the delay metric, and needs
exchanging in updating message to estimate many parameters for predicting the
optimum value using for dropping the message. Another work in [23] attempted
to migrate the custody message from congested node to other nodes, but it is stillin doubt that if there is no other node at that time or if all nodes are congested,
the migration does not work leading dropping mechanism to come out and take
into account. Therefore, in this section, we propose the newly heuristic congestion
control policy called Credit-based Congestion Control (CCC) to maintain highdelivery probability and reduce the number of replicas as our target. CCC isdynamic, independent, and isolated from history information, so there is noneed of beacon-updating.
CCC bases on the concepts that the message that is obsolete should bedeleted first as in oldest-drop, which performs high performance by using thetime-dependent credit. However, we also added the merit of most-forwarded firstdrop that the message has been forwarded many times should be dropped firstby applying the refilling and refunding technique when pair nodes get contact
and exchange their messages. Besides that, different applications or even in one
application may have different priorities for different messages, which need to
be standardized and is still an open issue. No matter how, getting along priority

Heurictic Congestion Control in Delay Tolerant Network

7

aspect we can easily rule the priority by adjusting the initial value of credit ashigh and low for higher and lower priority, respectively. The DTN router along
these credit-based policies will drop the message that has the least credit (canbe negative) when the buffer becomes full to allocate vacant space for the newcoming message. CCC can be described as follows.
Once a message is generated from any DTN node, it will carry maximum
amount of credit as its initial value. In consequence of that, host generating
the message can set how much credit should be initiated for different priorities
depending on various applications. Later, one credit will be decreased at every
time unit similar to age decreasing. However, the key technique of this policy isrefilling and refunding amount of credit when pair nodes encounter at any pointof transmission. We can construct function for refilling and refunding methods
in many approaches, however, in this paper, we propose an additive approachcalled Simple CCC and Half-Twice CCC as a multiplicative one.
4.1

Simple Credit-based Congestion Control (S-CCC)

Simple Credit-based Congestion Control policy manages amount of refill andrefund as follows. When two nodes get contact, they exchange the messages
depending on applied routing protocol. After that, each node refunds the sent
message’s credit for amount of value named penalty at the sender side, and refills
the received message’s (the same id, but the replicated one) credit for amount ofvalue named reward at the receiver. As a result, such a message that has been
replicated will have lower credit in sender host, but higher credit in receiver one.
We can easily formulate it into equation 1,

Csender (t + 1) = max (Csender (t) − ”penalty” , minimum value)(1a)Creceiver (t + 1) = min (Csender (t) + ”reward” , initial value)(1b)where Csender is the credit of message in sender side, Creceiver is the credit
of replicated message in receiver side, before (time=t) and after (time=t+1)
pair nodes encounter. However, the credit after refill cannot exceed the initiallymaximum value, and in practical, the credit should eventually equal to a certain
minimum value depending on the number of bits using as a header, or manuallyconfigure.
4.2

Half-Twice Credit-based Congestion Control (HT-CCC)

For S-CCC, we proposed the additive approach; however, HT-CCC on the other
hand applies multiplicative function to adjust refunding and refilling credit. With
this policy, it defines the multiplicative scheme following equation 2. The functions in equation 2 are constructed similarly from the sense of nature so-called
half-life theory. However, the complexity of equation 2 is because the credit value

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Lertluck Leela-amornsin, Hiroshi Esaki, The University of Tokyo

Fig. 3. The Simple Credit-based Congestion Control (S-CCC)

can be negative. Allowing credit to be negative can slow down the rate of refilling
and refunding when the credit of the message is near zero and decrease rapidly
when the credit is negative, which means that this message has no credit andis going to be in debt so we should ignore it. Whenever pair nodes get contact
and completely replicate the message, the credit of the message at the sender isrefunded as a half of credit before replicating but no less than a certain value,
which is limited by the number of bits we use as a header or we may manuallyconfigure. In contrast, the credit of the message at the receiver becomes twice
but no more than maximum value, which equals to the initial credit. Both policies can maintain high delivery probability, at the same time, they can decrease
the replica ratio as we validate in the next section.

Csender (t+1) =(max (⌊Csender (t) ÷ 2 ⌋ , minimum value) ; if Csender (t) > 0; if Csender (t) < 0
max (Csender (t) × 2 , minimum value)(2a)(min (Csender (t) × 2 , initial value); if Csender (t) > 0
Creceiver (t + 1) =min (⌊Csender (t) ÷ 2 ⌋ , initial value) ; if Csender (t) < 0(2b)5

Simulation and Evaluation

In this paper, we use the ONE simulator [19], NetLab, Helsinki University, whichis the simulator for DTN supporting GUI and providing many of libraries for
movement models, routing protocols, but no buffer management policies. Hence,
we implement relating buffer management policies, our Effective Looping Control
and including our Credit-based Congestion Control into the simulator. Later, weHeurictic Congestion Control in Delay Tolerant Network

9

Fig. 4. Performance of Effective Looping Control.

use the random waypoint movement model for 100 nodes over 1 km x 1 km world
size, for 2 days simulation time. Each node has 10-meter transmitting range, 2Mbps bandwidth, and moves with speed uniformly random from 0 km/h to 5
km/h. Besides that, we create message by using MessageEventGenerator classattached in the ONE simulator to randomly generate the source and destination
nodes for a 500 kB to 1 MB message every 1 to 30 seconds interval, uniformly.
Since we are focusing on the reactive message deletion control, thus we assume
the TTL value is equal to the observation time, 172800 seconds (2 days).
5.1

Evaluation in Effective Looping Control Mechanism

In this section, we compare most standing out policies to those policies themselves but applying our heuristic mechanism, effective looping control. We also
compare the difference of time in junk parameters ranging from 3600 seconds(1 hour), 43200 seconds (12 hours) to 172800 seconds (2 days). Then, we show

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Lertluck Leela-amornsin, Hiroshi Esaki, The University of Tokyo

the relationship among the delivery probability, replica ratio and the buffer size
ranging from 8MB, 32MB, 128MB, 256MB to 1024MB, as shown in figure 4.
From figure 4, we can see that when using Effective Looping Control to Epidemic routing we could increase the delivery rate of FIFD for any buffer size,
even we changed the time in junk parameter. In addition, other policies also
obtained higher delivery probability in most cases, but not much different inoldest-drop.
However, the replica ratio when applying effective looping policy is reduced
in all cases, compared to the cases without this mechanism. Especially, when the
traffic is very congested (many messages are generated or small buffer size of anode), our looping control could help reducing replicas up to 70% for LEFD, 65%
for MOFD, around 15% for FIFD, and also, 15% for oldest-drop when buffer isvery congested. Although the delivery ratio drops in some environments, it isless than 2% comparing to the replica ratio we can reduce. Corresponding to ourassumption, we could maintain high delivery probability and reduce the numberof replicas for most of all reactive policies. However, the performance is the samefor all when the buffer is large enough (1024MB). This is because the network
is not congested, so there is no message loss in the network. Remark that, thedelivery probability is not equal to one because there are still some messages justgenerated and may reach the destination out of range of our observation time.
In this sense, the Effective Looping Control can relieve such kind of problem byadjusting proper time in junk parameter to that system.
5.2

Evaluation in Credit-based Congestion Control

For evaluating the CCC, we compare among simple congestion control policies and our CCC policies with different initial credit values 3600 credits (HTCredit3600), 43200 credits (HTCredit43200), and 172800 credits (HTCredit172800)
for Half- Twice CCC, and at 172800 credits for simple version fixing 10 credits
for both penalty and reward. While, the minimum credit value in this simulationis set to the minimum value of integer (for Java programming = −231 ). We show
the performance results in table 1 for different buffer sizes 8 MB, 32 MB, and
256 MB, which easily congest, through Epidemic [8] and Prophet [10] routing.
Table 1. Comparison of Replica Ratio among Reactive Buffer Management Policies.

Buffer Size (MB)
FIFD
MOFD
OldestDrop

Epidemic Routing
Prophet Routing
Delivery Probability
Replica Ratio
Delivery Probability
Replica Ratio
8
32
256
8
32
256
8
32
256
8
32
256
0.1362 0.3111 0.6664 52.4140 28.4857 12.6230 0.1667 0.3718 0.7033 34.9224 22.1443 11.6573
0.0444 0.1288 0.6339 201.6181 70.0129 13.3065 0.0676 0.1647 0.6441 117.1928 54.3162 13.1769
0.1712 0.4848 0.7249 51.5952 17.7563 11.5215 0.1797 0.5056 0.7301 44.9874 16.7499 11.2092

SimpleCredit172800 0.1777 0.4833 0.7334 41.6650 17.6398 11.3637 0.1834 0.5051 0.7422 31.3373 15.9291 10.9750
HTCredit3600
0.1788 0.4874 0.7313 41.3886 17.4339 11.4217 0.1842 0.5079 0.7413 31.3349 15.8285 10.9874
HTCredit43200 0.1814 0.5335 0.7322 43.5313 15.8513 11.3993 0.1854 0.5449 0.7446 33.7201 14.7224 10.9486
HTCredit172800 0.1810 0.5200 0.7331 43.6944 16.2970 11.3530 0.1847 0.5226 0.7439 33.9030 15.5421 10.9617

Heurictic Congestion Control in Delay Tolerant Network

11

We evaluated our algorithm and obtained the result as shown in table 1. Both
S-CCC and HT-CCC could maintain high delivery probability, while reduce thenumber of replicas up to 20% for Epidemic routing, and to 10% for Prophet
routing, respectively to the best existing policy of each case. Even if we need to
add some information into header of the message to provide the current credit
field and initial credit value, it is very small comparing to whole message in DTN
whose size can be up to 1 MB. Therefore, it is worth to trade off some field inheader to many large replication messages those we can reduce. Although theperformance of CCC is close to the oldest-drop policy since its credit behaves
like an age of the message, it makes use of the advantage of refilling credit toenhance a chance for replicated message in the next-hop node, and refunding
credit to devalue the message at present node, as a result that it can reduce
useless replicas but still keep high delivery rate not only in Epidemic routing
but also in Prophet routing. In addition, in the Half-Twice approach also gavethe same tendency. This means that we can control the different priorities ofmessages corresponding to the initial credit values, and get high performance,
concurrently.

6

Conclusion and Future work

In this paper, we investigated the problem of congestion control in DTN. Since,some reactive buffer management policies have mined the looping problem, we
proposed the Effective Looping Control to prevent uselessly looping. We also
propose the congestion control policies in the heuristic way. Hence, our parameters used in our work need to be studied and standardized before using in thereal system for any general and/or any particular environment following theheuristic process. However, this work presents an intuitive idea but reasonable
as a prototype to control message deletion when buffer congests so that we canachieve high performance via reducing overhead without decreasing in delivery
rate. Therefore, in the future, we will apply this credit-based policy by refunding and refilling with respect to other resource values and/or knowledge metrics
such as remaining buffer, number of forwarded, etc., based on game theory andwill further expand to real world implementation.

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