www.ijecs.in
International Journal Of Engineering And Computer Science ISSN: 2319-7242
Volume 4 Issue 12 Dec 2015, Page No. 15165-15171
Review of Efficient Routing In Delay Tolerant Network
1
1, 2
Shyama Prasad Mukherji College (For Women), University Of Delhi, India
3
1
Sonia Kumari, 2 Pratibha Yadav, 3 Manvendra Yadav
Atma Ram Sanatan Dharma College , University Of Delhi , India
soniakumari.ducs@gmail.com, 2pratibhamcadu2011@gmail.com, 3ymanvendra@gmail.com
ABSTRACT
Delay Tolerant Networking (DTN)[2][3] has been proposed as a potential solution for this technical
challenge as it is designed to offer a solution for routing in a network which is not always connected. Adhoc network[6] is a decentralized type of wireless network. There is no pre existing infrastructure, like the
base station to coordinate the flow of messages between the nodes. To deliver a message to a node, the
shortest path to the destination node is computed. Ad hoc networks can use flooding for forwarding the data.
Ad hoc routing assumes that there is an end to end connectivity between the nodes. However this not the
case for DTNs. DTNs or the Delay Tolerant Networks are ad hoc networks in which contacts are not
available at all times. The messages are buffered till the contacts are available and are then forwarded on the
contacts when available. In this paper, we evaluate some of the most prominent routing protocols for DelayTolerant Networks such as Epidemic[1], PRoPHET , Contact Graph Routing and Dynamic social grouping
(DSG)
with the variable delay and loss rate values, render
the existing networking technologies inefficient.
Keywords
While various aspects of space communications,
Ad hoc network, Delay-Tolerant Networks
like transport protocols, have attracted extended
(DTN), Routing Protocols.
research interest, only little progress has been
1. INTRODUCTION
made as far as routing is concerned. Routing in
Delay Tolerant Networks becomes a matter of
Network nodes may need to communicate during
utmost importance as the number of space
opportunistic contacts, where the sender and
elements constantly increases. The total duration
receiver make contact at unscheduled time, like
of opportunistic contacts is considerable,
moving people, vehicles and aircrafts exchanging
alternative paths exist, and a multihop architecture
information. Also there can be scheduled contacts
becomes a viable solution. These network
between nodes. Delay-tolerant networks (DTN)
characteristics, along with the challenged
are the networks which can tolerate delays. In a
environment, pose restrictions to the development
DTN, an end-to- end path may not be available at
of reliable and efficient routing protocols. The
all times, so the messages are buffered till the
concept of static based routing algorithm such as
contacts are available. Delay Tolerant Networking
Simbet and Bubble Rap is also there. In this paper
(DTN) [2][3] composes an emerging network
, we investigate the performance of some of the
architecture that facilitates data transfers in
most prominent routing solutions when long
challenged networks, characterized by intermittent
delays are present. Epidemic Routing[1] achieves
connectivity, high loss rates and long propagation
a relatively good performance, although lower
delays. In this context, DTN is the key technology
than CGR, in the expense of extensive network
to support future space communications, since the
load . In this paper we have also analyzed the
constant movement of space elements together
1
Sonia Kumari, IJECS Volume 04 Issue 12 December 2015, Page No.15165-15171
Page 15165
DOI: 10.18535/Ijecs/v4i12.14
unique characteristics
protocols.
of
different
routing
Store and forward message switching
In store and forward message switching[6] the
messages are moved from the buffers on one node
to the buffer of another node, and the message
eventually reach the destination. The buffers can
hold message indefinitely. When a nodes buffer is
full and new messages arrive, then depending on
the buffer management scheme used some of the
messages are dropped. Nodes need to maintain
buffers as:
 The communication link to next hop may
not be available for long.
 Some nodes may send or receive data
faster or more reliably than the
other nodes in the network.
 A message may need to be retransmitted if
an error occurs.
2. APPLICATIONS OF DTN
DTN[3] are the networks operating in
interplanetary and deep space communication.
DTN are useful in network access in extremely
remote
areas,
where
regular
satellite
communication is difficult, like in the polar circle.
Also DTN provides mobility services to nomadic
users. DTNs can handle connectivity interruptions
due to gaps in radio coverage or due to speed of
movement. Examples of such networks include:
 Terrestrial Mobile Networks: Networks
may be partitioned due to node mobility
may be expected to be partitioned in a
periodic, predictable manner. As a node
travels from place to place, it provides a
message switching service to nodes to
communicate with distant nodes it will
visit in the future.
 Exotic
media
Networks:
Exotic
communication media includes near Earth
satellite communications, very long
distance radio links (e.g deep space
communications), and some free-space
optical communications. These systems
are subjected to high delays with
predictable interruption (e.g. due to
planetary dynamics or the passing of a
scheduled ship), may suffer outage due to
environmental conditions (e.g. weather), or
1


may provide a predictably available storeand-forward network service that is only
occasionally available (e.g. low-earth
orbiting satellites that pass by one or more
times each day).
Military Ad-Hoc Networks: These
systems are expected to operate in
situations where the environmental factors,
or intentional jamming may be cause for
disconnection. Data traffic may have to
wait several seconds or more while highpriority traffic is carried on links. For such
systems security is also critical.
Sensor and Sensor/Actuator Networks:
These networks are characterized by
extremely limited end-node power,
memory,
and
CPU
capability.
Communication within these networks is
often scheduled to conserve power.
3. DTN NETWORK MODEL
In DTN[3] graph nodes and edges is a directed
multi-graph, in which more than one edge (also
called link) may exist between a pair of nodes.
The link capacities are time-dependent. The
capacity is zero when there is no contact(link)
available.
Contact: A contact is an opportunity to send data
over an edge. It is a special edge and a
corresponding time interval during which the edge
capacity is strictly positive.
Messages: A message is the data to be transmitted.
The set of all messages is called the traffic
demand. Storage: As the contacts are not available
at all times so any datam to be transmitted has to
be stored at the intermediate nodes till the contacts
are available. All the nodes have a definite storage
capacity to buffer the messages till the contacts
are available. Routing: Is done in a store and
forward fashion. The routing algorithm decides
the next edge(s) to which the message should be
forwarded. Messages not immediately forwarded
are buffered until they are assigned to contacts by
the routing algorithm[3]. Nodes may be connected
by multiple edges. Each node j performs storeand-forward routing. An edge is parameterized by
its source and destination nodes plus a capacity,
c(t) and delay function, d(t).
Sonia Kumari, IJECS Volume 04 Issue 12 December 2015, Page No.15165-15171
Page 15166
DOI: 10.18535/Ijecs/v4i12.14
DTN[3] as the efficient routing algorithm. Based
on when the routing decisions are taken, there are
types of routing algorithms:


Figure 1 DTN Network Model
4 . DTN ROUTING ISSUES
In this section, we consider a number of important
issues in any routing algorithm: the routing
objective, the amount of knowledge about the
network required by the scheme, when routes are
computed, the use of multiple paths, and the use
of source routing. We focus on how these issues
arise in the context of the DTN routing problem.
4.1 Routing Objective
The routing objective of traditional routing
schemes has been to select a path which
minimizes some simple metric (e.g. the number of
hops). For DTN networks, however, the most
desirable objective is not immediately obvious.
One natural objective is to maximize the
probability of message delivery. Messages could
potentially be lost due to creation of a routing loop
or the forced discarding of data when buffers are
exhausted. As an approximation, we focus on
minimizing the delay of a message (the time
between when it is injected and when it is
completely received). While DTN[2] applications
are expected to be tolerant of delay, this does not
mean that they would not benefit from decreased
delay. Furthermore, we believe this metric is an
appropriate measure to use in exploring the
differential evaluation of several routing
algorithms in an application-independent manner.
Minimizing delay lowers the time messages spend
in the network, reducing contention for resources
(in a qualitative sense). Therefore, lowering delay
indirectly improves the probability of message
delivery.
5. ROUTING IN DTNS
Routes the messages in such a way that there is a
higher probability of message being delivered to
the destination, while considering the available
buffering at each node. Also the messages should
be delivered with minimum delay. This shows the
1
Static based routing algorithm
Dynamic based routing algorithms
5.1 SIMBET
Networks may consist of cliques where metrics
based on direct or indirect encounters may not
find a suitable carrier for the message. However
node when a node s is involved in a highly
cliquish cluster in which none of the nodes have
directly or indirectly met destination node d. This
makes the decision of selecting a node to forward
data difficult. There exist a crucial bridge between
the three tightly connected groups, and these
groups would not be connected if not for the
existence of these weak ties. Simbet[2] uses te
information to calculate the betweenness and
similarity values which is used in the
identification of these bridges and the
identification of nodes that reside within the same
cluster as the destination node. Estimating a nodes
centrality[5] in the network in order to identify
bridges.
5.2 BUBBLE RAP
A Pocket switch Network (PSN)[10], centrality
represent the importance of a node as a potential
traffic relay for other nodes in the system.
BUBBLE[9], a hybrid algorithm, that selects high
centrality nodes and community members of
destination as relays. There may be no a prior
information so distributed detection of node
popularity and node communities, and the use of
these for forwarding decisions are crucial.
Community detection, can help us to uncover and
understand local community structure in both
offline mobile trace analysis and online
applications, so we can exploit this kind of
community information to select forwarding paths
. Centrality represent the importance of a node as
a potential traffic relay for other nodes in the
system.
5.3 EPIDEMIC ROUTING
When a node contacts another node, the two nodes
exchange messages until their buffer contents are
Sonia Kumari, IJECS Volume 04 Issue 12 December 2015, Page No.15165-15171
Page 15167
DOI: 10.18535/Ijecs/v4i12.14
synchronized. Each node has a buffer where it
stores these messages. It has high delivery ratios
and does not require any knowledge of the
communication pattern and is thus suitable for
networks where the contacts between nodes are
unpredictable. Goals of epidemic routing[1] are
to:
1. Maximize message delivery rate
2. Minimize message latency
3. Minimize the total resources consumed in
message delivery.
Epidemic routing[1] is very expensive in terms of
the number of message transmissions and buffer
space. It relies heavily on buffers so an efficient
buffer management scheme can improve the
delivery ratio.
5.4 PROBABILISTIC ROUTING
The Probabilistic Routing Protocol using History
of Encounters and Transitivity (ProPHET)[7]
utilizes 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 (delivery
predictabilities) and replicating messages during
opportunistic encounters only if the node that does
not have the message, appears to be a better
chance of delivering it. Each individual nodes
having a set probability of successfully delivering
a message to a base station[7]. This probability is
based on the set of neighbours which the node
regularly interacts with Nodes individually begin
with a set delivery probability and transfer
the messages they are carrying to neighbours with
higher delivery probability, with the increase in
their own probability. If they are carrying a
message when it times out their probability is
reduced to reflect the nodes inability to transfer. It
does not take advantage of any knowledge other
than past contacts.
5.5 Dynamic Social Grouping based routing
algorithm(DSG)
The routing algorithms are more effective when
more information regarding the mobility patterns
of nodes, contacts and storage space is present. In
Probabilistic routing , the nodes which interact
with a set group of nodes in the past will do so
1
again in the future that is its depends upon the
history of contacts. In the grouping method social
routing , the nodes that are assigned to the same
social network (classroom, project team) will
regularly interact with members again .With these
groups identified, consistent routes to basestations
are identified for the reliable delivery of message
from node to the basestation. The basestations are
immobile, A given node may belong to several
social groups and will attempt to merge together
groups who share common members. Once the
social groups are identified, routing occurs
through these groups based on which group has
more reliable access to the basestations .This
algorithm is known as Dynamic social Grouping
(DSG)[11].
5.6 DSG -N2
Ad-hoc Networks are a collection of computing
devices
connected
through
wireless
communications, such as Bluetooth or wireless
LAN. Each node can move freely throughout the
network. Routing algorithms are more effective
when they rely on information regarding the
contact patterns of nodes. In Social routing , the
nodes assigned to the same social network (class
room, project team, department faculty, etc.) will
regularly interact with members of that social
group. Once these groups are identified, consistent
routes to nodes are recognized based on the
delivery history of a group or node. This
algorithm is called Dynamic Social Grouping
Node-to-Node (DSG-N2 )[12]
5.7 CONTACT GRAPH ROUTING
Contact Graph Routing[13] is a dynamic routing
system that computes routes through a timevarying topology composed of scheduled,
bounded communication contacts in a Delay
Tolerant Network. CGR is an energy saving
protocol, given that all contacts between nodes are
predefined and no exchange of connectivity
information takes place during the transmission. A
basic form of Quality of Service is provided in
CGR, since critical bundles can be handled in
exception; urgent data is transmitted similarly to
epidemic routing, making sure
that it is successfully delivered. In terms of
applicability, CGR composes a natural evolution
of current static routing in space environment, as
Sonia Kumari, IJECS Volume 04 Issue 12 December 2015, Page No.15165-15171
Page 15168
DOI: 10.18535/Ijecs/v4i12.14
it utilizes all predetermined connections without
being a completely autonomous and, therefore,
possibly unstable solution. The main drawback of
Contact Graph Routing is the need to schedule all
active connections prior to transmission; in this
context, opportunistic contacts cannot be
exploited.
Figure 2 summarizes the characteristics of the
aforementioned routing protocols as far as energy
consumption, autonomy, contact exploitation and
Quality of Service are concerned. As noticed,
Epidemic and PRoPHET routing may be
autonomous and able to exploit both scheduled
and opportunistic contacts, however none of them
is energy-efficient or provides QOS, like Contact
Graph Routing.
Figure 2: Characteristics of Epidemic , PRoPHET
Contact Graph Routing
and
6. ANALYSIS OF ROUTING PROTOCOLS
Each of the proposed routing protocols for space
communications carries unique characteristics,
depending on the applied architecture. These
attributes affect significantly the performance and
the efficiency of the protocols. In this we used a
topology of 4 nodes to compare Epidemic,
PRoPHET and Contact Graph Routing under
increasing RTT values, using Task Completion
Time as a metric. The topology of the experiment
consists of one sender, one receiver and two
intermediate nodes, as shown in Figure 3 below.
The intermediate nodes provide two alternative
routing paths, only one of them connected to the
endpoints at any given time. The sender transmits
100 packets of 10 KB each to the receiver with a
time interval of 5 seconds between two
consecutive transmissions.
Figure 4 demonstrates the Task Completion Time
of each protocol in relation to Round Trip Time.
Figure 4 : Round-Trip Time impact on Task Completion
Time
As depicted in this figure, CGR outperforms both
PRoPHET and Epidemic routing as RTT values
increase; for almost zero second delay we observe
that the performance of all three protocols is
almost identical, whereas for RTT values greater
than 1 sec PRoPHET’s performance degrades, in
contrast to the relatively stable performance of
both CGR and Epidemic routing. CGR, however,
achieves better performance by utilizing
predetermined information on each node’s
position and movement. In order to investigate
PRoPHET’s poor performance, we utilized a
simple single-hop topology (Figure 5).
Figure 5 : Single-hop topology
In this scenario, the sender transmits 50 data
packets throughout the duration of the emulation.
Our aim is to highlight PRoPHET’s inability to
quickly dispatch data packets, even when a path
towards the receiver exists. Figure 6 shows the
Task Completion Time of PRoPHET routing with
various RTT values. The results show that as RTT
values increase, there is a significant increase in
the Task Completion Time.
Figure 3 : 2-hop , alternate path topology
1
Sonia Kumari, IJECS Volume 04 Issue 12 December 2015, Page No.15165-15171
Page 15169
DOI: 10.18535/Ijecs/v4i12.14
1. D. Vahdat, D. Becker, “Epidemic Routing for
Partially-Connected Ad Hoc
Networks”, Duke Tech Report CS-2000-06, 2000.
2. V. Cerf, et al, “Delay-Tolerant Network
Architecture”, IETF RFC 4838, Internet
Engineering
Task
Force,
2007
,
http://www.ietf.org/rfc/rfc4838.txt.
Figure 6: Task Completion Time for varying RTT
In this context, it is understood that although
PRoPHET is advertised as a suitable routing
protocol for DTN, in fact it does not perform well
in space environment, where transmission delay is
in the order of seconds or even minutes. The
reason behind this, is PRoPHET’s need to
exchange routing information before each
application data transmission, resulting to waste of
valuable time resources.
7. CONCLUSION
In this paper, we study the performance of
Epidemic Routing and two sophisticated routing
protocols for Delay-Tolerant Networks[2][3],
namely PRoPHET and CGR. DTN routing
appears to be a rich and challenging problem. It
requires techniques to select paths, schedule
transmissions, estimate delivery performance, and
manage buffers. The problem of networking on
frequently-disconnected networks is receiving
more attention as the desire to have data
connectivity in devices which may be mobile or
into regions that may only be reachable by nonconventional network devices (e.g. motorbikes)
increases. We believe that in many frequently
disconnected
scenarios,
communication
opportunities may be predictable. In this paper,
we have developed a framework for evaluating
DTN routing algorithms, suggested and evaluated
several individual algorithms, and provided a
basis for future work in the area. Networks with
plentiful communication opportunities, the need
for smart routing algorithms is minimal. In
situations where resources are limited (contact
opportunities, bandwidth or storage, in our case)
smarter algorithms may provide a significant
benefit.
REFERENCES :
1
3. K. Fall. “A Delay-Tolerant Network
Architecture for Challenged Internets”. In ACM
SIGCOMM, Aug. 2003.
.
4. Sushant Jain, Kevin Fall and Rabin Patra.
“Routing in Delay Tolerant Network”. In
Proceedings of the 2004 conference on
Applications , technologies, architectures and
protocols for computer communications , pages
45-158 , 2004.
5. E. M. Daly and M. Haahr. “Social Network
analysis for routing in Disconnected DelayTolerant Manets”. In Proceedings of the 8th ACM
International symposium on Mobile ad hoc
networking and computing , pages 32-40 ,2007.
6. Amin Vahdat and David Becker. “Epidemic
routing for partially
connected Ad Hoc Networks”. Duke University
Technical report CS- 2000-06, July 2000.
7. A. Lindgren, A. Doria and O. Schelen.
“Probabilistic Routing in Intermittently Connected
Networks”.
ACM
SIGMOBILE
Mobile
Computing and Communications Review, vol. 7,
2003.
8. Martin Everetta and Stephen P. Borgatti. “Ego
network betweenness”. In Social Networks ,
Elsevier , vol 27 , pages 31-38 , 2005.
9. Pan Hui, Jon Crowcroft and Eiko Yoneki.
“BUBBLE Rap: Social-Based Forwarding in
Delay-Tolerant Networks”. In Proceedings of the
9th ACM international symposium on Mobile ad
hoc networking and computing , pages 241-250,
2008.
10. Pan Hui, Augustin Chaintreau, James Scott,
Richard Gass, Jon Crowcroft and Christophe Diot.
Sonia Kumari, IJECS Volume 04 Issue 12 December 2015, Page No.15165-15171
Page 15170
DOI: 10.18535/Ijecs/v4i12.14
“Pocket Switched Networks and Human Mobility
in Conference environments”. In Proceedings of
the ACM SIGCOMM workshop on DelayTolerant networking , pages 244-251, 2005 .
11. Roy Cabaniss, Sanjay Madria, George Rush,
Abbey Trotta and Srinivasan S. Vulli. “Dynamic
social grouping based routing in a mobile ad-hoc
network”. In Proceedings of the IEEE
International Conference on Mobile Data
Management , pages 295-296, 2010.
12. Roy Cabaniss , James M. Bridges , Andrew
Wilson and Sanjay Madria. “DSG-N2: A GroupBased Social Routing Algorithm”. In proceedings
of IEEE International Conference on Wireless
Communication and Networking , pages 504-509 ,
2011.
13. S. Burleigh, “Licklider Transmission Protocol
– Motivation”, IETF RFC 5325, Internet
Engineering
Task
Force,
2008
,http://www.ietf.org/rfc/rfc5325.txt.
1
Sonia Kumari, IJECS Volume 04 Issue 12 December 2015, Page No.15165-15171
Page 15171