Snapshot: a forwarding strategy based on analyzing networktopology in opportunistic networks
Junyeop Lee • Sun-Kyum Kim • Ji-Hyeun Yoon •
Sung-Bong Yang
Published online: 29 January 2015
� Springer Science+Business Media New York 2015
Abstract We study a forwarding strategy in opportunistic
networks which are one of the most challenging networks
among mobile ad-hoc networks. In opportunistic networks,
a node does not have knowledge about the entire network
topology, which is essential in the mobile ad-hoc network’s
forwarding strategy. Thus, node behavior is exploited to
calculate future contact opportunities for forwarding a
message. Utilizing social network analysis (e.g., similarity
and centrality) has been proposed to improve the accuracy
of the calculation task. This paper proposes a forwarding
strategy based on an analysis of network topology. In the
proposed strategy, each node takes a sequence of snapshots
of its first-order neighbors during a warm-up period. Each
node exchanges its own snapshots with each other, and
then aggregates the snapshots in order to extract the net-
work topology information. The extracted network topol-
ogy is analyzed by social network analysis methods:
compactness and algebraic connectivity. Forwarding
decisions are made using the analysis of the features
(compactness and algebraic connectivity). We present
simulations using NS-2 and the home-cell community-
based mobility model to show that the proposed forwarding
strategy results in delay performances similar to the epi-
demic forwarding scheme, while maintaining reasonable
network traffic. In addition, we demonstrate that the pro-
posed strategy outperforms the SimBet and PRoPHET
forwarding schemes with various communication ranges
and memory space.
Keywords Opportunistic networks � Social networks
analysis � Forwarding � Network topology
1 Introduction
In recent years, research on ad hoc networks has focused on
the challenges of new network paradigm instead of the
traditional ‘client–server’ network system, because main-
taining the traditional system requires high maintenance
costs. In addition, it is difficult to build and maintain the
traditional system in some tough environments, such as
battlefields, in places with no electricity, the deep sea, and
the outer-space [10, 20, 28]. Hence, it is necessary to
develop a cost effective and easily applicable network
system. An opportunistic network system as a new alter-
native solution is one of the most challenging networks in
which there are non-stable forwarding paths and intermit-
tent connections [6, 7].
One of the main topics of research on opportunistic
networks is the development of a feasible forwarding
scheme, because finding paths towards the destination is
not a trivial matter due to a lack in topological network
information [27]. If a forwarding scheme is able to obtain
the estimated network topology information, its forwarding
performance may improve considerably. However, there
are tradeoffs and limitations to obtaining such topological
information.
The forwarding strategies in opportunistic networks
without infrastructure can be classified into two types [27]:
J. Lee � S.-K. Kim � J.-H. Yoon � S.-B. Yang (&)
Department of Computer Science, Yonsei University, Seoul,
Korea
e-mail: [email protected]
J. Lee
e-mail: [email protected]
S.-K. Kim
e-mail: [email protected]
J.-H. Yoon
e-mail: [email protected]
123
Wireless Netw (2015) 21:2055–2068
DOI 10.1007/s11276-015-0900-9
the context-based scheme and the dissemination-based
scheme. In the context-based scheme, each node utilizes
the contexts of the nodes to determine the best relay nodes.
Thus, the context-based scheme reduces unnecessary net-
work traffic. Typical context-based schemes have been
proposed in [1, 15, 17, 22, 24, 30]. However, the context
scheme should overcome the privacy issue to obtain indi-
vidual context information. In addition, it’s necessary for
the schemes to update the context information since the
network environment and the context information of the
nodes change dynamically. On the other hand, in the dis-
semination-based scheme, each node disseminates the
message all over the network because each node hardly
knows a route towards either the destination or the best
next hop [33]. There are two drawbacks in the dissemina-
tion-based scheme. One is that each node does not have
enough memory space to keep the messages to be for-
warded due to the limitation of the memory size. Espe-
cially, nowadays, when much of the memory space is
consumed by large size messages, such as videos or photos.
The other drawback is that traffic congestion may not be
avoided since all the messages are forwarded without any
consideration to the reduction of traffic. To control the
network congestion, various schemes have been proposed
[4, 8, 14, 23, 35]. Most of these schemes use some local
information such as relationships or similarities among the
nodes in the network. However, they may suffer from
much longer transmission delays. We assume that each
node cannot completely access other nodes’ context
information due to privacy issues. Thus, we focus on
developing a dissemination-based scheme.
In this paper, we propose a forwarding scheme that
overcomes the aforementioned drawbacks. The proposed
scheme is called Snapshot; it begins by periodically col-
lecting the global information, such as the network topol-
ogy. Each node accumulates and shares the contact
information in order to obtain the network topology
information, because an opportunistic network can be
viewed as a distributed network system. Note that the
accumulated and shared information may depict a partial
network topology. Each node analyzes the respective
topological information with the social network analysis
methods, and then extracts some topological features from
the analyzed information. Simulations with NS-2 [25] to
compare the epidemic [33], PRoPHET [23] and SimBet [8]
schemes were performed, showing that Snapshot is supe-
rior in terms of both the transmission delay time and net-
work traffic. The simulation results also show that the
network topology obtained during each time period is fairly
accurate and that the extracted topological features have
been applied properly for a better performance.
The technical contributions of this paper can be sum-
marized as follows.
• A novel scheme known as Snapshot is devised to obtain
network topology in each time period: most forwarding
schemes focus on how to exploit the local information.
However, in the proposed scheme, we extract some
essential information from the network topology to
control all the nodes as a whole in the network.
• Network topology is periodically considered: the pro-
posed scheme keeps all the observed topological
information at specific times in order to dynamically
obtain the network features. Hence, the proposed
scheme is able to adapt to any opportunistic network
environment and control the network accordingly.
• The social network concept is applied to a forwarding
scheme: most forwarding schemes focus on utilizing
features of the nodes. However, we use two features
that are used in social network analysis: the compact-
ness of a connected component and the algebraic
connectivity.
The rest of the paper is organized as follows. Section 2
explains the related work, and Sect. 3 describes how the
proposed scheme works in detail. In Sect. 4, we present the
simulation results. Finally, we conclude the paper in Sect.
5.
2 Related work
2.1 Classification of forwarding schemes
in opportunistic networks
The forwarding schemes in opportunistic networks can be
classified into two groups: dissemination-based schemes
and context-based schemes. Dissemination-based schemes
are basically to manipulate flooding. Context-based
schemes utilize the knowledge of nodes in order to deter-
mine the best next hop.
In the dissemination-based schemes, each node diffuses
a message all over the network because the schemes may
not have comprehensive knowledge about stable paths
towards either the destination or the best next hop. A
message will finally be delivered to the destination by
relaying the message. The dissemination-based schemes
clearly perform well in terms of delay time, because there
are plenty of contact opportunities among the nodes.
However, they suffer from too much network traffic due to
too many relayed messages. On the other hand, the context-
based schemes utilize the context information of the nodes
to find proper relay nodes that deliver the message to the
destination. Thus they significantly reduce duplicate mes-
sages. However, they are subject to a longer transmission
delay, because they may not choose appropriate relay
nodes all of the time. In order to determine appropriate
2056 Wireless Netw (2015) 21:2055–2068
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relay nodes, the context-based schemes usually require a
more intensive computation process than the dissemina-
tion-based schemes. In addition, the context-based schemes
should update the context information of the nodes since
the network environment changes dynamically.
2.2 Dissemination-based forwarding schemes
The epidemic forwarding scheme [33] spreads the mes-
sages all over the network like the epidemic spread of
viruses. Once a node receives a message, it keeps the
message in its memory space and sends the message to
other nodes encountered. However, the dissemination
procedure may be impeded by a limited hop count [time to
live (TTL)]. If the hop count limit is one, the delivery
process cannot be accomplished except for when the sender
node encounters the destination. Our proposed scheme
partially adopts the basic strategies of the epidemic
scheme, but our scheme determines a proper TTL length
with the network topological information.
The PRoPHET forwarding scheme [23] allows each node
to collect the contact patterns of other nodes. Each node
computes the predictability that a node delivers the message
to the destination. Two nodes then exchange the summary
vectors when they encounter each other, including the
delivery predictability information with respect to the des-
tination. Afterwards, each node determines the next best hop
from the summary vector information. Our proposed scheme
utilizes the contact patterns to obtain the network topology.
The two-hop multi-copy scheme [13, 14] lets the source
node keep sending the message to all the encountered nodes.
Only a node that receives the message from the sender keeps
a copy of the message. The nodes with the message copies
send the messages to other nodes whenever possible. This
process may drastically reduce network traffic due to con-
fining of the number of nodes that carry the messages. Our
proposed scheme maintains a proper number of nodes (called
messengers) that carry the messages, but the number of
messengers is determined by the network topology.
The SimBet forwarding scheme [8] utilizes the social
information. When two nodes meet, they exchange infor-
mation about data messages along with the list of neigh-
bors. Each node then determines the ‘betweeness’ and
‘similarity’ values from the information. The betweeness
value of a node is the number of shortest paths from all
nodes to all others that pass through the node. The simi-
larity value is the number of common neighbors between
two nodes. The betweeness and similarity values are used
to predict which node is the best next hop. Our scheme also
utilizes some structural properties used in the social net-
work analysis in order to acquire essential features such as
algebraic connectivity, compactness, and the number of
components.
2.3 Context-based forwarding schemes
The context-aware routing protocol [24] lets each node
compute the delivery probabilities for all the destination
nodes. Each node then determines the best carrier based on
the nodes’ context such as the current battery level or the
degree of mobility. The best carrier then saves the message,
and forwards it to the destination or a node with higher
probability.
In the MobySpace forwarding scheme [22], each node
uses mobility patterns as the context information. Moby-
Space is defined as a multi-dimensional Euclidean space
where each axis denotes a possible contact between two
nodes, and the distance is the contact probability. If two
nodes are close to each other in the MobySpace, they may
have similar contact patterns. Thus the best carrier is
chosen as the node that is closest to the destination in the
MobySpace. In order to construct a more precise Moby-
Space, the contexts of all the nodes are required.
3 Proposed scheme
3.1 Overview
Most forwarding schemes exploit the user similarity or
contact information of the nodes in a network. These for-
warding schemes could reduce network traffic, while they
could not avoid a longer transmission delay. Thus, we
propose a novel forwarding scheme to resolve such a
problem. In our proposed scheme, each node takes ‘snap-
shots’ of the network during the warm-up period and
extracts important topological information from the snap-
shots as well as the period a message is forwarded.
By ‘taking snapshots’ we mean that each node periodi-
cally collects the information of the first-order neighbors
during the warm-up period. Note that a node also
exchanges its snapshots with its first-order neighbors to
update its own topological information. At the end of the
warm-up period, each node extracts (1) the change period
of the membership in the connected component [a con-
nected component (or just the component) in an undirected
graph is a maximal connected subgraph], (2) the com-
pactness of each component, and (3) the number of con-
nected components from the topological information. Next
we will describe the proposed scheme in detail.
3.2 The proposed scheme
The proposed scheme consists of three steps.
Step 1: Each node takes a snapshot in a regular time
interval during the warm-up period; that is, each node
Wireless Netw (2015) 21:2055–2068 2057
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collects the IDs of its first-order neighbors and records time
ts, where s = 1,…,w, when the receptions occur. Note that
the time of the receptions from its neighbors is assumed to
be identical while taking a snapshot. We assume that each
node in the network knows the wall-clock time and each
snapshot in a node has a time stamp that shows the time it
was taken. Therefore, when a node encounters another
node, they swap their own snapshots each other. Each node
merges its snapshots with the snapshots received according
to the time stamps. For example, if there are w snapshots
taken at times t1, t2,…, tw each, then both encountered
nodes have the same copies of w snapshots after merging.
Observe that as time passes each snapshot at ts of a node is
reinforced as if we keep finding ‘‘the missing puzzle pie-
ces’’ to try to achieve the entire network topology at time
ts. Hence the proposed scheme doesn’t need any special
mechanism for synchronization for taking snapshots among
the nodes. Table 1 shows sample snapshot information for
nodes.
The snapshot information is also exchanged between
the nodes in the warm-up period. The purpose of
exchanging the snapshot information is to construct a
network topology at ts. Note that since the network is
constructed based on the snapshots of the node and the
snapshots gathered from the neighbors, it may be a
partial network. With the snapshot information of all the
nodes in the network we can construct the entire topol-
ogy of the network at ts because the accumulated snap-
shot information includes all the connections among all
nodes in the network at that time.
Figure 1 illustrates the snapshots (adjacency matrices)
of n1 before and after exchanging the snapshot information
with n2. At time t1, node n1 encounters nodes n2, n3, and n4.
At the same time, n2 encounters n1, n4, and n5, as shown in
Fig. 1a. Figure 1b shows the snapshot information of both
n1 and n2. Figure 1c, d show the adjacency matrices of n1
before and after exchanging the snapshot information,
respectively.
Step 2: A node extracts three network features from the
adjacency matrix: (1) the change period of membership in
the component, (2) the compactness of each component
and (3) the number of components.
1. The change period of membership in the component
is used to determine the ‘pumping’ period. By
pumping we mean that a node that has the message
in its memory sends it to its neighbors and each
(relay) node that receives the message also imme-
diately sends it to its neighbors. Note that a relay
node does not keep the message in its memory, in
order to have a tolerance towards memory size. To
find the appropriate pumping period for node ni, we
measure what fraction of the nodes in the compo-
nent to which ni belongs has changed. Node nipumps the message only when a certain percentage
of the members in the component have changed.
This pumping contributes to reducing the network
traffic.
Each ni node looks into the adjacency matrix at each tsand finds the connected component, ci
s, to which ni belongs
at ts. It then tries to find the first component whose mem-
bers have changed at least a fraction, 1 - k of them at ts,
where k is the average algebraic connectivity of cis for all
s = 1,…,w. Note that the algebraic connectivity is defined
to be the second-smallest eigenvalue of the symmetric
normalized Laplacian matrix of a component [16, 19]; the
symmetric normalized Laplacian matrix is defined as
follows:
Lnorm := I � D�12AD�1
2 ð1Þ
where I is the identity matrix, A is the adjacency matrix and
D is the degree matrix [5].
According to the Rayleigh–Ritz theorem [12], the
solution to the graph partitioning problem for two par-
titions is precisely given by the eigenvector correspond-
ing to the second smallest eigenvalue [26, 31, 34].
Unlike the traditional connectivity, the algebraic con-
nectivity depends not only on the number of nodes in the
graph but to what extent the nodes are connected to each
other. Thus, the algebraic connectivity captures the
membership change ratio of a component well, because a
component with a low algebraic connectivity may easily
be broken apart. Let ts be the time when at least a
fraction (1 - k) of the component’s members are chan-
ged for the first time. Then s - f is the period that at
least a fraction (1 - k) of the members are changed in
the component since tf, where f is initialized to 1. nikeeps finding the next such components and computes
the average period. In Algorithm 1, P denotes the set of
the (s - f) periods.
Table 1 Sample snapshot
information for node niTime Neighbor nodes
t1 n2, n3, n4
t2 n2, n5
t3 n4
… …tw n2, n7, n10, n12
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Algorithm 1 Determining change period
Period set: ;
for do
if then
end if
end forreturn the average of all the values in P
2. The compactness of components: In periodic pumping,
the diameter of the component at time ts is utilized as
the TTL of the forwarding message. Traffic congestion
may be reduced by the TTL, since a message can reach
the members in a component within the TTL. How-
ever, the diameter of the components may not be a
proper factor for determining the TTL. For instance, in
a graph, G = K100 [ P4; the diameter of G is five, but
the geodesic between most pairs of nodes is one. Thus,
if the TTL is set to five, unnecessary network traffic
would be generated. For this reason, we use the com-
pactness of a component, as in [3]. The compactness is
defined to be the sum of the inverse geodesic of con-
necting nodes with the normalization of n(n - 1),
where n is the number of nodes in a component. The
compactness of a component can be written as in the
following equation, where dij is the geodesic distance
between a pair of nodes, ni and nj, in the component.
compactness ¼P
i6¼j 1=dij� �
nðn� 1Þ ð2Þ
However, we cannot use the compactness directly for
the TTL, because the compactness is not related to the
diameter. We therefore devise an equation with the com-
pactness and the longest diameter, D(G), of all components
as follows.
T ¼ DðGÞ1�compactness ð3Þ
In Eq. (3), as compactness becomes 1.0, the TTL T is
close to 1.0, indicating that a short TTL is good for con-
nected components with high compactness. On the other
hand, as compactness becomes 0, T is close to D(G),
indicating that a long TTL is appropriate for connected
Fig. 1 Example of exchanging
snapshots
Wireless Netw (2015) 21:2055–2068 2059
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components with low compactness. Note that relationship
between TTL T and the compactness is non-linear, because
Eq. (3) is an exponential function. As the compactness is
increased from 0.0 to 1.0, T is rapidly decreased in the
earlier stage. However, as the compactness gradually
approaches 1.0, T decreases to 1.0 extremely slowly.
According to the percolation theory [32], before the com-
pactness reaches a critical point, the network topology
consists of many small-sized components, each of which
has a relatively large compactness. After reaching the
critical point, a giant component (a connected component
that contains a constant fraction of the total number of
nodes in a network) may suddenly emerge from the net-
work topology. Note that the compactness of the giant
component is very small, since the geodesics in the com-
ponent become much longer. Such relationship naturally
fits the non-linearity between the compactness and TTL.
3. The number of components with consideration to the
component size (the number of nodes): We want each
component to maintain at least one node, called the
messenger, which keeps the copy of the message and
which pumps the message as the sender node does.
However, if the number of messengers, k, is equal to
that of the components, there may be superfluous
network traffic, because some components may be
trivial in size and other non-trivial sized components
may get more than one messenger. To determine an
appropriate value for k, we incorporate the number of
components with the component size, as shown in Eq.
(4), where Cs is the number of components at time ts,
mis is the size of component i, i = 1,…,Cs at time ts, w
is the number of snapshots, and n is the number of the
nodes in the network.
k ¼P
s
Pi min ms
i � Csn; 1
� �
wð4Þ
In Eq. (4), msi � Cs
ndenotes the ratio of the size of compo-
nent i to the average size of the components. We call it the
density of component i. We set the maximum value of the
density to 1.0, since more than one messenger in a component
may generate unnecessary traffic. We obtain k by dividing the
sum of the densities of all components for all s by w.
Each node should determine k messengers in the network
that are more likely to meet the destination. To do so, each
node looks into each component to which the destination
belongs in all the snapshots and finds kmessengers that appear
the most in all the components belonging to the destination.
In Fig. 2, we assume that n1 is the sender, n10 is the
destination, and k = 3. To select the k messengers, n1 looks
into each snapshot and finds the component to which the
destination belongs. That is, {n6, n7, n8, n9, n10} at t1, {n9,
n10} at t2, {n4, n7, n10} at t3, and {n2, n4, n5, n9, n10} at t4.
Hence, n1 chooses the nodes that appear most in the
components; n9, n7, and n4 are selected as messengers
because n9 appears three times, and n7 and n4 appear twice
each.
In the forwarding phase, whenever n1 encounters n9, n7,
and n4 for the first time, let each messenger keep the copy
of the message. Note that the sender may not encounter all
of the messengers and hence there may be less than
k copies of the message during the forwarding phase. Each
messenger who received the message plays the same role
as the sender independently.
Step 3: As soon as the warm-up period ends, each node
determines the pumping period, TTL, and k messengers
from the extracted network features in Step 2. Each sender
then pumps the message with the IDs of the messengers to
its neighbors. Each neighbor node then sends them to its
neighbors, and so on. If the destination receives the mes-
sage, the delivery has been accomplished. Or, if a mes-
senger receives the pumped information, it then acts like
the sender. Such pumping lasts until the TTL is computed
by the messenger.
4 Performance evaluations
4.1 Simulation environment
Our simulation model is similar to that used in [11, 29]. We
used the network simulator, NS-2 v2.35 [18, 25], to eval-
uate our proposed scheme, Snapshot, because the NS-2 is
suitable for analyzing the correlation between the network
traffic and transmission delay.
We compared Snapshot with typical dissemination-
based schemes such as the epidemic, SimBet and PRo-
PHET schemes. In our simulation, 40 mobile nodes follow
the home-cell community-based mobility model (HCMM)
[2] which is a widely used mobility pattern in mobile
network simulations. The network area is set to
150 m 9 150 m with four special zones called home
communities. A home community can be defined as a set of
members who gather socially at a certain place. So the
members in the same home community spend more time
with each other at their physical place. Each home com-
munity has ten nodes of which only one is the traveler
node. A traveler node has more chances to encounter other
nodes of different communities so that it can generally acts
as a bridge between communities far apart, while a non-
traveler node mostly stays within its home community or
stochastically follows a traveler node. The speed of each
node is 2–10 m/s. A node can recognize other nodes within
1, 5, 10, 20, 30, 40, 50 m. Each node in the network ran-
domly selects a destination to send a message. We
2060 Wireless Netw (2015) 21:2055–2068
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measured the delay time (s) and network traffic (the
number of received messages) until all 40 messages
arrived at their destinations. The simulations were con-
ducted 20 times to obtain the average results. The param-
eters for epidemic, PRoPHET and SimBet are given in
Table 2. Through extensive experiments the parameter
values have been determined in order for other schemes to
achieve their best experimental results in the environments,
while the proposed scheme is able to automatically adjust
its own parameters appropriately. Note that other schemes
should rely on manual adjustments of their parameters for
the environments. There is a warm-up period of 500 s.
During the warm-up period, each node gathers the snapshot
information in the form of an adjacency matrix. The pro-
posed scheme needs a warm-up period of 500 s. However,
its length is relatively short when compared with the ser-
vice time for the rest of the simulation that takes up to 12 h.
Such a lengthy period of time for the service time is not
unusual because DTN assumes very long end-to-end delays
due to lack of node contacts. In addition, the warm-up
period is only needed once in the beginning of network
services. Note that both SimBet and PRoPHET also require
the warm-up periods for collecting appropriate information
for their schemes like our proposed scheme and it is not
difficult to find other articles published that resort to the
warm-up periods [8, 9, 21, 23]. During the warm-up period,
each of PRoPHET, SimBet and Snapshot generates control
packets, while epidemic doesn’t need to generate any
control packets. Therefore, epidemic has more effective
than other schemes to reduce network traffic. In practice,
we have evaluated the number of received control packets
during the warm-up period of 500 s in the default network
environment. Snapshot, PRoPHET and SimBet exchange a
control packet whenever a node encounters another node;
Fig. 2 Snapshots at ts, where s = 1, 2, 3 and 4
Wireless Netw (2015) 21:2055–2068 2061
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each scheme uses about 26,000 control packets. However,
we assume that the size of a control packet is significantly
smaller than that of a data message packet because a
control packet in Snapshot has only a small size matrix of
binary information; that is, the size of a control packet is
500 times smaller than that of a data message packet. Thus,
the network traffic incurred by the control packets is neg-
ligible in analyzing the performance of Snapshot. Table 2
summarizes the parameters of the simulation environment.
In Fig. 3, the shaded cells indicate the locations of the four
communities.
4.2 Simulation results
Figure 4 presents the average network traffic and trans-
mission delays of epidemic, SimBet, PRoPHET, and
Snapshot. In the Fig. 4, the network traffics of epidemic,
SimBet, and PRoPHET are about 454.3, 323.6 and
223.9 % larger than that of Snapshot, respectively. The
transmission delay time of Snapshot is about 23.6 and
6.0 % smaller than those of SimBet and PRoPHET,
respectively. As we expected, Snapshot significantly
reduces the network traffic while maintaining a reasonable
delay time, because Snapshot maintains proper control
values, as given in Table 3.
We tested Snapshot for the above three control values in
dense networks to verify why Snapshot could reduce net-
work traffic significantly with a comparable delay time.
Snapshot was first tested with ten different pumping peri-
ods (1–10 s), as shown in Fig. 5, to verify that a proper
value for the pumping period is three. The Fig. 5 shows
that when the pumping period is three, Snapshot performs
quite well in terms of both network traffic and delay; when
it is two, traffic increases but the delay remains similar. On
the other hand, when it is four, not much changes with the
traffic but the delay gets longer. For other pumping periods,
there are wider gaps in the results. This is especially the
case when the pumping period is equal to or longer than
four; there are more nodes that do not get the messages,
resulting in a longer delay time with a smaller amount of
network traffic.
Next, we tested Snapshot with ten different numbers of
messengers, as shown in Fig. 6, to verify that the proper
number of messengers is three. Note that we obtained the
number of components for the number of messengers. In
Fig. 6, when there are three messengers, Snapshot per-
forms best in terms of both network traffic and delay time.
When there are four or more messengers, the network
traffic increases and the delay time improves slightly,
because more nodes receive duplicate messages from the
messengers. Hence, Fig. 6 confirms that three is indeed
properly chosen as the number of components.
Finally, we show that the average TTL chosen by
Snapshot successfully reduces the network traffic. After
the warm-up period, Snapshot determines the average
TTL to be nine with DðGÞ1�compactnessof the components
in each snapshot. Note that the average TTL is the longest
diameter among all of the components. We tested Snap-
shot with ten different TTLs (3–12). Figure 7 shows that
when the TTL is nine, Snapshot exhibits the least amount
of network traffic with almost the shortest delay time.
When the TTL is less than nine, the performance is poor,
because a message may not be spread into all the nodes in
a component.
4.3 Results with various communication ranges
We measured the network traffic with various communi-
cation ranges between 1, 5, 10, 20, 30, 40, 50 m. Figure 8
shows the simulation results of epidemic, SimBet, PRo-
PHET, and Snapshot. Both epidemic and SimBet increaseFig. 3 Community pattern
Table 2 Simulation parameters
Parameter (unit) Value (default)
Number of nodes 40
Size of the network (m2) 150 9 150
Number of communities 4
Community size (m) 50 9 50
Node speed (m/s) 2–10
Radius of communication range (m) 1, 5, 10, 20, 30, 40, 50 (30)
TTL for epidemic, PRoPHET, SimBet 10
Control value a for SimBet 0.5
Transitivity factor for PRoPHET 0.2
Aging factor for PRoPHET 0.8
Initial probability factor for PRoPHET 0.2
Number w of snapshots taken 50
Warm-up period (s) 500
2062 Wireless Netw (2015) 21:2055–2068
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network traffic as the communication range gets longer,
since more nodes could communicate with each other
directly. PRoPHET shows a similar trend as epidemic, but
when the communication range is 10 m, PRoPHET gen-
erates more network traffic than epidemic because PRo-
PHET requires a longer time to send all 40 messages to the
destinations. Note that we measured the delay time and
network overhead until all 40 messages arrived at their
destinations. On the other hand, Snapshot maintains low
network traffic with various communication ranges because
our scheme controls the number of messengers, the length
of the pumping period and the TTL according to the net-
work environment.
We also investigate the transmission delay with various
communication ranges. Figure 8 shows that epidemic
exhibits the shortest delay in each communication range.
The transmission delays of all the schemes are reduced as
the communication range increases. When the range is
50 m, Snapshot exhibits an almost similar delay time to
epidemic, since our scheme deploys an appropriate number
of messengers.
DTN routing schemes should allow reasonable trans-
mission delay and network traffic because DTN basically
assumes very sparse network environment. Thus, we also
measured the transmission delays and network traffics with
Fig. 4 Average network traffic and transmission delay in the default network
Fig. 5 Average network traffic and transmission delay with various pumping periods
Table 3 The average control values of snapshot
Snapshot controller Value
Pumping period 3
The number of messengers 3
TTL T 9
Wireless Netw (2015) 21:2055–2068 2063
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Fig. 6 Average network traffic and transmission delay with various numbers of messengers
Fig. 7 Average network traffic and transmission delay with various static TTLs
Fig. 8 Performance results with various communication ranges
2064 Wireless Netw (2015) 21:2055–2068
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the communication ranges of 1 and 5 m. In such very
sparse environments, the schemes show very similar trends
to those in other progressively dense environments. SimBet
shows very poor performance in very sparse networks
because it is extremely difficult for a node to find its
neighbors. If nodes hardly find the neighbor nodes in
SimBet, the similarities and betweenness utility values of
nodes have unintended meanings. However, as shown in
Fig. 8, Snapshot has well adapted to the sparse environ-
ments, because Snapshot seems to mimics epidemics by
adjusting the compactness, TTL, and period values
appropriately
4.4 Results with limited memory space
Figure 9 compares the robustness of the schemes in terms
of the memory space. The unit of memory space is the
number of slots, where each message fits into a single
slot. The results clearly indicate that the memory size of
our proposed scheme barely affects the transmission delay
time and the network traffic because the proposed scheme
uses the pumping method. Note that the pumping method
allows relay nodes to pass messages without keeping
them in their memory. However, the carry and forward
schemes such as epidemic, SimBet and PRoPHET permit
each relay node to keep the messages in its memory.
Therefore, these schemes may be easily influenced by
memory size.
According to Fig. 9, the larger the memory size is of the
schemes, the less network traffic that is acquired. Note that
a longer transmission delay usually incurs more network
traffic, because we stop the experiment when all 40 mes-
sages are successfully delivered. As expected, a larger
memory size leads to a reduction in the transmission delay
(Fig. 9).
4.5 Results with various community patterns
We measured the network traffic and transmission delay
with various community patterns in order to show the
robustness of the proposed scheme. We have chosen a few
other community patterns as shown in Fig. 10. They are
chosen according to the dispersion of the communities
within the network area; that is, we want to have a few
typical patterns depending on how communities are close
to each other. Note that the distance between a pair of
communities decides the distances that the nodes belonging
to the communities move around. The default community
pattern in Fig. 3 represents the most dispersed pattern,
while Fig. 10c represents a more concentrated pattern and
Fig. 10a, b represent intermediate patterns when compared
with both Figs. 3 and 10c.
In Figs. 11, 12 and 13, the simulation results in the
community patterns show very similar trends each other.
For epidemic, the average network traffic of the three
patterns in Figs. 11, 12 and 13 (the sum of traffics in the
three figures/3) is 15.0 % smaller than the network traffic
in Fig. 4. Such traffic reductions are attributed to the fact
that the messages are delivered earlier in the three com-
munity pattern environments of Fig. 10 than in the default
pattern environment. Note that we measured the delay time
and network overhead until all 40 messages arrived at their
destinations.
For SimBet, PRoPHET, and Snapshot, the average
traffics in Figs. 11, 12 and 13 are 15.6, 27.4, and 35.0 %
larger than the traffics for the default case in Fig. 4,
respectively. However, for epidemic, SimBet, PRoPHET,
and Snapshot, the average delay results in Figs. 11, 12 and
13 are about 3.5, 6.6, 12.8, 23.6 % smaller than the delays
for the default case in Fig. 4, respectively. Larger traffics
and shorter delays of the schemes are caused by the fact
Fig. 9 Performance results with various memory spaces
Wireless Netw (2015) 21:2055–2068 2065
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that in HCMM a node mostly moves from its community to
another community, and hence shorter inter-community
distances in the three community pattern environments of
Fig. 10 contribute to shorter traveling distances of nodes as
well as more frequent encounters among the nodes.
5 Conclusions
Our work examined two main questions, the first of which
is how to devise a new carry and forward scheme that is not
sensitive to the size of memory space. We provide the
(a) The first pattern (b) The second pattern (c) The third pattern
Fig. 10 Various community
patterns
Fig. 11 Average network traffic and transmission delay for the pattern in Fig. 10a
Fig. 12 Average network traffic and transmission delay in the pattern in Fig. 10b
2066 Wireless Netw (2015) 21:2055–2068
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pumping method in response to this question. The pumped
messages are forwarded to the neighbors right after they
are received without keeping them in the memory space. In
addition, pumping is periodically performed in each node.
By applying this pumping process, we devise a forwarding
scheme which is not sensitive to memory size. Therefore,
by reducing the reliance on memory size, the proposed
approach performs well at forwarding a message in an
environment that has limited memory or deals with large-
sized messages. However, this pumping approach itself
generates high network traffic and creates a longer trans-
mission delay. Therefore, we employed a graph theoretical
notion called the compactness of each component in order
to control the TTL.
The second main question we have addressed is how to
analyze a network topology in opportunistic networks and
how to exploit the features of a network topology. We
provided a snapshot mechanism so as to get a network
topology in a distributive manner. In the Snapshot scheme,
each node stores the social interactions for a certain period
of time. Each node then exchanges the stored social
interactions. The interactions are accumulated in the form
of an adjacency matrix to represent a network graph at a
specific time. We extracted two other essential features of a
network topology, the change period of membership in the
component and the number of components with consider-
ation to the component size.
The extensive simulation results show that the utiliza-
tion of the three network topology features is quite effec-
tive, and also our scheme outperforms other approaches in
terms of the transmission delay and network traffic. We are
currently working on the impact of different network fea-
tures with social network concepts.
Acknowledgments This research was supported by the Basic Sci-
ence Research Program through the National Research Foundation of
Korea (NRF) funded by the Ministry of Education, Science and
Technology (2013R1A1A2011114).
References
1. Boldrini, C., Conti, M., Jacopini, J., & Passarella, A. (2007).
Hibop: A history based routing protocol for opportunistic net-
works. In Proceedings of WoWMoM 2007 (pp. 1–12).
2. Boldrini, C., & Passarella, A. (2010). HCMM: Modelling spatial
and temporal properties of human mobility driven by users’
social relationships. Computer Communications, 33(9),
1056–1074.
3. Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2013). Analyzing
social networks. Washington, DC: SAGE Publications Limited.
4. Burns, B., Brock, O., & Levine, B. N. (2008). MORA routing and
capacity building in disruption-tolerant networks. Ad Hoc Net-
works, 6(4), 600–620.
5. Chung, F. R. (1997). Spectral graph theory (Vol. 92). Wash-
ington, DC: American Mathematical Society.
6. Conti, M., Giordano, S., May, M., & Passarella, A. (2010). From
opportunistic networks to opportunistic computing. IEEE Com-
munications Magazine, 48(9), 126–139.
7. Conti, M., & Kumar, M. (2010). Opportunities in opportunistic
computing. IEEE Computer, 43(1), 42–50.
8. Daly, E. M., & Haahr, M. (2009). Social network analysis for
information flow in disconnected delay-tolerant MANETs. IEEE
Transactions on Mobile Computing, 8(5), 606–621.
9. Dang, H., & Wu, H. (2010). Clustering and cluster-based routing
protocol for delay-tolerant mobile networks. IEEE Transactions
on Wireless Communications, 9(6), 1874–1881.
10. Doria, A., Uden, M., & Pandey, D. (2009). Providing connec-
tivity to the saami nomadic community. Generations, 1(2), 3.
11. Ferreira, A., Goldman, A., & Monteiro, J. (2010). Performance
evaluation of routing protocols for MANETs with known con-
nectivity patterns using evolving graphs. Wireless Networks,
16(3), 627–640.
12. Gould, S. H. (1995). Variational methods for eigenvalue prob-
lems: An introduction to the methods of Rayleigh, Ritz, Weinstein,
and Aronszajn. Mineola: Courier Dover.
13. Groenevelt, R., Nain, P., & Koole, G. (2005). The message delay
in mobile ad hoc networks. Performance Evaluation, 62(1),
210–228.
Fig. 13 Average network traffic and transmission delay in the pattern in Fig. 10c
Wireless Netw (2015) 21:2055–2068 2067
123
14. Grossglauser, M., & Tse, D. (2001). Mobility increases the
capacity of ad-hoc wireless networks. In Proceedings of IEEE
INFOCOM (pp. 1360–1369).
15. Guangchun, L., Zhang, J., Ke, Q., & Haifeng, S. (2012). Loca-
tion-aware social routing in delay tolerant networks. IEICE
Transactions on Communications, 95(5), 1826–1829.
16. Hossmann, T., Spyropoulos, T., & Legendre, F. (2010). Know thy
neighbor: Towards optimal mapping of contacts to social graphs
for DTN routing. In Proceedings of IEEE INFOCOM (pp. 1–9).
17. Hui, P., Crowcroft, J., & Yoneki, E. (2011). Bubble rap: Social-
based forwarding in delay-tolerant networks. Mobile Computing,
IEEE Transactions on, 10(11), 1576–1589.
18. Issariyakul, T., & Hossain, E. (2011). Introduction to network
simulator NS2. New York: Springer.
19. Jamakovic, A., & Van Mieghem, P. (2008). On the robustness of
complex networks by using the algebraic connectivity. In Pro-
ceedings of NETWORKING 2008 Ad Hoc and Sensor Networks,
Wireless Networks, Next Generation Internet (pp. 183–194).
20. Juang, P., Oki, H., Wang, Y., Martonosi, M., Peh, L. S., & Ru-
benstein, D. (2002). Energy-efficient computing for wildlife
tracking: Design tradeoffs and early experiences with ZebraNet.
ACM Sigplan Notices, 37(10), 96–107.
21. Lee, J., & Kim, S. (2011). FSRS routing method for energy
efficiency through the new concept of flooding restriction in
wireless ad-hoc networks. IEICE Transactions on Communica-
tions, 94(11), 3037–3048.
22. Leguay, J., Friedman, T., & Conan, V. (2005). DTN routing in a
mobility pattern space. In Proceedings of the 2005 ACM SIG-
COMM Workshop on Delay-tolerant Networking (pp. 276–283).
23. Lindgren, A., Doria, A., & Schelen, O. (2003). Probabilistic
routing in intermittently connected networks. ACM SIGMOBILE
Mobile Computing and Communications Review, 7(3), 19–20.
24. Musolesi, M., Hailes, S., & Mascolo, C. (2005). Adaptive routing
for intermittently connected mobile ad hoc networks. In Pro-
ceedings of IEEE WoWMoM 2005 (pp. 183–189).
25. Network Simulator-2. (2014). http://www.isi.edu/nsnam/ns/.
Accessed May 22, 2014.
26. Ng, A. Y., Jordan, M. I., & Weiss, Y. (2001). On spectral clus-
tering: Analysis and an algorithm. In Proceedings of Advances in
Neural Information Processing Systems. Cambridge, MA: MIT
Press.
27. Pelusi, L., Passarella, A., & Conti, M. (2006). Opportunistic
networking: Data forwarding in disconnected mobile ad hoc
networks. IEEE Communications Magazine, 44(11), 134–141.
28. Pentland, A., Fletcher, R., & Hasson, A. (2004). Daknet:
Rethinking connectivity in developing nations. Computer, 37(1),
78–83.
29. Ramanathan, R., Hansen, R., Basu, P., Rosales-Hain, R., &
Krishnan, R. (2007). Prioritized epidemic routing for opportu-
nistic networks. In Proceedings of MobiSys (pp. 62–66).
30. Shih, T. F., & Yen, H. C. (2008). Location-aware routing protocol
with dynamic adaptation of request zone for mobile ad hoc net-
works. Wireless Networks, 14(3), 321–333.
31. Spielman, D. (2009). Spectral graph theory. Lecture Notes, Yale
University.
32. Stauffer, D., & Aharony, A. (1991). Introduction to percolation
theory. New York: Taylor and Francis.
33. Vahdat, A., & Becker, D. (2000). Epidemic routing for partially
connected ad hoc networks. Technical Report CS-200006, Duke
University.
34. Von Luxburg, U. (2007). A tutorial on spectral clustering. Sta-
tistics and Computing, 17(4), 395–416.
35. Widmer, J., & Le Boudec, J. Y. (2005). Network coding for
efficient communication in extreme networks. In Proceedings of
ACM SIGCOMM (pp. 284–291).
Junyeop Lee is currently an
M.S. candidate in Computer
Science at Yonsei University in
Korea. His research interests
include mobile social networks,
delay tolerant networks and
social network analysis.
Sun-Kyum Kim received his
M.S. in Computer Science from
Yonsei University in Korea in
2012. He is currently a Ph.D.
candidate at Yonsei University.
His research interests include
mobile social networks, delay
tolerant networks and social
network analysis.
Ji-Hyeun Yoon is currently an
Ph.D. candidate in Computer
Science at Yonsei University in
Korea. His research interests
include mobile social networks,
delay tolerant networks and
social network analysis.
Sung-Bong Yang received his
M.S. and Ph.D. from the
Department of Computer Sci-
ence at the University of Okla-
homa in 1986 and 1992,
respectively. He has been a
professor at Yonsei University
since 1994. His research inter-
ests include graph algorithms,
mobile computing, and social
network analysis.
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