hcs: hierarchical cluster-based forwarding scheme for mobile social...
TRANSCRIPT
HCS: hierarchical cluster-based forwarding scheme for mobilesocial networks
Sun-Kyum Kim • Ji-Hyeun Yoon • Junyeop Lee •
Sung-Bong Yang
Published online: 19 December 2014
� Springer Science+Business Media New York 2014
Abstract Clustering has been shown to be a highly
effective way to reduce network traffic in mobile ad hoc
networks. Many clustering schemes have been proposed.
However, none of these schemes can be directly applied to
a mobile social network because they are designed for
well-connected networks and require timely information
sharing among the nodes. In this paper, we propose the
hierarchical clustering-based forwarding scheme (HCS),
which implements hierarchical clustering on social infor-
mation. Each node constructs hierarchical clusters based on
common neighbor similarity at the end of the warm-up
period. The nodes then forward a message to other nodes
based on the clustering information and similarity scores.
HCS exploits the shortcuts on the path toward the desti-
nation node with the help of social similarity and node
movement patterns. Experiments were performed on an
NS-2 network simulator. The results show that HCS
reduces network traffic compared to non-clustering
schemes, such as Epidemic, SimBet, PRoPHET, and
common neighbor similarity schemes, while maintaining
acceptable transmission delay compared to the Wait
scheme.
Keywords Hierarchical clustering � Forwarding �Routing � Mobile social network (MSN) � NS-2
1 Introduction
With advances in wireless communication technologies
and the popularity of mobile devices, mobile social net-
works (MSNs)—also known as opportunistic networks [1]
or pocket switched networks [2]—have rapidly become an
increasingly popular field of networking research. MSNs
are applicable to mobile wireless communications such as
Sami Network Connectivity Project [3], Zebranet [4],
Shared Wireless Info-Station [5], Vehicular Delay Tolerant
Networks [6], and so on. MSNs have evolved from mobile
ad hoc networks (MANETs) [7] and delay-tolerant net-
works (DTNs) (also known as disruption tolerant networks)
[8] with social characteristics [9]. MSNs carry a more
general concept with human-carried devices and include
DTNs. Also, MSNs do not assume any compatibility with
the Internet architecture, nor any a priori knowledge about
the network topology, the areas of disconnections, or future
link availability [10]. MSNs use contact opportunities and
rely on devices carried by humans to relay messages for
others [9]. However, like DTNs, MSNs suffer from inter-
mittent connectivity and long-lasting disconnections due to
low node density, short transmission ranges, and free node
mobility. In addition, there may be no complete paths
between the source and destination nodes [1]. Thus, the
forwarding or routing schemes for MANETs are not
applicable, and development of such schemes in MSNs
have become a challenging problem.
In MSNs, nodes communicate with the multi-hop and
relay nodes that forward message addresses to other nodes
in MSNs. In this case, however, forwarding is not ‘‘on the
S.-K. Kim � J.-H. Yoon � J. Lee � S.-B. Yang (&)
Department of Computer Science, Yonsei University,
Seoul, Korea
e-mail: [email protected]
S.-K. Kim
e-mail: [email protected]
J.-H. Yoon
e-mail: [email protected]
J. Lee
e-mail: [email protected]
123
Wireless Netw (2015) 21:1699–1711
DOI 10.1007/s11276-014-0876-x
fly’’ because the relay nodes store messages when no for-
warding opportunity exists, such as when there are no
nodes within the transmission range. Moreover, they
exploit any contact opportunity with other nodes in order to
forward the message [10]. This forwarding mechanism is
called a store, carry, and forward scheme and is performed
hop by hop. In MSNs, node mobility creates opportunities
for communication, whereas mobility in MANETs is
viewed as a disruption of connections among nodes [10].
Therefore, the key issue in message forwarding is selection
of proper nodes for message (or a copy of the message for a
multi-copy scheme) handed [11].
Clustering has been shown to be a highly effective way
to reduce network traffic in MANETs, and many clustering
schemes have been proposed. However, none of them can
be directly applied to MSNs because they are designed for
well-connected networks and require timely information
sharing among the nodes [12]. Among the proposed clus-
tering schemes in MSNs, a distributed clustering scheme
based on an exponentially weighted moving average [12]
has been proposed. However, this scheme requires gateway
nodes, each of which plays a role as a bridge between two
nodes in different clusters. If a gateway node operates
unpredictably, the scheme suffers from low performance.
The DTN hierarchical routing (DHR) scheme [13] has been
proposed to improve routing scalability. However, DHR is
based on a deterministic mobility model in which all nodes
move according to strict, repetitive patterns. Hence, the
method is very difficult to implement in MSNs.
Because mobile nodes have limited resources, such as
bandwidth, power consumption, channel utilization, net-
work size, and so on, the nodes in MSNs experience some
communication difficulties. Therefore, as network traffic
increases, network problems, such as bottlenecks, slow
communication, and noise problems, are unavoidable. In
addition, applications in MSNs should be relatively delay-
tolerant. However, it is still of interest to minimize the
delay whenever possible [24].
To resolve this problem, we propose the hierarchical
cluster-based forwarding scheme (HCS) to reduce network
traffic while maintaining acceptable transmission delay.
HCS constructs hierarchical clusters [14] using social
information with the home-cell community-based mobility
model (HCMM) [15]. Hierarchical clustering is a highly
effective way for clustering nodes in MSNs because each
node plays a similar role in flat clustering. This method is
simple and effective in small networks but not applicable to
large-sized MSNs [14]. HCS exploits agglomerate hierar-
chical clustering in the same way as in DHR. However,
HCS adopts the common neighbor similarity to construct
hierarchical clusters instead of the contact probability used
in DHR. In HCS, each node utilizes both hierarchical
clustering and similarity scores. The clustering information
helps deliver the message with a smaller number of hops
owing to social similarity and node movement patterns.
The experimental results show that HCS reduces
network traffic compared to non-clustering schemes, such
as Epidemic [18], SimBet [24], PRoPHET [29], and com-
mon neighbor similarity [32] schemes, while maintaining
decent transmission delay compared to the Wait scheme.
Mobile nodes in MSNs generally more frequently visit
certain places, like home communities, while visiting other
locations only occasionally [16]. Because the home com-
munity-based mobility model (HCMM) reflects such a
characteristic, it is well-suited for use in MSNs.
The main contributions of this paper can be summarized
as follows.
1. We propose a scheme using agglomerative (bottom-
up) hierarchical clustering with common neighbor
similarities. Cluster level-based forwarding is
introduced.
2. To more efficiently deliver a message to the destina-
tion node, similarity-based forwarding compensates for
level-based forwarding. Using this technique, a node
sends a message to other nodes with higher similarity
scores with respect to the destination.
3. We conduct extensive simulations for experiments
with the network simulator NS-2 and compare the
results with non-clustering schemes, such as Epidemic,
Wait, SimBet, PRoPHET, and common neighbor
similarity schemes.
The rest of this paper is organized as follows. In Sect. 2,
we discuss related work. After introducing the simplified
MSN model in Sect. 3, we describe the proposed scheme in
Sect. 4. The simulation environment and results are pre-
sented in Sect. 5. Finally, the conclusion and future work
are outlined in Sect. 6.
2 Related work
Existing routing protocols for MANETs [7], such as
Dynamic Source Routing, Ad Hoc On-Demand Distance
Vector, Split Multipath Routing, Shortest Multipath
Source, and AntHocNet, have been introduced. A double-
layered peer-to-peer system using clustering was also
proposed for improved routing performance [17]. None of
the current schemes are applicable to MSNs regardless of
improved performance because of the requirement for no
complete path between the source and destination in
MSNs.
The opportunistic routing schemes can be classified in
two categories: zero knowledge schemes and non-zero
knowledge schemes. Zero knowledge schemes use no
social information, while non-zero knowledge schemes
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take advantage of the information about node behaviors or
social relationships in order to make decisions for for-
warding messages. Non-zero knowledge schemes are the
ones utilized in MSNs.
Zero knowledge schemes include Epidemic [18], Spray-
and-Wait [19], Controlled routing protocol for DTN based
on hierarchy forwarding and cluster control (CRHC) [20],
Backpressure with adaptive redundancy (BWAR) [21],
Backpressure-based routing scheme [22], Homing spread
[16], and Hotspot-based forwarding scheme (HFS) [23]. In
Epidemic when each node meets other nodes, it distributes
the message to each of them, creating the replicas of the
message. In Spray-and-Wait a node ‘‘sprays a number of
copies into’’ some nodes in the network and then ‘‘waits’’
until one of these nodes meet the destination. First, a fixed
number k of messages is transferred by spraying a half of k-
copies. When each node has the last message after spraying
messages, it waits until the moment when it meets the
destination node during the wait phase. In CRHC, a node
spreads a half of the messages to other nodes until meeting
the destination when the destination is in the same cluster.
On the other hand, a node sends a message to the cluster-
head in the different cluster first, when the destination node
is in a different cluster. However, it is not applicable to our
environment because the network should select cluster
heads and stable nodes in advance, where we can easily
predict their moving patterns. BWAR takes advantages of
replication to reduce delay under low load conditions. It
creates copies of packets in a new duplicate buffer upon an
encounter, when the transmitter’s queue occupancy is low.
In Backpressure-based routing scheme a node can make
source rate, packet routing, and forwarding decisions
without the notion of end-to-end routes, using information
about queue backlogs and link states. Both BWAR and
Backpressure-based routing schemes are applicable to the
environment considering queue, link states and load con-
ditions. Homing Spread utilizes community structure to
identify suitable relay nodes. These approaches spread
messages to the detected communities via relay nodes;
however, they incur extra delivery overhead for mobile
nodes. HFS floods messages only in hotpots, where nodes
often interact; however, the size of the hotspots is limited.
Various non-zero knowledge schemes have been pro-
posed for MSNs [11, 24–31]. The non-zero knowledge
schemes can be further classified into three schemes: cen-
trality/similarity-based, social context-based, and proba-
bility-based. The centrality/similarity-based schemes
include SimBet [24], Bubble Rap [25], and SANE [26].
SimBet makes use of the exchange of both betweenness
centrality metrics and the locally determined social ‘‘sim-
ilarity’’ to the destination node. When a node encounters
other nodes, it transfers a message to the node with the
higher utility values of betweenness centrality and
similarity until reaching the destination node. Bubble Rap
takes advantage of both global and local centrality. The
bubble-up operations transmit a message to the destination
node or its community. However, when the destination
belongs only to a community whose members all have low
global centrality values, such a strategy may fail. In this
case, a relay node in the same local community as the
destination node cannot be identified. SANE utilizes user
interests and similarity.
Social context-based schemes include Label [27] and
HiBop [28]. In Label, each node is assumed to hold the
label information of other nodes in its social community,
similar to name tags used in a conference. Based on the
labels, the routing scheme selects nodes for directly for-
warding messages to the destination or for acting as the
next-hop node that shares the same label as that of the
destination. HiBop requires personal information, such as
residence, work, hobbies and fun, as well as system
information.
Finally, probability-based schemes include PRoPHET
[29], PeopleRank [30], and MobySpace [31]. PRoPHET
first estimates a probabilistic metric called the delivery
predictability, P(a, b), which indicates how likely it is that
node b will receive a message from node a during a warm-
up period. Two nodes exchange the summary vector of the
information on the messages and the delivery predictability
vector. The information in the summary vector is used to
determine which messages should be sent for requesting
information from other nodes. PeopleRank uses the Page-
Rank algorithm of Google as a guide for forwarding
decisions. Whenever two neighbor nodes in the social
graph meet, they exchange their current PeopleRank values
and their numbers of social graph neighbors. MobySpace
takes advantage of the knowledge concerning node
mobility; however, it requires global information for
routing.
Non-zero knowledge schemes are very effective in for-
warding messages. However, most non-zero knowledge
schemes require global information for forwarding deci-
sions. These schemes therefore exploit real datasets for
their simulations. These real datasets can be processed in
advance because they contain information on mobility,
contact trace, and social interaction graphs [23].
3 Simplified MSN model
This system obeys the rules of typical message forwarding,
whereby each node forwards a message to the destination
node. We assume the network is represented by the graph
G = hV, Ei, where the vertex set V consists of all nodes,
and edge set E consists of the social links between nodes.
Each node in MSNs has a unique identifier and is denoted
Wireless Netw (2015) 21:1699–1711 1701
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by Ni, i = 1, 2, …, m, where m is the number of nodes in
the network. Each node Ni keeps track of a set Ci of nodes
that Ni has encountered. Each node Ni belongs to a single
community, called its home community [2] so that it has a
label [27] that indicates its home community, denoted by
Hi, where Hi is one of {1, 2, …, r} and r is the number of
communities.
Each node moves freely from its own home community
to other communities and is aware of its own speed and
current location. Each node periodically assesses its loca-
tion. To determine the speed and location, each node is
assumed to have positioning system equipment. For sim-
plicity, we do not consider resources such as buffer size,
bandwidth, or power. Although such measurements would
result in additional computational costs, we do not consider
such computational costs in this paper because the com-
putation can be performed with simple equations. More-
over, the focus of this paper is not on computational cost
reduction. The following table summarizes key notations
used in describing HCS; some notations are introduced in
the following sections (Table 1).
4 Proposed scheme
4.1 Information exchange in the warm-up period
The greater is the number of common associates between a
pair of persons, the more likely they are to be friends with
each other, and the more frequently they are likely to meet
each other. Such a social concept is naturally suited for the
‘‘common neighbor similarity’’ in the network [32] if we
interpret a neighbor of a node as a commonly encountered
node. A pair of nodes becomes more ‘‘similar’’ to each
other as the number of common neighbor nodes increases.
During the warm-up period, nodes exchange and update
their information, including their own similarity scores.
The purpose of updating the similarity scores is to
accumulate the node contact information so that each node
is able to obtain the estimated global information on the
entire network. The similarity score between a pair of
nodes, Ni and Nj, i = j, can be computed with |Ci \ Cj|
that is the number of nodes encountered by both Ni and Nj.
Each node Ni maintains the information vector of
(Ni, Hi, Ci, Si), where Si is the similarity score lists. During
the warm-up period, whenever Ni meets other nodes, Ni
exchanges its information vector with each of the
encountered nodes and updates both Ci and Si. Figure 1
shows a data structure of Si, where |Ci \ Cj| is the number
of nodes encountered by both Ni and Nj. Observe that Sicontains not only the contact information of Ni itself but
also the contact information of other nodes; for example, in
Fig. 1, Si also contains the contact information of Nj and
Nk. If any of Nj and Nk have met other nodes before
encountering Ni, there would be more entries in Si for their
contact information.
We now explain how two nodes update their own sim-
ilarity scores after exchanging the information vectors each
other. Assume that two nodes, say N1 and N2, are
approaching each other at time t1 with their similarity score
lists S1 and S2 as shown in Fig. 2(a). Assume that N1 had
encountered N3 and N4 met N3 for the first time, respec-
tively, and later N1 has encountered N4. Therefore,
|C1 \ C4| = 1, indicating that both N1 and N4 commonly
encountered N3 before they meet each other. All such
encounters have been recorded at S1. Similarly, N2 is
assumed to have its contact information as in S2.
At time t2, N1 and N2 exchange their information vectors
and update their similarity score lists as shown in Fig. 2(b).
In the first and second entries of S1 and S2, (N2,1) and
(N1,1) have been created with score |C1 \ C2| = 1, indi-
cating that both N1 and N2 had encountered N3 earlier than
t2. In Fig. 2(b), S1 has the shaded entries that came from
S2 at time t1, while S2 has the unshaded entries that came
from S1 at time t1. However, when S1 and S2 have a
common entry such that one entry has larger similarity
Table 1 Key notations used in HCS
Notation Definition
m The number of nodes in the network area
r The number of communities in the network area
Ni Node i, where i = 1, 2, …, m
Hi Home community of Ni
Ci A set of nodes that Ni has encountered
Si The similarity score lists of Ni
Li[j] Nj’s level in the hierarchy of clusters constructed by Ni
Ki[j] A set of nodes in the cluster at level j in the hierarchy of
clusters constructed by Ni
d A threshold of level to quit the clusteringFig. 1 Data structure of Si
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score than the other, the entry with smaller score is updated
with the larger one. Notice that S1 and S2 are equivalent
after updating.
By the end of the warm-up period, each node accumu-
lates the contact history with similarity scores to extract the
global information on the network. After the warm-up
period, each node builds hierarchical clusters based on the
obtained similarity score lists.
4.2 Hierarchical clustering
Hierarchical clustering is widely used for finding commu-
nity structures in a network. HCS adopts bottom-up hier-
archical clustering based on common neighbor similarity.
In bottom-up hierarchical clustering, also known as
agglomerative clustering, each node serves as a single
cluster in the beginning of the warm-up period, and clusters
are iteratively merged until a certain condition is satisfied.
In HCS, each node hierarchically builds based on its sim-
ilarity score lists at the end of the warm-up period. We
modify bottom-up hierarchical clustering in two ways.
First, clustering ceases at some predetermined level of the
hierarchy. Second, more than two clusters can be merged in
a larger cluster.
Each node performs clustering greedily in terms of
similarity scores; that is, clusters containing the nodes with
the highest similarity score are first merged into a cluster.
Note that, once the nodes with the highest score belong to
the same cluster, their similarity scores will no longer be
considered for future clustering steps. Figure 3 illustrates
how a node, say N1, performs clustering with a threshold
d = 4.
In Fig. 3, {N4} and {N5} are merged first at level 1,
because |C4 \ C5|, the similarity score between N4 and N5,
is assumed to be the largest. We store such clustering
information in an 1-dimensional array K1, where Ki[j]
denotes a set of nodes in the cluster at level j in the hier-
archy of clusters constructed by Ni. Hence K1[1] =
{N4} [ {N5} = {N4, N5}. We also store the level number
‘‘1’’ for each of N4 and N5 in an 1-dimensional array L1;
L1[4] = L1[5] = 1. Then, {N4, N5} and {N3} become one
cluster at level 2, assuming that |C3 \ C4| is the next
largest; K1[2] = {N4, N5} [ {N3} = {N3, N4, N5}. We
assign level ‘‘2’’ to N3; L1[3] = 2. In the figure, at level 3 {N3,
N4, N5}, {N1}, and {N2} are merged into a single cluster,
because |C1 \ C3| = |C2 \ C3| are assumed to be the next
largest. Hence, K1[3] = {N3, N4, N5} [ {N1} [ {N2} =
{N1, N2, N3, N4, N5} and L1[1] = L1[2] = 3. Finally, {N6}
and {N7} are merged at level 4 in the above example;
K1[4] = {N6} [ {N7} = {N6, N7} and L1[6] = L1[7] = 4.
The reason why N6 and N7 are merged into a different cluster
from K1[3] is that |C6 \ C7| is assumed to be the next largest
similarity score and neither N6 nor N7 belongs to K1[3] =
{N1, N2, N3, N4, N5}.
In the above hierarchical clustering, the higher the
similarity is, the lower level the nodes are merged at. HCS
stops merging clusters at a certain level d, which is
Fig. 2 Update of the similarity
score lists. (a) Before the
encounter (b) During the
encounter
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determined through extensive simulation. Algorithm 1
formally describes the hierarchical clustering algorithm for
HCS.
4.3 Hierarchical clustering-based forwarding scheme
In this section, we describe the proposed HCS forwarding
scheme. In HCS, each node Ni uses either level-based
forwarding or similarity-based forwarding. In level-based
forwarding, Ni forwards the message to Nj if Li[j]\ Li[i].
Note that, when nodes are clustered at lower levels, they
are more likely to be similar; that is, they have encountered
more common nodes. Hence, in the future, they are
expected to meet ‘‘valuable’’ nodes, including the desti-
nation node. By valuable we mean that a node is more
likely to reach the destination node.
On the other hand, if Ni encounters only Nj such that
Li[j] C Li[i], then Ni stops using the clustering information,
and instead simply uses the similarity scores in its Si. In
other words, Ni forwards the message to a node with the
highest similarity score with respect to the destination
node. Such forwarding is called similarity-based forward-
ing. Hence, similarity-based forwarding serves to com-
pensate for cases when the level of the destination node is
relatively higher.
Themessage forwarding process in level-based forwarding
mimics the way people forward messages in virtual clusters
with similar movement patterns. Within such a cluster, it is
highly likely that some people could travel around the desti-
nation of a message. Note that HCS does not compute the
delivery probabilities for forwarding messages. Instead, it
exploits shortcuts on the path toward the destination nodewith
the help of social similarity and node movement patterns. To
implement this concept, each node in HCS creates its own
hierarchical clusters in which nodes can frequently interact
with each other. Even though each node independently builds
its own hierarchical clusters, we expect that the clustering
results of the nodes are quite similar to each other, because the
nodes continuously exchange their information vectors and
update similarity score lists whenever encountering other
nodes during the warm-up period. Such clustering results can
be viewed as the global network information. In HCS, clus-
tering in each node is not updated during the forwarding pro-
cess, because it causes additional communication overheads
and does not improve the overall performance noticeably.
Figure 4 illustrates how message forwarding is achieved
in HCS. Figure 4(a) shows how level-based forwarding is
performed. Suppose that N2 is the source node and N5 is the
destination node. A solid arrow indicates a possible
Fig. 3 An example of
hierarchical clustering
1704 Wireless Netw (2015) 21:1699–1711
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forwarding, while a dotted arrow denotes that there is no
message forwarding. N2 forwards the message only if the
level of the encountered node is lower than that of N2.
Here, N2 can forward to N3, N4, or directly N5, if they are
within the communication range of N2. If N3 receives the
message from N2, N3 first looks into its level information,
and then forwards the message to the encountered nodes
with lower level than the level of N3. If N4 receives the
message from N2, N4 can send the message to N5 when they
encounter each other.
Figure 4(b) illustrates how similarity-based forwarding
is accomplished. We assume that the source node is N5 and
the destination node is N2. The integer next to each node is
the similarity score with respect to the destination node. In
this case, because the level of the destination node is higher
than that of the source node, HCS exploits the similarity
scores. Therefore, N5 can forward the message to N3, N4, or
N7 whenever they are encountered, because they have
higher similarity scores than N5; that is, |C5 \ C2|\|C4 \ C2|\ |C3 \ C2|. Note that they can check their
similarity scores with the destination N2 in their similarity
score lists without additional communication with N2.
However, N5 never sends the message to N1 nor N6 because
their scores are lower than that of N5. In general, once node
Ni has a message, it can forward the message to a node that
has higher similarity score than that of Ni with respect to
the destination.
At the end of the warm-up period, each node indepen-
dently constructs its own clusters and generates a message
to send. When each node Ni encounters a set of nodes
within its communication range, it executes Algorithm 2.
Let E be the set of (encountered) nodes within the com-
munication range of Ni. Assume that Ns is the source node
and Nd is the destination node. In Line 2, when Nd is in E,
Fig. 4 Examples of forwarding process. (a) Level-based forwarding (b) Similarity-based forwarding
Wireless Netw (2015) 21:1699–1711 1705
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Ni simply forwards the message, and the forwarding pro-
cess is done. Otherwise, Ni sends the message with level-
based forwarding if Li[d]\ Li[s]; that is, if the level of the
destination node is lower than that of the source node.
Therefore, Ni attempts to find node Nj, whose level is lower
than Li[i]. If Li[d] C Li[s], Ni sends the message with
similarity-based forwarding. Therefore, it tries to finds
node Nj whose similarity score is higher than that of Ni
with respect to Nd. In either case, Ni sends the message to
Nj if found in E in Line 9. If Ni cannot find Nj in Lines 6 or
8, Ni does not transmit the message at this time.
5 Experimental results
5.1 Simulation environments
In the experiment, we use the network simulator NS-2
v2.35 [33] for the simulations. The network area is set to
450 m 9 450 m, and the community size is 150 m 9
150 m. The number of grids is 9. The number of com-
munities among the grids is 4, and each community has 10
or more nodes. The number of nodes is set to 40, 50, 60 or
70. The communication range varies from 10 to 50 m. The
movement of a node follows HCMM [15], which is a fre-
quently used movement pattern in MSN simulations.
Because the node contact information is not available in
HCMM, a warm-up period is required to obtain estimated
social (global) information in order to utilize the social-
aware forwarding schemes. The velocity of a node ranges
from 1 to 9 m/s, which is appropriate for either people or
vehicles. In our simulator, a mobile node issues one mes-
sage to a random destination right after the warm-up per-
iod. After a source node transmits its message to other
nodes, it still keeps the message. The warm-up period is set
to 1,000 s for collecting enough node contact information.
Both SimBet and PRoPHET also need the warm-up period
to obtain the information required. In particular, the cluster
level varies from 1 to 10 for constructing hierarchical
clusters based on our extensive experiments. The total
simulation time is 8,000 s. We run each scheme 20 times
and determine the average results. Table 2 summarizes the
parameters used in our simulation. All simulation envi-
ronments are as in [23].
We evaluated the proposed scheme with the following
performance metrics:
1. Delivery ratio: Ratio of the number of delivered
messages to the total number of messages issued.
2. Network traffic: Total number of messages sent and
received.
3. Delay: Time required for a message to travel from the
source to destination nodes.
We do not consider the network traffic during the warm-
up period, since most schemes in MSNs ignore network
traffic [24, 29]. We found that the amount of traffic in the
warm-up period is not big enough to affect the total amount
of traffic. For 8,000 s, we simulate and compare the pro-
posed scheme HCS with non-clustering schemes such as
Epidemic, Wait, SimBet, PRoPHET, and common neighbor
similarity schemes. During this period, all the schemes
except Wait achieve 1.0 delivery ratio. Epidemic and Wait,
which are typical social-oblivious schemes, Epidemic has
the highest network traffic and the lowest transmission
delay, while Wait shows the highest transmission delay and
the lowest network traffic. In the rest of the schemes,
SimBet, which is the centrality/similarity-based scheme,
uses the betweenness centrality. PRoPHET, which is the
predictability-based scheme, takes advantages of contact
probability. During the warm-up period, SimBet collects
common neighbor similarities and betweenness centralities
of nodes, obtains the SimBet utility values by combining
both metrics. PRoPHET calculates the delivery predict-
ability P(a, b) during the warm-up period. In both schemes,
each node forwards a message to another node with higher
SimBet utility or higher delivery predictability for the
destination, respectively. Common neighbor similarity
scheme uses only common neighbor similarity that HCS
uses for clustering. Hence, we compare these schemes
against HCS.
5.2 Simulation results
5.2.1 Effect of cluster level
We examine the performance of HCS at different cluster
levels when the communication range is 10 m. Figure 5
shows network traffic and transmission delay of HCS with
various cluster levels. The cluster level varies from 1 to 10.
As shown in Fig. 5(a), when the level is low, HCS shows
higher network traffic. However, as the level increases,
Table 2 Simulation parameters
Parameter Value (default)
Network area 450 9 450 m2
Community size 150 9 150 m2
Number of grids 9
Number of communities 4
Number of nodes 40, 50, 60, 70, (40)
Communication range 10, 20, 30, 40, 50, (10) m
Velocity of nodes 1 * 9 m/s
Warm-up period 1,000 s
Cluster level 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, (5)
Simulation time 8,000 s
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HCS increasingly reduces network traffic. Such a phe-
nomenon arises because HCS utilizes clustering informa-
tion very well at high clustering levels.
On the other hand, as shown in Fig. 5(b), as the level
becomes higher, HCS shows longer transmission delay
because it requires longer time to find the nodes with lower
levels. However, because HCS appropriately uses both
level-based and similarity-based forwarding, its traffic
amount is still smaller than those of the non-clustering
schemes. It is evident that a proper value of d in hierar-
chical clustering for this environment is 5, because we get
relatively lower traffic as well as acceptable delay. How-
ever, d should be appropriately chosen according to the
given environment.
5.2.2 Delivery ratios and network traffic by time
Figure 6 shows the delivery ratios and network traffic as
the simulation time reaches 8,000 s. The results are
shown after 1,000 s in order not to include the results of
the warm-up period. The number of nodes is set to 40. In
Fig. 6(a), the non-clustering schemes, except Wait,
achieve the maximum delivery ratio that is faster than that
of HCS, because they allow multiple copies of messages.
Because Wait does not distribute a message but instead
waits for the message to encounter its destination, it
requires much longer time to reach the 1.0 delivery ratio.
However, HCS uses an appropriate number of copies of a
message, thereby resulting in a somewhat slower time in
Fig. 5 Effect of cluster level.(a) Network traffic (b) Transmission delay
Fig. 6 Delivery ratios and network traffic by time. (a) Delivery ratio (b) Network traffic
Wireless Netw (2015) 21:1699–1711 1707
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reaching the 1.0 delivery ratio compared with other
schemes.
For network traffic shown in Fig. 6(b), each scheme
experiences higher network traffic as time passes. HCS
shows much lower traffic than the non-clustering schemes
except Wait. Note that HCS distributes the messages to the
nodes with lower levels or higher similarity scores.
5.2.3 Effect of number of nodes
We evaluate the performance of the schemes as the number
of nodes increases. Figure 7(a) shows the average delivery
ratio with 40–70 nodes. Figure 6(a) compares the delivery
ratios of the schemes; the non-clustering schemes, except
Wait, reach a 1.0 delivery ratio faster than HCS, because
HCS maintains only an appropriate number of copies of a
message. Figure 7(b) shows the network traffic when the
number of nodes increases. As expected, the amount of
traffic in the non-clustering schemes explosively increase
as the number of nodes increases. In particular, the dif-
ference between HCS and each of the non-clustering
schemes is large. HCS shows the lowest traffic except for
Wait. In HCS, as the number of nodes increases, so does the
number of ‘‘valuable’’ nodes that play a critical role in
message delivery. Figure 7(c) shows transmission delay.
The delays of most schemes decrease as the number of
nodes increases. However, Wait demonstrates similar pat-
terns regardless of the number of nodes. The delay in HCS
is higher than those of the non-clustering schemes because
of their multiple message copies. However, HCS well
maintains the balance between network traffic and trans-
mission delay.
Fig. 7 Effect of the number of nodes. (a) Delivery ratio. (b) Network traffic. (c) Transmission delay
1708 Wireless Netw (2015) 21:1699–1711
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5.2.4 Effect of communication range
Finally, we compare the effect of the communication range
of the nodes for each scheme. The number of nodes is set to
40. Figure 8(a) shows the average delivery ratios with
10–50 m of the communication range. In Figs. 6(a) and
7(a), the delivery ratios of HCS increase little bit slower
than those of other schemes except Wait because HCS
maintains a smaller number of copies even if the commu-
nication range increases. As shown in Fig. 8(b), as the
communication range becomes wider, all the schemes
except Wait experience increased network traffic. PRo-
PHET shows a moderate increase because only the nodes
with higher contact probability to the destination partici-
pated in the delivery of the messages. However, HCS
shows the lowest traffic except Wait when the
communication range is 10 because the nodes that are
involved in message delivery are properly chosen. Such a
result confirms that HCS can be well implemented in a
sparse network. Figure 8(c) shows the transmission delays
of the schemes. It is natural that most schemes incur shorter
delays as the communication range increases. Epidemic
shows the shortest delay. SimBet and Common exhibit
similar results. However, PRoPHET suffers from a longer
delay compared with HCS, except when the communica-
tion range is 10. The transmission delays of HCS and Wait
significantly decrease as the range increases. In HCS, the
percentages of level-based forwarding and similarity-based
forwarding during the entire forwarding processes are 91.3
and 8.7 %, respectively. It is evident that hierarchical
clustering on social similarity in HCS is well constructed;
therefore, relay nodes are properly chosen with the
Fig. 8 Effect of communication range. (a) Delivery ratio, (b) Network traffic, (c) Transmission delay
Wireless Netw (2015) 21:1699–1711 1709
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clustering information. Hence, the transmission delay of
HCS becomes significantly lower as the communication
range becomes larger. The results in both figures demon-
strate that HCS maintains a well-balanced performance
between network traffic and transmission delay.
6 Conclusion
In MSNs, non-clustering schemes suffer from higher net-
work traffic. To reduce network traffic, we proposed a for-
warding schemeusing hierarchical clustering based on social
information. The proposed scheme effectively distributes the
messages to the nodes via shortcuts by exploiting clustering
results. Experimental results demonstrated that the scheme
reduced network traffic compared to non-clustering
schemes, while delay was acceptable compared to the Wait
scheme. In future work, we plan to study more enhanced
dynamic forwarding schemes with varying resources in
consideration of continuous information updating in MSNs.
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).
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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.
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.
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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