A social overlay-based forwarding scheme for mobile socialnetworks

Sun-Kyum Kim1• JunYeop Lee1 • Sung-Bong Yang1

Published online: 18 December 2015

� Springer Science+Business Media New York 2015

Abstract In mobile social networks, forwarding is a

challenging problem since there may be no end-to-end

paths. Existing schemes using overlays are not applicable

to pure mobile social networks since they use environments

different from the mobile social networks. To resolve this

problem, we propose a novel forwarding scheme that

considers the common neighbor similarity and contact

probability. In the proposed scheme, the more nodes have

in common, the more likely they are to be friends, and

higher contact probabilities between nodes increase the

chances they will arrive at the destination. The proposed

scheme constructs an overlay using two layers with the

common neighbor similarity and the contact probability

and forwards messages to the destinations through the

overlay. Experimental results show that the proposed

scheme outperforms most known forwarding schemes in

terms of balancing the network traffic and transmission

delay.

Keywords Mobile social network � Overlay �Forwarding � Common neighbor similarity � Contactprobability

1 Introduction

As the advances of wireless communication technologies

and the popularity of mobile devices, this emerging para-

digm is applicable to various types of mobile sensing such

as mobile cloud computing [1–3], mobile crowdsourcing

[4], and wireless communications such as wireless sensor

networks [5], multi-hop wireless networks [6], cognitive

radio networks [7], green communications [8, 9], vehicular

networks [10], heterogeneous networks [11], software-de-

fined networks [12], mobile ad hoc networks [13, 14],

delay tolerant networks [15, 16], social networks [17], and

mobile social networks [18–20]. Among them, mobile

social networks (MSNs; also known as opportunistic net-

works [21, 22] or pocket switched networks [23]) have

attracted a lot of attention for wireless communication

paradigms. MSNs are advanced networks of mobile ad hoc

networks (MANETs) and delay-tolerant networks (DTNs)

with social properties of human behaviors [24]. Due to the

short transmission range and node mobility, such networks

suffer from intermittent connectivity and frequent discon-

nections in sparse environments. For this reason, MSN

applications are used for the Sami Network Connectivity

Project [25], Zebranet [26], the Shared Wireless Info-Sta-

tion (SWIM) [27], the N4C project [28] including web

caching, email/not-so-instant messengers, hiker’s applica-

tions, meteorological data transfer applications, animal

tracking applications, and TIRE applications [29], provid-

ing the connection between technology and the users.

Since there may be no stable end-to-end paths in MSNs,

the forwarding or routing schemes [30] for ad-hoc net-

works are so inaccessible that forwarding or routing has

become a challenging problem in MSNs. The forwarding

mechanism operates hop by hop in a store, carry, and

forward manner, where the relay nodes store and carry

& Sung-Bong Yang

sbyang@cs.yonsei.ac.kr

Sun-Kyum Kim

skyum@yonsei.ac.kr

JunYeop Lee

kotzyi@yonsei.ac.kr

1 Department of Computer Science, Yonsei University, Seoul,

Korea

123

Wireless Netw (2016) 22:2439–2451

DOI 10.1007/s11276-015-1162-2

messages and then contact the other nodes for opportunities

to forward the messages until a message is delivered to the

destination. Hence, the key issue in forwarding or routing

is properly selecting relay nodes to which the message (or a

copy of the message for a multi-copy scheme) should be

forwarded [22].

An overlay is an effective way to support new applica-

tions, as well as protocols in the underlying network [31]. It

can provide flexible, scalable, and reliable services [32].

The connections between the overlay nodes are provided

by overlay links, each of which is usually composed of one

or more virtual or physical links. Several studies [50, 51]

exploit overlays with social information, However, it is

difficult to know more precise contact information without

global information for grouping. Hence they are hardly

applicable to forwarding for pure MSNs.

To resolve these problems, we propose a novel for-

warding scheme, called the social overlay-based forward-

ing scheme (SOF), for pure MSNs. SOF constructs an

overlay between two asynchronous social layers consisting

of the common neighbor similarity and contact probability

with social information obtained from the home-cell

community-based mobility model (HCMM) [33]. Basi-

cally, the more nodes have in common, the more likely

they are to be friends and the higher contact probability the

nodes have the more likely they are to meet the destination.

In the SOF model, using the common neighbor similarity

and contact probability characteristics, nodes construct

clusters according to the common neighbor similarity of

encountered nodes in the social similarity-based layer, and

create a social graph with the refined contact probability in

the contact probability-based layer. The nodes then forward

the messages according to contacts in an overlay consisting

of both metrics. Consequently, SOF reduces the network

traffic and hops count while maintaining an accept-

able transmission delay, considering the overlay in MSNs.

We employ extensive simulations to compare the proposed

scheme, SOF, with most of the known forwarding schemes.

The main contributions of this paper can be summarized

as follows.

• We propose a novel forwarding scheme that constructs

an overlay between two asynchronous social layers

using the common neighbor similarity and contact

probability.

• We refine the contact probability by considering the

frequency, longevity and regularity of the contact of nodes.

• We conduct simulations for experiments with the

network simulator NS-2 and analyze the results with

Epidemic, Wait, SimBet, ProPhet, and the pure com-

mon neighbor similarity scheme.

The rest of this paper is organized as follows. In Sect. 2,

we first discuss the related work. Next, we describe system

modelling in Sect. 3. After introducing the proposed

scheme in Sect. 4, the simulation environment and results

are described in Sect. 5. Finally, the conclusion and future

work are provided in Sect. 6.

2 Related work

Forwarding or routing issues are typical challenges in

various types of networks [5–20]. Furthermore, routing

protocols for low power and lossy networks are applicable

to advanced metering infrastructure network [34] which is

the full management and collection system to evaluate data

related to the usage of various utility resources such as

electricity, gas, and water.

In MSNs, the forwarding or routing schemes can be

classified into two categories: zero knowledge schemes and

non-zero knowledge schemes. The zero-knowledge

schemes do not use the social information at all. On the

other hand, the non-zero knowledge schemes take advan-

tage of the information about nodes’ behaviors or social

relations to make decisions for forwarding messages in

MSNs.

In the zero knowledge schemes, Epidemic [35] dis-

tributes the message to each of them and creates replicas of

the message, when each node meets other nodes. In Spray-

and-Wait [36], a node ‘‘sprays a number of copies into’’

some nodes in the network and then ‘‘waits’’ until one of

these nodes meets the destination. CodePipe [37, 38],

which is the reliable multicast with pipelined network

coding, when a destination has received enough coded

packets for decoding, it could proactively serve packet

delivery in cooperation with the original source for other

destinations. Backpressure with adaptive redundancy

(BWAR) [39] creates copies of packets in a new duplicate

buffer upon an encounter to reduce delay under low load

conditions, when the sender’s queue occupancy is low.

Homing spread [40] identifies suitable relay nodes by the

community structure. Since this scheme spreads messages

to the detected communities via the relay nodes, it creates

extra delivery overhead for mobile nodes. HFS [41] floods

messages only in hotspots, which is a space where nodes

often meet each other, but the size of the hotspots is

limited.

Various non-zero knowledge schemes have been pro-

posed for MSNs [42–51]. The non-zero knowledge

schemes can be further classified into four schemes: cen-

trality/similarity-based schemes, social context-based

schemes, probability-based schemes, and overlay-based

schemes. The centrality/similarity-based schemes are

SimBet [42], Bubble rap [43], and SANE [44]. In SimBet,

when a node encounters other nodes, it gives a message to

the node that has a higher betweenness centrality and

2440 Wireless Netw (2016) 22:2439–2451

123

similarity utility value until the destination node is met.

Bubble rap takes advantage of both the 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

the user interests and their similarity.

The social context-based schemes include Label [45]

and HiBop [46]. In Label, each node is assumed to have the

label information of the other nodes in its social commu-

nity, similar to the name tags used at conferences. Based on

the labels, the routing scheme selects some nodes to

directly forward messages to the destination or to the next-

hop node which belongs to the same label as that of the

destination. HiBop requires personal information such as

the residence, employer, and hobbies, as well as the system

information.

The probability-based schemes include ProPhet [47],

PeopleRank [48], and MobySpace [49]. ProPhet first esti-

mates a probabilistic metric called the delivery pre-

dictability, P(a, b), which indicates how likely node b is to

receive a message from node a during the warm-up period.

The information in the summary vector of the messages’

information and the delivery predictability vector are used

to decide which messages should be sent to request from

other nodes. PeopleRank adopts the Google’s PageRank

algorithm for forwarding decisions. When two neighboring

nodes in the social graph meet, they exchange their current

PeopleRank values and the number of social graph neigh-

bors they have. MobySpace takes advantage of the

knowledge concerning a node’s interest and mobility

information such as coordinates and locations, but it needs

the global information for routing.

Finally, mGroup [50] and Socio-Aware overlay [51] have

been introduced as the overlay-based schemes. mGroup uses

the selected relay nodes based on multi-level cross-com-

munity social information. It applies a simple group for-

mation method to historical encounters (i.e., social

relationships in the physical world) and/or the social profiles

of mobile users (i.e., social relationships in the social world)

and builds multi-level cross-community social groups.

Socio-Aware Overlay constructs an overlay using the cen-

trality of a community and a backbone for publish/subscribe

communication which is not applicable for pure MSNs.

The non-zero knowledge schemes are very effective at

forwarding messages. However, most non-zero knowledge

schemes require global information for forwarding deci-

sions. Accordingly, these schemes exploit real datasets for

their simulations. These real datasets can be preprocessed

in advance since they contain information on mobility,

contact trace, and social interaction graphs.

3 Simplified MSN model

Our system is based on the system in MSNs. This system

obeys the rules for typical message forwarding, where each

node delivers a message to the destination node. We model

an interaction between mobile nodes in MSNs as an

undirected graph, G = (N, L), that consists of a finite set of

nodes, N, and a finite set of communication links, L, where

each element is a tuple. We assume that each node gen-

erates a message for its destinations and has local memory

space for keeping the messages. There are m nodes,

N1, N2,…, Nm, and no central server. The set of encountered

nodes of node Ni is denoted by Ei. We use the HCMM as the

node mobility model in this paper. It is the most-used

mobility model, because it models spatial and temporal

properties of human mobility in social information. Each

node moves freely from its own home-community to other

communities up to the maximum speed and is aware of its

own speed (or velocity) and location. Each node periodically

measures its location. To determine the speed and location,

each node is assumed to have positioning system equipment.

For more details, we make the following assumptions.

• Each node in MSNs has a unique identifier, a node ID.

The nodes in the MSN are denoted by N = {N1, N2,

N3, … Nm}, where m is the number of nodes in the

network. The destination of Ni is denoted by Di.

• Each node belongs to one community. Each community

has a unique identifier, a community ID. All commu-

nities are denoted by H = {H1, H2, …, Hr}, where r is

the number of communities. In these communities, the

start position of a node located in the community is

called the home-community [45].

• Nodes are unaware of the global information of the

network because there is no central server in the

network.

• For simplicity, we do not consider resources such as the

bandwidth, message size, memory space, and power.

Although such measurements would result in additional

computational costs, we do not address reducing the

computational cost as this is not our main concern. The

following Table 1 summarizes key notations used in

describing SOF; some notations are introduced in the fol-

lowing sections.

4 Proposed scheme

4.1 Overview

In this section, we provide an overview of the proposed

scheme, SOF. Unlike forwarding in MANETs, each node

cannot keep a routing table for forwarding because there

Wireless Netw (2016) 22:2439–2451 2441

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may be no stable end-to-end paths. Thus, it is very

important to select a proper relay node for delivering the

message to the destination within an acceptable delay.

Each node takes advantage of the common neighbor sim-

ilarity and the contact probability with respect to the des-

tination. When each node contacts other node, each node

exchanges and updates the common neighbor similarity

and contact probability for each of the two asynchronous

social layers during the warm-up period. Then, each node

builds clusters by the obtained common neighbor similarity

in ascending order of the similarity scores with respect to

its destination. Afterwards, each node constructs an overlay

by linking the last cluster in the common neighbor simi-

larity-based layer, where the destination is, to nodes in the

contact probability-based layer. After each node generates

a message, it starts forwarding the message. Since the

network has no infrastructure for global information and it

may be difficult for a node to know the metrics of all other

nodes that may change dynamically, each node continu-

ously updates and exchanges the constructed overlay, the

common neighbor similarity, and the contact probability

information with the encountered nodes. The current node

properly forwards the message until the node belonging to

the cluster where the destination belongs is met and then

the received node sends the message to other nodes with

higher contact probabilities within the overlay until the

message is delivered to the destination.

4.2 Calculation of common neighbor similarity

Basically, the more people have in common, the more

likely they are to be friends. This concept is represented as

the ‘‘common neighbor similarity’’ in the network. Two

nodes are more similar if they have a number of common

neighbor nodes and they frequently meet one another. We

define the common neighbor similarity score of Ni and Nj,

S(i, j), as follows.

S i; jð Þ ¼ Ei \ Ej

��

�� ð1Þ

Equation (1) indicates the number of neighbor nodes

that Ni and Nj have in common. Note that Ni’s total simi-

larity scores Si consists of all common neighbor nodes

between Ni and Nj that Ni has encountered, including all

common neighbor nodes between Nj and other nodes that

Nj has encountered. Since each node Ni does not know

global information, Ni estimates all common neighbor

similarity scores by exchanging with Nj and adding to Siwhenever Ni meets Nj. Newman [52] computed and veri-

fied a correlation between the number of common neigh-

bors of Ni and Nj at time t and the probability that they will

collaborate in the future. SOF uses the common neighbor

similarity for building clusters.

4.3 Calculation of contact probability

In general, a node with a higher contact probability to the

destination is highly likely to forward the message to the

destination. The contact duration and the inter-contact time

present how frequently and how long each node contacts

other nodes, respectively [11]. We consider these metrics

to refine the following equation, assuming an exponential

inter-contact time.

P i; jð Þ ¼ 1� exp �U i; jð ÞI i; jð Þ

� �

; ð2Þ

where P(i, j) indicates the contact probability of Ni and Nj,

U(i, j) is the last contact duration of Ni and Nj, and I(i, j) is

the last inter-contact time between Ni and Nj. In Eq. (2), the

contact duration, U(i, j) is divided by the inter-contact time,

I(i, j). Note that the longer the contact duration is, the

higher P(i, j) is, while the shorter the inter-contact time is,

the higher is P i; jð Þ. Meanwhile, if it has been a long time

since Ni and Nj have encountered each other, their contact

probability needs to be modified with the aging factor c in

terms of time t as

P i; jð Þ ¼ P i; jð Þold�ct; ð3Þ

where t is the time that has elapsed since their last

encounter. In addition, the contact probability needs to

incorporate the transitivity property; that is, when Ni meets

Nj after Nj encounters Nk, Ni updates P(i, k) according to

P(i, j) and P(i, k) as follows.

P i; kð Þ ¼ 1� 1� P i; jð Þð Þ � 1� P j; kð Þð Þ; ð4Þ

Table 1 Key notations used in SOF

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

Ei A set of nodes that Ni has encountered

Si The similarity score lists of Ni

Pi The contact probability information of Ni

Ii The inter-contact time information of Ni

Ui The contact duration information of Ni

Ri The similarity score range for clustering of Ni

Fi The cluster levels of nodes encountered by Ni.

d A number of clusters for clustering

2442 Wireless Netw (2016) 22:2439–2451

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Notice that Ni does not update P(i, k) if Ni had

encountered Nk previously, since P(i, k) was already

updated at that time. Finally, SOF makes the layer based on

the contact probabilities of the nodes according to these

equations.

4.4 Information exchange

Each node Ni maintains the information vectors,\ IDi, Ei,

Si, Pi, Ii, Ui[ , to update the similarity score and the

contact probability, where IDi is the ID of Ni, Ei is the set

of encountered nodes, Si is the similarity scores of Ni,

denoted by Si = {S(i, 1), S(i, 2), …, S(i, m), Si (2, 1), …S(2, n), S(3, 1), … S(3, n), … S(m, n)}, and Pi = {P(i, 1),

P(i, 2),…, P(i, m), P(2, i), P(3, i),… P(m, i)}, Ii = {I(i, 1),

I(i, 2), I(i, 3), … I(i, m)}, and Ui = {U(i, 1), U(i, 2), U(i,

3), … U(i, m)}. Whenever Ni meets Nj, Ni evaluates Ei, Si,

Ii, Ui, exchanges and updates its information vectors with

Nj for clustering during the warm-up period. At this time,

Ni also computes P(j, i) at the same time to reduce extra

communication between them. After the warm-up period

and message generation, each node Ni constructs an over-

lay for forwarding. Since information vectors are changed

dynamically, each node Ni exchange the information vec-

tors with the destination and its constructed overlay

information in the forwarding process.

4.5 Construction of overlay

The more nodes have in common, the more likely they are

to be friends, and when nodes have higher contact proba-

bilities, they are more likely to meet the destination. These

are the characteristics of the common neighbor similarity

and contact probability. SOF constructs an overlay con-

sisting of two asynchronous social layers with these metrics.

Note that the two social layers are different from each

other. Both types of social layers are complementary since

these layers consist of a lot of social information for con-

tact such as the number of common neighbor nodes, the

total contact count, the contact duration and the inter-

contact time. Mei et al. [44] found that nodes with similar

social information tend to have contacts more often in

MSNs. Therefore, it is also possible to construct an overlay

by considering the social information or relationships

regarding the common neighbor similarity and contact

probability among nodes to improve the performance of

MSN forwarding.

Before constructing the overlay, each node, Ni, builds

two layers, the common neighbor similarity-based layer

and the contact probability-based layer, using the social

information it obtained. When Ni meets another node, Ni

exchanges and updates the social similarity and the contact

probability by Eqs. (1), (2), and (3). Then, Ni builds clus-

ters in the common neighbor similarity-based layer and

creates a social graph in the contact probability-based

layer. Ni sorts the obtained similarities to the destination in

ascending order and extracts the last score, which is the

highest similarity score to the destination in the sorted

similarities. Ni divides the sorted nodes into clusters of

equal size according to the range of the similarity scores

and the number of clusters. The highest similarity score

that a node has in each cluster becomes the cluster’s sim-

ilarity (i.e., delegated similarity).

Afterwards, Ni constructs an overlay by combining the

similarity-based layer and the contact probability-based

layer. The construction of the overlay is implemented only

in the last cluster in the similarity-based layer that the

destination belongs to. Ni filters the nodes with higher

contact probabilities by virtually linking the nodes

belonging to a cluster to one another. Since the network has

no servers for global information, Ni updates its own

overlay information including the common neighbor sim-

ilarities and contact probabilities even though the warm-up

period has ended.

Figure 1 shows the construction of an overlay of Ni. We

assume that Ni and Di are the source and the destination,

respectively. The similarity score of Ni to Di is six. Ni

builds the social similarity-based layer. Since the maxi-

mum similarity to Di is 20 and the number of clusters is set

to four, the four clusters are evenly built at intervals of five

similarity score units. Ni belongs to the second cluster. The

delegated similarity of each cluster is 5, 10, 15, and 20.

Then, only the nodes in the last cluster, which has the

destination, link to one another according to the contact

probabilities.

Algorithm 1 describes how to construct an overlay. dindicates the number of clusters as a parameter. maxValueiand maxLeveli denote the highest similarity score of Ni for

clustering and the maximum cluster level, respectively. Fi

is the cluster levels of nodes encountered by Ni. Ri is the

score range for clustering of Ni, which is calculated by

maxValuei, maxValuei, and the number of encountered

nodes. m0 denotes the total number of encountered nodes of

Ni. After sorting Si in ascending order according to the

similarity to the destination Di, Ni extracts the last (i.e., the

maximum) similarity score and keeps it in maxValuei. Ni

then divides the encountered nodes into clusters according

to Ri and stores cluster levels (i.e., the social similarity-

based layer information) in Fi in Lines 7–14. Finally, Ni

links the nodes to one another.

Wireless Netw (2016) 22:2439–2451 2443

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The selection of the number of clusters, d, could affect

the performance significantly. If d is large, each cluster has

higher network traffic in terms of communications since

each cluster has a large number of nodes. On the other

hand, if d is small, each cluster has a lower transmission

delay because each cluster has a small number of nodes.

Thus, we perform extensive experiments to find a proper d(explained in Sect. 5.3.1).

4.6 Message forwarding

In this section, we describe how each node forwards a

message in SOF. Each node, Ni, uses either the cluster-

based forwarding or the probabilistic forwarding. In the

cluster-based forwarding, Ni forwards the message to Nj

belonging to a cluster with a higher delegated similarity.

Note that when nodes are clustered at a higher similarity

score, they are more likely to be similar to the destination;

that is, they are more likely to meet the destination fre-

quently. On the other hand, when Ni encounters only Nj

belonging to a cluster with a lower delegated similarity, Ni

does not forward the message to Nj.

When a message arrives at a node belonging to the

cluster with the highest delegated similarity (where the

destination is), the received node, Ni, forwards the message

to an Nj with a higher contact probability until the message

is delivered to the destination. Such forwarding is called

probabilistic forwarding. Since Ni uses the clustering-based

forwarding inter-clusters and probabilistic forwarding in

the last cluster, the messages can be delivered reliably and

the network traffic can be reduced while maintaining an

acceptable delay.

Fig. 1 Construction of an

overlay

Algorithm 1. Pseudo code for constructing the overlay cluster01: ConstructingOverlay(δ){02: Sort Si in ascending order according to the similarity to the destination Di;03: maxValuei← the last (highest) value in the sorted Si;04: maxLeveli ← δ;05: for n ← 1 to maxLeveli06: Ri[n] ← (maxValuei / maxLeveli) * (n+1);07: for n ← 1 to 08: if Ri[0] > S(i, n) { // the lowest delegated similarity09: Fi[n] ← 1;10: for k ← 1 to maxLeveli – 1 11: if Ri[k] = S(i, n) && Ri[k+1] > S(i, n) { // the mid-value delegated similarity12: Fi[n] ← k+1;13: if S[maxLeveli -1] < S(i, n) && maxValue > S(i, n) // the highest delegated similarity14: Fi[n] ← maxLeveli;15: Link node n to one another belonging to Fi[n] = maxLevel;16: }

2444 Wireless Netw (2016) 22:2439–2451

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Figure 2 illustrates how message forwarding is done

based on Fig. 1 in SOF. Each cluster has a delegated

similarity, DS, such as 5, 10, 15, and 20. The source node,

Ni, and the destination node, Di, each belongs to the clus-

ters with a DS of 10 and a DS of 20. The large circles and

the small circles indicate clusters and nodes, respectively.

Using cluster-based forwarding, Ni forwards the message to

nodes belonging to the clusters with a DS of 15 or 20 and

does not forward to nodes belonging to the cluster with a

DS of 5 since Ni’s cluster has a higher DS than the cluster

with a DS of 5. When a node belonging to the cluster with a

DS of 20 receives the message, the node forwards to the

other nodes with a higher contact probability that belong to

the same cluster until the message is delivered to the des-

tination using probabilistic forwarding. At this time, each

node forwards the message to the nodes with higher contact

probabilities that belong to the cluster with the highest DS.

The forwarding algorithm in SOF is outlined inAlgorithm

1. Assume that Ni has a message destined for Di. Since all

values in the information vectors are changed dynamically,

Ni updates its information vectors whenever Ni meets Nj

during the forwarding process. Ni exchanges Ni’s informa-

tion vectors including Di and Fi with Nj. Ni then updates the

similarity scores, Si, and the contact probability, P(i, j), with

I(i, j) andU(i, j).Ni also updates each P(i, k) for k = i = j in

Pi using Eq. (3) with Pj. Next, Ni (re-)constructs an overlay

using Si andPi. Finally,Ni compares its cluster levelFi. Since

Ni also exchanges Fi, the forwarding process is implemented

according to the source node’s cluster level. IfNj belongs to a

clusterwith a higher delegated similarity thanNi,Ni forwards

the message to Nj. Otherwise, Ni does not forward the mes-

sage to Nj. However, if they belong to the same cluster with

the highestDS,Ni compares its contact probability toDi. IfNj

has a higher contact probability, Ni sends the message to Nj.

Fig. 2 Message forwarding in

SOF

Algorithm 2. Pseudocode for SOF forwarding 01: // After discovery of an encountered node Nj02: SOF_Forwarding() {03: Exchange_Information (Ni, Di, Fi); // Exchange information vectors of Ni, adding Di and Fi04: Update_SimilarityScore(Si); // Update the similarity score of Ni05: Update_ContactProbability(Pi); // Update the contact probability of Ni

06: ConstructingOverlay(δ); // Update an overlay07: if Di = Nj08: Forward the message to Nj;09: else10: if Fi [Ni] < Fi [Nj] // Cluster-based forwarding11: Forward the message to Nj;12: else13: if Fi [Ni] = Fi [Nj] && Fi [Ni] = maxLeveli // Probabilistic forwarding14: if P(Ni, Di) < P(Nj, Di) 15: Forward the message to Nj;16:}

Wireless Netw (2016) 22:2439–2451 2445

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5 Experimental results

5.1 Simulation environments

Our simulation model follows the model in [41]. We exploit

the network simulator NS-2 v2.35 [53] for the simulations.

The network area and the community size are set to 450 m x

450 m and 150 m x 150 m, respectively. The number of

communities is four among nine grids. Each community has

10 or more nodes. The number of nodes is set to 40, 50, 60,

and 70. The communication range of a node varies from 10

to 50 m. The node movement follows the HCMM, which is

a widely used mobility pattern in MSN simulations. In the

HCMM, we are not allowed to manipulate the input data to

our advantage. The velocity of a node ranges from 1 to 9 m/

s. Since the HCMM does not have social information, we

need the warm-up period to obtain the information to sim-

ulate the non-zero knowledge forwarding schemes; thus, the

total simulation time is 8000 s, including the 1000 s warm-

up period for constructing an overlay. The number of

clusters varies from 1 to 10 to construct the overlay for our

extensive experiments. In our simulator, each node issues

one message after the warm-up period. After transmitting a

message to other nodes, the originating node does not delete

the message. We run each scheme 20 times to determine the

average of the results. Table 2 lists the parameters used in

our simulation.

We evaluate the proposed scheme using the following

three performance metrics:

• Delivery ratio: the ratio of the number of delivered

messages to the total number of messages issued.

• Network traffic: the total number of messages sent and

received.

• Delay: the time needed for a message to travel from the

source to the destination.

• Hop count: the average number of hops required for a

message to travel from the source to the destination.

5.2 Forwarding schemes for simulations

For 8000 s, we simulate and compare the proposed scheme,

SOF, with non-clustering schemes such as Epidemic, Wait,

SimBet, ProPhet, and the common neighbor similarity-

based scheme. During this period, except for Wait, the

schemes achieve a delivery ratio of one.

• Epidemic [35]: The source node spreads messages to

any node it encounters.

• Wait: The source node waits to send a message until it

meets the destination node.

• SimBet [42]: The source node sends a message to a

node with a higher SimBet utility value through

considering the similarity between the source node

and the destination and the betweenness centrality.

• ProPhet [47]: The source node sends a message to a

node with a higher contact probability, p(a,b).

• Common: The source node sends to a node with a

higher social similarity by using Eq. (1).

5.3 Simulation results

5.3.1 Effect of the number of clusters

We examine the performance of SOF in terms of the number of

clusters, d, in the common neighbor similarity-based layer when

the communication range is 10 m.This simulation indicates how

many clusters are appropriate for the performance.We need this

result since there is one overlay and the performance depends on

d. Figure 3(a)–(c) shows the network traffic, the transmission

delay and the hop count of SOF with the number of clusters

increasing from1 to10.As shown inFig. 3(a),whend is smaller,

SOF has low network traffic whereas SOF has higher network

traffic when d increases. Such phenomena arise because as dincreases, SOF hasmany clusters that each node sendsmessages

to.On theother hand, inFig. 3(b) asd increases, the transmission

delay of SOF decreases since SOF has a lot of nodes joining to

forward and the messages to the destination node quickly. In

Fig. 3(c), since thenodes act as bridgenodes, asd increases,SOFhas a larger number of hop counts.

According to Fig. 3(a)–(c), it turns out that as d increases

and each node communicates with many other nodes, the

network traffic of SOF increases, the transmission delay

decreases, and the number of hop counts increases. However,

since SOF appropriately uses both the social cluster-based

forwarding and probabilistic forwarding, its traffic amount is

still smaller than other schemes.Clearly, a suitabled is four forthis environment, since we get relatively lower traffic (about

180,000) as well as acceptable delay and hop counts (delay of

less than 600 s and hop count of about two). However, an

appropriate number of clusters should be chosen according to

a given environment.

Table 2 Parameters in our simulation

Parameter Value (default)

Network area 450 9 450 m2

Community size 15,050 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 1000 s

Number of clusters 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, (5)

Simulation time 8000 s

2446 Wireless Netw (2016) 22:2439–2451

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5.3.2 Effect of the simulation time

We evaluate the performance of all schemes based on the

simulation time. Figure 4(a)–(c) show the simulation

results for various simulation times. The number of nodes

and the communication range are set to 40 and 10 m,

respectively. We set the number of social clusters, d, tofour as a result of the previous simulation in Sect. 5.3.1. In

Fig. 4(a), most schemes achieve the maximum delivery

ratio faster than Wait due to multiple copies of messages

being used. Since Wait does not distribute a message and

instead waits for the message to reach its destination, it

Fig. 3 Effect of the number of clusters, d. a Network traffic, b transmission delay, c hop count

Fig. 4 Effect of the simulation time. a Delivery ratio, b network traffic

Wireless Netw (2016) 22:2439–2451 2447

123

needs a greater amount of time to reach the 1.0 delivery

ratio. Owing to sufficient copies of messages by cluster-

based forwarding and probabilistic forwarding, SOF

achieves 1.0 slightly slower than other schemes. Each

scheme undergoes a high amount of network traffic as the

simulation time increases [Fig. 4(b)]. Wait is better than

SOF because a single copy of the message is maintained.

However, other schemes are worse than SOF, since SOF

only distributes the messages to the nodes belonging to

clusters with higher delegated similarities and only com-

municates with the nodes belonging to the cluster with the

highest delegated similarity score; their contact probabili-

ties consider frequency, longevity and regularity. Thus, as

the simulation time passes the network traffic difference

between SOF, the non-clustering schemes increases while

maintaining comparable delivery ratios.

5.3.3 Effect of the number of nodes

We evaluate the performance of the schemes as the number

of nodes increases. The communication range is set to

10 m. The number of clusters, d, is four. Figure 5(a) shows

the network traffic when the number of nodes increases. As

expected, except for Wait and SOF, the schemes’ traffic

greatly increases. SOF in particular has lower network

traffic than the other schemes. In SOF, nodes only com-

municate with nodes belonging to the clusters with higher

delegated similarity even if the number of nodes increases.

Since nodes forward the messages between clusters (except

for the last cluster), the clusters act like large-sized nodes.

Thus, SOF has lower network traffic. Figure 5(b) shows the

transmission delay. The delay for most schemes decreases

as the number of nodes increases. However, Wait has

similar patterns regardless of the number of nodes. ProPhet

and SOF decrease slightly in terms of the transmission

delay since these schemes need to find the nodes with

higher contact probabilities. However, since the actual

gap between SOF and the other scheme is somewhat large,

it is acceptable. Even though the number of nodes in

the network increases, SOF maintains the balance between

the network traffic and transmission delay well.

Figure 5(c) shows the hop counts of the schemes.Wait does

Fig. 5 Effect of the number of nodes. a Network traffic, b transmission delay, c hop count

2448 Wireless Netw (2016) 22:2439–2451

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nothing until each destination is encountered; hence, it has

a hop count of one. Basically, as the number of nodes

increases, the hop counts of all of the schemes also

increase. However, SOF has a smaller number of hop

counts than the other schemes because it handpicks nodes

belonging to the clusters with higher delegated similarities

to send the messages to.

5.3.4 Effect of the communication range

Lastly, we examine the effect of the nodes’ communication

range on each scheme. The number of nodes and the

number of clusters, d, are set to 40 and 4, respectively. In

Fig. 6(a), as the communication range increases, the net-

work traffic of SOF and the other schemes increases. SOF

traffic is higher thanWait but lower than the other schemes.

Furthermore, the actual gap between SOF and the other

schemes is large since SOF nodes do not distribute the

messages to nodes belonging to lower delegated similari-

ties. Figure 6(b) shows the transmission delay. Most

schemes experience a decreased delay as the number of

nodes increases. The transmission delays of SOF and Wait

decrease significantly. Figure 6(c) shows the hop counts.

As the communication range increases, the hop counts of

all of the schemes basically increase. However, the hop

counts of SOF and ProPhet decrease. This is because in

both schemes only the nodes with higher contact proba-

bility to the destination participate in the delivery of

messages within their communication range. Interestingly,

in ProPhet, when the communication range increases, the

network traffic slowly increases, however, the transmission

range slowly decreases and the hop count decreases

because nodes forward the messages to other nodes with

higher contact probabilities within their communication

range. Since SOF uses both cluster-based forwarding and

probabilistic forwarding, the network traffic of SOF slowly

increases, the transmission delay decreases sharply, and the

hop count also decreases. SOF benefits from the advan-

tages of both kinds of forwarding methods. Consequently,

SOF maintains the network traffic and transmission delay

levels due to the existence of sufficient copies of the

messages in the constructed overlay.

Fig. 6 Effect of the communication range. a Network traffic, b transmission delay, c hop count

Wireless Netw (2016) 22:2439–2451 2449

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6 Conclusion

We proposed an effective forwarding scheme for MSNs,

SOF, which effectively uses an overlay. SOF constructs an

overlay by using social information and takes advantage of

cluster-based forwarding and probabilistic forwarding in

the overlay that is built. In cluster-based forwarding, a

source node belonging to a cluster with a high delegated

similarity forwards a message. A node that receives a

message in the cluster with the highest delegated similarity

only sends the message to another node with a higher

contact probability through probabilistic forwarding. Since

SOF distributes the messages to the nodes in the clusters, it

reduces the network traffic in comparison to the non-

clustering schemes; the transmission delay is acceptable in

comparison to Wait. In future work, we plan to study more

enhanced, dynamic, forwarding schemes with varying

resources 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.

JunYeop Lee 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 Dept.

of Computer Science at the

University of Oklahoma in 1986

and 1992, respectively. He has

been a professor at Yonsei

University since 1994. His

research interests include graph

algorithms, mobile computing,

and social network analysis.

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