A forwarding scheme based on swarm intelligence and percolationcentrality in opportunistic networks

Jiho Park1 • Junyeop Lee1 • Sun-Kyum Kim1• Kiyoung Jang1 • Sung-Bong Yang1

� Springer Science+Business Media New York 2015

Abstract Incorporating social relationship properties into

forwarding schemes in opportunistic networks has become

a more and more important paradigm. Communication

among the nodes in an opportunistic network relies on

intermittent contacts without complete end-to-end paths.

Most of these forwarding schemes take advantage of social

information such as contact information and social rela-

tionship among the nodes in the network. In this paper, we

propose a social information based forwarding scheme in

opportunistic networks through mimicking honey bees’

behaviors in an artificial bee colony. In the proposed

scheme, we adopt the percolation centrality in the social

networks to assign certain nodes as ‘‘influential’’ bees. The

proposed forwarding scheme is aim at balancing the level

between network traffic and transmission delay using the

home-cell community-based mobility model (HCMM).

Experiments were performed on the network simulator NS-

2. The results show that the proposed scheme has out-

standing performance for the level of network traffic and

transmission delay in comparison to other schemes such as

Epidemic, PRoPHET and SimBet when using the HCMM.

Keywords Opportunistic networks � Social information �Artificial bee colony � Percolation centrality � HCMM

1 Introduction

Recent advances in wireless communication technologies

have resulted in the emergence of opportunistic networks

[1–3] (OPPNETs, also known as Pocket Switched Net-

works [4]). An OPPNET is one of the most challenging

networks in which there are no complete forwarding paths

due to intermittent connections among the nodes [1]. One

of the main research topics regarding OPPNETs is the

development of feasible forwarding schemes [2, 3, 5–12].

Forwarding decisions in OPPNETs are made in a hop-

by-hop fashion. The problem with message forwarding is

thus the selection of ‘proper’ relay nodes. Although a

number of forwarding schemes have been proposed for

OPPNETs [2, 7], it still remains as a continuing challenge

that a forwarding scheme achieves an appropriate balance

between network traffic and transmission delay due to the

existence of the tradeoff between them. Among the for-

warding schemes, the flooding-based schemes [13] result in

extremely high network traffic with very low transmission

delay, since they transmit multiple copies of messages. On

the other hand, the wait-based schemes [14] suffer from

much longer transmission delay and have very low network

traffic, because they use a single copy of a message and let

the sender wait until it encounters the destination node.

Thus, it is important to select desired relay nodes for the

next hops and to make adequate copies of the message with

consideration for both network traffic and transmission

delay.

Since mobile nodes have limited resources such as

bandwidth, power consumption, and channel utilization,

& Sung-Bong Yang

sbyang@cs.yonsei.ac.kr

Jiho Park

jhpark54@yonsei.ac.kr

Junyeop Lee

kotzyi@yonsei.ac.kr

Sun-Kyum Kim

skyum@yonsei.ac.kr

Kiyoung Jang

wall2010@yonsei.ac.kr

1 Department of Computer Science, Yonsei University, Seoul,

Korea

123

Wireless Netw

DOI 10.1007/s11276-015-1113-y

the nodes in OPPNETs undergo difficulties in communi-

cation. Thus, as network traffic increases, network prob-

lems such as communication disruption and noise cannot

be avoided. In addition, although applications using OPP-

NETs should be relatively delay-tolerant, it is still of

interest to minimize the delay [15–26]. Therefore, it is

essential to develop an appropriate forwarding scheme to

resolve such difficulties and we expect that both contact

information and the social relationships among nodes play

crucial roles in enhancing the performance of the system.

With regards to the social relationships, people tend to

live their daily lives routinely; in other words, in weekdays

some people spend daytime at their workplaces, while

others move around all over the place. Hence it can be

viewed as if a particular job is assigned to each person

based on a predetermined role.

Therefore, in this paper, we propose a social informa-

tion-based forwarding scheme in OPPNETs, called

ABCON, through mimicking the behaviors of honey bees in

the artificial bee colony (ABC) proposed in [27]. The ABC

algorithms are generally used for multidimensional and

multimodal optimization problems. But we modify slightly

the roles of the bees by defining their roles as follows. A

bee is called a Scout if it has no message and is looking for

a message. A bee with a single message is called an Em-

ployed bee who carries with it the message and forwards it

to a bee called an Onlooker. An Onlooker in the dance area

of the hive accumulates the received messages from

Employed bees. Note that in our proposed scheme an

Onlooker is able to hold a number of different messages so

that they can transmit the messages to Scouts.

Among the bees in the proposed scheme, Onlookers are

extremely important since they are supposed to play the roles

of relay nodes. Hence we assign certain nodes as Onlookers

using a modified percolation centrality. Note that in social

networks the percolation centrality determines the relative

importance of nodes based on both their topological connec-

tivity and their percolation states. Hence the percolation

centrality is quite suitable to measure how certain nodes can

perform suitable roles well in forwarding messages. However,

we modify the definition of the percolation centrality slightly;

that is, time is not considered and the percolation state of a

node is changed to reflect how ‘important’ a node is in

transmitting messages. Note that in OPPNET environments it

is not easy to implement a system clock because generally

there is no centralized server for the nodes in the network.

Extensive simulations have been performed on the net-

work simulator, NS-2 ver. 2.35 [28, 29] with the home-cell

community-based mobility model (HCMM) [30]. We

compared ABCON with the Epidemic [31], PRoPHET [32]

and SimBet [33] schemes. The experimental results show

that ABCON outperforms all others in terms of both the

network traffic and transmission delay.

The main contributions of this paper can be summarized

as follows.

• We exploit the ABC algorithm since human behavior

resembles the social behavior of bees in swarm

intelligence. We applied this concept of each individual

acting according to an individual role, similar to the

way a bee hive is organized into Employed bees,

Onlookers and Scouts to our study of the human social

structure.

• Percolation centrality is used to deliver the messages

more effectively according to the role of each node. In

specific, we define a modified percolation centrality to

determine if nodes Onlookers, most influential nodes

among other nodes. Especially, when applying the

modified percolation centrality, the ego-network con-

cept is also embraced so that the percolation compu-

tations can be done more precisely.

The rest of this paper is organized as follows. Section 2

explains the related work, and Sect. 3 describes the pro-

posed scheme in details. In Sect. 4, we present the simu-

lation results. Finally, we conclude the paper in Sect. 5.

2 Related work

2.1 Forwarding schemes in OPPNETs

Many studies for forwarding schemes in MANETs have

been made over the few past decades [3, 34–36]. However,

these schemes are not applicable straightforwardly to

OPPNETs due to lack of any complete routing paths

between the source and the destination [1, 5]. The for-

warding schemes in OPPNETs can be classified into two

groups in general: social-oblivious schemes and social-

aware schemes. Social-oblivious schemes do not use social

relationship information. On the other hand, social-aware

schemes use social information about node behaviors or

social relationships in order to make decisions for for-

warding messages [37, 38].

Social-oblivious schemes are fundamentally flooding-

based methods in which they are beneficial to delay mes-

sages; however, this results in a negative impact on net-

work traffic. This is because the social-oblivious schemes

do not take into account the relationships among nodes as

well as nodes’ behaviors. In addition, such schemes allow

nodes to forward messages indiscriminatingly to other

nodes because they are unaware of both complete routes

and the information on the best next hop. A message is

finally delivered to its destination by relaying the message

whenever nodes encounter other nodes. Since there are

presumably enough contact opportunities among the nodes,

social-oblivious schemes show superior performances in

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terms of message delay. However, they have a negative

effect on network traffic because each node has to store

relay messages, causing much higher network traffic.

Typical social-oblivious forwarding schemes include Epi-

demic [31, 39]. However, there are other social-oblivious

forwarding schemes such as Spray-and-Wait [40] in which

a node ‘sprays’ a number of copies into some other nodes

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

interacts with the destination.

On the other hand, social-aware schemes show much

better performances, since message deliveries are made

intelligently among ‘proper’ nodes utilizing social infor-

mation. Thus, they significantly reduce unnecessary mes-

sages with moderate delays. In order to determine the relay

with social information, these schemes usually require a

more intensive computation process than social-oblivious

schemes. Recently, social-aware forwarding schemes have

been introduced in various fields in OPPNET environments

[19, 20]. SimBet [33] and Bubble Rap [41] are well-known

centrality-based schemes. In SimBet, when two nodes

encounter each other, they exchange information about the

messages along with the list of their neighbors. Then, each

node exchanges both the ‘betweenness centrality’ and

‘social similarity’ values. The betweenness centrality value

of a node is the number of shortest paths from all nodes to

all others that pass through the node in the entire network

topology. The social similarity value is the number of

common neighbor nodes. Bubble Rap uses both global and

local centralities. However, when the destination belongs

to a community in which all nodes have low global cen-

trality values, message forwarding could fail. Therefore, in

this case, a relay node in the same local community as that

of the destination node could not be identified. Finally,

PRoPHET [32] and PeopleRank [42] are the representative

probability-based forwarding schemes. In PRoPHET, each

node is allowed to collect the contact patterns of other

nodes. Each node computes the predictability of message

delivery to the destination. Two interacting nodes exchange

the delivery predictability information with respect to the

destination for finding the next best hop. PeopleRank uses

the PageRank algorithm of Google as a guide for for-

warding decisions. Whenever two neighbor nodes in the

social graph encounter each other, they exchange their

current PeopleRank values as well as the numbers of social

graph neighbors.

2.2 Artificial bee colony algorithm in swarm

intelligence

Swarm intelligence is deeply embedded in a biological

study of self-organized behaviors in social individuals (or

insects) [43, 44]. Karaboga first introduced the ABC

algorithm for numerical optimization [27]. The algorithm

is based on the intelligent foraging behaviors of honey bees

that are grouped into three categories based on their for-

aging behaviors: Employed bees, Onlookers, and Scouts.

Karaboga further applied the algorithm to different topics

of study [45, 46].

From the routing of traffic in social networks to the

design of control algorithms for groups of autonomous

robots, the collective behaviors of animals have inspired

many of the foundational works in this emerging research

field [47–50]. Population characteristics could be used as a

good example in social relationship or community-based

fields [47, 49, 51].

The followings are some applications of the ABC

algorithm in wireless networks and ad hoc networks.

Wedde et al. [52] defined the bee characteristics based on

the roles of Foragers, Recruiters and Scouts and applied

these definitions to MANETs, employing the personalities

of bees in the waggle dance and tremble dance in the

communication area. Ozturk [53] utilized the ABC algo-

rithm in wireless sensor network environments in which the

bee roles are defined based on some fitness functions and

suggested an efficient forwarding scheme based on clus-

ters. Finally, Feng Xia [54] presented a new scheme by

incorporating socially-aware networking in the vehicular

ad hoc environment using the ABC algorithm. This

scheme uses community density and social tie information

to apply efficiently the social-aware-based scheme into

forwarding.

2.3 Percolation centrality

Most centrality measures how important each node in a

social network. Betweenness centrality [55, 56] measures

the fraction of the number of shortest paths from all nodes

to others that pass through the network. A node with high

betweenness centrality has the capacity to enhance inter-

actions between its neighbor nodes [56]. The betweenness

centrality of node BC(v) is defined as

BCðvÞ ¼ 1

ðN � 1ÞðN � 2ÞX

s 6¼v 6¼r

rs;tðvÞrs;t

; ð1Þ

where rs;t is the number of shortest paths between the

source node s and the target node t, and rs;t vð Þ is the

number of shortest paths between s and t that pass through

node v.

Percolation centrality [57] considers the state of each

node in a complex network at any given time. Hence, it is

appropriate to apply this to rapidly changing network

topologies. We define percolation centrality of a node v as

the proportion of the shortest paths that pass through v at a

given time, where the source node is ‘percolated’ (i.e.,

‘infected’). The target node could be percolated, non-

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percolated, or in a partially percolated state. The percola-

tion centrality PC(v) of node v at time t is defined as

PCt vð Þ ¼ 1

N � 2

X

s6¼v 6¼r

rs;r vð Þrs;r

xtsPxti½ � � xtv

; ð2Þ

where rs;r is the total number of percolated paths from

node s to node r and rs;r vð Þ is the number of those paths

that pass through v. In more detail, we look into the fraction

wts;v ¼

xtsPxti½ � � xtv

; ð3Þ

where xti is the percolation state of node i at time t, indi-

cating that how much node i is percolated at time t.

Therefore, if it is 0, then i is at a non-percolated state and if

it is 1.0, then i is a fully percolated state. The sum in the

denominator is the total extent of percolation in the net-

work, ranging from 0 to 1. If all nodes are fully percolated,

then wts;v ¼ 1, otherwise it is 0. We subtract xtv from the

sum for proper normalization. Hence, wts;v indicates how

much node v is percolated at time t; that is, it tells us how

important role v plays in the process of contagion at time t.

3 Proposed scheme

3.1 Overview

For a forwarding scheme in OPPNETs, we assign a task to

each node. In the proposed scheme we apply the ABC

algorithm with modified roles (tasks) of honey bees; there

are three types of bees, Employed bees, Onlookers, and

Scouts. In the proposed scheme, each Employed bee holds

a single message and sends it to an encountered Onlooker.

As soon as an Employed bee has sent its message to an

Onlooker, it becomes a Scout. Scouts are supposed to look

for other messages. Finally, each Onlooker has a buffer to

hold multiple messages each of which was received from

an Employed bee. An Onlooker chooses a message in its

buffer randomly and sends the message to a Scout in the

vicinity. Since Onlookers receive multiple of messages

from Employed bees, an Onlooker’s buffer can be viewed

as a beehive, allowing the conceptualization of the nectar

storage in a beehive. As soon as a Scout receives a message

from an Onlooker, it becomes an Employed bee. We use a

modified percolation centrality to identify Onlooker bees.

Note that if a node has a higher percolation centrality it is

likely to play a more important role in delivering messages

effectively. The proposed scheme ABCON consists of two

steps.

Step 1 [Warm-up period]: In this step, each node builds up

its ego-network, collecting contact information from the

encountered nodes. At the very end of this step, each node

computes the modified percolation centrality of itself from

its ego-network to see whether it can be an Onlooker or not.

Step 2 [Forwarding Stage]: In the beginning of this step,

each node generates a single message to a randomly chosen

destination. Note that there is no Scout in the beginning of

this step. Afterwards the following actions are taken in this

step.

1. When an Employed bee encounters an Onlooker, the

message is sent to the Onlooker. As soon as an

Employed bee gives its message to an Onlooker, it

becomes a Scout.

2. Each Scout keeps looking for other Onlookers to get a

message.

3. When an Onlooker encounters a Scout, it delivers a

message that is chosen randomly in its buffer to the

Scout.

3.2 Determining Onlookers

During the warm-up period, nodes in the network move

around the network area. Whenever a node encounters

another node, they exchange the contact information

accumulated so far so that at the very end of the period

each node comes up with a network, called ego-network

[58]. In the proposed scheme, the ego-network of node Ni

consists of only up to two-hop neighbors of Ni, because

further expansion of the network may lead to erroneous

connections in the network.

In the social network analysis, the percolation centrality

is a crucial measure to identify important nodes through the

percolation paths in a network. We define the percolation

centrality with slight modification to identify Onlookers,

because we want to distinguish a set of nodes to play a

certain role and there is no wall-clock in OPPNET envi-

ronments.

Algorithm 1. Pseudocode of determining OnlookersInput: The adjacency matrix A of Ni’s ego-network obtained at the end of the warm-up period 01: Calculate Ni’s percolation centrality value. 02: if (the percolation centrality value of Ni > δ) // δ is the percolation threshold. 03: then Ni becomes an Onlooker 04: else Ni is an Employed bee

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The process for determining an Onlooker is given in

Algorithm 1. At first, each node Ni computes A2Æ [1 - A]

to computers;r Nið Þrs;r

for each node r = s = Ni, where s is the

source node [33]. Note that A is an adjacency matrix where

each entry (i, j) is either 1 or 0, indicating whether Ni

encountered Nj or not, respectively. Note that each entry (i,

j) in A2 represents the number of paths between Ni and Nj

of length two or less. The term [1 - A] represents the

subtraction of matrix A from the matrix 1 in which all

elements are 1’s; that is, the result of [1 - A] is a matrix in

which an entry has ‘0’ if its corresponding entry of A is ‘1’

and an entry has ‘1’ if its corresponding entry of A is ‘0’.

Hence we can consider only paths between two nodes that

are not connected by an edge. Finally A2Æ [1 - A] is

obtained from element-wise product of A2 and [1 - A];

that is, each entry in A2 is multiplied by its corresponding

entry in [1 - A]. Figure 1 shows an example for calcu-

lating A2Æ [1 - A].

Figure 1(b) shows A2 = A 9 A; for example, there are 4

paths from N1 to itself of length two or less, and there is 1

path between N1 and N2 of length two or less in the ego-

network. In Fig. 1(c) [1 - A] is computed by comple-

menting 0’s to 1’s and vice versa; the purpose of such

complementation is to consider only nodes that are not

connected directly. We consider only non-zero values in

the upper triangle of the matrix in Fig. 1(d), since the

adjacency matrix is symmetric; that is, an ego-network is

an undirected graph. So, from the non-zero elements of the

upper triangle in A2Æ [1 - A] of N1 we can compute

rs;r N1ð Þrs;r

by adding the reciprocals of non-zero values [33]; that is,

1/1 ? 1/1 ? 1/1 ? 1/1 = 4.

After each node Ni calculatesrs;r Nið Þrs;r

from its own ego-

network, it then calculates the modified percolated weight

value w0s;i as in Eq. (4). Note that we do not consider time t

as in Eq. (3), because it is difficult for each node to know

the wall-clock time in OPPNET environments. Although xidenotes the percolation state in Eq. (3), we let it denote the

number of nodes encountered by Ni, since we want w0s;i to

show how much Ni contributes tors;r Nið Þrs;r

in the network.

w0s;i ¼

xsP½xj�=2 � xi

; ð4Þ

In this equation the sum in the denominator is the

number of nodes encountered by all of the nodes in the

ego-network of Ni and xs is the number of nodes encoun-

tered by the source node s. Again as in Eq. (3), xi is sub-

tracted from the sum in the denominator for proper

normalization.

In Fig. 2, the percolation centrality of N1 is

1.0 9 0.4 ? 1.0 9 0.4 ? 1.0 9 0.4 ? 1.0 9 0.4 = 1.6,

because each of four non-zero entries in the upper triangle

of A2Æ [1 - A] is 1 and w

0

s;1 = 0.4. Therefore, if we set a

certain percolation threshold, say 0.5, both N1 and N7

become Onlookers in this sample network because their

percolation centrality values are larger than 0.5. However,

if there are too many Onlookers, there would be too much

network traffic, while if there are a few Onlookers, the

messages would not be delivered within a reasonable

time. Hence, in Sect. 4.2.1, we investigate what is a

proper percentage of Onlookers among the nodes in the

network.

3.3 Forwarding process

The forwarding process of the proposed scheme is outlined

in Algorithm 2. When an Employed bee E encounters an

Onlooker O, E sends the message M in its buffer to O, and

then E becomes a Scout. If O happens to be the destination

of M, O receives M and O keeps playing the role of

Onlooker. When an Onlooker O encounters a Scout S,

O sends a randomly chosen message M from its buffer, if it

has it. If M is a message that S has received before, then

S discards M and remains a Scout until it meets other

Onlookers. Otherwise S receives M and becomes an

Employed bee.

Fig. 1 Calculation of A2 Æ [1 - A] for N1. a adjacency matrix A, b A2, c [1 - A], d A2 Æ [1 - A]

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Algorithm 2. Pseudocode of forwarding in ABCON01: [Forwarding Stage] 02: When an Employed bee E encounters an Onlooker O03: E sends its message M to O04: E becomes a Scout 05: When an Onlooker O encounters a Scout S06: O sends a randomly chosen message M from its buffer to S, if any 07: if O has no message 08: S remains as a Scout until S meets other Onlooker 09: else10: if (S once had M before) 11: S discards M12: else13: S receives M14: if (M is destined to S) 15: S requests other message from O, if any 16: S becomes an Employed bee 17: end if

4 Performance evaluations

4.1 Simulation environment

We used the network simulator NS-2 v2.35 [28, 29] to

evaluate our proposed scheme, ABCON, due to its suit-

ability for analyzing the correlation between network

traffic and transmission delay.

Table 1 summarizes the parameters of the simulation

environment. We compared ABCON with typical for-

warding schemes such as the Epidemic, SimBet and PRo-

PHET schemes in OPPNETs. In our simulation, 40 mobile

nodes follow the HCMM, which is a widely used mobility

pattern in mobile network simulations. The network area is

set to 450 9 450 m2 with four special zones called home

communities [50]. A home community can be defined as a

set of members who gather socially at a certain place [59].

Therefore, the members in the same home community

spend more time with each other at a specific physical

location. In the simulation, each home community has ten

nodes. The speed of each node ranges from 1 to 9 m/s and

the communication ranges are 5, 10, 20, 30, 40 and 50 m.

Each node in the network selects a destination node ran-

domly to send a message. We measure the delay time

(s) and network traffic (number of received messages) until

all 40 messages arrive at their destinations. The simula-

tions were conducted 20 times to obtain the average results.

The parameters for Epidemic, PRoPHET and SimBet are

also given in Table 1.

Through extensive experiments, the parameter values at

which other schemes achieved their best experimental

results in each environment were determined, while the

proposed scheme is not able to rely on its parameters. Note

that other schemes rely on manual adjustments of their

parameters. The proposed scheme needs a warm-up period

of 600 s. However, its length is relatively short compared

with the operation time for the rest of the simulation, which

takes up to 12 h. In addition, the warm-up period is only

Fig. 2 Determining Onlookers based on the percolation threshold

Table 1 Simulation parameters

Parameter (unit) Value (default)

Number of nodes 40

Number of grids 9

Size of the network (m2) 450 9 450

Number of communities 4

Community size (m2) 150 9 150

Node speed (m/s) 1–9

Radius of communication range (m) 5, 10, 20, 30, 40, 50 (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

Threshold of percolation centrality (%) 12.5–35.0 (25.0)

Warm-up period (s) 600

Simulation time (s) 6000

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needed once in the beginning of network services. We

therefore do not consider the network traffic during the

warm-up period, since most schemes in OPPNETs ignore

network traffic during the warm-up period. We found that

the amount of traffic in the warm-up period was not large

enough to affect the total amount of traffic.

Note that both SimBet and PRoPHET also need the warm-

up periods to adjust their control parameters of their schemes.

During the warm-up period, each of PRoPHET, SimBet, and

ABCON generates control packets except Epidemic. We

measure the number of received control packets during the

warm-up period. ABCON, PRoPHET and SimBet exchange a

control packet whenever a node encounters another node.

Additionally, we evaluate the proposed scheme with the fol-

lowing three performance measures:

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. Transmission delay: Time required for a message to

travel from the source node to the destination node.

4.2 Simulation results

4.2.1 Determining the percolation threshold

In this section we want to determine a proper percentage of

Onlookers among the nodes in the network. We have tested

various percolation thresholds from 12.5 % to 35.0 %

when the communication range is set to 10 m. Figure 3

shows the results of the network traffic and transmission

delays. When the threshold is too low, then there are a few

Onlookers and therefore message delivery takes too long.

On the other hand, if the threshold is too high, there are too

Fig. 3 Results with various

percolation thresholds

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much network traffic. In the figure, when the percolation

threshold is 25.0 %, the performance is well-balanced

between the network traffic and the transmission delay.

However, the percolation threshold may be appropriately

chosen according to a given environment.

4.2.2 Result with average values

Figure 4 shows the average network traffic and transmis-

sion delays of Epidemic, PRoPHET, SimBet and ABCON.

The results in the figure have been computed without the

warm-up period. The number of nodes is set to 40. Fig-

ure 4(a) shows the average network traffic within 10 m of

the communication range, where ABCON significantly

reduces the network traffic compared to Epidemic, PRo-

PHET and SimBet. ABCON could reduce much of the

traffic because each Employed bees and Scouts has a buffer

capacity of only a single message. Figure 4(b) shows the

average transmission delay within 10 m of the communi-

cation range. With regards to the transmission delay, it

turned out that ABCON shows almost similar performance

to PRoPHET and SimBet schemes in the transmission

delay. Such a performance has been achieved through a

proper selection of Onlookers with the modified percola-

tion centrality; that is, Onlookers play a vital role as hubs in

delivering the messages. And Fig. 4(c) shows the average

delivery ratio with the 10 m communication range. The

various schemes all reached up to 1.0 delivery ratio inde-

pendently. Excluding Epidemic, the fastest scheme to reach

the 1.0 delivery ratio is ABCON. However, it attains this

delivery ratio slower than other schemes initially because

Onlookers have to gather sufficient messages from

Employed bees.

4.2.3 Results with various communication ranges

Figure 5(a) compares the network traffic of the schemes

with the communication ranges of 5, 10, 20, 30, 40 and 50

m. The number of nodes and total simulation time are set to

40 and 6000 s, respectively. As the communication range

increases, so does the network traffic of each scheme,

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Fig. 4 Network traffic and transmission delays in the default

network. a Network traffic, b transmission delays, c delivery ratio

Fig. 5 Results with various communication ranges. a Network

traffic, b transmission delays

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because the nodes could get more chances to communicate

with each other. Especially, in comparison to other

schemes, ABCON shows the most significant decrease in

network traffic for sparser environments.

The transmission delays of the schemes are given with

various communication ranges in Fig. 5(b). The transmis-

sion delays of all the schemes are reduced as the commu-

nication range increases. When the communication range is

5 m (a very sparse environment) SimBet exhibits a very

poor performance because it is extremely difficult for a

node to find its neighbors. As with the SimBet scheme,

PRoPHET also has a low performance as well, because

each node of PRoPHET is not likely to meet each other to

compute the delivery predictability for the destination in

such a sparse environment. However, in sparser environ-

ments, ABCON shows more outstanding performance

because Onlookers play hubs (that is, influential role as

relay nodes) in delivering the messages.

And also, as shown in Fig. 5(a), ABCON significantly

decreases the network traffic compared to both SimBet and

PRoPHET because it is able to use effectively different

buffer sizes in response to its roles. Therefore, ABCON is

well-adapted to sparse environments.

5 Conclusions

We proposed a novel forwarding scheme called ABCON in

which Onlookers are chosen with the modified percolation

centrality. In the forwarding stage, Onlookers behave like

hubs in the network and Employed bees and Scouts play

appropriate roles for transmitting messages to achieve an

enhanced system performance. The proposed scheme out-

performs other schemes in terms of balancing between the

network traffic and the transmission delay. As for future

work, we plan to look into more sophisticated ways to

adjust the system parameters so that the forwarding

scheme could achieve robust performance in ever-changing

social environments.

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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Jiho Park is currently a 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.

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Junyeop Lee is currently a

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.

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.

Kiyoung Jang is currently a

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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