a forwarding scheme based on swarm intelligence and...
TRANSCRIPT
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
Jiho Park
Junyeop Lee
Sun-Kyum Kim
Kiyoung Jang
1 Department of Computer Science, Yonsei University, Seoul,
Korea
123
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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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10000
20000
30000
40000
50000
60000
70000
Epidemic PRoPHET SimBet ABCON
Net
wor
k T
raff
ic(t
he n
umbe
r of
rec
eive
d m
essa
ge)
307.0725
597.46842 578.37539 590.91938
0
100
200
300
400
500
600
700
Epidemic PRoPHET SimBet ABCON
Tra
nsm
issi
on D
elay
(sec
)(a)
(b)
(c)
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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