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Distributed InformationBased Cooperative
Strategy Adaptation in Opportunistic Mobile
Networks
Sudip Misra, Senior Member, IEEE, Sujata Pal, Member, IEEE, Barun Kumar Saha
Email: {smisra, sujata.pal, barun.kumar.saha}@sit.iitkgp.ernet.in
Abstract—Cooperation among nodes is a fundamental necessity in Opportunistic Mobile Networks (OMNs), where the messages are
transferred using the storecarryandforward mechanism, due to sporadic internode wireless connectivity. While multiple works have
addressed this issue, they are often constrained in their assumptions on solutions (e.g., requirement of central authority, and tracing
the recipient nodes for providing reward or punishment). In this work, we address this research lacuna by taking an evolutionary theory
based approach. In evolutionary theory, the players analyze alternative strategies and select the best one to survive in a population.
Inspired by this, in this work, we propose a Distributed InformationBased Cooperation Ushering Scheme (DISCUSS) to promote
cooperation in message forwarding between nodes. In this scheme, the nodes maintain and exchange information with one another
during contacts about the messages created and delivered in the network. Based on this, the nodes evaluate their own performance and
compare that with the approximated network performance to adapt the most successful forwarding strategy. Simulation results show
that the message delivery ratio in the network improves upto 31%, when the nodes dynamically switch their strategies, as compared to
the case when they do not. Furthermore, the DISCUSS scheme fared closely to its variant with the nodes having complete knowledge
about the networkwide performance.
Index Terms—Cooperation, Opportunistic Mobile Networks, Strategy adaptation.
F
1 INTRODUCTION
Opportunistic Mobile Networks (OMNs) are a sub
class of delaytolerant networks (DTNs) [1]. In tra
ditional wireless networks, endtoend communication
paths usually exist between the source and destination
node pairs, which, however, is not true for the OMNs.
The messages in OMNs are “opportunistically” routed
to their destinations using the storecarryandforward
transfer mechanism. Mobility among users creates new
opportunities to connect and communicate with one
another using wireless devices [2]. Since the source
destination pairs might not be connected simultaneously,
route maintenance exhibits a huge challenge. So, the
routes are typically computed at each node based on
the local knowledge of the node. A source node delivers
its message to the destination node, if both the nodes
come in contact of each other. Otherwise, the message is
forwarded to the intermediate nodes, which then store
and carry the messages, until one of them meets with the
destination and delivers, or they can ﬁnd other nodes
which can do the same. Thus, message forwarding in
OMNs signiﬁcantly depends on the cooperation of the
intermediate nodes.
Existing routing protocols such as [3]–[7] make the as
sumption that the nodes are fully cooperative. Since the
•Sudip Misra, Sujata Pal, and Barun Kumar Saha are with the School of
Information Technology, Indian Institute of Technology Kharagpur, India.
Email: {smisra, sujata.pal, barun.kumar.saha}@sit.iitkgp.ernet.in
nodes are typically unmanaged, it is difﬁcult to coordi
nate different nodes having different goals. So, we do not
always consider fully cooperative behavior among all
nodes. Mobility in OMNs causes nodes to meet oppor
tunistically. These opportunistic connections help certain
nodes to transfer their messages through intermediate
nodes to the corresponding destination nodes. Some
nodes may take help from others for forwarding their
own messages, but may not always help in forwarding
others’ messages. This selﬁsh behavior heavily affects
the network performance. The mobility and the greedy
nature of the nodes require a distributed and efﬁcient
cooperation scheme that can stimulate cooperation and
discourage noncooperative behavior.
Enforcing cooperation among nodes has been exten
sively studied in the literature. One way of promoting
cooperation among nodes is to give reward or pun
ishment. However, for giving reward or punishment,
the recipient node needs to be traced. Zhu et al. [8]
proposed ‘iTRUST’, in which a periodically available
trusted authority judges each node and detects the
misbehaving nodes. Creditbased incentive schemes [9]–
[11] were proposed, in which credit is given to the
forwarding nodes in terms of virtual pricing unit. Wei et
al. [12], and Zhang et al. [13] proposed reputationbased
incentive schemes for encouraging cooperation among
nodes. Wu et al. [14] proposed a game theoretic approach
that encourages cooperation among nodes. However, the
scheme proposed by these authors is based on the use
of a server (Credit Clearance Centre) for updating each
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2
transaction. In another scheme, ‘Pi’ [15], the source node
attaches some incentives on the forwarding message.
In this work, we consider three groups of nodes based
on three strategies of message forwarding – cooperate,
exploit, and isolate – similar to [16]. The cooperators help
the other nodes in forwarding their messages. The ex
ploiters use the other nodes as “freeriders”, without
helping them in forwarding their messages. The isolators
neither take help, nor provide so, to the other nodes in
forwarding their messages. It is pertinent to clarify at
this juncture that the existing work [16] considers the
above mentioned strategies. It, however, merely shows
the existence of a cyclic equilibrium among the three
strategies, cooperate, exploit, and isolate. Additionally, it
studies the aspect of cooperation in opportunistic mobile
networks, combining the concept of RockScissorsPaper
game, and Simpson’s paradox. The current work, on
the contrary, presents a distributed mechanism, using
which each node dynamically selects the most successful
strategy. Our mechanism prevents the nodes from se
lecting the exploit and isolate strategies, which, in turn,
stimulates them to cooperate. The design of a mecha
nism for the enforcement of cooperation is new to this
work. Further, in this work, we present the theoretical
characterization of the proposed scheme together with
extensive performance evaluations.
Evolutionary theory [17] postulates that each individ
ual in a population periodically checks the alternative
strategies and selects the best one for survival. Inspired
by this, we consider an OMN, in which the nodes peri
odically compare their individual performance with the
locally available networkwide performance (in terms of
delivery ratio of messages) and switch to the strategy
of the most successful group, if it is not the node’s
strategy already. We develop a Distributed Information
baSed Cooperation UShering Scheme (DISCUSS), using
which the nodes locally exchange information among
themselves to acquire knowledge about the performance
of one another, without requiring a central authority
to do it. We show that the proposed scheme provides
high delivery probability and low latency on message
delivery. For evaluating the effectiveness of the proposed
scheme, we proposed a variant of DISCUSS, in which the
nodes are assumed to possess global knowledge – every
node is assumed to possess precise information about the
messages created and delivered by any node. In the rest
of the manuscript, we refer to this variant as “DISCUSS
with global knowledge”.
The contributions of this paper are summarized in the
following points.
•Proposing a scheme, inspired by evolutionary the
ory, following which the nodes dynamically adapt
their message forwarding strategies based on dis
tributed information over time.
•Proposing the distributed information based co
operation ushering scheme, DISCUSS, to promote
cooperation amongst nodes for message forwarding
through selfevaluation using their own and the
network’s performance information.
•Establishing the effectiveness of DISCUSS by evalu
ating the Jaccard similarity index [18] between DIS
CUSS and its variant, in which global knowledge is
available.
•Analyzing the theoretical characteristics of the
scheme in terms of complexity and convergence.
•Demonstrating the efﬁciency of the proposed
scheme through extensive simulations using both
reallife traces and synthetic mobility models.
The remaining part of this work is organized as fol
lows. Section 2 presents a review of the related work.
Section 3 provides an overview of the system model.
This is followed by Section 4, in which we describe
the proposed scheme, DISCUSS. Section 5 contains the
theoretical description of the characteristics of DISCUSS.
Sections 6 and 7 present the simulation setup and analy
sis of results. Finally, in Section 8, we conclude this work.
2 RE LATED WORK
The operation of message delivery in OMNs is gen
erally reliant upon cooperation by intermediate nodes.
Due to resource limitations, the intermediate nodes do
not always want to help the other nodes. Each node
in the network always tries to fulﬁll its own goal by
utilizing the other node’s resources. So, to promote
cooperation between nodes in such type of networks,
different cooperationenforcing schemes [19] were pro
posed. Credit and reputationbased approaches [9]–[13]
are commonly known to promote cooperation among
nodes. In the creditbased incentive schemes, virtual
currency or pricing acts as the credit. On the other hand,
in the reputationbased schemes, reputation of the nodes
are calculated by their neighbors based on the message
forwarding actions.
2.1 Creditbased Schemes
Shevade et al. [10] ﬁrst proposed a creditbased incentive
mechanism for DTNs, which motivate the selﬁsh nodes
to cooperation. Their results show that the presence
of selﬁsh nodes degrade the overall network perfor
mance. To alleviate this behavior, they proposed the
pairwise TitforTat (TFT) incentive mechanism. They
considered two constraints – generosity and contrition
– to maximize cooperation among nodes. Zhu et al.
[9] proposed SMART, a Secure Multilayer Credit based
Incentive scheme for DTNs. SMART stimulates cooper
ation among nodes by preventing the malicious users
from cheating credits based on layered coins. Layered
coin provides virtual credits for charging and rewarding
of data forwarding in DTNs. On the other hand, another
scheme, MobiCent [11], provides incentive to the selﬁsh
nodes for forwarding other node’s messages. Lu et al.
[15] proposed the “Pi” protocol, where the selﬁsh nodes
are stimulated to cooperate by forwarding the messages
of other nodes. Pi attaches incentives to the messages
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3
and sends it to the intermediate nodes, which attracts
the participation of other nodes in forwarding. However,
this method need a Trusted Authority (TA) for storing
the credit and reputation of each nodes. “iTrust” [8]
encourages cooperation among the forwarding interme
diate nodes, by providing incentives, and detecting and
punishing the misbehaving nodes. “iTrust” introduces
the concept of a periodically accessible trusted author
ity, which detects the misbehaving nodes based on the
forwarding history evidence.
2.2 Reputationbased Schemes
MobiID [12] is a reputationbased incentive scheme. It
assumes an ofﬂine system manager, where each node
registers to join the network. This scheme uses selfcheck
and communitycheck of either the previous/nexthop
nodes or their community before forwarding a message.
A reputation value is assigned to the forwarding node,
when it forwards a message to another node. Nodes
are ostracized when their corresponding reputations are
less than a given threshold. So, each node exerts itself
to cooperate in forwarding other node’s messages, in
order to earn increased levels of reputation. Zhang et
al. [13] proposed a practical reputationbased incentive
“Pri” scheme. Pri also assumes the use of Ofﬂine Security
Manager (OSM), which is in charge of key distribution.
Nodes register in OSM before joining the network.
Resta and Santi [20] analyzed the performance of
epidemic routing for different degrees of node coopera
tion. They derived the probability distribution of packet
delivery delay and communication cost. Communication
cost is computed by determining the number of nodes
having the message in the network by the timing a
copy of the message is ﬁrst delivered to the destination.
Apart of these works, Buttyan et al. [21] and Yin et al.
[22] proposed a noncooperative game theoretic model
that avoids selﬁsh behavior among nodes and stimulates
cooperation among individuals in a network. Elwhishi et
al. [23] proposed a message scheduling framework for
epidemic routing in DTNs, which improves the message
delivery ratio.
From the above works, it can be synthesized that co
operation enforcement poses a great challenge in OMNs
and requires the use of tamperproof hardware for up
dating the reputation values of every node. Otherwise,
for giving reward or punishment, the recipient nodes
need to be traced, which is costlier in OMNs. On the
other hand, the proposed approach does not need a
central server or any incentive mechanism. On every
contact, a node maintains and exchanges information
with the other neighboring nodes, about the messages
created and delivered in the network. Based on this
local information, a node evaluates its own performance
and compares it with the approximated network perfor
mance of others. A node adapts, other strategy, if its own
performance is inferior than the others performance.
3 SYSTEM MODEL
3.1 Assumptions
The proposed schemes “DISCUSS” and its variant “DIS
CUSS with global knowledge”, makes the following
assumptions.
1) Each node follows any one of these three message
forwarding strategies at a time – cooperate,exploit,
and isolate.
2) In the DISCUSS scheme, the nodes reliably ex
change information with their neighboring nodes.
3) In case of DISCUSS with global knowledge, the
nodes can obtain message delivery information
from a Central Authority (CA).
4) In DISCUSS with global knowledge, every node
shares its relevant information with the CA. So,
each node has “complete” information (number of
generated and delivered messages) about the other
nodes.
3.2 Strategy DeﬁnedOpportunistic Mobile Network
We represent an OMN as (N, M, S ), where Ndenotes
the set of nodes in the network, Mdenotes the set of
messages generated by the nodes, and Sdenotes the
set of strategies selected by any node in forwarding
the messages; S={Cooperate, Exploit, I solate}. Each
node may act as a source, a destination or an interme
diate relay node. We consider three groups of nodes
–cooperators,exploiters, and isolators – based on these
strategies. The following section elaborates the behavior
of the individual nodes.
3.2.1 Cooperators
These nodes with the strategy “cooperate” not only
forward their own messages, but help the other nodes as
well in doing so. In other words, the cooperators act as
relays by receiving, storing and forwarding the messages
generated by the other nodes.
3.2.2 Exploiters
On the other hand, the exploiters forward their messages
to the other nodes (cooperators) for delivery. They re
ceive other node’s messages, but instead of storing them,
they silently drop those messages. The exploiters take
help from others for forwarding their own messages as
free riders, without helping them.
3.2.3 Isolators
The isolators only receive the message for which they are
the destinations. They do not take help from the other
nodes for forwarding their messages, neither do so for
others. The isolators directly deliver their messages, when
they meet with the corresponding destination node.
Deﬁnition 1: An OMN is said to be Strategy Deﬁned
Opportunistic Mobile Network (SDOMN), if all the nodes
in the OMN follow any message forwarding strategy
described by the set S={Cooperate, Exploit, I solate}.
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4
TABLE 1: List of Notations
CM i
tCreated messages list of the node iat time t
DM i
tDelivered messages list of the node iat time t
DP i
tDelivery probability list of the node iat time t
Ψg
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Group weight of the group g
Ψi(t)Group weight of the node iat time t
pi
kDelivery probability of kth node of node i
tkTime stamp of kth node
αi
gDelivery probability of the group g,
g∈ {C, E, I }, as known by the node i
ωi
gWeighted factor of the group g,g∈ {C, E, I },
as computed by the node i
γi
gWeighted average delivery ratio of group g,
g∈ {C, E, I }, as computed by the node i
At any point of time, a given node chooses only one
strategy from S.
Let nC,nE, and nI, respectively denote the number of
cooperators, exploiters, and isolators in the SDOMN at
time instant t. Therefore, we have N=nC+nE+nI. The
objective of SDOMN is to motivate the noncooperators
in the network into cooperation in order to optimize the
performance of the network. Therefore, the goal function
can be write as,
max
t∈RnC(t),s.t. 0< t ≤Γ,0< nC(t)≤ N(1)
where, Γis the lifetime of the system. When the con
cerned performance objective is the delivery ratio (i.e.
the fraction of the created messages that have been deliv
ered to their respective destinations) of the messages, the
performance function should be maximized. Therefore,
in this context, the goal function of this work, can be
alternatively represented as,
max
t∈RX
s∈S
αs(t),s.t. 0< t ≤Γ,0< αs(t)≤1(2)
where, αs(t)denotes the delivery ratio of the group of
nodes with strategy s∈Sat any time instant t. We
validate this in Section V.
3.3 Network Performance Information
Initially, each node has a message forwarding strategy.
Each node distributively shares information with the
other nodes, without requiring a CA. Each node has
limited information about the effect of its actions. When
two nodes mutually encounter, they interchange their
past history (own delivered message list and nodes’
delivery probability list) with the neighboring nodes.
Based on the local knowledge of past history, each
node updates its behavior. The nodes select their own
communication mode – whether they cooperate,defect or
isolate in message forwarding, based on their past history.
In case of DISCUSS with global knowledge, we assume
that all nodes are managed by a CA. The nodes share
their relevant information such as received message ids,
message sender node ids, and own delivery probability
with the CA. Every node collects information such as
delivery probabilities of the other nodes’ from the CA.
They select their own communication modes based on
these information.
4 DISTRIBUTED INFORMATIONBASED COOP
ERATION USHERING SCHEME
We now discuss a scenario where a node dynamically
switches its forwarding strategy, if required, to the most
successful strategy in the concerned SDOMN. This is
inspired by the existing research in evolutionary theory
(ET) [17], which shows that the size of groups in a pop
ulation1changes with the various competing strategies.
In particular, the following presents a mapping of the
concepts from Darwin’s Natural Selection Theory [24]
into our context involving SDOMN.
•Nodes in SDOMN exhibit variation in their mes
sage forwarding actions.
•Such variation occurs as the nodes follow different
kinds of strategies, as deﬁned by the set S.
•The most successful strategy among the set Ssur
vives in the population and is propagated to the
other nodes over time.
We present the proposed scheme, DISCUSS, in the back
drop of ET. Table 1 list the notations used to represent
the system parameters with a short description.
DISCUSS comprises of two phases that are repeated in
every generation interval (τ). (1) Acquiring information on
the performance of the SDOMN, and (2) Strategy adap
tation. The ﬁrst phase requires information on – (a) The
messages created (CM ) by the nodes, (b) The delivered
messages (DM ), and (c) The delivery probabilities (DP )
of the nodes.
4.1 Acquiring Information
Let iand jbe two nodes that come in contact with
each other at time t. Let CMi(t)be the list of generated
messages by node iat time t. Then,
CM i(t) = {(mi
r, tr)},
where mi
ris the rth message created by the node i, at
time tr,r≥0. In other words, CM i(t)consists of the list
of messages created by node itill time t. Similarly, we
deﬁne DM i(t)as:
DM i(t) = {(mi
q, tq)},
where mi
qis the qth delivered message of the node iat
time tq. Therefore, DMi(t)⊆CM i(t).
Each node imaintains its own delivery probability, as
well as its view of the other nodes’ delivery probabilities
known till time tin DPi
tas:
DP i
t={(k, pi
k, ti
k)}, k = 1 to N, t ≥tk,∀k
1. Population in ET and set of nodes in our case
5
Here, pi
kis the last updated information stored at node
ion the delivery probability of the node kknown at time
tk. The delivery probability (pk) of any node k,∀k∈N,
is determined as:
pk=DM k(t)
CM k(t)(3)
Information are updated between pairs of nodes,
when they come within their respective communication
ranges. More speciﬁcally, node i,i∈N, maintains a list
of delivery probabilities for every node it has met before.
Delivery probability (pi
k) of the list DP i
tis updated in
every connection, according to the following rule:
pi
k=(pj
k,if tj
k> ti
k,
pi
k,otherwise. (4)
Algorithm 1 describes the steps required for updating
node i’s lists on encountering node j,∀i, j.
Algorithm 1: Delivery probability update by node i
on contact with node j
Inputs :CM i
t, CM j
t, DM i
t, DM j
t, DP i
t, DP j
t
Output: Updated values of DM i
tand DP i
t
1C← CM i
t;// Number of messages in CM i
2D← DM j
t;// Number of messages in DM j
3/*Check, whether DM jlist contain any
created messages of node i. If so, add
it to DM i.*/
4for u←1, C do
5for v←1, D do
6if (mu=mv)then
7Add mvto DM i
t;
8end
9end
10 end
11 // Find delivery probability of iand j
12 DP i
t← DM i
t/CM i
t;
13 DP j
t← DM j
t/CM j
t;
14 Pi← DP i
t;Pj← DP j
t;
15 /*Node icompare its own DP list with
node j’s DP list */
16 for u←1, Pido
17 for v←1, Pjdo
18 if tv
k> tu
kthen /*Compare time stamp
of kth node at node iand the same
kth node at node j.*/
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19 pu
k←pv
k, tu
k←tv
k;/*Update delivery
probability and time stamp of
kth node at node i*/
20 end
21 end
22 end
Figure 1 illustrates an example of comparison and
update process of two nodes iand j. On every contact,
node iupdates its information with node j, as given
Fig. 1: Update information on each contact
below.
•Node ihas three lists CM i,DM i, and D P i, at time
t. Similarly, node jhas CM j,DM j, and D P j.
•Node igenerates two messages, M8and M12, at
t= 150sand 200s, respectively. Similarly, node j
has messages M24 and M5created at t= 321sand
101s, respectively.
•Nodes iand jlist the past delivered messages
of nodes. Node ichecks node j’s list of deliv
ered message, and updates its own list of de
livered messages. Initially, nodes iand jhave
DM i={hM8,400i,hM2,700i} and DM j=
{hM24,800i}. After updating, node ihas DM i=
{hM8,400i,hM2,700i,hM24,800i}.
•Node iﬁnds its delivery probability (pi) at current
time tas pi=CM i/DMi, and updates its deliv
ery probability list.
•Node icompares its own DP ilist with the jth
node’s DP jlist, and then updates its list.
•Let node ichoose the kth node time stamp (ti
k) from
the DP ilist, and compare it with the kth node’s
time stamp (tj
k) of node j’s DP jlist. If ti
k< tj
k,
node iupdates its kth node’s delivery probability
by dpj
k. Node ichecks the 1st node’s time instant,
i.e. 300 >0, and consequently makes no change.
Again, it checks the 2nd node’s time instant, i.e.,
400 <800, and then updates the message delivery
probability of this node by 0.5. Similarly, it repeats
this for the rest of the nodes of node i. Hence,
DP i
t={h0.2,300i,h0.5,800i,h0.3,600i}.
Algorithm 1 repeats exactly in the same way as described
for updating the list of other nodes.
Further, each node i,i∈N, maintains a record of
the group weights,Ψi
g(t),g∈ {C, E, I }. Initially, at time
t= 0,Ψi
g(t)is empty. At any time t, when a node i
comes in contact with another node jbelonging to a
group g, where g∈ {C, E, I }, node iupdates its value
of the corresponding group weight as:
Ψi
g(t)=Ψi
g(t−1) + Ii
g(t),(5)
where, Ii
g(t)is the indicator decision variable, which, in
turn, is deﬁned as follows:
Ii
g(t) = (1,if node ireceives a message from group g,
0,otherwise.
6
For example, Ψi(t) = {Ψi
C,Ψi
E,Ψi
I}indicates that until
the time instant t, node ireceives Ψi
C,Ψi
Eand Ψi
I
number of messages, respectively, from the cooperators,
exploiters, and isolators, respectively. Thus, Ψgdenotes
the relative importance of each group over the others.
4.2 Strategy Adaptation
Cooperation is necessary and important in OMNs for
increasing the performance of the networks. So, for
enhancing cooperation among nodes, we propose a dy
namic strategy adaptation mechanism. This mechanism
bridges the nodes’ selfrecommendation approach based
on the local information gathered by a node during
contacts with other nodes. The objective of the proposed
approach is to maximize the expected delivery proba
bility of messages in the Kth timeslots by motivating
the exploiters and isolators to shift their strategies to
cooperation.
We evaluate the proposed approach using two scenar
ios: static and dynamic. In the static scenario, the number
of cooperators (nC), exploiters (nE), and isolators (nI)
remain invariant over time t, i.e.,
nC(t) = nC(0),
nE(t) = nE(0),
nI(t) = nI(0),
(6)
and nC+nE+nI=N,∀t.
In the dynamic scenario, the number of cooperators
(nC), exploiters (nE), and isolators (nI) vary with time.
Ageneration is a stage in the life cycle of a node. The
strategies of individuals (nodes) remain same in a genera
tion. The generation interval (τ), where τ={1,2,3, ..., K},
is the time required to complete a generation. In Fig
ure 2, initially, the size of cooperators, exploiters and
isolators are the same. After a generation interval, every
Fig. 2: Strategy adaptation by the nodes in the SDOMN
after a generation interval (τ).
node compares its individual performance with that of
the other groups, and dynamically changes its strategy,
if required, accordingly. Such a self–evaluation can be
performed by each node individually during the run
time based on information exchanged with the other
nodes, as discussed below.
Each node ihas a delivery probability list (DP i
t),
where it stores the delivery probabilities of the other
nodes based on the most recent information available,
together with the corresponding time when the infor
mation was obtained, and then timestamps it. Any
node i,i∈N, computes the average delivery ratio αg,
g∈ {C, E, I }of cooperators, exploiters, and isolators, as
follows:
αi
C=Pk∈NCpi
k
NC
αi
E=Pk∈NEpi
k
NE
αi
I=Pk∈NIpi
k
NI
(7)
where, αC,αE, and αI, respectively, denote the average
delivery ratio of the cooperators, exploiters, and isolators
in the OMN. Here, NC,NE, and NIdenote the set of
cooperators, exploiters, and isolators that node i,i∈N,
met within time interval τ.
Deﬁnition 2: The weighted factor, ω, of a group g,g∈
{C, E, I }is deﬁned as:
ωg=Ψg
Ps∈{C,E,I }Ψs
Node idetermines the weighted factor of each group from
Ψi(t). The weighted factor of cooperators (ωi
C), exploiters
(ωi
E), and isolators (ωi
I) of node iare calculated using
Deﬁnition 2, as follows:
ωi
C=Ψi
C
Ψi
C+ Ψi
E+ Ψi
I
ωi
E=Ψi
E
Ψi
C+ Ψi
E+ Ψi
I
ωi
I=Ψi
I
Ψi
C+ Ψi
E+ Ψi
I
(8)
Deﬁnition 3: The weighted average message delivery
ratio γgof a group gis deﬁned as:
γg=αg×ωg.(9)
From Deﬁnition 3, the weighted average delivery ratio of
cooperators (γC), exploiters (γE), and isolators (γI) of
node iis calculated as follows:
γi
C=αi
C×ωi
C
γi
E=αi
E×ωi
E
γi
I=αi
I×ωi
I
(10)
Finally, node idetermines the most successful group,
gsucc, as follows:
gsucc =
C, γC> γE, γC> γI
E, γE> γC, γE> γI
I, γI> γE, γI> γC
(11)
Now, node ichanges its group to gsucc , if its delivery
probability, computed using Equation 3, is less than the
maximum of the weighted average delivery ratio of the
Accepted Version (For Personal Use Only)
7
three groups, i.e.,
pi< max(γi
C, γi
E, γi
I)(12)
The Strategy Adaptation mechanism is repeated exactly
in the same way for every node after each generation
interval for switching to a successful strategy. In par
ticular, we note that, if a node fails to perform self
evaluation due to one reason or another (for example,
when a node does not have information about the other
nodes available yet), it continues with its own previous
strategy. The ﬂowchart presented in Figure 3 shows the
sequence of steps performed by a node executing the
DISCUSS algorithm in an OMN.
Fig. 3: Flowchart depicting the steps in DISCUSS.
5 CHARACTERISTICS OF DISCUSS
This Section presents the complexity analysis and theo
retical analysis on the characteristic of DISCUSS.
5.1 Theoretical Analysis
Deﬁnition 4: A SDOMN with Nnodes is said to have
reached an equilibrium, when (1−δ)N(where 0≤δ < 1)
nodes have a common strategy, s∈S, and the remaining
δN nodes have different strategy/strategies. The com
mon strategy of the (1 −δ)Nnodes is said to be the
equilibrium strategy.
Deﬁnition 5: A SDOMN is said to have reached (K, δ)
convergence, if, for Ksuccessive generations, where K≥
1, at least the (1 −δ)Nnodes have the same common
strategy, s∈S, while the remaining nodes could possibly
have different strategy/strategies.
Theorem 1: Necessary criteria for (K, δ)convergence: Let
sibe the equilibrium strategy at the ith generation. If
si=sj,∀i,j∈ {1,2, ..., K}, SDOMN has attains (K, δ)
convergence.
Proof: The proof follows from Deﬁnition 5.
Theorem 2: Sufﬁcient criteria for (K, δ)convergence: Let
sibe the equilibrium strategy for the ith generation. It
is sufﬁcient to say that SDOMN moves toward (K, δ)
convergence with δ→0, when,
γsimax
si
{γs0
i}, s0
i∈S− {si}
Proof: Let Nsibe the set of nodes with strategy si,
and Ns0
ibe the set with strategies other than si. Using
Equation (9), we have,
γsi=X
n∈Nsi
αn
siωn
si
Assuming, γsiγs0
i, we have
X
n∈Nsi
αn
siωn
siX
n∈Ns0
i
αn
s0
i
ωn
s0
i
⇒X
n∈Nsi
αn
siΨn
siX
n∈Ns0
i
αn
s0
i
Ψn
s0
i
⇒X
n∈Nsi
pn
si
NsiΨn
siX
n∈Ns0
i
pn
s0
i
Ns0
iΨn
s0
i
The above holds true if any one of the following con
ditions holds: (1) NsiNs0
i, (2) individual αsiΨsi
αsi
0Ψsi
0, (3) both (1) and (2). Let us look at the individual
cases.
Case 1:
NsiNs0
i
⇒Nsi
Ns0
i
1
⇒Nsi+Ns0
i

Ns0
i
1
⇒1
δ1⇒δ1⇒δ→0.
This implies that SDOMN moves towards convergence.
Case 2: αsiΨsiαsi
0Ψsi
0implies that most of the nodes
from Ns0
iswitch to Nsi, which, in turn, implies that in
the next generation, SDOMN will attain convergence.
Case 3: If both Cases (1) and (2) occur, SDOMN will
attain convergence. Hence, the proof.
Theorems 1 and 2 bear signiﬁcance in the character
izability of the convergence time of strategies in the
concerned SDOMN. In other words, for given Kand
δ, one can determine if and when the OMN converges
to an equilibrium strategy. This is revisited in Section 7
in the context of experimental results.
Theorem 3: The message delivery ratio of the SDOMN
increases with the increasing number of cooperators.
Accepted Version (For Personal Use Only)
8
TABLE 2: Datasets
Accepted Version (For Personal Use Only)
Dataset Mobility type Device Network type No. of devices Duration (days)
Infocom06 [25] Real iMote Bluetooth 78 4
PMTR2[26] Real PMTR RTXRTLP 44 19
Sassy [27] Real Tmote Bluetooth 27 79
KAIST [28] Real Tmote GPS receiver 4 (92trace) 78
HCM [29] Mapbased  Bluetooth 100 5
RWP [29] Random way point  Bluetooth 100 5
Mathematically,
limNC→NX
s∈S
αs(t)=1
Proof: Let NC Ns0, where s0∈S− {C}. The
chances of message delivery for a group gincreases
with the increase in the corresponding Ψg. So, we can
approximate αsas αs(t)≈Ψs
Ps∈SΨs. As the number of
cooperators is high, ΨCis also high, since the coopera
tors forward the messages of exploiters as well. So, we
can write ΨCΨs0. Therefore,
αC(t) = ΨC
Ps∈SΨs
≈ΨC
ΨC
= 1.
Moreover, as Ψs0→0,αs0→0,s0∈S− {C}. Therefore,
X
s∈S
αs(t) = αC(t) + X
s0∈S−{C}
αs0(t)=1.
Hence, the proof.
Theorem 3 holds true in a SDOMN, when one of the
following conditions is satisﬁed.
1) The trafﬁc generation rate is low, but messages are
generated throughout the lifetime of the system
considered.
2) The trafﬁc rate is high and messages are generated
for a short period of time (possibly during the
initial generation of the SDOMN).
In the ﬁrst case, due to low trafﬁc rate, less number of
messages are generated in the time period considered.
Theoretically, almost all of them can be delivered to
the respective destination if most of the nodes are co
operators, so that limNC→NPs∈Sαs(t)=1. In reality,
depending on the contact patterns among the nodes,
the same may be less than unity. In the second case,
although more messages are created in the system, a
comparatively longer lifetime allows the delivery of
most of them. Again, when most of the nodes are
cooperators, limNC→NPs∈Sαs(t)=1. Moreover, this
theorem veriﬁes the goal function presented in Equation
2.
The case of high trafﬁc rate, however, deserves further
discussion. In real life, there are practical constraints
on the buffer capacity of the nodes and/or lifetime
of the messages. Even if such constraints are relaxed,
continuous generation of messages throughout the life
time of the system does not provide sufﬁcient time to
the nodes to deliver them using the storecarryand
forward approach that is typical of OMNs. This can be
realized from the fact that in OMNs, the average message
delivery latency is typically a few thousand seconds,
or may be more. Therefore, in such cases, the claim of
Theorem 3 can be suboptimal.
5.2 Complexity Analysis
Space complexity: Each node caches the CM ,DM, and
DP lists in its buffer. Let σ,ϕ,λ, and µbytes be
required to store a node’s address, message identity,
timestamp, and delivery probability, respectively. Let
us further assume that a node generates Xnumber of
messages, out of which Ymessages are delivered. Let
Nbe the total number of nodes in the SDOMN. We
compute the space required for storing Xmessages in
CM and Ymessages in DM. The space complexity for
storing CM ,DM, and DP at the nodes, respectively, are:
S(CM ) = X×(ϕ+λ)∈O(X), X (ϕ+λ)
S(DM ) = Y×(ϕ+λ)∈O(Y), Y (ϕ+λ)
S(DP ) = N×(σ+µ+λ)∈O(N), N σ+µ+λ)
So, the space complexity for storing these lists in a node
is O(n), where n=X+Y+N. As an illustration, let
σ= 8 byte (typical for Bluetooth hardware address),
ϕ=λ=µ= 4 byte and N= 100. So, the size of
the list DP is, DP = 100 ×(8 + 4 + 4) = 1600 byte.
Similarly, C M= 8Xbyte, and DM = 8Ybyte. So,
the total buffer overhead, B= 1600 + 8(X+Y)byte.
With X=Y= 100,B= 3200 byte. Figure 4 shows
the buffer requirement versus the number of messages
for storing the lists, when X=Y. This is insigniﬁcant,
given the fact that today’s smart phones have storage
capacities ranging upto a few gigabytes.
0
2
4
6
8
10
100 200 300 400 500
Buffer required (kilobyte)
Number of messages (X)
N = 100 N = 50 N = 25
Fig. 4: Storage overhead of DISCUSS as a function of the
number of messages.
9
Time complexity: The time complexity of Algorithm
1 is max{O(XY ), O(N2)}=O(m2), where m=N
or m=number of messages created or delivered. Thus,
the time complexity is quadratic in terms of the number
of nodes in the network, or the number of messages cre
ated and delivered – whichever is maximum. The most
time consuming part of DISCUSS algorithm (as shown
in Figure 3) is the phase of acquiring information, which
is described in Algorithm 1. The remaining steps of the
DISCUSS algorithm are linear or constant, and do not
affect the order of complexity. So, the time complexity
of DISCUSS is O(m2).
5.3 DISCUSS with Global Knowledge
For the sake of completeness and evaluating the effec
tiveness, we also consider a version of DISCUSS, where
the nodes have complete information about the SD
OMN. In this case, we assume the presence of a Central
Authority (CA) in the network, with which the nodes can
communicate instantaneously. Whenever a new message
is created (or delivered), the CA is informed by the
concerned node. Based on these information, the CA
computes the DP of each node. At the end of each τ, all
the nodes get the DP information of all the nodes in the
network from the CA. Based on this, the nodes adapt
their strategies to the most successful one, if required.
6 SIMULATION DESIGN
This Section gives the overview of the simulation setup,
data sets, and metrics used for evaluation.
6.1 Simulation Setup
We evaluated the DISCUSS scheme using the ONE
simulator [29] considering the synthetic mobility traces,
Helsinki City Map (HCM) and Random Way Point (RWP),
together with the realworld traces, as summarized in
Table 2. For the HCM and RWP mobility models, we
executed the simulations for 2 days with 100 mobile
nodes deployed in terrains of size 4500 ×3400 m2and
450×340 m2, respectively, moving at a speed of 0.5−1.5
m/s. Messages were generated uniformly at random be
tween every 30−40 seconds, with two randomly chosen
nodes as the sourcedestination pair. The message size
was chosen to be 25 KB and the TimetoLive (TTL) of
the messages was 5hours. Each node had a buffer size of
3MB, transmission speed of 250 Kbps, and transmission
range of 10 meter.
The nodes used the binary Spray and Wait [3] routing
protocol with maximum 10 copies per message. How
ever, it is important to point out that the proposed
approach is also applicable to other routing schemes.
At the beginning of the simulation, each node was
assigned to an initial strategy from the strategy set S, in
such a way that the percentage of the nodes having each
strategy was equal. In the static scenario, the nodes do
not change their respective strategies. On the other hand,
in the dynamic scenario, at the end of each generation
interval, the nodes decided whether or not to change
their respective strategies. Based on the information
available at the end of every generation interval (τ),
each node determined the most successful strategy and
switched to it if its strategy was not already the same.
Unless otherwise speciﬁed, the generation interval was
taken to be 1 hour.
The average value of the results taken over 30 runs to
gether with the corresponding 95% conﬁdence intervals
are reported.
6.2 Performance Metrics
We evaluated the performance of the proposed scheme,
DISCUSS, using the following metrics.
6.2.1 Group composition (size)
The percentage of cooperators, exploiters, and isolators
in each generation with respect to time.
6.2.2 Delivery ratio of messages
It is the ratio of the total number of messages success
fully delivered to the intended destination nodes to the
total number of messages generated in the OMN.
6.2.3 Average message delivery latency
It is the average time taken for delivering a message from
the source node to its corresponding destination node.
We also compared the results of performance eval
uation obtained for three variants of the DISCUSS al
gorithm: (a) DISCUSS in a dynamic scenario (referred
to as “dynamic” in the plots), (b) DISCUSS in a static
scenario (referred to as “static” in the plots), and (c)
DISCUSS with global knowledge, as discussed in Section
5.3 (referred to as “global” in the plots).
7 SIMULATION RESULTS
The results obtained in this manuscript are derived
considering the datasets given in Table 2. In this Section,
we present some results of DISCUSS using the metrics
given in Section 6.2.
7.1 Effects of Generation Interval
Figure 5 shows the delivery ratio of messages for dif
ferent mobility scenarios and different generation in
tervals (τ). In the KAIST and RWP mobility scenarios,
the message delivery ratio was higher when τ= 1
hour, and it decreased as τincreased. This is because,
as τincreased, the number of exploiters and isolators
remained the same in respective strategies for τtime.
So, the message drops increased in the OMN. It was
observed that in 1hour, almost all nodes had information
about the other nodes. So, they attempted to switch
their respective strategies. The sooner they changed their
strategies, the better they earned in terms of delivery
ratio. So, the overall networkwide message delivery
Accepted Version (For Personal Use Only)
10
0.2
0.3
0.4
0.5
0.6
1 2 3 4 5 6 7 8 9 10 11 12
Delivery ratio
Generation interval (hour)
(a) Infocom06
0.08
0.09
0.1
0.11
0.12
0.13
1 2 3 4 5 6 7 8 9 10 11 12
Delivery ratio
Generation interval (hour)
(b) Sassy
0.08
0.1
0.12
0.14
0.16
1 2 3 4 5 6 7 8 9 10 11 12
Delivery ratio
Generation interval (hour)
(c) KAIST
0.3
0.4
0.5
0.6
0.7
1 2 3 4 5 6 7 8 9 10 11 12
Delivery ratio
Generation interval (hour)
(d) HCM
0.3
0.4
0.5
0.6
0.7
1 2 3 4 5 6 7 8 9 10 11 12
Delivery ratio
Generation interval (hour)
(e) RWP
0.24
0.25
0.26
0.27
0.28
1 2 3 4 5 6 7 8 9 10 11 12
Delivery ratio
Generation interval (hour)
(f) PMTR
Fig. 5: Message delivery ratio for different generation intervals.
increased, as the generation interval was less. Similarly,
using the Infocom06 trace as well, the message delivery
ratio increased when τ= 1 hour. But in the HCM
scenario, the delivery ratio improved marginally, when
τ= 2 hour, because in this case, in the 1st hour, the
number of direct contacts was more than the indirect
contacts, which implies that almost all messages of nodes
are delivered. So, less number of nodes changed their
strategies. This is ascribed to the fact that delivery ratio
was high when τ≈1hour. So, hereafter, we consider
τ= 1 hour for all simulations. We present the exper
imental results using the mapbased (HCM), random
way point (RWP) mobility model and real mobility traces
(Infocom06 [25], KAIST [28], Sassy [27], and PMTR [26]).
7.2 Similarity Measurement
Similarity index is used to determine the similarity be
tween two documents. Using this metric, we established
the effectiveness of DISCUSS by comparing it with its
variant, in which global knowledge is available. Jac
card similarity index has been widely used in several
works related to DTNs such as in the work by Hui
and Crowcroft [30] as well as, wireless networks [31],
in general. The application of Jaccard similarity index
is suitable to evaluate the similarity of two sets, as
considered here, unlike many other similarity indices
such as, cosine similarity index, which operates upon
two vectors. Let, after a generation interval, NDand
NG, respectively, be the set of nodes using DISCUSS
and its variant with global knowledge. In particular,
NG=ND=N;NGand NDdiffer only in terms of
the strategies adapted by the different nodes, e.g., node
imay have strategy Cin NG, and Ein ND. Then, the
Jaccard similarity index [18] is deﬁned as:
Js=NG∩ND
Accepted Version (For Personal Use Only)
NG∪ND(13)
A measure of Js∈[0,1] indicates the closeness in per
formance of DISCUSS with local and global knowledge.
0
0.2
0.4
0.6
0.8
1
Infocom06 HCM KAIST Sassy PMTR RWP
Similarity
1h 5h 10h 15h 20h 1d 2d
Fig. 7: Jaccard Similarity Index between DISCUSS and
its variant with global knowledge.
Figure 7 shows the similarities between DISCUSS with
global and local knowledge for different time intervals.
It was observed that the value of Jaccard similarity
index increased with time, since the nodes obtained
more accurate information as time increased. For the
Infocom06, HCM, KAIST, and RWP data sets, the Jaccard
similarity index was observed to be above 85% at the end
of 2 days. However, for Sassy and PMTR, the similarities
varied between 55% to 70%. In RWP, initially, in the 1st
hour, very less number of nodes changed their strategies.
So, the similarity is higher at the beginning. In RWP,
the nodes met less frequently and for short durations.
Therefore, using the DISCUSS scheme, the nodes did
not exhibit accurate possessing information about other
groups. So, the similarity decreased signiﬁcantly (30%)
between 1 to 10 hours using RWP.
7.3 Effects on the Group Composition
Figure 6 shows the variation in group composition with
time. Initially, in all the simulation scenarios, the per
11
0
20
40
60
80
100
0 1h 4h 8h 12h 1d 2d
Group size (%)
Time
Cooperators Exploiters Isolators
(a) HCM
0
20
40
60
80
100
0 1h 4h 8h 12h 1d 2d
Group size (%)
Time
Cooperators Exploiters Isolators
(b) RWP
0
20
40
60
80
100
0 1h 4h 8h 12h 1d 2d
Group size (%)
Time
Cooperators Exploiters Isolators
(c) KAIST
0
20
40
60
80
100
0 1h 4h 8h 12h 1d 2d
Group size (%)
Time
Cooperators Exploiters Isolators
(d) Infocom06
0
20
40
60
80
0 1h 4h 8h 12h 1d 2d
Group size (%)
Time
Cooperators Exploiters Isolators
(e) PMTR
0
20
40
60
80
100
0 1h 4h 8h 12h 1d 2d
Group size (%)
Time
Cooperators Exploiters Isolators
(f) Sassy
Fig. 6: Variation in group composition with time.
centages of cooperators, exploiters, and isolators were
the same. In all the scenarios, the residual number of
cooperators varied between 85100% and the number of
exploiters and isolators varied between 010%, except for
the PMTR case. Using the PMTR trace, the percentage
of cooperators, exploiters, and isolators were 78%, 18%,
and 4%, respectively. We observed from Figure 6 that
the group size of the cooperators increased with time.
The reason behind this is that, generally, the weighted
delivery ratio (Equation (10)) of the cooperators is more,
because the cooperators help in forwarding other nodes’
messages. So, after each generation interval, more nodes
switched their strategy to cooperate, instead of exploit
and isolate. Another interesting phenomenon that was
observed is that for all the traces, approximately after
1 day, 80% nodes chose cooperation as their strategy
because cooperate is the most successful strategy. In the
HCM, RWP, KAIST, and Infocom06 traces, the number
of isolators reached 0% after 1 day and number of
exploiters reached 23%, except in the case of the HCM
and PMTR traces.
(K, δ)convergence: From Figure 6(d), it can be observed
that beyond 24 hour, the percentage of cooperators is at
least 91%. Let us consider the time duration between 24
hour and 36 hour consisting of 12 generations, with τ= 1
hour. We can infer from Figure 6(d) that the SDOMN
attained (K, δ)convergence, with K= 12 and δ= 0.1.
Similar inferences can be drawn from others as well.
7.4 Delivery Ratio of Messages
We evaluated DISCUSS with respect to the static,dy
namic, and global scenarios. Figure 8 shows the cumu
lative delivery ratio as a function of time. Here, ﬁrst
we discuss the comparison of DISCUSS in the static and
dynamic scenarios, and then we discuss DISCUSS in the
global and dynamic scenarios.
It can be observed that in all the mobility traces,
DISCUSS, in the dynamic scenario exhibited higher de
livery ratio, than in the static scenario. For example, in
the Infocom06 and HCM traces, the dynamic scenario
outperformed the static one by 31% and 17%, respec
tively, at the end of 2 days. The reason behind this is
that, in the static scenario, the groupwise percentage
of nodes remained the same throughout the simulation.
As discussed earlier, exploiters and isolators do not
cooperate in forwarding other nodes’ messages. So, the
message drop probability increased over time and the
delivery probability decreased for the static scenario. On
the other hand, in the dynamic scenario, the groupwise
percentage of nodes changed over time and most of the
nodes switched their strategy to cooperate. Hence, the
message delivery ratio increased over time.
Further, in the Infocom06 trace, the message delivery
ratio was less between 212 hours, because during this
time, the number of contacts per hour was quite less, as
shown in Figure 9(b). So, during this time, less number
of messages were delivered to the destination nodes.
However, in the HCM mobility trace, as shown in Figure
9(a), the number of contacts varied between 350 to 550,
which is nearly similar throughout the simulation dura
tion. So, the probability of message delivery increased
over time when the cooperators increased with time.
In KAIST mobility trace, the message delivery ratio
decreased after 12 hour, because the number of con
tacts was observed to be less among nodes, in between
12 −48 hour and message generation rate was uniform
Accepted Version (For Personal Use Only)
12
0
0.2
0.4
0.6
0.8
1
1h 4h 8h 12h 1d 2d
Delivery ratio
Time
Dynamic
Static
Global
(a) HCM
0
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0.4
0.6
0.8
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Delivery ratio
Time
Dynamic
Static
Global
(b) RWP
0
0.2
0.4
0.6
0.8
1
1h 4h 8h 12h 1d 2d
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Static
Global
(c) KAIST
0
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Delivery ratio
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Dynamic
Static
Global
(d) Infocom06
0
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0.8
1
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Static
Global
(e) PMTR
0
0.2
0.4
0.6
0.8
1
1h 4h 8h 12h 1d 2d
Delivery ratio
Time
Dynamic
Static
Global
(f) Sassy
Fig. 8: Message delivery ratio using DISCUSS in different scenarios.
Accepted Version (For Personal Use Only)
100
200
300
400
500
600
1 6 12 18 24 30 36 42 48
Number of contacts
Time (hour)
(a) HCM
0
2000
4000
6000
8000
10000
1 6 12 18 24 30 36 42 48
Number of contacts
Time (hour)
(b) Infocom06
Fig. 9: Number of contacts in the network.
throughout the simulation duration. The delivery ratio
of RWP, KAIST, PMTR, and Sassy were 51%, 15%, 25%,
and 16% respectively, due to assorted number of contacts
on different mobility scenarios.
We compared the delivery ratio of the messages in
global and dynamic scenarios. For the global scenario,
accurate and same information were possessed by all
the nodes and each node took decision accordingly. This
implies that the delivery ratio for the global case is
more accurate than the distributed case in the dynamic
scenario. In the KAIST, PMTR, and Sassy scenarios, the
delivery ratio was almost similar for both the global and
dynamic scenarios, as the number of contacts between
nodes were higher and repetitive. In RWP, the delivery
ratio of DISCUSS, in the global scenario was more than
that in the dynamic scenario, because in the later case,
the nodes did not get the complete information about the
other groups of nodes due to less number of contacts.
However, in the HCM scenario, the contacts were more
and repetitive. Initially, the nodes had partial informa
tion about network performance. As time progressed,
they came in contact with more number of nodes. So,
the delivery ratio in the case of the dynamic scenario
was almost the same as that of the global scenario after
1 day. Similarly, in the Infocom06 scenario, the delivery
probability of the dynamic scenario was approximately
the same as that in the global scenario.
0.4
0.5
0.6
0.7
0.8
0.9
1
20 40 60 80 100
Delivery ratio
Cooperators (%)
2030
3040
4050
5060
(a) HCM
0.3
0.4
0.5
0.6
0.7
20 40 60 80 100
Delivery ratio
Cooperators (%)
2030
3040
4050
5060
(b) Infocom06
Fig. 11: Variation in percentage of cooperators under
light trafﬁc load.
Figure 11 shows the delivery ratio of the messages
in SDOMN, under light trafﬁc load, which validates
the claim of Theorem 3. Here, the message generation
duration was taken as 1day. We considered the SD
OMN with static scenarios, by varying the percentage of
cooperators and message generation rates. It is observed
from Figure 11 that as the percentage of cooperators
increased, the delivery ratio of the messages increased,
correspondingly, towards unity.
7.5 Message Delivery Latency
Figure 10 shows the average delivery delay of the mes
sages of the three scenarios on different mobility data
sets. The average delivery latency of DISCUSS in the
dynamic scenario was observed to be almost similar
to that in the global scenario, for all the mobility data
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9000
1h 4h 8h 12h 1d 2d
Average delivery latency (second)
Time
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Static
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(a) HCM
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Average delivery latency (second)
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(b) KAIST
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Average delivery latency (second)
Time
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Global
(c) RWP
Accepted Version (For Personal Use Only)
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3000
4500
6000
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9000
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Time
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(d) Infocom06
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10000
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Average delivery latency (second)
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Global
(e) PMTR
0
2000
4000
6000
8000
10000
12000
1h 4h 8h 12h 1d 2d
Average delivery latency (second)
Time
Dynamic
Static
Global
(f) Sassy
Fig. 10: Message delivery delay using DISCUSS in different scenarios.
sets, except the Sassy mobility trace. Figure 10 shows
some variation in delay between the global and dynamic
cases because of incomplete information. In the HCM,
Infocom06, and RWP cases the average delivery latency
of the messages in the dynamic scenario is less than that
in the static scenario. In these mobility data sets, the
number of internode contacts is more. So, as the size of
the cooperator increased, more messages were delivered
and the delivery delay was reduced in the dynamic
scenario. For example, using the HCM mobility trace,
latency decreased to 136% at the end of 2 days. In the
Infocom06 trace, initially the delay was more between
4 to 12 hour, even in the presence of more number of
cooperators, as less number of contacts are available. As
the number of contacts increased, the delay gradually
decreased. In the KAIST, PMTR, and Sassy traces, the
average delay in the dynamic scenario is more than that
in the static scenario, because less number of contacts
are available. So, the message delivery latency is less.
8 CONCLUSION
In this paper, we proposed a distributed cooperation
mechanism, named DISCUSS, for stimulating the non
cooperative nodes to cooperate for forwarding the other
nodes’ messages. After a generation interval, each node
compares its own performance with the performance of
the other groups and selects the most successful strategy
accordingly. We veriﬁed the similarity and effectiveness
of DISCUSS in different scenarios. We compared the per
formance of dynamic DISCUSS with the static DISCUSS,
without changing the initial chosen strategies. Extensive
simulation results show that the dynamic DISCUSS is
better than the static DISCUSS. In the future, this work
can be extended by exploring other possibilities of non
cooperative node strategies. Also, the proposed mecha
nism can be extended by considering the privacy and
security aspects of SDOMN. We, further, plan in the
future to validate the results using a reallife testbed.
ACKNOWLEDGEMENT
This work was partially supported by a fellowship spon
sored by the Tata Consultancy Services (TCS), India.
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Dr. Sudip Misra is an Associate Professor in the
School of Information Technology at the Indian
Institute of Technology Kharagpur. He received
his Ph.D. degree in Computer Science from Car
leton University, Ottawa, Canada. His research
interest is broadly on wireless networks. He has
won 8 research paper awards in different confer
ences. He was awarded the IEEE ComSoc Asia
Paciﬁc Outstanding Young Researcher Award at
IEEE GLOBECOM 2012. He was also awarded
the Canadian Governments NSERC Post Doc
toral Fellowship and the Humboldt Research Fellowship in Germany.
Sujata Pal is currently pursuing her PhD degree
from the School of Information Technology at the
Indian Institute of Technology Kharagpur, India.
She received the MTech degree in Multimedia
and Software System from the West Bengal
University of Technology, India, in 2007 and the
BE degree in Computer Science from the North
Orissa University, India, in 2002. She has more
than 5 years of teaching experience. Her current
research interests are DTNs and MANETs.
Barun Kumar Saha is pursuing MS at School of
Information Technology, Indian Institute of Tech
nology Kharagpur. His research interests include
MANETs, DTNs, OMNs together with human
aspects and their applications, and the use of
technology for advancing education. He is the
lead developer of the Software Engineering and
Advanced Network Technologies Virtual Labs.
He maintains a tutorial on DTNs at http://delay
tolerantnetworks.blogspot.com/