Ghani et al

*Dr. Anwar Ghani, Department of Computer Science & Software Engineering, International Islamic University Islamabad,

Department of Computer Science & Software Engineering, International Islamic
University Islamabad

# SOS: Socially Omitting Selfishness in IoT for Smart and Connected
Communities

Ghani ur Rehman  Anwar Ghani  Muhammad Zubair  Muhammad Imran Saeed  Dhananjay
Singh (Senior Member  IEEE)  Department of Computer Science & Software
Engineering, International Islamic University, Islamabad 44000, Pakistan
Faculty of Computer Science & Bioinformatics, Khushal Khan Khattak University,
Karak 27000, Pakistan  Department of Electronics Engineering, Hankuk
University of Foreign Studies, Yongin 449-791, South Korea
<EMAIL_ADDRESS>G. Rahman  A. Ghani  M. Zubair  M. I. Saeed  D. Singh

(26 April 2016; 6 June 2016; 6 June 2016)

###### Abstract

[Summary]Smart and Connected Communities (SCC) is an emerging field of
Internet of Things (IoT), and it is having potential applications to improve
human life. The improvement may be in terms of preservation, revitalization,
livability, and sustainability of a community. The resources of the nodes and
devices in the SCC have certain constraints that may not allow the devices and
nodes to cooperate to save their resources such as memory, energy, and buffer,
or simply maximize their performance. Thus, to stimulate the nodes to avoid
selfish behavior, SSC needs a novel and well-organized solution to motivate
nodes for cooperation. This article aims to resolve the issue of selfish
behaviors in SCC and to encourage the nodes for cooperation. A novel mechanism
Socially Omitting Selfishness (SOS) has been proposed to manage/eradicate
selfishness using a socially-oriented election process. The election process
elects different heads based on weight and cooperation (using VCG model). The
election of heads and incentive mechanism encourages the nodes to show
participation and behave as highly cooperative members of the community.
Furthermore, an extended version of the Dempster-Shafer model has been used to
discourage the selfish behavior of the participating nodes in the SOS scheme.
It uses different monitoring and gateway nodes to efficiently employ the
proposed scheme. A mathematical model has been developed for the
aforementioned aspects and simulated through NS2 simulation environment to
analyze the performance of SOS. The results of the proposed scheme outperform
the contemporary schemes in terms of average delivery delay, packet delivery
ratio, throughput, and average energy.

, , , , and (2019), SOS: Socially Omitting Selfishness in IoT for Smart and
Connected Communities, International Journal of ABC, XYZ, 2019;00:1–16

###### keywords:

Sustainability, IoT, Revitalization, Smart and connected communities, Social
selfishness, Selfish behaviors,Incentive techniques

††articletype: Research Article00footnotetext: Abbreviations: SOS, Socially
Omiting selfishness; IoT, Internet of Things; SCC, Smart and Connected
Communities

##  1 INTRODUCTION

Internet of things (IoT) is an emerging area in modern communication networks.
It consists of different devices (things) such as machines, people, animals,
and objects embedded with sensors and actuators 1. Data sensed by these
sensors and actuators are relayed to remote servers for further processing.
The processing time and storage capacity depends on the capabilities of IoT
objects 2, 3, 4, 5, 6, 7, 8. The advancements in IoT are influential on the
smart and connected Communities (SCC). The main considerations of the SCC are
the present past and future of the major areas of society 9, 10. Aims and
objectives of SCC are the preservation, livability, revitalization, and
sustainability of human life to make life easy by remembering the past of
human life, focus on present, and plan for a better future. The cultural
heritage for communities is preserved and living needs are referred to as
livability 11. Sustainability is the need of making plans for the future
having three important pillars such as social, environmental, and economic
aspects 12. The nodes in SCC can store and forward data to the destination hop
by hop. It means nodes need to be socially cooperative to relay data. However,
some of the nodes adopt a selfish nature to save their resources.

Limited resources, preservation of bandwidth, manipulation, energy savings and
other social behavior of the nodes make the node self-centered and selfish.
Selfishness may be categorized as individual and social 13, 14, 15. The
individual selfishness has the same degree of selfishness with all other nodes
that may not forward messages even to its neighbor. Social selfishness has a
different degree of selfishness because the relationship varies with all other
nodes in the network. The nodes having a variable relationship with neighbor
nodes may forward messages to their friends, and refuse to forward messages to
the strangers. The selfish behavior of the node has an adverse effect on the
throughput of the network and can affect the performance of the network by
losing connectivity 16. In this article, the problem of selfishness either
individual or social has been considered to design a scheme that can omit the
selfishness using a social mechanism of election in SCC.

There are many existing techniques such as the Game-theoretic reward-based
system and the Cooperative Watchdog System that addressed the issue of
selfishness and motivate node for cooperation. Different types of cards are
assigned to nodes according to their selfishness level in the first scheme. In
the second scheme, a reputation score is assigned to each node. However, these
schemes have issues like (i) Individual importance factor problem: in 17, only
individual importance of a node is considered for trust. (ii) monitoring nodes
can be selfish: this scheme only monitors the behaviors of the selfish node.
But monitoring node can also be selfish as they give wrong information about
the selfish or cooperative nodes (iii) strict punishment issue: the node can
not join the network once it has been punished 18. Node Selfishness is omitted
differently in proposed SOS scheme. It also monitors the behavior of the
monitoring nodes in the network that is ignored in the above two schemes.
Another feature of the proposed scheme is the computation of the collective
trust of monitoring nodes. The collective trust called Collective Importance
Factor (CIF) is computed to entitled any monitoring node to be cooperative or
selfish. It also gives a chance to the selfish node after showing selfishness
for the first time. A participating node can rejoin the network by making
negative payment first. At last, the node can be expelled from the network for
showing selfish behavior repeatedly.

In this article, a new motivation system is presented called socially omitting
selfishness. In this system, selfish nodes are encouraged to take part in the
election. SOS communities are headed by a different head, elected in the
election process. The different types of elected heads are community head
($CH$), monitoring head ($MH$), and incentive head ($IH$). These community
heads are elected based on certain features such as number of votes they get
in election, weight, and cooperation. Each of these heads has their own duties
in the community. The nodes that have malicious or selfish behaviors are
fined. SOS scheme motivates the selfish nodes for cooperation in SCC. The
primary contributions of the proposed SOS scheme in SCC are:

  * •

To analyzed the selfish behavior of nodes in SCC both individually and
socially. Furthermore, to analyze the effect of nodes participating in the
overall performance of the network.

  * •

To design a novel strategy that motivates the non-cooperative nodes for
participation within the community to improve network performance in SCC.

  * •

To formulate criteria taking into account certain parameters like weight and
cooperation that can be used to elect various heads such as Community Head,
Monitoring Head, and Incentive Head.

  * •

To develop a mechanism for solving the weight tie issue by adding cooperation
as a parameter for the next nomination criteria in the election of heads in
the election process.

  * •

To introduce an effective monitoring scheme that constantly monitors the
selfish behavior of the nodes to calculate the Collective Importance Factor
(CIF) and track the behavior of the monitoring head as well to avoid any
discriminatory behavior on the part of the monitoring head itself.

  * •

To perform comparative analysis of the proposed scheme, different incentive-
based schemes are compared with the proposed scheme to gauge the performance
of the proposed scheme in terms of improving human livability, revitalization,
improved packet delivery ratio, average delivery delay, and energy constraint.

The remaining of the paper is organized in the following sections: Section 2
discusses related works. Section 3 shows the detailed design of SOS. Section 4
shows the performance evaluation of the proposed SOS scheme. The paper is
concluded and future work is discussed in section 5.

##  2 RELATED WORK

SCC has the number of nodes that are selfish and non-cooperative in nature.
The selfish behavior of the nodes has been widely examined and is highly
interested in the researchers. Selfish nodes are degrading the overall
performance of the network 19. The incentive-based techniques are implemented
to encourage the selfish nodes for cooperation and share its resources
altruistically with other nodes in the network 20, 21. The incentive-based
technique is further classified into four classes such as reputation-based,
game-theoretic-based, credit-based and barter-based system.

The reputation-based motivation system depends on the degree of node
cooperation within the network. Compared to non-cooperative nodes, cooperative
nodes are extremely appreciated. The credit-based scheme operates to give the
nodes some benefits for showing cooperation. The nodes can subsequently
utilize this awarded credit for their purpose later. Barter-based motivation
systems, also known as the Tit-For-Tat (TFT) approach, where nodes share the
same information.

Yuxin et al. 22 proposed a game-based incentive scheme. Two types of
relationship such as competitive and cooperative are considered. To motivate
the nodes for cooperation, a contribution measurement is given during the
game. Annalisa et al. 23 proposed a social scheme called SORSI for detecting
selfishness and encourage the selfish nodes for data forwarding. Ning et al.
24 proposed a CAIS scheme to discourage selfishness in social networks. In
this scheme, social interactions among nodes make communities in the network.
The nodes are rewarded two types of credit namely social credit and non-social
credit for data forwarding within the same communities or different
communities.

Wang et al. 25 proposed a hop limited flooding scheme to tackle the issue of
selfishness in Delay Tolerant Networks. Jedari et al. 26 proposed a social-
based watchdog system to detect selfish nodes in an opportunistic mobile
network. This scheme differentiates the degree of the selfishness of the nodes
because the punishment and rewarding process employed to encourage nodes might
not be the same for all nodes in the network. Wang et al. 27 presented an
incentive approach to resolve the issue of selfishness in the urban
environment. Seregina et al. 28 addressed the issue of selfishness by proposes
a reward-based mechanism to motivate the mobile nodes for cooperation.

Lu et al. 29 proposed a Geographic information and node selfish-based scheme
to tackle the problem of selfishness. To choose a decent next-hop relay node,
the readiness of the relay node is merged with geographic informations. Wei et
al. 30 proposed a community-based and reputation-based incentive scheme to
motivate a selfish node to take part in data forwarding. In this scheme each
node can retain, update and show reputation for verification whenever
necessary. The critical factor in this scheme is the altruism function that
kicks out selfish nodes.

In the research article 17, the author presents a game theoretical reward-
based system to manage selfish nodes in the network. Dias et al. 18 proposed a
cooperative watchdog system to tackle the issue of selfishness by assigning a
reputation score to all nodes in the network. Fawaz et al. 31 proposed an
incentive mechanism to motivate the selfish nodes for cooperation in the
vehicular networks. In the research article 32, the author proposed a barter-
based scheme to resolve the issue of selfishness in the network. Liu et al. 33
also proposed the barter-based scheme among different communities to encourage
selfish nodes for taking part in message forwarding.

Li et al. 34 proposed a Social scheme to manage selfishness in the network. In
a social community or group, the nodes with strong social interactions with
one another are likely willing to forward data. In the research article 35,
the author proposed an incentive system to manage selfish nodes in vehicular
networks. In this scheme, an incentive is given as a reward to the nodes for
sharing different information. The provided information is related to the
construction of roads, traffic congestion, and road accidents. In 36, the
author proposed stable and reliable data dissemination that forward data to
other nodes in the network intelligently. Sobin et al. 37 proposed a
selfishness and buffer-aware routing (SBR) to deal with the issue of
selfishness. Yamini et al. 38 proposed an advanced collaborative mechanism to
deal with the problem of selfishness in Mobile Ad hoc network. Ganesan et al
39 proposed Semi Markov process inspired selfish aware cooperative scheme
(SMPISCS) for wireless sensor networks In this scheme, selfish nodes are
encouraged for cooperation in the network. Muhammed et al. 40 presented Game
Theory-Based Cooperation for Underwater Acoustic Sensor Networks. In this
scheme, the selfish nodes are motivated for showing cooperation in the
network. Terence et al. 41 proposed behavior based routing misbehavior
detection (BRMD) for wireless sensor networks to identify false advertiser
node in the network. In this scheme, the sensor nodes are constantly monitored
by the neighbor nodes. In smart and connected communities environment, safe
and stable communication is required 42.

Thus, SOS is a socially-oriented system that is used to forward data to the
participating nodes in SCC depending on social interaction among nodes.
Election scheme is used by SOS to encourage the selfish nodes for
participation in a network. During the election, the elected heads motivate
the selfish nodes for cooperation in a community. Nodes are given some
incentive for their cooperative behaviors with other nodes that have greatly
improved the performance of the network. Table 1 provide the comparison of SOS
with some existing Schemes.

Table 1: Comparison of SOS with some Existing Schemes Paper and Authors |  Contributions |  Limitations |  Comparison with SOS
---|---|---|---
Umar et.al 17 |  A game-theoretical reward-based system to manage selfish nodes in the network. Different types of cards are assigned to nodes according to their selfishness level in scheme. |  Individual importance factor problem: Only the individual importance of a node is considered for trust. Monitoring nodes can be selfish: This scheme only monitors the behaviors of the selfish node. |  Collective Importance Factor (CIF) is computed to entitled any monitoring node to be cooperative or selfish. It also monitors the behavior of the monitoring nodes in the network
Dias et al. 18 |  Proposed a cooperative watchdog system to tackle the issue of selfishness by assigning a reputation score to all nodes in the network. |  Strict punishment issue: the node can not join the network once it has been punished. It only detects Selfish nodes. |  Node Selfishness is omitted in the proposed SOS scheme. Nodes are get warned for showing selfish behavior for the first time.
Terence et al. 41 |  Proposed behavior-based routing misbehavior detection (BRMD) for wireless sensor networks to identify false advertiser node in the network. |  No punishment scheme is defined for selfish nodes by showing selfish behavior repeatedly. |  A Proper punishment scheme is defined in the SOS scheme.
Lo et al. 43 |  Proposed a multi-head clustering algorithm in vehicular ad hoc networks to handle the issue of selfishness |  Weight tie Problem: when the weights of two nodes are the same, then there is no alternate criteria to elect heads in the election. |  Cooperation is considered as the next criteria to elect heads during election

##  3 SYSTEM MODEL

The proposed scheme SOS relies primarily on node involvement in the network.
Nodes in the community have various performance-related activities, like
forwarding of messages, monitoring, and tracking of nodes in the community.
These activities are considered to be the main duties of the participating
nodes in the election process. The nodes in the community are motivated to
participate and function as a unit. One of the important features of the
community-based node is to monitor the behavior of the neighbor node. This
characteristic of a node provides complete control over the message
transmitting and receiving. Therefore, an incentive system in the shape of
reputation is suggested to encourage and stimulate the nodes to accomplish
their tasks in the network by the proposed scheme. Nodes with malicious or
selfish behaviors are motivated to engage in the voting system and act as
cooperative nodes. Selfish nodes are also punished in the form of removal from
the community for showing malicious behaviors repeatedly. This punishment
message is broadcasted in the community regarding such nodes. Since the
proposed system is divided into two phases such as election scheme and payment
method. Thus, the nodes initiate involvement in the election process which
establishes the election process.

The community is controlled through the election process periodically. During
the voting system, different heads are elected on the basis of higher weight,
cooperation and a higher percentage of votes in the election. A node receiving
a greater amount of votes, weight, and cooperation are elected as CH, second
as MH, and third as IH. Election table is used to keep the records of all
elected heads. The proposed scheme is applicable in IoT for smart and
connected communities. Nodes are encouraged to cooperate with one another and
due to this, selfishness is omitted which improves the livability,
preservation, revitalization, and attainability of the community. The
selfishness and cooperative nature of the nodes are determined by monitoring
nodes through some trust value. The range of the trust value is in [0,1].
Nodes having a trust value greater than 0.5 is considered as cooperative and
if the trust value is less than 0.5 then it is considered as selfish. Table 2
lists the notations used in the proposed scheme.

Table 2: Notations Notation |  Description and Explanation
---|---
$x$ |  It is the elected Community Head $CH$ during the election process on the basis of higher votes, weight, and cooperation
$cp_{x}$ |  Contacts of the node $x$ with other nodes in the community
$W_{x}$ |  Weight of node $x$ shows the remaining resource it possesses
$Vt_{x}$ |  Node $k$ voted for community head $CH$
$M_{x}$ |  Monitoring node that constantly monitors the behavior of the nodes in the community
$P_{f}$ |  Fixed Payment that is given to nodes for their cooperative behaviors
$F_{b}$ |  Fixed Payment for each node that participates in the community election
$\Psi_{x}$ |  Per member payment
$IF_{x}$ |  Importance Factor shows the honesty of node $x$

###  3.1 Election Participation Payment

The proposed system SOS forward information in the community. The method
starts with the involvement of nodes in the voting phase. The method of
community formation and maintenance is an outreach for this article and
therefore not discussed here. Table 3 lists some of the important variables
that are used in the proposed scheme.

Table 3: Introduction of some key variables Variables |  Description
---|---
$Weight$ |  Weight is the number of remaining resources a node possesses.
$Cooperation$ |  Nodes are regarded as cooperative if they make contacts with the more nodes in the community i.e $cp_{x}>k$ where $k=\lceil\frac{n}{3}\rceil$.
$Selfishness$ |  To measure selfishness of the node, nodes with contact $cp_{x}\leq k$ are declared as selfish.
$Reputation$ |  Reputation of the monitoring nodes is calculated on its honest behavior in the network.
$CIFRule$ |  The Collective Importance Factor preludes the monitoring nodes from making prejudice decision about the node having a mutual relationship prior to it.

####  3.1.1 Election Procedure

The first phase of SOS conducts the election process based on certain
eligibility criteria. Two properties such as weight and cooperation of nodes
are eligibility requirements. Nodes are nominated for the election that has
greater (remaining) weight and cooperation. The weight is simply the total
amount of resources a node possesses.

Energy Ratio ($E_{x}$): The SCC devices are resource-constrained. Therefore,
energy is also limited. Let $E_{x}$ be the energy and is provided by:

$E_{x}=\frac{Er_{x}}{Emax_{x}}\times 100\%$ (1)

Where the current remaining energy of node $x$ is $Er_{x}$ and highest energy
of node $x$ is $Emax_{x}$.

Buffer Ratio ($B_{x}$): The space in buffer is decreased by storing more data
in it. Where $B_{x}$ is the proportion of the remaining buffer that reflects
the node position in the buffer. The remaining status of the buffer is
provided by:

$B_{x}=\frac{Br_{x}}{Bmax_{x}}\times 100\%$ (2)

Where $Br_{x}$ is the remaining buffer of the node $x$ presently and
$Bmax_{x}$is the highest buffer.

Remaining Time-To-Live $(TTL)$: $TTL$ is related to the delivery of a bundle.
Each node should forward bundle before its $TTL$ is expired. Node is not
considered for payment after its $TTL$ is expired. The status of node in the
form of $TTL$ is given by:

$T^{x}_{{ID}_{m}}=\frac{TTLr_{x}}{TTL}\times 100\%$ (3)

where $T^{x}_{ID_{m}}$ is the remaining $TTL$ of bundle $ID_{m}$ carried by
node $x$ presently and $TTLr_{x}$ is the remaining $TTL$ of the bundle.

Node Degree $ND_{x}$: This indicates the number of nodes as neighboring nodes
in the node $x$ transmission range.

$ND_{x}=\sum_{y\in n,y\neq x}\big{\\{}y\mid dis(x,y)<T_{range}\big{\\}}$ (4)

Relative Distance $(RD_{x})$: By 43, the SCC nodes have certain features of
how near they are to each other. Each node computes its own proximity to the
mean distance and is provided by the formula given below:

$RD_{x}=\mid_{pos}-\omega_{pos}\mid=\sqrt{((X_{x}-X_{\omega})^{2}+(Y_{x}-Y_{\omega})^{2})}$
(5)

In the given equation, $x_{pos}$ indicates the location of $x$, the mean
location of any node with its neighbors of $x$ is depicted by $\omega_{pos}$ ,
$(X_{x},Y_{x})$ is the $X$ and $Y$ coordinates of node $x$ and
$(X_{\omega},Y_{\omega})$, shows the coordinate of $\omega$ position. Once the
results of all five characteristics are computed, their weight is determined
by:

$W_{x}=E_{x}.wt_{1}+B_{x}.wt_{2}+T^{x}_{{ID}_{m}}.wt_{3}+ND_{x}.wt_{4}+RD_{x}.wt_{5}$
(6)

where $W_{x}$ is the node $x$ weight, the weights
$(wt_{1},wt_{2},wt_{3},wt_{4},wt_{5})$ are randomly chosen, where the absolute
weight is equivalent to 1 comparable to 44. The scenario where the weight of
two node is same, then cooperation is adopted as the next criteria for
nomination of nodes for election. Cooperation is provided by:

$\displaystyle cp_{x}=\sum_{n\in N}Rc_{x}(n),ifcp_{x}>k$ (7)

Where $k=\lceil\frac{n}{3}\rceil$, $cp_{x}$ is the node $x$ cooperation, all
nodes in the community is $n$ and $Rc_{x}$ is node $x$ total contacts. Nodes
are regarded as cooperative if they make contacts with the more nodes in the
community i.e $cp_{x}>k$. To measure selfishness of the node, nodes with
contact $cp_{x}\leq k$ are declared as selfish. Node with greater weight and
cooperation is nominated as an election candidate. The node makes
communication in its range for a short duration. There is a chance that the
information given by the node may be incorrect in terms of its weight and
cooperation. A node may illustrate its weight as underweight and overweight.
The underweight illustration will prevent it from being elected as community
head and the overweight will offer it some opportunities to become a leader of
the community. VCG model is used to develop and increase the trust behavior of
the nodes within the community. The aim of this model is to expose the
incorrect information regarding the node weight. Algorithm 1 provides the
details of the election procedure. In this algorithm, nodes are nominated for
election based on two characteristics weight and cooperation. The nodes having
higher weight and cooperation are nominated for election. After nomination,
heads such as community Head $CH$, monitoring Head $MH$, and Incentive Head
$IH$ are elected in the election by getting a higher number of votes. All the
nodes that participated in the election are also awarded some sort of payment.
Election table is used which contains all the record of the nodes that
participated in the election.

Algorithm 1 Election procedure & Payment to participating nodes

####  3.1.2 Vickrey, Clarke, and Goves (VCG) Approach

Vickrey, Clarke, and Groves (VCG) is a helpful technique that utilizes game
theory tools. This model is used to demonstrate the behaviors of all nodes
within the network and also encourage the node to tell the truth 45. Let $P$
be all the members of VCG model, where each member $m\in\\{{1,2,3,...n}\\}$
has some personal information $\theta_{m}$, known as member type. Let
$Z_{m}\in\Theta$ be any strategy that a member $m$ can use to input in a
mechanism. Let $P=\\{{P_{1},P_{2},P_{3},...,P_{n}}\\}$ be the specific payment
vector. To calculate explicit payment vector
$P=\\{{P_{1},P_{2},P_{3},...,P_{n}}\\}$, the VCG model takes input from all
the members to generate overall output $O=O\\{{Z_{1},Z_{2}...,Z_{n}}\\}$. The
output generated shows the preference of each node as cost function
$C_{m}=(\Theta,O)$. The handling of such information shows the usefulness of
each member as calculated by cost function $U_{m}=P_{m}-C_{m}(\theta_{m},O)$.

To cope with the features of SCC, a slight modification is made to the present
model in this article. Previously, only the energy level of a node was
considered as its persona data. To illustrate the energy level of a node, the
truth-telling behavior of the VCG model was used 46. Here in the proposed
scheme, the energy level of a node and as well as some other parameters such
as buffer ratio, message $TTL$, relative distance and node degree are
considered as the weight of a node. The individual personal information of a
node is the weight of a node. Every node is awarded a real number called
reputation value that depends on the reward or penalty a node receives from
the community head. Nodes reputation improves or reduces after every voting
process and is dependent on nodes cooperation within the network.

####  3.1.3 Post Election Payment Based on VCG Model

There are $n$ nodes in the game. Each node is a community player. The nodes in
the contest or game need to disclose their weight to initiate the electoral
process. In the election process, some nodes are elected as heads and
recognize others as participants. Payment in the shape of reputation is made
to both elected heads and participants. Every node in the contest tries to
improve its reputation $R$. Higher reputation nodes get more network
utilities. A reputation table known as $RTable$ is maintained by each node in
the community. This table has all the details regarding the reputation of the
neighbors and it is modified whenever necessary. Algorithm 2 provides the
details of the payment process and operations of all heads elected in the
election. In this algorithm, the monitoring nodes are assigned the
responsibility to constantly check the behaviors of neighbor nodes in the
community. The incentive head is responsible for making payment to the nodes.
Nodes are awarded some incentive for showing cooperation in the community. But
for showing selfish behavior in the community, nodes are also punished.
Sometime Monitoring nodes can also be selfish. In such cases, $CH$ also
computes the Importance factor of all monitoring nodes.

Algorithm 2 Operational Phase & Packet forwarding Payment Process

##### A. Community Head $(CH)$ Payment

After the completion of the election process, every node in the network gets
its payment. The payment to the $CH$ is made based on the votes given to it by
the participating nodes. All the nodes in a community election process that
voted for $CH$ are awarded incentive that is considered as the cost of the
$CH$ elected in the election. The cost vector that is actually the weight of a
node is expressed by $W_{1},W_{2},....,W_{n}$, where $n$ is the number of
nodes in total. The differentiation between receiving and making incentive is
the $CH$ profit.

$\displaystyle P_{m}(x)=\sum_{k\in
n}(Vt_{x}(W,k))\times(F_{b})\times(\Psi_{x})$ (8)

Where $(Vt_{x}(W,k)$ in the election scheme generates particular value (equals
1 if $k$ votes for $x$, 0 is produced otherwise). The $IH$ also determines
particular fixed budget$F_{b}$ for each node involved in election (this
payment is well-know and fixed to all nodes) and $\Psi_{x}$ is node payment as
provided below:

$\displaystyle\Psi_{x}$ $\displaystyle=W_{x}+\frac{1}{\sum_{k\in
n}Vt_{x}(W,k)}\times(\sum_{l\in n}(W_{y})).\times{\sum_{k\in
n}Vt_{y}}(W|W_{x}=\infty,k)-{\sum_{y\in n}}(W_{y})$ $\displaystyle{\sum_{k\in
n}}(Vt_{y}(W,K))$ (9)

##### B. Community Members Payment

On the basis of fixed payment $F_{b}$, the absolute cost of the nodes is
shared among all the nodes by the $CH$ (nodes that gave the vote). The cost
function $Cost_{x}$ determined by $CH_{x}$ is given under:

$\displaystyle C_{s}(x)=\frac{1}{W_{x}}\ast(W_{y}-W_{x})\sum_{k\in
n}(Vt_{x}(W,k))\times(F_{b})\times\Psi_{x}$ (10)

where $W_{x}$ and $W_{y}$ indicate the maximum and second maximum nodes
weights of the node involved in the election process. The elected heads in a
community deducted the absolute cost from their payment to calculate their own
reputation.

$R_{p}(x)=P_{m}(x)-C_{s}(x)$ (11)

The absolute cost of the nodes is shared among them depending on the nodes
reputation. The $CH$ announces a payment to member nodes through some
$CH_{ack}$ notification. The messages are signed and checked using standard
message authentication. Each of the nodes updates the $RTable$.

###  3.2 Packet Forwarding Payment in a Community

The $CH$ and Gateway nodes are constrained for forwarding of messages only
acting as relay nodes. The relay nodes may not forward some packets by showing
selfish behavior. Such selfish behaviors of a node in SCC have undesirable
effects on community performance. It also manipulates community nodes
disconnection and added to the percentage of packet drop ratio. Each node
receives an incentive for message forwarding in the form of reputation 44. The
monitoring nodes that control the function of relay nodes make the payment
scheme more efficient.

####  3.2.1 Payment for Relay Nodes in a Community

Payment is made to the node for each packet forwarding in the proposed scheme.
The incentive head makes a fixed payment $p_{f}$ to the nodes in the network.
This $P_{f}$ is made on the cooperative behavior of the nodes. Therefore, the
monitoring system is presented, that collects impervious of all monitoring
nodes. These evidences contribute to the computation of the decision-making
behavior of the relay nodes.

##### A. Collective Trust of Monitoring Nodes in a Community

In the proposed scheme, a relay contains four monitoring nodes. In these four
monitoring nodes, one of them is monitoring head that is elected in the
election process. The other three monitoring nodes are participating nodes
picked in a round-robin mode. A packet hash 47 is generated by each node to
maintain the packet genuine and prevent the packet from being changed by the
forwarder. Furthermore, the hash score of the forwarded packet will be
verified when the packet arrives at the next relay node. It ensures that the
packet sent will be consistent whenever the hash results match. In a case,
when hash results do not match, then the relay node is labeled as selfish and
collect a negative reward. Each node keeps a record of the forwarded packets
in its buffer. These packets will be sent next after some expected lifetime.
The monitoring node generates a trust report regarding the behavior of nodes
after certain threshold time period. The trust report specifies nodes
intentions to forward messages. The trust report shows the behavior of the
nodes as selfish or cooperative. The four monitoring nodes send a report to
$CH$ to compute the trust value. If the Collective trust assessment of the
cooperative nodes surpasses the malicious conduct, the forwarder is labeled
genuine and receives favorable payment (reputation) otherwise selfish nodes
will receive negative payment after repeatedly showing selfish behavior
(punishment). To obtain comparable outcomes (avoid inconsistent results), the
Collective Importance Factor (CIF) principle is presented to compute the trust
depends on evidence from distinct nodes.

##### B. Use of Extended Dempster-Shafer to Merge Evidences

The Dempster-Shafer uses mathematical formulas to resolve uncertainty
situations in a network 48, 49. This model is primarily used in finding
routing attacks in mobile ad hoc networks 45 and for calculation of collective
trust. The evidence theory is calculated by $\delta$, which is a frame of
judgment and probability assignment function BPA 44. A frame of judgment
$\delta$ shows a mutually exclusive and exhaustive hypothesis, showing only
one of them is true. BPA function shows $b:2^{\delta}\longleftarrow[0,1]$
satisfying two of the condition:

$b(\emptyset)=0$ (12)

and

$\sum_{A\subseteq\vartheta}b(A)=1$ (13)

Where $\vartheta$ is null set having A is any subset of $\delta$ to calculate
two BPA functions $b_{1}$ and $b_{2}$, the DS theory gives the following rule:

$b(C)=\frac{\sum_{A\cap B=C}b_{1}(A)b_{2}(B)}{1-\sum_{A\cap
B=\emptyset}b_{1}(A)b_{2}(B)}$ (14)

The limitations of DS theory has that it treat all the evidences equal and the
priorities of the evidences are not considered. 50 has introduced the
importance factor IF in the proposed Extended Dempster-Shafer (EDS) rule.
Where IF is a real number calculated on the basis of the importance of
evidence. 50 defines basic probability assignment rules for two importance
factor $IF_{1}$ and $IF_{2}$ having $EV_{1}$ and $EV_{2}$ evidences:

$b(C,IF_{x},IF_{w})=\frac{\sum_{A_{x}\cap
B_{x}=C}\bigg{[}(b_{1}(A_{x})^{\frac{IF_{x}}{IF_{w}}}b_{2}(B_{w})^{\frac{IF_{w}}{IF_{x}}}\bigg{]}}{\sum_{C\subseteq\theta,C\neg=\emptyset},\sum_{A_{x}\cap
B_{x}=C}\bigg{[}(b_{1}(A_{x})^{\frac{IF_{x}}{IF_{w}}}b_{2}(B_{w})^{\frac{IF_{w}}{IF_{x}}}\bigg{]}}$
(15)

However, in some situations, both DS and EDS theories generate output which is
irrational 51.

#####  C. Trust Calculation Based on Collective Importance Factor

The Collective importance factor preludes the monitoring nodes from making
prejudice decision about the node having a mutual relationship prior to it. It
may declare the node as selfish and punish it. Thus, an importance factor that
distinguish between honest and dishonest monitoring nodes in a community is
essential. The reputation of the monitoring nodes is calculated on its honest
behavior in the network. Monitoring nodes importance factor is its honesty.
The $IF$ of monitoring nodes $x$ is equal to the reputational score over the
actual reputational score of all monitoring nodes in the community. $IF_{x}$
indicates the importance factor of monitoring node x and $R_{1},R_{2},R_{3}$,
and $R_{4}$ are the four monitoring nodes taking part in a relay.

$\displaystyle IF_{x}=\frac{R_{x}}{\Sigma^{4}_{x=1}R_{x}}$ (16)

Implicitly if any node $x$ report the behavior of any node $y$, the accurate
or genuine judgment is equal to the importance factor $(IF)$ of a node
reporting the behavior of a node. Any node $x$ having an $IF_{x}$ report that
node $x$ is cooperative.

$\displaystyle M_{x}(Cooperative)=IF_{x}$ (17) $\displaystyle
M_{x}(Selfish)=1-IF_{x}$ (18)

In a similar manner, if any node $w$ reported $k$ as non-cooperated or selfish
then

$\displaystyle M_{w}(Selfish)=IF_{w}$ (19) $\displaystyle
M_{w}(Cooperative)=1-IF_{w}$ (20)

The CIF rule calculates the collective trust as under. Suppose
$(A,b_{1}),(B,b_{2})....(N,b_{n})$ are two discrete evidence produced by $n$
watchdog having $IF_{1},IF_{2}$ and $IF_{n}$ as an importance factor.
$EV_{1},EV_{2}....EV_{k}$ shows $k$ combination of elements in $\delta$ given
in Eq(21). For each $EV_{w}$, $w\in k$ we associate a value
$b^{\prime}(EV_{w})$ as,

$\displaystyle
b^{\prime}(EV_{w})=\displaystyle\sum_{x=1}^{n}b_{x}(EV_{w})^{\frac{IF_{x}}{\Sigma^{n}_{i=1,i\neq
x}IF_{i}}}-\prod_{x=1}^{n}b_{x}(EV_{w})^{\frac{IF_{x}}{\Sigma^{n}_{i=1,i\neq
x}IF_{i}}}$ (21)

Finally, BPA is assigned to $EV_{x}$ as,

$\displaystyle
b(EV_{w})=\frac{b^{\prime}(EV_{w})}{\Sigma^{n}_{x=1}b^{\prime}(EV_{w})}$ (22)

##### D. Descriptive instance of Collective Importance Factor Rule

Consider four monitoring nodes $M_{1},M_{2},M_{2},$ and $M_{4}$ with a
reputation score of 70, 30, 10 and 40 respectively produce a forwarder trust
report on $node_{1}$. The $node_{1}$ is cooperative according to the report of
$M_{1}$. However, $M_{2}$, $M_{3}$ and $M_{4}$ report $node_{1}$ is not
cooperative. So, the importance factors $IF_{1}$, $IF_{2}$, $IF_{3}$, and
$IF_{4}$ of the four monitoring nodes are calculated for their total
reputation score as, $\frac{70}{100}=0.7$, $\frac{30}{100}=0.3$,
$\frac{10}{100}=0.1$, and $\frac{40}{100}=0.4$ respectively. Thus,

$M_{1}(cooperation)=0.7$ $M_{1}(Selfishness)=0.3$

$M_{2}(Selfishness)=0.3$ $M_{2}(Cooperation)=0.7$

$M_{3}(Selfishness)=0.1$ $M_{3}(cooperation)=0.9$

$M_{4}(Selfishness)=0.4$ $M_{4}(cooperation)=0.6$

Subsequently, the collective trust value on node 1 is computed as,

$M^{\prime}(Selfishness)=(0.3)^{\frac{0.7}{0.3}}+(0.3)^{\frac{0.3}{0.7}}+(0.1)^{\frac{0.1}{0.9}}+(0.4)^{\frac{0.4}{0.6}}-(0.3)^{\frac{0.7}{0.3}}(0.3)^{\frac{0.3}{0.7}}(0.1)^{\frac{0.1}{0.9}}(0.4)^{\frac{0.4}{0.6}}=1.95$

$M^{\prime}(Cooperation)=(0.7)^{\frac{0.7}{0.3}}+(0.7)^{\frac{0.3}{0.7}}+(0.9)^{\frac{0.1}{0.9}}+(0.6)^{\frac{0.4}{0.6}}-(0.7)^{\frac{0.7}{0.3}}(0.7)^{\frac{0.3}{0.7}}(0.9)^{\frac{0.1}{0.9}}(0.6)^{\frac{0.4}{0.6}}=2.73$

Therefore, $M(Selfishness)$ and $M(Cooperation)$ can be computed as,

$M(Selfishness)=\frac{1.95}{1.95+2.73}=.416$

$M(Cooperation)=\frac{2.73}{1.95+2.73}=.583$

The evidences provided by all the nodes in the network will decide the honesty
of the forwarder for making payment. CIF principle decides the selfish and
cooperative nature of nodes. The CIF rule states as if three monitoring nodes
declare a node selfish but the final trust calculated is less than the fourth
one, the node will still be cooperative. This implies the node having
cooperative nature for a long time has its importance. As of final
calculation, the new reputation is computed from the nodes given incentive and
contemporary reputation, and it is broadcasted by the $CH$.

####  3.2.2 Monitoring Nodes Payment in a Community

Monitoring nodes are paid for submitting trustworthy reports. The $CH$ is
making the payment to the monitoring node based on some trust score.
Monitoring nodes trustworthiness is determined by the final score. For
$(P_{m}(M)>0)$, implies that the monitoring nodes are trustworthy as the final
trust score matches the trust report. For $(P_{m}(M)<0)$, implies that the
final trust value has a deviation from the trust value and termed the node as
misbehavior monitoring node.

Some changes have been made to the new model to handle the devices in the
community. Therefore, the nodes weight is regarded as individual personal
information that is one of the eligibility requirements for participation in
the process of election. In addition, a nodes reputation is a real number
allocated to each participating node calculated on node behavior. This value
varies according to the truth-telling behavior of the nodes in the network.

####  3.2.3 Reputation Carry and Forward

At first, all nodes have zero reputation as they join the network. The
reputation of the nodes varies during the electoral phase. This is reported by
the $CH$ periodically to the nodes in the network. Node switches its position
to $GW$ as it gets updates from $CH$. The node constantly updates the $RTable$
with other $CH$ as it gets updated in $Rtable$ in either community. Community
head in the network confirms the accuracy and integrity of the information
transmitted by $GW$ node. It affirms that the $CH$ knows about the information
about two hope communities in both directions. For instance, there are three
communities namely $A,B,$ and $C$. Suppose a new node $X$ is joining the
community $B$. Community $B$ apprehend all the reputation score of all the
nodes in $A$ and $C$ as per the proposed scheme. Thus, the reputation score of
the node $X$ is recognized to the community $B$ even before it joins $B$. If
the $CH$ is unsure of the new node reputation score, a new node will be
recognized as a new node within a network.

###  3.3 Selfish Nodes Punishment in a Community

Community head stimulates the selfish nodes to take an active part in the
election process. This participation in election process labels them as
cooperative nodes. The head of the community can punish the nodes with selfish
behavior in three ways. The community head stimulates the node to cooperate
after being selfish for the first time but in such case, no incentive is
awarded. Secondly, a node receives negative payment after warning from the
community heads. Finally, the community heads can remove the selfish nodes
from the community as punishment for a certain time. But sometimes a node can
enter the network again and act cooperatively, so in such scenario, the
negative payment should be paid first.

##  4 PERFORMANCE EVALUATION

This section outlines the performance of SOS in Network Simulator NS-2.34 52,
53 by comparing it with current algorithms. NS-2 is a network simulator for
both wired and wireless networks. MANET routing protocols can be implemented
in NS-2. Here, the simulation setup and metrics are presented first and then
simulation results are discussed.

###  4.1 Simulation Setup

The simulation is performed in six modules. The first module shows how the
reputation of nodes changes with selfish nodes variation. It is presented in
the simulation during the election process. The second module show nodes
behavior changes with respect to its variation in reputation. In the third
module, the results for the CAIS protocol is obtained based on our setup. In
the fourth module, we examined the SSAR protocol. The fifth module is about
the proposed scheme. In CAIS, SSAR, and SOS, 4% selfish nodes are injected in
it. The results are compared and evaluated in the sixth module. The variations
in the selfish nodes are used as tools to test all three protocols. The system
is assumed to be normal under 0% selfish nodes, which means that all the nodes
are cooperative in nature. The selfish nodes are variable ranging from 4% to
90% in the network during the simulation. SSAR protocol is used as a
benchmark. The simulation parameters are given in Table 4.

Table 4: Parameters values for Simulation Parameter | Values
---|---
Area | $500\times 500m^{2}$
Base Protocols | CAIS, SSAR
Number of nodes | 50
Node Distribution | Uniform
Initial Energy | 90
$R_{x}$ Power | 0.3
$T_{x}$ Power | 0.6
Movement Trace | OFF
Malicious Activity | 4%,10%, 25%, 50%, 75%, and 90%
Comparison | CAIS, SSAR with selfish nodes (Proposed Work)
Size of Packet Header | 4 bytes
Address Size | 4 bytes
Max. number of messages/packet | 4bytes
Traffic Source | CBR
Packet Protocol | TCP

###  4.2 Metrics

The simulation uses throughput, average delivery delay, average energy
consumption, and packet delivery ratio (PDR) as metrics of performance in a
network. Throughput 54 is the number of successfully delivered packets to the
total packets. The packet delivery ratio 34 is the successful delivery of the
messages over generated messages. The average delivery delay 24 is defined as
the time is taken by the message to reach its destination. Energy consumption
55 of the individual nodes to energy consumption by the entire number of nodes
is called average energy. SOS is compared with the following protocols: CAIS:
A Copy Adjustable Incentive Scheme 24 and Social selfishness Aware Routing
(SSAR) 34. Both CAIS and SSAR are an incentive-based and social scheme that
handles the issue of selfishness. Thus, these two schemes are used as a
benchmark for SOS.

###  4.3 Results and Discussion

The nodes get payments through VCG model. Nodes involved in the election are
paid for exhibiting cooperation in the network and become community head,
monitoring head and Incentive head via the election process. The nodes with
selfish behavior or action and not exhibiting the desired duty receive
negative payments as punishment.

####  4.3.1 Variation of Reputation

The behavior of nodes has an effect on the reputation of the nodes. Reputation
of nodes varies with its behavior. A node can be selfish and cooperative,
determined by its behavior in the network. The variation in node behavior is
shown in figure 1(a) in the election process.

((a)) Total reputation of nodes with modifying selfish nodes

((b)) Variation in Reputation over the simulation time

Figure 1: Total reputation of nodes with modifying selfish nodes & Variation
in Reputation over the simulation time

The final simulation results show that participated nodes are decreased as the
selfish nodes increases during the election process. It also generates lesser
payments to the selfish nodes by the heads of the community. Figure 1(b) shows
the behavior of the node in the election process. The reputation of the nodes
in the network increases with an increase in cooperation and gets decreased as
the number of selfish nodes increased. The ratio of reputation is higher in
the cooperative nodes than the selfish nodes reputation as shown in the
simulation results. Variation in the reputation ratio is due to the payment of
incentives to the nodes that varies during the election process. It depends on
the actively participating nodes in the network. Fixed payment to the relay
node and monitoring nodes is made based on node behavior to forward messages
to its neighbor. It implies that the nodes reputation increases with an
increase in the participating nodes in the electoral process and number of the
message forwarded by the node to its neighbor. The reputation of the nodes
decreased when negative payments are made to the node.

####  4.3.2 Comparison for Injecting 4% Selfish Nodes

The routing performance is studied in terms of packet delivery ratio,
throughput, average delay, and average energy consumption when 4% of nodes are
selfish. Figure 2(a) shows the results of the performance metrics packet
delivery ratio. The SOS technique gains the highest packet delivery ratios of
the packets. At pause time 8 sec, the SOS has the highest packet delivery
ratio of 0.9 (packet sent/rec) that is approximately 25% and 27% higher than
SSAR and CAIS respectively. Figure 2(b), 2(c) and 2(d) show the results of the
performance metrics, throughput, average delivery delay, and average energy
consumption.

((a)) Packet Delivery Ratio

((b)) Throughput

((c)) Average Delivery Delay

((d)) Average Energy

Figure 2: Performance comparisons of the algorithms when 4% of nodes are
selfish

At pause time 8 sec, the throughput of SOS is 221 kbps that is 37% and 78%
higher than SSAR and CAIS respectively. At pause time 10 sec, the average
delay of SOS is 14.67 ms that is 50% lower than CAIS and 23% lower than SSAR.
In addition, the average energy consumed by SOS is 12.77 joule at pause time 4
sec that is 14% and 19% higher than SSAR and CAIS respectively. The energy of
the node is sometimes saved by not giving a due response to other nodes or by
using a blacklisting mechanism. The SOS technique has comparatively high
throughput, minimum delay, and energy consumption. It is due to the core
reason that the SOS technique stimulates the selfish nodes in the network to
participate in the network and effectively forward the messages. Comparing the
results with SSAR, here the messages are forwarded on the node contact history
and willingness level. The SOS technique takes some initial time
(configuration and loading time) for the arrangements of all the factors
involved in the simulation environment. The values of the simulation are not
accurate for the initial two seconds. The values (Throughput and PDR) become
consistent after pause time 2 sec. It can be shown in figure 2 that the
performance of SOS is better than the two existing techniques.

The comparison of SOS, SSAR, and CAIS for 4% selfish nodes for all performance
metrics are shown in Table 5. It can be seen in Table5, the SOS technique
gains the highest packet delivery ratios of the packets. At pause time 8 sec,
the SOS has the highest packet delivery ratio of 0.9 (packet sent/rec) that is
approximately 25% and 27% higher than SSAR and CAIS respectively. At pause
time 8 sec, the throughput of SOS is 221 kbps that is 37% and 78% higher than
SSAR and CAIS respectively. At pause time 10 sec, the average delay of SOS is
14.67 ms that is 50% lower than CAIS and 23% lower than SSAR. In addition, the
average energy consumed by SOS is 12.77 joule at pause time 4 sec that is 14%
and 19% higher than SSAR and CAIS respectively 5.

Table 5: Performance comparisons of SOS, SSAR, and CAIS for 4% selfish nodes |  |  PDR |  |  |  Throughput |  |  |  Avg.Delay |  |  |  Avg.Energy |
---|---|---|---|---|---|---|---|---|---|---|---|---
Pause time |  SOS |  SSAR |  CAIS |  SOS |  SSAR |  CAIS |  SOS |  SSAR |  CAIS |  SOS |  SSAR |  CAIS
0 |  0.0 |  0.0 |  0.0 |  0.0 |  0.0 |  0.0 |  0.0 |  0.0 |  0.0 |  0.0 |  0.0 |  0.0
2 |  .4 |  .68 |  .31 |  0.0 |  134 |  21.08 |  0.0 |  120 |  440 |  3.6 |  15.4 |  16.3
4 |  .84 |  .68 |  0.7 |  215 |  133 |  33.6 |  9.0 |  116 |  402 |  12.7 |  15.6 |  16.6
6 |  .93 |  0.7 |  .68 |  218 |  130 |  33.2 |  8.5 |  116 |  309 |  12.9 |  15.8 |  17.8
8 |  .99 |  0.7 |  .67 |  221 |  130 |  33.7 |  8.4 |  115 |  263 |  13.0 |  16.0 |  17.0
10 |  .98 |  0.7 |  .62 |  219.4 |  126 |  30.9 |  14.67 |  117 |  242 |  13.25 |  16.12 |  17.2
12 |  .96 |  .45 |  .55 |  218.9 |  125 |  28.61 |  14.16 |  117.4 |  230 |  13.97 |  16.24 |  17.3
14 |  .95 |  .48 |  .55 |  218.6 |  126 |  28.87 |  14.1 |  118.1 |  220 |  13.53 |  16.46 |  17.4
16 |  .95 |  .51 |  .46 |  218.4 |  127 |  25.61 |  14.05 |  117.4 |  211 |  13.69 |  16.66 |  17.5

####  4.3.3 Influence of Different Percentage of Selfish Nodes

The performance of SOS is compared with SSAR and CAIS for obtained network
properties. The selfish nodes of 10%, 25%, 50%, 75%, and 90% are injected and
results are checked for packet delivery ratio, average deliver delay,
throughput and average energy.

By injecting 10% selfish nodes in the network, SOS again outperforms CAIS and
SSAR in terms of throughput, packet delivery, average energy and average
delivery delay as shown in Figure 3.

((a)) Packet Delivery Ratio

((b)) Throughput

((c)) Average Delivery Delay

((d)) Average Energy

Figure 3: Performance comparisons of the algorithms when 10% of nodes are
selfish

At pause time 8 sec, the packet delivery ratio of SOS is 0.67 (packet
sent/rec) that is almost 37% and 31% higher than SSAR and CAIS respectively as
shown in Figure 3(a). The throughput of SOS is 204.5 kbps at pause time 4 sec
that is 34% and 77% higher than SSAR and CAIS respectively as shown in Figure
3(b). In addition, the average delay of SOS is 15.94 ms at pause time 8 sec
that is 14% and 38% lower than SSAR and CAIS respectively as shown in Figure
3(c). Similarly, the average energy consumed by SOS is 3.6 joule at pause time
4 sec that is 17% and 23% lower than SSAR and CAIS respectively as shown in
Figure 3(d).

By injecting 25% selfish nodes in the network, the packet delivery ratio and
throughput of SOS is higher than CAIS and SSAR as showed in Figure 4(a) and
4(b).

((a)) Packet Delivery Ratio

((b)) Throughput

((c)) Average Delivery Delay

((d)) Average Energy

Figure 4: Performance comparisons of the algorithms when 25% of nodes are
selfish

At pause time 6 sec, the packet delivery ratio of SOS is 21% and 42% higher
than SSAR and CAIS respectively. This is because of the fact that monitoring
nodes constantly monitored the behavior of selfish nodes in SOS. At pause time
8 sec, the throughput of SOS is 36% and 79% higher than SSAR and CAIS
respectively. The average delay and average energy of SOS is much lower than
CAIS and SSAR as shown in Figure 4(c) and 4(d). At pause time 4 sec, the
average delay of SOS is 22.5 ms that is 14% lower than SSAR and 41% lower than
CAIS. In addition, the average energy consumed by SOS at pause time 4 sec is
17% lower than SSAR and 20% lower than CAIS. Similarly, the average delay and
average energy consumed by SOS is lower. Thus, the proposed scheme SOS
outperform the both the existing scheme namely SSAR and CAIS in terms of
packet delivery ratio, throughput, average delivery delay, and average energy
when there are 25% selfish nodes are present in the network. This is due to
the fact that the nodes in the SSAR and CAIS have a weak social relationship
with each other and hence ignore to forward messages to other members nodes in
a community. The comparison of SOS, SSAR, and CAIS for 25% selfish nodes for
all performance metrics are shown in Table 6.

Table 6: Performance comparisons of SOS, SSAR, and CAIS for 25% selfish nodes |  |  PDR |  |  |  Throughput |  |  |  Avg.Delay |  |  |  Avg.Energy |
---|---|---|---|---|---|---|---|---|---|---|---|---
Pause time |  SOS |  SSAR |  CAIS |  SOS |  SSAR |  CAIS |  SOS |  SSAR |  CAIS |  SOS |  SSAR |  CAIS
0 |  0.0 |  0.0 |  0.0 |  0.0 |  0.0 |  0.0 |  0.0 |  0.0 |  0.0 |  0.0 |  0.0 |  0.0
2 |  0.4 |  .68 |  .12 |  54 |  133.5 |  6.01 |  0.0 |  330.6 |  1395 |  3.6 |  15.5 |  16.2
4 |  0.6 |  .60 |  0.2 |  209.8 |  133.6 |  16.89 |  8.97 |  225.3 |  635.8 |  12.7 |  15.7 |  16.3
6 |  0.9 |  .69 |  .48 |  217.6 |  133.5 |  26.34 |  9.05 |  190.2 |  1144 |  12.9 |  16.0 |  16.5
8 |  0.9 |  .60 |  .56 |  220.5 |  132.8 |  28.93 |  9.17 |  177.8 |  877.7 |  13.0 |  16.2 |  16.6
10 |  0.9 |  .42 |  .48 |  220.1 |  132.8 |  26.28 |  9.62 |  177.8 |  786.9 |  13.2 |  16.4 |  16.7
12 |  0.9 |  .31 |  .49 |  220.1 |  132.8 |  26.42 |  9.62 |  177.8 |  889.0 |  13.3 |  16.6 |  17.0
14 |  0.8 |  .31 |  .62 |  220.1 |  121.9 |  0.31 |  9.62 |  168.5 |  977.4 |  13.4 |  16.9 |  17.4
16 |  0.8 |  .40 |  .64 |  212.8 |  123.4 |  31.41 |  10.84 |  159.7 |  862.9 |  13.6 |  17.1 |  17.7

It can bee seen in Table 6, at pause time 6 sec, the packet delivery ratio of
SOS is 21% and 42% higher than SSAR and CAIS respectively. Similarly, at pause
time 16 sec, the packet delivery ratio of SOS, SSAR, and CAIS is .8, .40, .64
that is 40% and 16% higher than SSAR and CAIS respectively. This is because of
the fact that monitoring nodes constantly monitored the behavior of selfish
nodes in SOS. At pause time 8 sec, the throughput of SOS, SSAR, and CAIS is
220.5 kbps, 132.8 kbps, and 28.93 kbps, that is 36% and 79% higher than SSAR
and CAIS respectively. In addition, At pause time 16 sec, the throughput of
SOS, SSAR, and CAIS is 212.8 kbps, 123.4 kbps, and 31.41 kbps. Thus, is still
observed that, the throughput of SOS is 37% and 75% higher than SSAR and CAIS
respectively. Furthermore, the average delay and average energy of SOS is much
lower than CAIS and SSAR. At pause time 4 sec, the average delay of SOS is
22.5 ms that is 14% lower than SSAR and 41% lower than CAIS. In addition, At
pause time 16 sec, the average delay of SOS, SSAR, and CAIS is 10.84 ms, 159.7
ms, and 862.9 that is 9% lower than SSAR and 56% lower than CAIS. Similarly,
the average energy consumed by SOS, SSAR, and CAIS at pause time 4 sec is 12.7
joules, 15.7 joules, and 16.3 joules respectively. So it is observed that the
energy consumed by SOS is 17% lower than SSAR and 20% lower than CAIS. In
addition, the average energy consumed by SOS, SSAR, and CAIS at pause time 16
sec is 13.6 joules, 17.1 joules, and 17.7 joules respectively. So it is
observed that the energy consumed by SOS is 20% lower than SSAR and 22% lower
than CAIS.

A similar conclusion can also be drawn by injecting 50% and 75% selfish nodes
in the simulation as shown in Figure 5 and Figure 6.

((a)) Packet Delivery Ratio

((b)) Throughput

((c)) Average Delivery Delay

((d)) Average Energy

Figure 5: Performance comparisons of the algorithms when 50% of nodes are
selfish

((a)) Packet Delivery Ratio

((b)) Throughput

((c)) Average Delivery Delay

((d)) Average Energy

Figure 6: Performance comparisons of the algorithms when 75% of nodes are
selfish

For 50% selfish nodes in the network, the performance of SOS is better for
performance metrics. At pause time 6 sec, the Packets delivery ratio of SOS is
.8 (packet sent/rec) that is 28% and 50% higher than CAIS and SSAR
respectively as shown in Figure 5(a). The throughput of SOS is almost 204.76
kbps at pause time 4 sec that is again 32% higher than SSAR and 69% higher
than CAIS as shown in Figure 5(b). In addition, the average delay of SOS is
20.1 ms at pause time 8 sec that is 59% lower than CAIS and 10% lower than
SSAR as shown in Figure 5(c). Similarly, at pause time 4 sec, the average
energy consumed by SOS is 12.76 joule that is 18% lower than SSAR and 15%
lower than CAIS as shown in Figure 5(d). The packet delivery ratio of SOS,
CAIS, and SSAR at pause time 6 sec are 0.84, 0.35 and 0.09 (packet sent/rec)
respectively as shown in Figure 6(a). Thus, the packet delivery ratio of SOS
is 49% and 75% higher than CAIS and SSAR respectively. At pause time 8 sec,
the throughput of SOS is 217.91 kbps that is 43% and 78% higher than SSAR and
CAIS respectively as shown in Figure 6(b). In addition, the average delay of
SOS at pause time 4 sec is 10.7 ms that is 9% lower than SSAR and 43% lower
than CAIS as shown in Figure 6(c). Similarly, at pause time 4 sec, again the
average energy consumed by SOS is 12.77 joule that is 17% and 20% lower than
SSAR and CAIS respectively as shown in Figure 6(d).

By injecting 90% selfish nodes in the network, the SOS scheme still
outperforms SSAR and CAIS in terms of packet delivery ratio, throughput,
average delay, and average energy as shown in Figure 7.

((a)) Packet Delivery Ratio

((b)) Throughput

((c)) Average Delivery Delay

((d)) Average Energy

Figure 7: Performance comparisons of the algorithms when 90% of nodes are
selfish

It can be seen in Figure 7(a), at pause time 8 sec, the packet delivery ratio
of SOS, SSAR, and CAIS are .86, .48, and .46 that is 38% and 40% higher than
SSAR and CAIS respectively. In addition, at pause time 14 sec, the packet
delivery ratio of SOS, SSAR, and CAIS are .87, .31, and .41 that is 56% and
46% higher than SSAR and CAIS respectively. Similarly, at pause time 8 sec,
the throughput of the SOS, SSAR, and CAIS scheme are 210.91 kbps, 110.14 kbps,
and 32.76 kbps, that is 42% and 74% higher than SSAR and CAIS respectively as
shown in Figure 7(b). In addition, at pause time 14 sec, the throughput of the
SOS, SSAR, and CAIS scheme are 210.37 kbps, 110.73 kbps, and 34.29 kbps, that
is still 41% and 73% higher than SSAR and CAIS respectively. Furthermore, at
pause time 8 sec, the average delay of SOS, SSAR, and CAIS are 22.72 ms,
139.89 ms, and 967.83 ms, that is 7.3% and 67% lower than SSAR and CAIS
respectively as shown in Figure 7(c). In addition, at pause time 14 sec, the
average delay of SOS, SSAR, and CAIS are 21.27 ms, 134.95 ms, and 551.15 ms,
that is still 8% and 38% lower than SSAR and CAIS respectively.

Similarly, at pause time 8 sec, the average energy consumed by SOS, SSAR, and
CAIS are 13.86 joules, 16.94 joules, and 17.55 joules, that is 17% and 20%
lower than SSAR and CAIS respectively as shown in Figure 7(d). Similarly, at
pause time 16 sec, the average energy of SOS is 13.89 joules that is 18% and
21% lower than SSAR and CAIS respectively. The comparison of SOS, SSAR, and
CAIS for 90% selfish nodes for all performance metrics are shown in Table 7.

Table 7: Performance comparisons of SOS, SSAR, and CAIS for 90% selfish nodes |  |  PDR |  |  |  Throughput |  |  |  Avg.Delay |  |  |  Avg.Energy |
---|---|---|---|---|---|---|---|---|---|---|---|---
Pause time |  SOS |  SSAR |  CAIS |  SOS |  SSAR |  CAIS |  SOS |  SSAR |  CAIS |  SOS |  SSAR |  CAIS
0 |  0.0 |  0.0 |  0.0 |  0.0 |  0.0 |  0.0 |  0.0 |  0.0 |  0.0 |  0.0 |  0.0 |  0.0
2 |  0.4 |  .59 |  .10 |  53 |  119.46 |  6.52 |  2 |  129.82 |  1385.62 |  3.55 |  16.47 |  17.26
4 |  .65 |  .60 |  .20 |  209.4 |  111.25 |  14.9 |  21.75 |  155.0 |  630.54 |  13.45 |  16.60 |  17.34
6 |  .86 |  .61 |  .44 |  210.12 |  111.93 |  21.03 |  22.28 |  149.14 |  1060.18 |  13.86 |  16.78 |  17.55
8 |  .86 |  .48 |  .46 |  210.91 |  110.14 |  32.76 |  22.72 |  139.89 |  967.83 |  13.86 |  16.94 |  17.55
10 |  .86 |  .40 |  .42 |  210.66 |  110.16 |  32.47 |  21.98 |  139.19 |  764.24 |  13.86 |  17.09 |  17.60
12 |  .86 |  .28 |  .41 |  210.49 |  108.71 |  32.94 |  21.7 |  139.86 |  648.28 |  13.86 |  17.10 |  17.60
14 |  .87 |  .31 |  .41 |  210.37 |  110.73 |  34.29 |  21.27 |  134.95 |  551.15 |  13.89 |  17.10 |  17.60
16 |  .86 |  .21 |  .54 |  210.28 |  113.45 |  34.54 |  20.14 |  134.67 |  499.33 |  13.89 |  17.10 |  17.70

It can be seen in Table 7, the packet delivery ratio of SOS, SSAR, and CAIS
are .86, .48, and .46 that is 38% and 40% higher than SSAR and CAIS
respectively. In addition, at pause time 16 sec, the packet delivery ratio of
SOS is .86 that is 65% and 32% higher than SSAR and CAIS respectively.
Similarly, at pause time 8 sec, the throughput of the SOS, SSAR, and CAIS
scheme are 210.91 kbps, 110.14 kbps, and 32.76 kbps, that is 42% and 74%
higher than SSAR and CAIS respectively. In addition, at pause time 16 sec, the
throughput of the SOS, SSAR, and CAIS scheme are 210.28 kbps, 113.45 kbps, and
34.54 kbps, that is 38% and 73% higher than SSAR and CAIS respectively. In
addition, at pause time 8 sec, the average delay of SOS, SSAR, and CAIS are
22.72 ms, 139.89 ms, and 967.83 ms, that is 7.3% and 67% lower than SSAR and
CAIS respectively. Furthermore, at pause time 16 sec, the average delay of
SOS, SSAR, and CAIS are 20.14 ms, 134.67 ms, and 499.33 ms, that is 8% and 34%
lower than SSAR and CAIS respectively.

Similarly, at pause time 8 sec, the average energy consumed by SOS, SSAR, and
CAIS are 13.86 joules, 16.94 joules, and 17.55 joules, that is 17% and 20%
lower than SSAR and CAIS respectively. Similarly, at pause time 16 sec, the
average energy of SOS is 13.89 joules that is 18% and 21% lower than SSAR and
CAIS respectively. It is due to the fact that it stimulates the nodes to
participate in the network and forward messages in a cooperative manner in its
community. Cooperative nodes are given some incentive in the form of
reputation. The nodes showing selfish behavior are penalized in the form of
expulsion from the network. However, nodes are not directly expelled from the
network, it has given a warning first. The two other techniques have not
considered the effect of the selfish nodes on the community. Thus, the results
demonstrated in SOS shows that it has encouraged the nodes in a community to
cooperate with all other nodes in message delivery. Therefore, the SOS scheme
outperformed the existing two techniques namely CAIS and SSAR in terms of
packet delivery ratio, throughput, average delivery delay, and average energy
consumed.

##  5 CONCLUSION AND FUTURE WORK

In this article, a new SOS scheme is proposed to stimulate the selfish nodes
in different communities to cooperatively forward messages for other nodes.
The proposed approach is based on the electoral system and generally omits
selfishness in IoT based SCC. In an electoral system, different heads are
elected such as Community Head, Incentive Head and monitoring Head in the
communities. These heads are elected based on two characteristics weight and
cooperation. Incentive in the form of reputation is awarded to the nodes for
their cooperation within the community using VCG model. Furthermore, nodes are
also penalized for showing repeated selfish behavior. In the proposed scheme
SOS, one of the important rules called the Collective Importance Factor (CIF)
principle is used that decides the selfish and cooperative nature of nodes.
This rule computes the trust depends on evidence from distinct nodes. These
evidences are then merged by using the Extended Dempster-Shafer model to
resolve uncertainty situations. For comparative analysis, two protocols namely
SSAR and CAIS are thoroughly simulated and analyzed. The results in terms of
data delivery ratio, network delay and average energy consumed are compared
with the proposed approach. The results indicate that SOS can possibly
accommodate a large number of selfish nodes by enabling them to collaborate in
a community to improve network performance. As a future enhancement of the
proposed approach, the bandwidth may be considered as reputation criteria of
nodes in the network for service delivery.

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