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

# Honesty Based Democratic Scheme to Improve Community Cooperation for IoT
Based Vehicular Delay Tolerant Networks

Ghani ur Rehman  Anwar Ghani  Muhammad Zubair  Shahbaz Ahmad Khan Ghayyure
Shad Muhammad  Department of Computer Science & Software Engineering,
International Islamic University, Islamabad 44000, Pakistan  Faculty of
Computer Science & Bioinformatics, Khushal Khan Khattak University, Karak
27000, Pakistan<EMAIL_ADDRESS>G. Rahman  A. Ghani  M. Zubair
S.A.Ghayyure  S. Muhammad

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

###### Abstract

[Summary]Many Internet of things (IoT) applications have been developed and
implemented on unreliable wireless networks like the Delay tolerant network
(DTN), however, efficient data transfer in DTN is still an important issue for
the IoT applications. One of the application areas of DTN is Vehicular Delay
Tolerant Network (VDTN) where the network faces communication disruption due
to lack of end-to-end relay route. It is challenging as some of the nodes show
selfish behavior to preserve their resources like memory, and energy level and
become non-cooperative. In this article, an Honesty based Democratic Scheme
(HBDS) is introduced where vehicles with higher honesty level are elected as
heads – during the process. Vehicles involved in the process would maximize
their rewards (reputation) through active participation in the network
activities whereas nodes with non-cooperative selfish behavior are punished.
The honesty level of the heads is analyzed using Vickrey, Clarke, and Groves
(VCG) model. The mathematical model and algorithms developed in the proposed
HBDS technique are simulated using the VDTNSim framework to evaluate their
efficiency. The performance results show that the proposed scheme dominates
current schemes in terms of packet delivery probability, packet delivery
delay, number of packets drop, and overhead ratio.

, , , , and (2020), Honesty Based Democratic Scheme to Improve Community
Cooperation for Internet of Things Using VDTN, Journal name XYz, publisher,
2020;00:1–16

###### keywords:

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

††articletype: Research Article00footnotetext: Abbreviations: Delay Tolerant
Networks, Selfish behavior, Honesty, incentive schemes, Internet of Things,
Trustworthiness, cooperative communication

##  1 Introduction

Normally the traditional internet is the connectivity of homogeneous devices
like computers but there are some emerging paradigm where Heterogeneous
devices like tablets, computers, and smartphones are connected for some
specific purposes in a wireless network like cloud and grid. One of the
similar vision paradigms is the Internet of Things (IoT) 1. In IoT, the
connected devices are heterogeneous and can be used for sensing, object
identification, tracking of vehicles, privacy and traffic control and many
other areas. In such scenarios, the underlying network is very important to
consider. The IoT applications may suffer from low connectivity, mobility of
nodes, and interruptions of links in the urban environment. Such unstructured
networks in the urban areas are termed as DTN. DTN 2 has nodes that cooperate
to forward messages to its connected node. In the DTN the mobile nodes are
intermediate and help in data transfer in the network. There are also
connectivity issues in the DTN, so it used the store and forward routing
method 3, 4. The node in the DTN stores the message until it finds another
node to forward the message. DTN packet forwarding depends on the mobile node
performance. Sometimes the mobile nodes are not willing to forward messages to
other nodes in the network 5. It can be useful in Interplanetary, the
military, and (VDTN).

The paper is about the behavior of node (vehicles) in the vehicular Delay
tolerant (VDTNs) 6, where the connected vehicles communicate wirelessly to
each other. Many of the applications of the VDTN are traffic congestion
notification, updates on the weather, emails and many others. The traditional
routing protocols DSR and AODV may not work properly due to broken
connectivity. The store and carry approach of these traditional routing has
some cooperation for forwarding the data to its destination irrespective of
the place. The messages are replicated by the DTN protocols to ensure the end
to end connectivity issue. The selfish nodes can degrade the performance of
the DTN network by showing misbehavior towards other nodes in a network. Some
of the research has shown that the misbehaved nodes can badly damage the
performance of the network 7, 8, 9. Other research has shown that the
misbehavior is due to selfish nodes in the network. The selfish nodes save its
resources such as (storage space, CPU time, and energy, etc) in order to be
chosen 10. This clearly indicates that selfishness is the real issue in the
DTN and needs to be addressed in a more sophisticated way. The two types of
nodes in the network is normal and abnormal nodes 11. A normal type of nodes
in the network actively participates in the network simulation and show its
cooperation with its neighbor nodes. Abnormal nodes are those nodes which are
not taking part in the network activities and can degrade the performance of
the network. This selfish behavior of node is shown to save its resources like
energy-saving, bandwidth, manipulation and other social behaviors 12.

For the simulation of the selfish nodes in the network, many different
mechanisms have been proposed. In the incentive and punishment-based scheme
13, nodes in the network get incentives based on cooperation and its weight.
In multi-heads clustering scheme 14, the incentive to the nodes is rewarded
only on its weight. The drawbacks with these two approaches are that, (i) the
contacts made are only for short duration in IPS Scheme (ii) there is chances
of weight tie problem in multi-leader election scheme. The proposed scheme
omits the selfish behavior of the nodes in different way as compared to these
techniques like nodes nomination criteria for the democratic system is based
on some honesty parameters. It is observed that when the nodes in the network
have long and frequent conversations, higher level of honesty is expected. The
following are the key contribution in the proposed scheme in VDTN;

  * •

Analyzing node behavior in VDTN.

  * •

To investigate the influence of the activities of the node on network
performance and to develop a methodology in which node with selfish behavior
are stimulated and encouraged to cooperate.

  * •

To boost the overall performance of the system on the basis of node honesty
with different parameters such as frequency of interaction, mutuality and
centrality and community of interest to elect heads e.g. Community Head,
Auxiliary Community Head and Incentive Head.

  * •

To design a Watchdog system that properly check the behavior of nodes in a
community.

  * •

A Comparative review is conducted to compare the performance of the proposed
mechanisms with all the other incentive-based techniques. In addition, the
proposed solution has increased network performance in terms of packet
delivery probability, packet dropping ratio, overhead and delivery delay.

Rest of the paper is organized as: Section 2 discusses related work, in
Section 3, HBDS detailed design is presented, it also covers system model. The
performance results of HDBS is presented in Section 4. The paper is finally
concluded in Section 5.

##  2 Related Works

IoT has greatly influenced the smart cities and helps it in making a
sustainable environment in it. The major objectives of smart and sustainable
societies are to provide livability, renewal, and a sustainable environment
for human life 15. Smart and sustainable cities are connected by heterogeneous
devices like computers, traffic lights, roadside units, healthcare units, and
smart homes. One of the important aspects of smart and connected cities 16, 17
is to make societies sustainable. The connected objects in such types of
societies are to provide a resource-efficient environment, ensure safety,
healthy living-hood, and provide a smart transport system 18, 19. The IoT
applications in the smart transport system depend on the VDTN paradigm of
networking 20. In VDTN, the vehicles are connected with roadside units,
lights, and other vehicles. For IoT applications to work in VDTN efficiently
the connected objects need to be cooperative and forward data within a network
21. However, some of the nodes in the VDTN have selfish behavior. Many
researchers have addressed the issue of selfishness in it. The selfish
behavior of the nodes degrades the performance of the network 22, 23.
Incentive-based mechanisms encourage the selfish nodes to simulate and share
its resources for cooperation in the network 24. Incentive schemes are credit-
based, reputation-based, game-theory and barter systems. All the cooperative
nodes are rewarded for showing cooperation by the credit-based approach in the
network. This reward will be used by the nodes in the network for their
purpose later on in the election process.

Jiang et al. 25 have designed a secure credit-based incentive scheme (SCIS)to
encourage the selfishness nodes for cooperation in opportunistic networks. The
selfish nodes are given some sort of incentive in the form of virtual credit
for their cooperation. Ning et al. 26 proposed a Copy Adjustable Incentive
Scheme(CAIS) is a type of mechanism where all the nodes in the community are
divided based on the social interaction in the network. In this scheme, two
types of incentive namely social and non-social credits are given to the
nodes. Park et al. 27 have proposed a bitcoin-based secure incentive scheme to
motivate nodes for cooperation in VDTN. Bitcoin is a very popular currency and
digital payment based some techniques of cryptography. Jiang et al. 28
proposed a credit-based congestion-aware incentive scheme (CBCAIS) to handle
the issue of selfishness in DTN. Nodes are given credit for their cooperation.
The selfish nodes are punished for not showing cooperation in the network.
Jedari et al. 29 proposed a game-theoretic incentive scheme for social-aware
routing in selfish mobile social networks (GISSO) to stimulate the selfish
nodes for cooperation.

The reputation-based incentives scheme calculates the nodes cooperation in the
network. Cooperative nodes in the network have higher values than the selfish
and non cooperative nodes. Rehman et al. 13 proposed an incentive and
punishment scheme for to discuss the issue of selfishness. In this scheme on
one hand nodes are given an incentive in the form of a reputation for their
cooperation within the community. On the other hands, nodes are punished for
showing selfish behavior repeatedly in the form of expulsion from the
community. The problem of selfishness in Vehicle Delay Tolerant Network is
presented in Dias et al. 30. In this approach, the score for a successful
transfer of data is calculated. If packets are received successfully, the
score will be increased; otherwise, a certain number will decrease the score.
Mantas et al. 31 proposed a reputation-based scheme to handled the issue of
selfishness. In this scheme monitor nodes are used to properly monitor the
behaviors of the nodes in the network and nodes are encouraged for
cooperation. Jedari et al. 32 proposed So-Watch (Social Watchdog system) uses
a reputation scheme that identifies the number of selfish nodes and the level
of selfishness. Kou et al. 33 proposed a strict reward and punishment model to
tackled the issue of selfishness. Nodes are stimulated for taking part in the
routing process. Sharma et al. 34 proposed a reputation-based scheme to detect
selfish and malicious nodes and also stimulates such nodes for cooperation in
Delay tolerant networks. Cai et al. 35 have presented an efficient incentive-
compatible routing protocol (ICRP) for DTNs. In this scheme nodes are rewarded
incentives in the shape of a reputation for showing cooperation.

In barter system, the nodes in contact share same amount of information which
is also called tit-for-tat scheme. Liu et al. 36 proposed that message can be
forwarded between the communities and the nodes based on the barter-based
mechanism. The nodes and the community in the network share the messages. The
messages are shared between node and community. Zhou et al. 37 that Tit-for-
Tit scheme for the message forwarding in the network. In this scheme a reward
is given to the cooperative nodes for forwarding of data to the other nodes.
Buttyan et al. 38 proposed a barter-based approach to deal with issue of
selfishness.

Some other approaches such as game-theoretic, social and trust-based have also
been proposed to deal with the issue of selfishness. SSAR 39 is a social-based
scheme that tackled the problem of selfishness in a network. In this approach,
a node having a strong social relationship in a community can take part in
data forwarding for other nodes. Lui et al. 40 proposed an incentive scheme to
share information that is related to a road accident, road construction and
delay in traffic, etc. Fawaz et al. 41 proposed Unmanned Aerial Vehicles
(UAVs) scheme to encourage vehicles for cooperation. Li et al. 42 proposed a
scheme which also stimulates vehicles for cooperation in a vehicle to vehicle
networks. Socievole et al. 43 proposed a social approach to encourage selfish
and misbehavior nodes for cooperation. Umar et al. 44 proposed a game-
theoretic scheme that motivates the nodes for cooperation to reduces the load
on nodes. Vamsi et al 45 proposed a trust-aware scheme to stimulate the nodes
to take part in the routing process. Kumar et al. 46 has proposed a dynamic
trust-based intrusion detection scheme track and isolate the selfish nodes
from the network. Pal et al. 47 proposed a trust-based approach for ensuring
the routing process. Dhurandher et al. 48 proposed a trust-based scheme that
motivates the nodes for cooperation in the network.

##  3 Proposed Scheme HBDS

The prime focus of the proposed scheme HBDS is to investigate the involvement
of node in DTN network. The nodes in the network are responsible for message
forwarding and watching over its fellow nodes. These two activities are main
responsibilities of the nodes in the democratic process. Nodes are encouraged
to participate in the network and coordinate to make better performance of the
network. In a community based network the node watches the fellow node
behavior for sending and receiving messages. This gives controls to the node
over message forwarding in a network. The proposed mechanism suggests
incentives to the nodes in the form of reputation to motivate the nodes for
simulation and to improve the performance of the network. The proposed
mechanism will show us the involvement of the nodes in the network and its
contribution towards network performance. The contribution of the node is from
message forwarding to the watching of the fellow nodes for the message
forwarding. The proposed mechanism makes incentive to the nodes for a)
involvement of the node in the Democratic process b) Messages forwarding to
its fellow nodes c) Watching the behavior of the other nodes. These will be
explained in later sections. The nodes with selfish behavior in the network
are simulated and encouraged to participate in the democratic process to
cooperate in the networks. The node with persistent selfish behavior are
punished and may be expelled from the community and this message will be
broadcasted in the community. The registration of the nodes in the VDTN will
be decided by centralized authority. Registration process depends on three
parameters, per-node budget, payment to the relay node, and the payment made
to the watchdog. The working of the proposed scheme presented in the figure 1.

Figure 1: Overall Structure of Proposed Scheme

###  3.1 Incentive for Participation in Democratic Process

The proposed HBDS scheme forwards the data to other nodes in the community.
The nodes in the network start the democratic process by participating in the
election. Some of the topics like community formation and its maintenance can
nor be covered in this paper and therefore it is not discussed here.

####  3.1.1 Democratic Process

The democratic process periodically manages the community. The eligibility
criterion of the democratic process is trust attribute (TA) honesty of the
node. It plays an important role in the assessment of the trust developed
among the nodes in the network. Without honesty the trust among the nodes will
not be genuine and strong relationships cannot exist as in the sociology
concept. Trust attribute (TA) shows the trustiness of the vehicle with the
trustor vehicle 49. The honesty property of the trust is chosen because
dishonest nodes can make delays in the network and can obstruct the overall
operations of the network delivery. Honest nodes will be nominated for the
election process. The three factors for the node honesty calculation are (a)
Frequency of interaction (b) Mutuality and Centrality and (c) Community of
interest. The node that scores the highest number of votes for honesty will be
elected as community head $CH$. Second, highest sorcerer will be elected as
auxiliary community head $ACH$ and the third-highest scorer will become
Incentive head $IH$. To bring stability to the community, the $ACH$ can become
$CH$ if the community head moves away from the community and is not part of
the community anymore. Heads in the community are responsible for assigning
unique community-id. After every democratic process, the $CH$ uses MAC-id to
generate community-ids to the nodes in the community. The unique community-ids
are stored and maintained by the succeeding $ACH$. The incentive head $IH$
makes payment to the cooperative nodes in the form of incentives. The watchdog
nodes are used to monitors the behavior of the nodes and are selected by the
heads in the network. Honesty calculation is a lengthy process. It combines
past information with the new information to predict the honesty level of the
node. The honesty level depends on the frequency of conversations and the
length of the conversation in the connected nodes. The cooperation of a node
can also be judged by the balanced of interactions among the nodes.
$H_{xy}^{a}(t)$ provides the evaluation of honest to object $y$ by object $x$
at time $t$, where $a$ represents the attributes. The calculation of node
honesty is explained as follow:

##### Frequency of Interaction:

Let us consider a conversations between nodes
$C=\\{c_{1},c_{2},,c_{3},……,c_{n}\\}$, the conversation between trustee nodes
over time t. On the basis of this, the degree of honesty between two nodes can
be calculated in equation 1.

$Honesty_{xy}^{TC}(t)=\sum_{i=1}^{j}\frac{\mid cl_{i}\mid}{\mid
tcl_{i}\mid}E(cl_{i})$ (1)

where $j$ is the number of contacts made, means the frequency of interactions
of a node with each other, $cl_{i}$ is the length of the $i^{th}$ successful
communication. $tcl_{i}$ is the overall length of communication and
$E(cl_{i})$ will measure the usefulness of the conversation and is called the
entropy function.

##### Mutuality and Centrality:

Common friends of two nodes show the honesty of the trustor and trustee. More
common friends between the nodes mean higher reliability of communication
between them. We can calculate the mutuality and centrality (credibility) of
the trustee node and is given in equation 2.

$Honesty_{xy}^{cen}(t)=\frac{\mid\mid M_{xy}\mid\mid}{\mid\mid N_{x}\mid\mid}$
(2)

where $M_{xy}$ is the number of common friends between two nodes and $N_{x}$
is the trustee friends.

##### Community of Interest:

We know that both the trustor and the trustee can be members of more than one
community. One of the important factors in honesty is to see how much interest
a node takes in a community. It also shows the common capabilities of nodes in
a community. Here $M_{x}y$ shows the common communities of the trustor and the
trustee nodes and $N_{x}y$ shows the communities of which trustee is a member
is shown in equation 3.

$Honesty_{xy}^{coi}(t)=\frac{\mid\mid M_{xy}^{coi}\mid\mid}{\mid\mid
N_{x}^{coi}\mid\mid}$ (3)

Once the values are calculated for all three parameters, then the final
honesty degree of a trustee is given in equation 4.

$FH_{xy}(t)=\alpha_{1}Honesty_{xy}^{TC}(t)+\alpha_{2}Honesty_{xy}^{cen}(t)+\alpha_{3}Honesty_{xy}^{coi}(t)$
(4)

where $FH$ is the final honesty, the $(\alpha_{1},\alpha_{2},\alpha_{3})$ are
selected randomly, where the total honesty is equal to 1. Algorithm 1 has
detail about the democratic process in the proposed scheme.

Algorithm 1 Democratic Process and Incentive algorithm

1:Number of nodes $n$

2:Democratic Process and Incentive to nodes participating in the Democratic
process

3:for $x=1:n$ do

4: Compute and broadcast $FH_{x}$

5:end for

6:for all $k\in n$ do

7: Nominate node $max(FH_{k})$ for election

8:end for

9:Votes are counted and nodes such as $CH$, $ACH$ and $IH$ elected in
democratic Process

10:$Pay=\sum_{k\in n}(Vt_{x}(FH,k)).Fb.\beta_{x}$ and
$Cost_{x}=\frac{1}{FH_{x}}\ast(FH_{y}-FH_{x})\sum_{k\in
n}(Vt_{x}(FH,k))Fb.\beta_{x}$

11:$CH$ new reputation, $Rep_{CH}=Rep_{CH}+Pay_{CH}-Cost_{CH}$

12:for all $k$, $k$ is not CH in community do

13: new reputation $Rep_{k}=Rep_{k}+Pay_{k}$

14:end for

15:for all $k\in n$ do

16: broadcast $CH_{ack}$=$Vt_{CH}(k)\parallel Pay_{k}\parallel Rep_{k}$

17:end for

18:Update Election Table

In this algorithm, honesty of all nodes are calculated and then broadcasted in
the community. The nodes are nominated for democratic process on the basis of
honesty and then incentive is given to all the nodes participating in the
democratic process. The running complexity of the algorithm is $O(nk)$.

A node in the community with a higher honesty level is considered as a
candidate for the democratic process of election. The node sometime can
declare itself as honest and report false information. The false report will
be either declaring itself as dishonest or over an honest node. To escape the
chances of becoming community head the node declares itself under honest and
it may declare itself over honest to get some incentives to become community
head. The trust behavior of the nodes in a community will be evaluated by VCG
model. To know about the false information reported by the nodes, VCG model is
used.

####  3.1.2 Vickrey, Clarke and Groves (VCG) Scheme

Vickrey Clarke and Groves is an important and valuable method of game theory.
It predicts the nodes behavior in the network and motivates the dishonest
nodes to participate in the network 50. It was previously assumed that energy
level and weight of the node as internal and private information of the node.
So the truth-telling behavior of the VCG was used to predict the behavior of
the node 51. In our proposed model, some of the parameters of the nodes
declare the honesty of the respective node. The honesty level of the node is
considered as internal and private information. The mobility parameters of the
node show the honesty level of the node in VDTN. The reputation of the node
reaches to certain level after every democratic process. The reputation of the
node s calculated on the basis of cooperation of the node in the network. The
payment procedure detail is given in Algorithm 2.

Algorithm 2 Responsibilities of Elected Heads and Incentive for packet
forwarding algorithm

1:Number of nodes $n$

2:Operations of Elected Heads& Incentive to nodes for Packet Forwarding

3:for $x=1:n$ do

4: for each relay in community

5: Elected Heads collectively assign Watchdog nodes WN1, WN2 and WN3

6: $WN_{i},\\{i=1,2,3\\}$, wait for time ‘t’

7: if The same packet overheard after time ‘t’ then

8: beh = Coop;

9: send $t(report)=^{\prime}Coop^{\prime}$

10: else

11: beh = Self;

12: send $t(report)=^{\prime}Self^{\prime}$

13: end if

14: $CH$ compute $IA$

15: $IA_{x}=\frac{R_{x}}{\Sigma^{3}_{x=1}R_{x}}$

16: $CH$ Calculate the behavior of all nodes $WN_{i}$

17: if $x_{coop}>x_{self}$ then

18: grant $Pay_{r}=Rep_{r}+F_{pay}$

19: else

20: grant $Pay_{r}=Rep_{r}-F_{pay}$

21: end if

22: if $t_{report}(WN_{i})=t_{final}$ then

23: grant $Pay_{WN_{i}}=Rep_{WN_{i}}+Pay(WN)$

24: else

25: grant $Pay_{WN_{i}}=Rep_{WN_{i}}-Pay(WN)$

26: update $R_{Table}$

27: end if

28:end for

In this algorithm watchdog nodes are collectively selected by the three heads.
The watchdog nodes are responsible for monitoring the behavior of all nodes in
the network. The importance aspect of all watchdog nodes are calculated. The
overall operations and responsibilities of the elected heads and payment
detail is also given in the algorithm. The complexity of the algorithm is
$O(n)$.

####  3.1.3 Payment after Democratic Process using VCG Model

The game has $n$ number of players. We consider each node to be a community
player. The democratic process starts with nodes disclosing their honesty
level. Democratic election process is used for nodes election as heads and
declared others as participants. The heads elected through the democratic
process and other participants will receive payments in the form of
reputation. Nodes in the network are always in search to increase its
reputation value $R$. the higher the value of $R$ the more services they
receive from the network. Each node in the network has $RTable$ to maintain
its reputation. The node updates its $Rtable$ when the node gets updates from
neighbor node.

##### Payment to Elected head $(CH)$ of Community

Each node gets payment after the democratic process ends. The $CH$ gets
payment on the basis of a number of votes it receives from the participants
nodes. the nodes participating in the democratic process also get payments for
voting, which denote the cost of the elected $CH$. $H_{1},H_{2},....,H_{n}$,
where $n$ is all number of nodes, represents the cost vector (nodes honesty).
Difference in the value of payment to the node and payment received is the
gain of $CH$ is given in equation 5.

$Pay=\sum_{k\in n}(Vt_{x}(FH,k)).Fb.\beta_{x}$ (5)

where $Vt_{x}(FH,k)$ provides certain values in the democratic process (value
is $1$ if $k$ node votes for $x$, otherwise 0 will be produced. $CH$ also
assigns a fixed budget $FB$ to every node participating in the democratic
process. This fixed budget is known to all the nodes in the network.
$\beta_{x}$ is a payment of node shown below in equation 6.

$\beta_{x}=FH_{x}+\frac{1}{\sum_{k\in n}Vt_{x}(FH,k)}\times\sum_{l\in
n}(FH_{y})\times{\sum_{k\in n}Vt_{y}}(FH|FH_{x}=\infty,k)-{\sum_{y\in
n}}(FH_{y}){\sum_{k\in n}}(Vt_{y}(FH,K))$ (6)

##### Payment to Community Members

The total cost of the nodes is divided among them based on the reputation of
the node. The $CH$ announces the payment to the nodes through $CH_{ack}$
message. All the messages once received are authenticated by the standard
message authentication mechanism. An update is made to the RTable in each
node. The fixed payment $FB$ is used to distribute the total cost among the
connected nodes who have participated in the voting process. We can calculate
the cost function $C_{x}$ determined by $CH_{x}$ and is given in equation 7.

$Cost_{x}=\frac{1}{FH_{x}}\ast(FH_{y}-FH_{x})\sum_{k\in
n}(Vt_{x}(FH,k))Fb.\beta_{x}$ (7)

where $FH_{x}$ and $FH_{y}$ represent the highest and second-highest nodes
honesty of the nodes participating in a democratic process. As noticed below,
the elected heads calculates their reputation by deducting the overall cost
form their own payment to calculate their own reputation and is shown in
equation 8.

$Rep_{x}=Pay_{x}-Cost_{x}$ (8)

Each node gets a share of the total cost based on the reputation of the node.
$CH_{ack}$ message is used to announce the payment to the nodes in the
network. These messages are verified by the standard authentication messages.
After each announcement the node updates its $RTable$.

###  3.2 Packet Forwarding Payment

The gateway nodes(GW) and the community head (CH) cannot be used as a relay
node. This restriction means that these nodes cannot be used to forward
packets. This is a negative and selfish behavior of the node and can degrade
the performance of the network. Selfish nodes can make disconnections and drop
packets, this selfish behavior has a negative impact on the performance of the
network. To stimulate the nodes for participation in the network, some
incentives have been awarded in the form of reputation. To make the payment
system effective, a monitoring system is used to monitors the relay nodes.
Table 1 has the notations used in the proposed scheme.

Table 1: Summary of notations and symbols Notation | Representation of the symbol
---|---
$x$ |  $CH$ is the Community Head that is elected in Democratic Process
$cl_{i}$ | Lenght of $i^{th}$ successful communication
$tcl_{i}$ | Overall length of communication
$E(cl_{i}$ | Entropy Function
$Rep_{x}$ | Node $m$ reputation
$Pay_{x}$ | Node $x$ payment
$Vt_{x}$ | Node $k$ which voted for $x$
$\beta_{x}$ | Per member node cost
$H_{x}$ | Honesty level of node $x$
$IA_{x}$ | Importance aspect of node $x$
$M_{xy}$ | Number of common friends
$N_{x}$ | Trustee Friends
$coi$ | Community of interest
$WN$ | Watchdog Node

####  3.2.1 Incentive for Relay Nodes

The $IH$ makes payment to the nodes for each data forwarding in our proposed
scheme. It pays fixed payment $(F_{pay})$ in the form of incentives and is
managed by incentive head. The $F_{pay}$ is allocated to the relay node on the
basis of its cooperative behavior. To monitor this a watchdog system is
introduced to monitors the behavior of all the relay nodes and then take the
behavioral decisions.

##### Cumulative Trust of Watchdog Nodes

In Proposed scheme, we have three watchdog nodes in a relay. One of them is
auxiliary community Head $ACH$, the second one is a predecessor to the $ACH$
and the third one is selected in round-robin as member node. The nodes in the
relay create hash 52, the function of the hash is to keep the packet genuine
and prevent the forwarding node from changing the packet. Every packets when
reach to its destination nodes, it is verified with hash function value. If
the value of the has function is matching, it means the packet is in steady-
state. If the hash value is not matching then the forwarder node is declared
as a selfish node, and it receives negative payment. The nodes in the network
keep a record of the recently forwarded packets in a buffer. Each packet has a
certain expected time to be sent next. So we can calculate direct and indirect
trust based on the node behavior. Direct trust is based on the interaction of
the node with its neighbor nodes. There are three watchdog nodes in our
proposed scheme. The watchdog function as monitor to observe the behavior of
the neighbor nodes in the network. Any unusual behavior is reported and trust
value is calculated using a hash function. A watchdog system constantly
monitors the conversation of the node with its neighbor node. It calculates
the total number of dropped and forwarded packets in a network. The mobility
of the node creates difficulty in calculating the trust value. It is because
the node while moving also discover new nodes and replaces an old neighbor. To
overcome this situation, the second opinion is taken to assess the rigorous
trust value. So indirect trust is calculated to improve the overall decision-
making system. So each watchdog calculates both direct and indirect trust
value for the total calculation of the trust value. The range value is [0,1]
for each watchdog node. The initial value starts at 0.5. Any value greater
than 0.5 is termed as a cooperative node and less than 0.5 is a selfish node.

The trust reported generated by the node is its individual opinion. It can be
cooperative or selfish, based on the individual behavior of the node. The $CH$
calculates the total trust value from three trust reports of the watchdog
nodes. If the total value of the trust is greater than the selfish behavior of
the node, the nodes is declared as genuine and incentive in the form of
reputation is made to it. In order to avoid contradiction in the results, the
$CIA$ cumulative importance aspect rule is introduced to calculate the trust
value from different nodes based on certain evidence.

##### Cumulative Important Aspect for Trust Calculation

The $CIA$ can report any bias, if the participating nodes have any prior
indulgence of declaring each other as cooperative nodes. It may also
unjustifiably declare a node as selfish. So the $CIA$ makes this distinction
of the selfish and cooperative node. The watchdog nodes important aspect is
its honesty. The honesty of the watchdog node is directly linked to its
reputation. The watchdog node $IA$ important aspect is equivalent to the
reputation level over the cumulative reputation level of all the watchdog
nodes in the community. $IA_{x}$ indicates the importance of the watchdog node
and $R_{1},R_{2}$ and $R_{3}$ are three relay-involved watchdog nodes and is
given in equation 9.

$IA_{x}=\frac{R_{x}}{\Sigma^{3}_{x=1}R_{x}}$ (9)

If any $x$ node reports the behavior of the any $n$ node, the accurate
judgment is considered to be equivalent to the importance aspect $(IA)$ of a
node reporting the nodes behavior. Therefore, any $x$ node with an important
aspect $IA_{x}$ states that node $x$ is cooperative and is given in equation
10.

$M_{x}(Coop)=IA_{x}$ (10) $M_{x}(Self)=1-IA_{x}$ (11)

Moreover, If any $y$ node reported $k$ as selfish then

$M_{y}(Self)=IA_{y}$ (12) $M_{y}(Coop)=1-IA_{y}$ (13)

The evidence from all the nodes will be used to calculate the final honesty
level of the node to award payment. The $CIA$ rule decides the behavior of the
node as selfish or cooperative. The $CIA$ rule state that even if two of the
watchdog nodes declare the node to be selfish, but the total value of trust is
less than the third watchdog node that claims the node to be cooperative. Then
according to $CIA$ rule, the node will be acknowledged as a cooperative node.
The final reputation is calculated from the awarded payment to the node and
current reputation. This is announced by the $CH$ in the network to all.

####  3.2.2 Watchdog Nodes Payment

Each trustworthy report submitted by the monitoring node gets payment for it.
The $CH$ makes payment to the nodes on their trust value. The final value is
the trustworthiness of the monitoring node. For $(Pay>0)$, shows that this
node is trustworthiness as the final trust value is matching with trust
report. For $(Pay<0)$, it shows that the final trust value is different from
the trust value and it termed the node as misbehaving watchdog node. Some
slight modification is made to deal with different features of the
participating devices in the community. So the honesty of the node is
considered as internal and private to the node. This is the basic eligibility
criteria for the participation in the democratic process. The awarded value of
the reputation is the real number of each node. This value alters as the
truth-telling behavior of the nodes changes.

####  3.2.3 Carry-Forward Reputation

The initial reputation of each node in the network is set to zero. The
reputation of the node changes during the election process. The change made to
the reputation value is updated and $CH$ node announces it after every update.
The node status changes to $GW$ after every update from the $CH$ node. Every
node shares its reputation table $RTable$ with community head $CH$ after each
update. The data forwarded by the $GW$ node is checked for authentication by
the $CH$ before it is broadcasted. This means that all the information about
the reputation is managed and stored by the $CH$ in a community. Let us
suppose that there are three community $a$, $b$ and $c$, and new node $N$
enters in the $b$ community. According to our proposed scheme community, $b$
knows about the reputation values of all the participated nodes in the $a$ and
$c$ community. So the community $a$ and $b$ has all the information about the
$N$ node even before it enters into $a$ and $b$ community. If $CH$ has no
information about the new node, then this new node will be termed as a new
node to the network.

###  3.3 Penalties for Selfish Nodes

The community head stimulates the selfish nodes in the network for
participation in the democratic process. It can also punish the selfish node
with repeated misbehavior in three ways. The $CH$ gives zero incentive to the
nodes if it behaves selfishly for the first time and encourages it to
participate. The $CH$ gives negative payment to the node if it shows selfish
behavior for the second time. The $CH$ node can punish the node with repeated
selfish behavior and expel it from the network for some time. It is also noted
that the node may join the network after some time and cooperate with the
nodes, so such a node should pay the negative payment first. The penalty
coefficient is used as an importance mechanism for feedback. This feature is
used to measure the dishonesty level of the node, which has shown such
behavior in the past. It is important to keep the honesty level at a certain
level, so an exponential downgrading system is used. $\mid\mid C\mid\mid$
shows the total number of conversations at time $t$ and $\mid\mid
U_{C}\mid\mid$ is ineffective conversations. The penalty is calculated in
equation 14:

$H_{p}(t)=\frac{\mid\mid{C}\mid\mid-\mid\mid U_{C}\mid\mid}{\mid\mid
C\mid\mid}e^{(\,{\frac{\mid\mid{C}\mid\mid-\mid\mid U_{C}\mid\mid}{\mid\mid
C\mid\mid}})\,}$ (14)

##  4 Performance Evaluation

In this section, a simulation tool VDTNSim was used to conduct the simulation
performance of HBDS scheme 53. VDTNSim is the extended version of the
Opportunistic Network Environment (ONE) Simulator 54. This makes it possible
to simulate the VDTN architectural solution, which involves the store-carry-
and forward relayed network just below the network layer. The Simulation
setup, performance metrics, and results are shown in the next subsections.

###  4.1 SIMULATION SETUP

The simulation takes place in three modules. The first module shows the
evaluation of selfish nodes effects on performance metrics such as packet
delivery probability, packet dropping ratio, overhead and delivery delay.
Based on our setup the results for the HBDS protocol are obtained in the
second module. In the third module, the results are contrasted and examined
with two existing protocols. Selfish node differences are used as techniques
for testing all three protocols. For 0% selfish nodes, the network is assumed
to be normal. The selfish nodes range from 0% to 80% in the network throughout
the simulation process. SSAR is used as a basis for comparison. Table 2 lists
the parameters used in the simulation.

Table 2: Simulation Parameters Parameters | Values
---|---
Simulation Area | $4500\times 3500m^{2}$
Number of nodes | 100
Transmission Range | 300m
Malicious Activity | 0% to 80%
Comparison | Proposed Scheme compared with SimBet and SSAR
Simulation Time | 24 hrs
Number of Relay Nodes | 05
Number of Terminal Nodes | 100
Terminal Nodes Buffer Capacity | 150 MB
Relay Node Buffer Capacity | 250 MB
Average Speed | $60km/h$
Variation in Nodes | 20 to 100
Interval for Packets Generation | [20, 30] sec
Node Communication | IEEE 802.11b
Size of Packets | [50, 650 KB]
Packets TTL | 320 minutes

###  4.2 METRICS

The simulation uses packet delivery probability, packet dropping ratio,
delivery delay, and overhead ratio as metrics of performance within a network.
The probability of packet delivery was computed as the percentage between the
number of the individual packets forwarded to their intended destination and
the number of individual packets created at source nodes. The packet delivery
delay for the packet is calculated as the average amount of time packets have
to transfer from source to destination. A nodes in the network dropped packets
because there is lesser space in the buffer, TTL expiry is soon and selfish
behavior of the node. The overhead ratio calculates a routing protocols
bandwidth performance. It means it has to calculate extra bundles for
necessary delivery that packet. HBDS is compared with incentive-based
protocols: SimBet 55 and Social selfishness Aware Routing (SSAR) 39. SimBet is
used as a basis for comparison.

###  4.3 RESULTS AND DISCUSSION

VCG method has been used to make payments to the nodes. The nodes
participating in the democratic process are rewarded incentives to show
collaboration and cooperation in a network. Such cooperative nodes can become
Community Head, Auxiliary Community Head, and Incentive Head via the
democratic process. Negative incentives are provided to selfish nodes for not
exhibit the appropriate commitment. The Nodes are also punished in the form of
removal from the network if they consistently exhibit selfish behavior.

####  4.3.1 Impact of Selfish Nodes on Performance Metrics

The analysis begins with an evaluation of the influence of selfish nodes on
all performance metrics such as packet delivery ratio, delivery delay,
overhead ratio, and number of packet dropped as shown in figure 2. It is
observed that the packets delivery ratio drastically decreases as the number
of selfish nodes increases in the network as shown in 2(a). This operation
shows how important it is to detect and take steps against such nodes (e.g.
punish or exclude them from the network). The selfish behavior of the nodes
also affects the time packets take to reach its destination. It is due to the
fact that the selfish nodes are not cooperating and it takes double effort
from the cooperative nodes to deliver the packets to the destination. This
process increases the amount of time a packet has to transfer between its
source and the intended destination as shown in figure 2(b).

By holding packets on node buffers for a prolonged period of time causes
buffer congestion, resulting in a higher proportion of dropped packets as
nodes must maintain their cooperative behavior so as to not deviate from the
protocol. In addition to affecting other nodes, the existence of selfish nodes
in the network will also have a massive impact on the routing process. The
overhead is shown in figure 2(c) that describes the routing protocols
bandwidth performance. It can be seen clearly when a number of selfish nodes
increases it also increase the overhead that actually degrades the network
performance. figure 2(d) shows that the number of packets dropped as a result
of the existence of selfish nodes.

((a)) Packet Delivery Probability

((b)) Packet Delivery Delay

((c)) Overhead Ratio

((d)) Number of Dropped Packets

Figure 2: Influence of different number of selfish nodes on Performance
Metrics

It was observed throughout this section, as the percentage of selfish nodes
increased it has drastically affected the performance of all the metrics. In
next subsection, the performance of the proposed HBDS scheme is checked by
incorporating the same percentage of selfish nodes in the network.

####  4.3.2 Effect of Selfish Nodes on Packet Delivery Ratio and Average
Delivery Delay

To evaluate the performance of the proposed scheme, the result of the HBDS
scheme was compared with a scheme where there is no selfishness omitting
procedure was used as shown in figure 3. This study begins with an assessment
of the percentage of packets delivered. As shown in figure 3(a), as the number
of selfish nodes increases, the probability of packet delivery continues to
decrease.

((a)) Packet Delivery Probability

((b)) Number of Dropped Packets

Figure 3: Packet Delivery and Delay when 20% to 80% nodes are selfish nodes

However, the HBDS succeeds in reducing the negative effect of selfish nodes.
Comparing HBDS packet delivery outcomes with a strategy in which no action is
taken toward selfish nodes. By incorporating 20% selfish nodes in the network,
the HBDS increases the packet delivery outcome by approximately 4%, 4%, 8%,
2%, 2%, 6%, 3%, 3% and 3% (for a number of nodes 20, 30, 40, 50, 60, 70, 80,
90, and 100 respectively). In addition, by incorporating 80% selfish nodes,
HBDS also enables to reduce the effects of selfish nodes by increasing the
probability of packet delivery. The packet delivery probability is increased
approximately by 3%, 3%, 3%, 3%, 4%, 3%, 2%, 3% and 7% (for number of nodes
20, 30, 40, 50, 60, 70, 80, 90, and 100 respectively. This is because, HBDS
rewards nodes in the form incentive (reputation) for their cooperative
behavior, making cooperative nodes even more resource-sharing.

It can also be seen in figure 3(b) that HBDS also produces a good performance
in terms of packet delivery delay when compared to a scheme in which there is
no strategy used for omitting selfishness in a community. It ensures that HBDS
is capable of delivering packets faster. When 20% selfish nodes are
incorporated in the network, the HBDS packets delivery approximately 5, 5, 5,
5, 6, 6, 6, 6, and 6 sooner (for a number of nodes 20, 30, 40, 50, 60, 70, 80,
90 and 100 respectively). Also, when 80% selfish nodes are incorporated in the
network, the HBDS packets delivery approximately 16, 16, 16, 16, 15, 16, 16,
16, and 16 sooner (for number of nodes 20, 30, 40, 50, 60, 70, 80, 90 and 100
respectively). The explanation for the observed results for both parameters is
due to the HBDS approach of considering a reputation score of nodes to
determine the number of resources they can share. The node reputation score
will be increased by allowing only interaction between cooperative nodes, that
is resulting in higher resource sharing.

####  4.3.3 Effect of Selfish Nodes on Number of Packets Drops and Overhead
Ratio

The performance of the proposed scheme HBDS is checked in terms of wasted
resources in this section. The evaluation begins with an assessment of the
overhead ratio and number of packets dropped for this reason as shown in
figure 4. It can be seen in figure 4(a), the selfish nodes do not have a huge
impact on the overhead ratio in the proposed scheme HBDS.

((a)) Overhead Ratio

((b)) Number of Dropped Packets

Figure 4: Overhead Ratio and Number of Dropped Packets when 20% to 80% nodes
are selfish nodes

This is a big achievement when the result of the proposed scheme HBDS is
compared with the result of an approach where no action has been taken toward
selfish nodes. For 20% selfish nodes the overhead of HBDS is decreased
approximately by 8, 7, 7, 8, 12, 16, 18, 17, and 16 packets (for a number of
nodes 20, 30, 40, 50, 60, 70, 80, 90 and 100 respectively). Similarly for For
80% selfish nodes, the overhead of the proposed scheme HBDS is reduced
approximately by 3, 5, 5, 4, 5, 7, 7, 8, and 6 packets (for a number of nodes
20 , 30, 40, 50, 60, 70, 80, 90 and 100 respectively). The proposed scheme
also significantly reduced the number of packets drops for different
percentage of selfish nodes as shown in figure 4(b).

For 20% selfish nodes, the HBDS scheme dropping packets approximately 200,
200, 200, 300, 200, 300, 200, 200, and 700 (for a number of nodes 20, 30, 40,
50, 60, 70, 80, 90 and 100 respectively) less than the approach where there is
no proper selfish nodes detection procedure used. In addition, for 80% selfish
nodes, the proposed scheme discard less 200, 200, 200, 300, 200, 300, 200,
200, and 700 packets (for a number of nodes 20, 30, 40, 50, 60, 70, 80, 90 and
100 respectively). This happens because HBDS helps to reduce the network
resource due to the proper monitoring of selfish nodes in the community and
avoiding contacts with such nodes. Thus, avoiding selfish node contact often
means that a higher number of copies of packets pass through the network,
improving their chances to reach the final destination.

####  4.3.4 Comparison of HBDS, SSAR, and SimBet for Different Percentage of
Selfish Nodes

These three techniques are evaluated for the network characteristics in case
of 20%, 40%, 60%, and 80% nodes are selfish. The HBDS scheme outperforms the
two schemes SSAR and SimBet in terms of Packet delivery probability, packet
delivery delay, overhead ratio and a number of dropped packets as shown in 5.
In figure 5(a), the packet delivery probability of the proposed HBDS scheme is
higher.

((a)) Packet Delivery Probability

((b)) Packet Delivery Delay

((c)) Overhead Ratio

((d)) Number of Dropped Packets

Figure 5: Performance Comparison of HBDS, SSAR and SimBet for Selfish Nodes
Percentage of 20% to 80% for all Metrics

For 20% selfish nodes, the packet delivery probability of HBDS, SSAR, and
SimBet is 49%, 35% and 30% respectively that is 14% and 19% higher than SSAR
and SimBet. Similarly for 80% selfish nodes, the packet delivery probability
of HBDS is 25% that is 7% and 13% higher than SSAR and SimBet respectively.
The key reason behind this is that the HBDS scheme encourages selfish nodes to
take part in the routing process and thus avoid the selfish behavior of nodes.
The other three techniques did not present in-depth the effect of selfishness
on the network. In figure 5(b), the packet delivery delay, overhead ratio and
a number of packets dropped of the Proposed HBDS scheme are lower. For 20%
selfish nodes, the packet delivery delay of HBDS is 110 minutes that is 20%
and 27% lower than SSAR and SimBet respectively. By incorporating 80% selfish
nodes, the packet delivery delay of the HBDS is 130 minutes that is 27% and
57% lower than SSAR and SimBet respectively. In figure 5(c), the overhead of
HBDS scheme is 25% and 37.5% lower than SSAR and SimBet respectively when
there are 20% selfish nodes in the network. The comparison of HDBS, SSAR, and
SimBet for packet delivery ratio for different percentage of selfish nodes is
shown in Table 3. For 20% selfish nodes, the packet delivery probability of
HBDS, SSAR, and SimBet are 49, 35, and 30 respectively. Thus, it can be seen
that the packet delivery ratio of HBDS is 23% and 31% higher than SSAR and
SimBet respectively. In addition, for 80% selfish nodes, the packet delivery
probability of HBDS, SSAR, and SimBet are 25, 18, and 12 that is 11.6% and
21.6% higher than SSAR and SimBet respectively.

The comparison of HDBS, SSAR, and SimBet for delivery delay for different
percentage of selfish nodes is shown in Table 4. For 20% selfish nodes, the
delivery delay of HBDS, SSAR, and SimBet are 110, 170, and 190 respectively.
Thus, it can be seen that the delivery delay of HBDS is 20% and 27% lower than
SSAR and SimBet respectively. In addition, for 80% selfish nodes, the delivery
delay of HBDS, SSAR, and SimBet are 130, 210, and 300 that is 56% and 26%
lower than SSAR and SimBet respectively.

Table 3: Comparison of HBDS, SSAR, and SimBet in case of 20%, 40%, 60%, and 80% Selfish nodes for Packet Delivery Probability No. of Selfish Nodes | HBDS | SSAR | SimBet
---|---|---|---
20% | 49 | 35 | 30
40% | 39 | 30 | 25
60% | 30 | 24 | 18
80% | 25 | 18 | 12
Table 4: Comparison of HBDS, SSAR, and SimBet in case of 20%, 40%, 60%, and 80% Selfish nodes for Delivery Delay No. of Selfish Nodes | HBDS | SSAR | SimBet
---|---|---|---
20% | 110 | 170 | 190
40% | 120 | 190 | 200
60% | 123 | 200 | 250
80% | 130 | 210 | 300

In addition, for 80% selfish nodes, the overhead of proposed scheme is 22% and
37% lower than SSAR and SimBet respectively. In figure 5(d), for 20% selfish
nodes, the number of packet drop in HBDS scheme is 7% and 22% less than SSAR
and SimBet respectively. The comparison of HDBS, SSAR, and SimBet for overhead
ratio for different percentage of selfish nodes is shown in Table 5. For 20%
selfish nodes, the overhead ratio of HBDS, SSAR, and SimBet are 30, 50, and 60
respectively. Thus, it can be seen that the overhead ratio of HBDS is 25% and
38% lower than SSAR and SimBet respectively. In addition, for 80% selfish
nodes, the overhead of HBDS, SSAR, and SimBet are 43, 60, and 72 that is 22%
and 37% lower than SSAR and SimBet respectively. The comparison of HDBS, SSAR,
and SimBet for packet delivery ratio for different percentage of selfish nodes
is shown in Table 6. For 20% selfish nodes, the rate of packet drop of HBDS,
SSAR, and SimBet are 5300, 6000, and 7500 respectively. Thus, it can be seen
that the overhead ratio of HBDS is 7% and 22% lower than SSAR and SimBet
respectively. In addition, for 80% selfish nodes, the rate of packet drop of
HBDS, SSAR, and SimBet are 6700, 7500, and 8500 respectively, that is 8% and
18% lower than SSAR and SimBet respectively. It is because the nodes avoid
sending messages to their neighboring nodes in other methods, if they do not
have a strong social interaction with their neighbors. The nodes are
considered to be cooperative residing in the same community in SimBet and SAAR
methods. So in this protocol, the level of selfishness is low. While the nodes
will modify their selfishness accordingly in our HBDS scheme if they have
sufficient incentive for data transfer. This improves network performance by
changing the selfishness of the node dynamically. Therefore, it concludes that
HBDS scheme has a better capacity to handle node selfishness and to encourage
nodes to collaborate in a better network performance.

Table 5: Comparison of HBDS, SSAR, and SimBet in case of 20%, 40%, 60%, and 80% Selfish nodes for Overhead Ratio No. of Selfish Nodes | HBDS | SSAR | SimBet
---|---|---|---
20% | 30 | 50 | 60
40% | 38 | 56 | 65
60% | 41 | 58 | 67
80% | 43 | 60 | 72
Table 6: Comparison of HBDS, SSAR, and SimBet in case of 20%, 40%, 60%, and 80% Selfish nodes for Number of Packets Drop No. of Selfish Nodes | HBDS | SSAR | SimBet
---|---|---|---
20% | 5300 | 6000 | 7500
40% | 5700 | 6500 | 7700
60% | 6300 | 7000 | 8000
80% | 6700 | 7500 | 8500

##  5 Conclusion and Future Work

In this article, the HBDS scheme is proposed for IoT using VDTN to stimulate
selfish nodes in a community for cooperation or forwarding of messages. The
HBDS scheme monitors selfish nodes and continuously watches their behavior.
The reward is offered as a reputation that makes the network node cooperative.
Nodes are also penalized for showing consistently selfish behavior. Following
the democratic process, the watchdog nodes evaluate the performance of their
neighbor nodes. Two protocols namely SSAR and SimBet were systematically
analyzed to test the performance metrics such as packet delivery probability,
packet delivery delay, overhead ratio and number of dropped packets. The
results show that the HBDS scheme can effectively manage a wide range of
selfish nodes by enabling more selfish nodes to work together in a community
to boost network performance.

The proposed scheme HBDS can be used in the future for cooperative sharing of
the information among the connected nodes in the network. It can be used for
efficient tracking of the selfish nodes to detect the network anomalies like
errors in packets delivery, damaged packets delivered, energy and power
constraints.

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#  A CNN with Noise Inclined Module and Denoise Framework for Hyperspectral
Image Classification

Zhiqiang Gong, Ping Zhong, , Jiahao Qi, and Panhe Hu Manuscript received XX,
2022; revised XX, 2022. This work was supported by the Natural Science
Foundation of China under Grant 62001502.(Corresponding author: Panhe Hu) Z.
Gong is with the National Innovation Institute of Defense Technology, Chinese
Academy of Military Science, Beijing 100000, China. e-mail:
(gongzhiqiang13@nudt.edu.cn).P. Zhong, J. Qi, and P. Hu are with the National
Key Laboratory of Science and Technology on ATR, College of Electrical Science
and Technology, National University of Defense Technology, Changsha 410073,
China (e-mail<EMAIL_ADDRESS>hupanhe13@nudt.edu.cn).

###### Abstract

Deep Neural Networks have been successfully applied in hyperspectral image
classification. However, most of prior works adopt general deep architectures
while ignore the intrinsic structure of the hyperspectral image, such as the
physical noise generation. This would make these deep models unable to
generate discriminative features and provide impressive classification
performance. To leverage such intrinsic information, this work develops a
novel deep learning framework with the noise inclined module and denoise
framework for hyperspectral image classification. First, we model the spectral
signature of hyperspectral image with the physical noise model to describe the
high intra-class variance of each class and great overlapping between
different classes in the image. Then, a noise inclined module is developed to
capture the physical noise within each object and a denoise framework is then
followed to remove such noise from the object. Finally, the CNN with noise
inclined module and the denoise framework is developed to obtain
discriminative features and provides good classification performance of
hyperspectral image. Experiments are conducted over two commonly used real-
world datasets and the experimental results show the effectiveness of the
proposed method. The implementation of the proposed method and other compared
methods could be accessed at https://github.com/shendu-sw/noise-physical-
framework.

###### Index Terms:

Hyperspectral Image Classification, Convolutional Neural Networks (CNNs),
Noise Inclined Module, Denoise framework, Diversity.

##  I Introduction

Hyperspectral images by airborne or spaceborne sensors have played an
important role in land cover detection, resource management, quality control,
and others [1, 2]. The hundreds of spectral channels in the image can provide
plentiful of spectral information to discriminate different objects of
interest in classification task which tries to assign the unique land-cover
label to each pixel. In spite of the high spectral resolution in hyperspectral
image classification, there exists great noisy in each pixel which would make
the great intra-class variance and inter-class similarity. This multiplies the
difficulty to extract discriminative features from the image and leads to the
degradation of the classification performance. Therefore, exploring an
effective and proper model to remove noise from the image is urgent to perform
effective image classification.

Nowadays, deep learning based methods have been introduced into hyperspectral
image classification and contributed to dramatic improvements [3]. Deep
learning networks (DNNs), especially convolutional neural networks (CNNs), can
capture both the local and global information and thus provide discriminative
features. To further improve the potential representational ability of CNNs to
extract spatial and spectral features for hyperspectral image classification,
most of prior works commonly focus on deepen or widen their network
architectures [4] through the utilization of multi-scale information, spatial
information, rotation variance, and others. For instance, [5] utilizes the
multi-scale information and proposes a multiscale covariance maps for
improvement of hyperspectral image classification. Similarly, [3] also focuses
on embedding the multi-scale information into the deep model. [6] and [7]
tries to leverage the spatial and spectral information of the hyperspectral
image. [8] proposes a pyramidal bottleneck residual blocks which gradually
increases the convolutional filters of the network to improve the spectral-
spatial features extracted by the deep model. These prior methods pursue good
performance over hyperspectral image classification by improving the model on
spectral and spatial features while ignore the intrinsic physical
characteristics of the image itself.

To utilize these intrinsic physical characteristics, some prior works utilize
such physical prior to construct the training loss and supervise the training
process of the deep model. As a representative work, [9] leverages the
statistical properties of each class of hyperspectral image and construct the
statistical loss for better representation. Similarly, [10] directly utilizes
the high dimension property and models the hyperspectral image as nonlinear
data manifold, and then constructs the training loss under such manifold
assumption. These methods consider the class distributions of the image and
construct the corresponding training loss which can supervise the training
process and embed such class information with back propagation of the network.
Thus good performance can be obtained with these additional information.
However, these methods mainly use the class distribution from the view of
training loss while ignore the potential of network module to extract these
intrinsic physical characteristics.

As for hyperspectral images, each pixel is generated by radiance signals
combined with the sensor noise as the adjacency radiance. These noises bring
statistical variance to the spectral signature of pixels from the same class
and therefore different classes in the image present great overlapping of the
spectral curves. Such noise characteristics would negatively affect the deep
model to discriminate different classes. To solve the problem, motivated by
[11], this work proposes a CNN to remove these noise and provide clean
features from the image for better classification performance. Our method
includes a noise inclined module to represent the generated noise from the
image and a denoise framework to remove such noise. Such framework can provide
clean features from the hyperspectral image and significantly improve the
performance of the deep model.

To be concluded, the contributions of this work are twofold. First, this work
develops a noise inclined module to extract noise information from the
hyperspectral image. The module utilizes the noise space to represent the
noise from the image. The diversity regularization and the large scale of
noise space can guarantee a good representation of the noise from the image.
Then, this work proposes a CNN framework to remove the extracted noise by the
noise inclined module from the features, and provide clean and discriminative
features to improve the classification performance.

The remainder of this paper is arranged as follows. Section II details the
construction of the proposed framework for hyperspectral image classification.
Experimental results and analysis over two real-world datasets are summarized
in Section III. Section IV concludes this paper with some future directions.

##  II Proposed Method

Denote $X=\\{{\bf x}_{1},{\bf x}_{2},\cdots,{\bf x}_{N}\\}$ as the set of
training samples of the hyperspectral image where $y_{i}$ is the corresponding
label of the sample ${\bf x}_{i}$ and $N$ is the number of training samples.

Figure 1: Flowchart of the proposed method for hyperspectral image
classification.

###  II-A Physical Modelling

The hyperspectral image can be physically modelled by at-sensor radiance
signal model. Generally, the total radiation signal reaching the sensor
consists of three components, namely the reflected radiance (radiation
reflected from the pixel of interest), the adjacency radiance (radiation
reflected from the surface surrounding the pixel of interest and scattered by
the air volume into the sensor) and other radiance from sensor noise. The
reflected radiance generates the spectral signature of a certain object and
the other radiances formulate the noise imposed on the object.

Therefore, for hyperspectral image processing, the spectral characteristics of
each object can be defined by a single class spectrum or a target subspace
combined with noise from the neighbors, sensors, and others [12, 13]. Let
${\bf x}_{\Lambda}^{m}$ denote the object of interest from $m$-th class and it
can be formulated as

${\bf x}_{\Lambda}^{m}={\bf s}^{m}+{\bf n}({\bf x}_{\Lambda}^{m})$ (1)

where ${\bf s}^{m}$ is the spectral signature of the $m$-th class, and ${\bf
n}({\bf x}_{\Lambda}^{m})$ is the random additive noise from the neighbors and
sensors imposed on this specific object.

To represent these noises imposed on the image, this work denotes the noise
space $S=\\{{\bf n}_{1},{\bf n}_{2},\cdots,{\bf n}_{k}\\}$ where ${\bf n}_{i}$
stands for $i^{th}$ base noise of the space. Then, the noise can be seen as
the linear weighted of noises from multiple sources in noise space and it can
be calculated as

${\bf n}({\bf x}_{\Lambda}^{m})=\lambda_{1}({\bf x}_{\Lambda}^{m}){\bf
n}_{1}+\lambda_{2}({\bf x}_{\Lambda}^{m}){\bf n}_{2}+\cdots+\lambda_{k}({\bf
x}_{\Lambda}^{m}){\bf n}_{k}$ (2)

where ${\bf n}_{1},{\bf n}_{2},\cdots,{\bf n}_{k}$ represents the base noise
from different sources and $\lambda_{i}({\bf x}_{\Lambda}^{m})$ denotes the
weight parameter of the noise ${\bf n}_{i}(i=1,2,\cdots,k)$.

###  II-B Noise Inclined Module

This subsection proposes the noise inclined module to learn these base noises
adaptively, estimate the weight parameters, and provide the representation of
the noise of each object in the noise space. The noise inclined module
consists of three parts, namely the noise extraction module to extract noise
from the features, the noise reconstruction mudule to reconstruct the noise in
the noise space and the self-supervised module to update the noise space.

####  II-B1 Noise Extraction Module

The noise extraction module is performed by the noise mapping operation. In
general imaging process, the noise ${\bf n}({\bf x}_{\Lambda}^{m})$ of ${\bf
x}_{\Lambda}^{m}$ is highly related to the signature ${\bf s}^{m}$, which can
be represented as ${\bf n}({\bf x}_{\Lambda}^{m})\propto{\bf s}^{m}$. Based on
Eq. 1, the noise is also related to the noised feature ${\bf
x}_{\Lambda}^{m}$,

${\bf n}({\bf x}_{\Lambda}^{m})\propto{\bf x}_{\Lambda}^{m}$ (3)

Considering the imaging mechanism, ${\bf n}({\bf x}_{\Lambda}^{m})$ can be
calculated through the non-linear transformation of ${\bf x}_{\Lambda}^{m}$,
which is implemented by the fully-connected layer in this work. Denote
$\phi({\bf x}_{\Lambda}^{m})$ as the learned features from the CNN where
$\phi(\cdot)$ represents the mapping function of the CNN. Then, the extracted
noise can be calculated by

${\bf n}_{f}({\bf x}_{\Lambda}^{m})={\bf W}\phi({\bf x}_{\Lambda}^{m})+b$ (4)

where ${\bf W},b$ represent the weight and the bias of the layer,
respectively.

####  II-B2 Noise Reconstruction Module

The key process for noise reconstruction is to reconstruct the noise in the
noise space based on the extracted noise from noise extraction module. The
noise reconstruction process can be seen as the following optimization,

$\min\limits_{\lambda_{1},\cdots,\lambda_{k}}L_{u}=L_{r}+L_{s}+\alpha L_{d}$
(5)

where $L_{r}=\|{\bf n}_{f}({\bf
x}_{\Lambda}^{m})-\sum_{i=1}^{k}\lambda_{i}{\bf n}_{i}\|$ denotes the
reconstruction term, $L_{s}=\sum_{i=1}^{k}|\lambda_{i}({\bf
x}_{\Lambda}^{m})|$ is the regularization term to enforce the unique
reconstruction solution, and $L_{d}$ is the noise diversity promoting augment
and $\alpha$ is a constant that controls tradeoff between task objective and
diversity. This paper defines a angle-based difference measure for diversity,
that is,

$L_{d}=\frac{1}{k(k-1)}\sum\limits_{l\neq m}{\bf n}_{l}^{T}{\bf n}_{m}$ (6)

It should be noted that $L_{d}$ mainly performs for the update of ${\bf
n}_{i}$.

Instead of obtaining the values of $\lambda_{i}({\bf
x}_{\Lambda}^{m})(i=1,\cdots,k)$ through optimization methods, this work tries
to estimate these values directly using the correlation between the base noise
and the noise features. Generally, the base noises ${{\bf n}_{1},\cdots,{\bf
n}_{k}}$ are expected to be indepedent to each other, therefore, this work
tries to use the cosine similarity to estimate the values and impose the
indepedent regularization over the base noises.

As [11], we compute the cosine similarity $s_{j}({\bf x}_{\Lambda}^{m})$ of
the $j$-th base noise ${\bf n}_{j}$ and the extracted noise $n_{f}({\bf
x}_{\Lambda}^{m})$.

$s_{j}({\bf x}_{\Lambda}^{m})=\frac{{\bf n}_{j}{\bf n}_{f}({\bf
x}_{\Lambda}^{m})}{\|{\bf n}_{j}\|\|{\bf n}_{f}({\bf x}_{\Lambda}^{m})\|}$ (7)

Then, the pre-reconstructed noise is calculated by

${\bf n}^{\prime}=\sum_{j=1}^{k}s_{j}({\bf x}_{\Lambda}^{m}){\bf n}_{i}$ (8)

To preserve the energy of the noise, the final estimation of $\lambda_{j}$
given ${\bf x}_{\Lambda}^{m}$ can be measured by

$\lambda_{j}({\bf x}_{\Lambda}^{m})=\frac{\|{\bf n}_{f}({\bf
x}_{\Lambda}^{m})\|}{\|{\bf n}^{\prime}\|}s_{j}({\bf x}_{\Lambda}^{m})$ (9)

Then, the final reconstructed noise can be formulated as

${\bf n}_{res}({\bf x}_{\Lambda}^{m})=\sum_{j=1}^{k}\lambda_{j}({\bf
x}_{\Lambda}^{m}){\bf n}_{j}$ (10)

####  II-B3 Noise Self-Supervised Module

These base noises in noise space are updated through self-supervised learning
based on the gradient descent of Eq. 5 and it can be calculated as

$\frac{\partial L_{u}}{\partial{\bf n}_{i}}=\frac{\partial L_{s}}{\partial{\bf
n}_{i}}+\alpha\frac{\partial L_{d}}{\partial{\bf n}_{i}}$ (11)

Based on derivation rule,

$\frac{\partial L_{s}}{\partial{\bf n}_{i}}=-2\lambda_{i}({\bf
x}_{\Lambda}^{m})({\bf n}_{f}({\bf
x}_{\Lambda}^{m})-\sum_{j=1}^{k}\lambda_{j}({\bf x}_{\Lambda}^{m}){\bf
n}_{j})$ (12)

Besides, $\displaystyle{\frac{\partial L_{d}}{\partial{\bf n}_{i}}}$ is used
to decrease the redundancy and make independent between different base noises,
and it can be calculated by

$\frac{\partial L_{d}}{\partial{\bf n}_{i}}=\frac{2}{k(k-1)}\sum\limits_{l\neq
i}{\bf n}_{l}$ (13)

In addition, this work uses the decay way for self-supervised update. The
final self-supervised update of the base noise can be formulated as

${\bf n}_{j}^{(k+1)}=\beta{\bf n}_{j}^{(k)}+(1-\beta)\times\frac{\partial
L_{u}}{\partial{\bf n}_{i}}$ (14)

where $\beta\in[0,1]$ is the decay rate. This strategy is also the self-
supervised updating just as [11].

###  II-C The Proposed Framework

This work proposes a novel framework with noise inclined module and denoise
operation to extract clean and discriminative features from the hyperspectral
image. The noise inclined module is used to extract the physical noise from
the image, reconstruct the noise in a noise space, and finally update the
noise space under self-supervised way.

Then, the denoise operation is followed to provide the clean features from the
image, and the denoise operation can be implemented by

$f_{clean}({\bf x}_{\Lambda}^{m})=\phi({\bf x}_{\Lambda}^{m})-{\bf
n}_{res}({\bf x}_{\Lambda}^{m})$ (15)

The obtained clean features are then used for the classification of different
objects in the image. The flowchart of the proposed method is shown in Fig. 1.

This work utilizes the center loss [14] for the training of the deep model to
increase the inter-class variance of the learned clean features, and the loss
can be formulated as

$L_{C}=\frac{1}{2}\sum\limits_{{\bf x}_{\Lambda}^{m}\in X}\|f_{clean}({\bf
x}_{\Lambda}^{m})-{\bf c}_{m}\|^{2}_{2}$ (16)

where ${\bf c}_{m}$ denotes the class center in the loss.

##  III Experimental Results

###  III-A Experimental Setups

To validate the effectiveness of the proposed method, we conduct experiments
over two real-world hyperspectral image datasets, namely Pavia University
dataset and Houston 2013 dataset.

Pavia University data [15] was collected by the reflective optics system
imaging spectrometer (ROSIS-3) sensor ranging from 0.43 to 0.86 $\mu m$ with a
spatial resolution of 1.3$m$ per pixel. The data contains $610\times 340$
pixels with 115 bands where 12 bands have removed due to noise and absorption
and the remaining 103 channels are used. 42776 samples from 9 classes have
been labeled for experiments.

Houston 2013 data [16] was acquired over the University of Houston campus and
the neighboring urban area with a resolution of 2.5 m/pixel, and provided as
part of the 2013 IEEE Geoscience and remote Sensing Society data fusion
contest. it consists of $349\times 1905$ pixels of which a total of 15011
labeled samples divided into 15 classes have been chosen for experiments. Each
pixel denotes a sample and contains 144 spectral bands ranging from 0.38 to
1.05 $\mu m$.

Pytorch is used as the deep learning framework and all the experiments are
carried out on a PC with an Intel@ Xeon(R) Gold 6226R CPU and Nvidia Quadro
RTX 6000 GPU. For pavia university data, 200 samples per class are randomly
selected for training and the remainder are for testing while for Houston2013
data, the training and the testing set are following the 2013 GRSS datafusion
contest. The dimension of the learned features is set to 400. The learning
rate is set to $1e^{-4}$ and the training batch is set to 4. The
hyperparameter $\alpha$, $\beta$ is set to $1$, 0.9, respectively.

Figure 2: CNN model adopted in the experiments to implement the proposed
method for hyperspectral image classification.

###  III-B General Performance

At first, we present the general performance of the proposed method for
hyperspectral image classification. In this set of experiments, the number of
base noise in the noise space is set to 1024. The backbone of the proposed
method is the 3-D CNN.

As showed in table I and II, the proposed method can improve the performance
of vanilla CNNs by introducing the physical information in the training
process. As for the Pavia University data and the Houston2013 data, the CNNs
with the proposed framework can significantly improve the CNNs’ performance.
For Pavia University, the accuracy can increase from $94.97\%\pm 0.69\%$ to
$96.56\%\pm 0.47\%$ with the proposed framework. For Houston2013 data, the
accuracy can be improved by about $6\%$. This indicates that the physical
information is effective for the training of the deep CNNs in the
hyperspectral image classification literature.

Furthermore, Fig. 3 and 4 shows the classification maps of different datasets.
It also demonstrates that the proposed method using the physical information
can remarkably decrease the classification errors and further improve the
classification performance.

TABLE I: Classification accuracies ($Mean\pm SD$) (OA, AA, and Kappa) of different methods achieved on the Pavia University data. Methods | SVM-POLY | 3-D CNN | HybridSN | PResNet | SSFTTNet | Proposed Method
---|---|---|---|---|---|---
Classification Accuracies (%) | C1 | $82.79\pm 1.42$ | $95.59\pm 1.15$ | $91.52\pm 1.76$ | $\mathbf{97.10\pm 0.87}$ | $94.97\pm 1.95$ | ${95.92\pm 1.56}$
C2 | $85.91\pm 0.74$ | $96.82\pm 0.69$ | $96.20\pm 2.65$ | $97.04\pm 1.85$ | $95.92\pm 1.81$ | $\mathbf{97.46\pm 0.74}$
C3 | $86.12\pm 0.34$ | $89.68\pm 2.12$ | $70.56\pm 9.31$ | $84.20\pm 4.69$ | $77.36\pm 0.37$ | $\mathbf{92.22\pm 1.07}$
C4 | $97.75\pm 0.12$ | $97.60\pm 1.30$ | $92.32\pm 3.51$ | $97.91\pm 0.74$ | $96.23\pm 2.77$ | $\mathbf{98.03\pm 0.92}$
C5 | $99.65\pm 0.12$ | $100.0\pm 0.00$ | $99.91\pm 0.12$ | $100.0\pm 0.00$ | $100.0\pm 0.00$ | $\mathbf{100.0\pm 0.00}$
C6 | $91.81\pm 0.25$ | $86.77\pm 4.42$ | $85.71\pm 3.57$ | $85.39\pm 8.62$ | $74.61\pm 11.74$ | $\mathbf{94.70\pm 3.28}$
C7 | $94.56\pm 0.06$ | $97.36\pm 0.92$ | $84.16\pm 12.89$ | $90.18\pm 5.76$ | $95.18\pm 3.69$ | $\mathbf{97.72\pm 1.08}$
C8 | $87.84\pm 1.08$ | $92.64\pm 2.19$ | $84.92\pm 10.60$ | $89.82\pm 0.75$ | $81.40\pm 9.32$ | $\mathbf{94.47\pm 2.05}$
C9 | ${\mathbf{100.0\pm 0.00}}$ | $99.71\pm 0.31$ | $99.46\pm 0.76$ | $99.93\pm 0.09$ | $99.40\pm 0.09$ | ${99.84\pm 0.22}$
OA (%) | $88.00\pm 0.19$ | $94.97\pm 0.69$ | $91.64\pm 1.16$ | $94.47\pm 0.44$ | $91.34\pm 1.95$ | $\mathbf{96.56\pm 0.47}$
AA (%) | $91.83\pm 0.06$ | $95.13\pm 0.60$ | $89.42\pm 1.67$ | $93.51\pm 1.83$ | $90.56\pm 3.04$ | $\mathbf{96.70\pm 0.55}$
KAPPA (%) | $84.29\pm 0.23$ | $93.32\pm 0.89$ | $88.96\pm 1.46$ | $92.66\pm 0.61$ | $88.58\pm 2.58$ | $\mathbf{95.41\pm 0.63}$
TABLE II: Classification accuracies ($Mean\pm SD$) (OA, AA, and Kappa) of different methods achieved on the Houston 2013 data. Methods | SVM-POLY | 3-D CNN | HybridSN | PResNet | SSFTTNet | Proposed Method
---|---|---|---|---|---|---
Classification Accuracies (%) | C1 | $81.48$ | $\mathbf{83.84\pm 2.56}$ | $80.10\pm 2.75$ | $82.24\pm 1.07$ | $82.29\pm 0.34$ | ${82.85\pm 0.08}$
C2 | $81.86$ | $83.57\pm 2.54$ | $82.94\pm 0.33$ | $83.88\pm 1.53$ | $89.24\pm 8.31$ | $\mathbf{90.04\pm 4.12}$
C3 | ${99.41}$ | $95.21\pm 2.13$ | $91.49\pm 0.84$ | $97.03\pm 0.00$ | $\mathbf{99.70\pm 0.14}$ | ${96.48\pm 0.84}$
C4 | $92.61$ | $94.34\pm 2.59$ | $91.10\pm 2.54$ | $93.04\pm 0.47$ | $97.35\pm 0.67$ | $\mathbf{97.99\pm 1.32}$
C5 | $96.88$ | $98.48\pm 0.88$ | $98.72\pm 1.54$ | $98.72\pm 0.33$ | $\mathbf{99.62\pm 0.00}$ | ${98.92\pm 0.46}$
C6 | $93.71$ | $91.19\pm 2.45$ | $82.17\pm 4.45$ | $96.15\pm 1.48$ | $\mathbf{97.90\pm 2.97}$ | ${94.83\pm 2.82}$
C7 | $80.22$ | $83.71\pm 5.16$ | $78.31\pm 0.73$ | $85.49\pm 4.02$ | $73.23\pm 8.31$ | $\mathbf{90.54\pm 1.48}$
C8 | $36.28$ | $63.84\pm 2.21$ | $67.47\pm 3.42$ | $66.81\pm 0.07$ | $\mathbf{81.48\pm 17.86}$ | ${70.65\pm 3.21}$
C9 | $72.90$ | $69.63\pm 4.58$ | $68.13\pm 0.87$ | $\mathbf{76.11\pm 0.53}$ | $69.88\pm 17.76$ | ${73.82\pm 3.87}$
C10 | $55.89$ | $49.44\pm 4.04$ | $56.56\pm 1.64$ | $51.59\pm 4.03$ | $\mathbf{60.38\pm 30.78}$ | ${59.13\pm 3.01}$
C11 | $\mathbf{75.14}$ | $62.52\pm 3.35$ | $72.01\pm 1.88$ | $73.29\pm 1.41$ | $67.47\pm 32.70$ | ${74.91\pm 2.60}$
C12 | $72.24$ | $69.34\pm 7.59$ | $77.86\pm 9.17$ | $71.37\pm 4.08$ | $62.58\pm 1.83$ | $\mathbf{88.49\pm 1.59}$
C13 | $72.28$ | $91.58\pm 2.90$ | $88.25\pm 3.72$ | $90.70\pm 2.23$ | $91.58\pm 0.99$ | $\mathbf{91.16\pm 1.52}$
C14 | $\mathbf{100.0}$ | $96.68\pm 0.78$ | $99.19\pm 1.15$ | $98.99\pm 0.29$ | $100.0\pm 0.00$ | ${94.98\pm 1.02}$
C15 | $\mathbf{98.31}$ | $93.66\pm 1.97$ | $96.83\pm 3.29$ | $93.45\pm 6.88$ | $96.41\pm 0.90$ | ${94.88\pm 2.87}$
OA (%) | $77.24$ | $78.40\pm 1.37$ | $79.46\pm 0.21$ | $80.61\pm 0.72$ | $80.96\pm 1.44$ | $\mathbf{84.41\pm 0.73}$
AA (%) | $80.61$ | $81.80\pm 1.03$ | $82.08\pm 0.12$ | $83.93\pm 1.00$ | $84.61\pm 1.24$ | $\mathbf{86.64\pm 0.76}$
KAPPA (%) | $75.51$ | $77.00\pm 1.43$ | $78.10\pm 0.21$ | $79.32\pm 0.76$ | $79.69\pm 1.51$ | $\mathbf{83.31\pm 0.76}$

Figure 3: Classification mapping by different methods over Pavia University
(Overall accuracies). (a) Ground truth; (b) 3-D CNN (95.42%); (c)
HybridSN(46.92%); (d) PResnet(94.16%); (e) SSFTTnet(92.72%); (f) Proposed
Method(97.24%).

Figure 4: Classification mapping by different methods over Houston2013
(Overall accuracies). (a) Ground truth; (b) 3-D CNN (77.28%); (c)
HybridSN(79.31%); (d) PResnet(81.12%); (e) SSFTTnet(81.98%); (f) Proposed
Method(85.46%).

###  III-C Performance with Different Number of Base Noise

In this set of experiments, we test the performation of the proposed framework
under different number of base noise in the noise space. The number of base
noise in the noise space denotes the representational ability of the noise
space. Generally, a larger number of base noise can provide a better
representation of the noise in the signature characteristics of the
hyperspectral image and thus the clean feature can be more discriminative.
However, excessive number of base noise increases the learned parameters and
makes redundancy between the base noise which makes the degradation of the
classification performance. Figure 5 shows the tendencies of the performance
under different number of base noise. As for Pavia University data, the
classification performance achieves the best with base noise of 2048 while for
Houston2013 data, the classification performance achieves best under 1024 base
noise. The tendencies of the classification performance over the two datasets
validate the conclusions before.

Figure 5: Classification performance with different number of base noise in
the noise space over (a) Pavia University; (b) Houston2013.

###  III-D Performance with Different Sizes of neighbors

In this set of experiments, we presents the performance under different sizes
of neighbors. Fig 6 shows the tendencies with different neighbor sizes over
the two datasets. As the figure shows, the 3-D CNN is not very friendly to
extract features from hyperspectral image with larger neighbor sizes.
Generally, spectral-spatial classification with larger neighbor size contains
more spatial information and thus provide more discriminative features for
classification. While due to the limitation of the backbone model, the
classification performance even decreases with excessive large spatial sizes
(e.g. $9\times 9$).

More importantly, as Fig. 6 shows, the classification performance can be
significantly improved with the proposed method by using physical information
under different sizes of neighbors. Especially, for Pavia University data, the
classification accuracy can be increased by 1.72% under $5\times 5$ neighbor
and 2.32% under $9\times 9$ neighbor. While for Houston2013 data, the
classification accuracy can even be increased by about 6% under $5\times 5$
neighbor.

Figure 6: Classification performance with different neighbor sizes over (a)
Pavia University; (b) Houston2013.

###  III-E Comparisons with Other Methods

To thuroughly evaluate the effectiveness of the proposed method, this work
selects Support Vector Machine(SVM), 3-D CNN [1], PResNet [17], HybridSN [18],
SSFTTNet [19] as baselines. Table I and II show the comparisons over the two
data sets, respectively. The experimental results in each table are with the
same experimental setups and $5\times 5$ neighbors are used as the spatial
information in the experiments.

From table I, we can find that the proposed method which achieves $96.56\%\pm
0.47\%$ OA outperforms these baseline methods over Pavia University data where
the best provides an OA of $94.97\%\pm 0.69\%$ by 3-D CNN. As for Houston2013
data, it can be noted that the proposed method also provides an superior
performance ($84.41\%\pm 0.73\%$ by proposed method vs $80.96\%\pm 1.44\%$ by
SSFTTNet). Furthermore, the proposed method can provide a better performance
by using a different backbone CNN. This indicates that the proposed method can
provide an comparable or better performance than other recent methods.

##  IV Conclusions

In this work, a novel CNN with noise inclined module and denoise framework is
developed for hyperspectral image classification. First, the physical noise
model is constructed to represent the noise information. Then, the noise
inclined module is developed to adaptively learn the noise from each object.
Finally, the denoise framework is followed to remove the extracted noise
information and privide clean features to better discriminate different
objects. The experimental results over two challenging datasets have
demonstrated the effectiveness of the proposed method.

In future work, it is suggested investigating other effective architecture
using the intrinsic information. Another important future avenue of
exploration is other intrinsic structures of the hyperspectral image for
effective learning of deep models, such as the manifold structure of class
distribution.

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