Grasshopper Optimization Based Clustering Algorithm (GOCA) For Adaptive Flying Ad-Hoc Network (FANET) To Enhance The Quality Of Service (Qos)

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Grasshopper Optimization Based Clustering

Algorithm (GOCA) For Adaptive Flying Ad-Hoc

Network (FANET) To Enhance The Quality Of

Service (Qos)

Ankur Pandey, Dr. Piyush Kumar Shukla, Dr. Ratish Agrawal, Ankur Khare

Abstract : Unmanned aerial vehicles (UAVs) are broadly used for disaster and rescue operations in flying ad-hoc networks (FANETs). UAVs generate problems like small time for flying and infertile routing due to limited power of battery and high velocity. In this paper, t hese issues will be condensed by utilizing efficient Grasshopper Optimization based Clustering Algorithm (GOCA). The performance of clustering is enhanced by using the grasshopper optimization algorithm and outputs are analyzed in terms of several performance parameters and compared with other optimization approaches like CLPSO, CACONET, and GWOCNET. The experimental results represent that the GOCA has been generated better efficiency than CLPSO, CACONET, and GWOCNET on the basis of packet delivery ratio (PDR), cluster lifetime, end to end delay and consumed energy.

Keywords: Clustering, Grasshopper Optimization Algorithm, Rescue Operations, Unmanned aerial vehicles.

————————————————————

1 INTRODUCTION

Unmanned aerial vehicles (UAVs) are highly useful rising systems which offered vigorous and consistent communication in various applications like disaster, rescue and military operation in vehicular ad hoc network (VANET) and mobile ad hoc network (MANET) [1, 2, 16]. UAVs are also utilized in multimedia communication with 5G [4] environment to permit the Internet of Things (IoT) for omnipresent computing [14]. The protection and communication [5] overhead issues are general shortcomings of UAVs utilized for smart-phones in wireless sensor networks [6]. Packet collision is also a major issue in UAVs. These issues are decreased by an on-require multi UAV [10] having reliable control through remote communication between IoT things [12]. Drones are specific UAVs having highest velocity and adaptive nature to find the target objects in minimum time duration [4]. Flying Ad-hoc Network (FANET) [4] is a bendable and manageable communication concept which generates communication among fast UAVs in 3D environment and OPNET is calculated the results in terms of end to end delay and throughput [3, 11]. FANET uses two routing protocols like AODV and OLSR which are also evaluated packet delivery ratio [15]. The major problems with power utilization and node velocity are decreased by using clustering in MANET but the low strength of cluster obtains system failures in the MANET. The clustering is performed by obtaining a cluster head node and their member nodes to reduce the length of paths and time of message transmission of UAVs [13] in Wireless Sensor Networks (WSN) [8]. Weighted Clustering Algorithm (WCA) is enhanced by modifying some parameter of WCA and improved the efficiency of network

and decreasing costs of computation and communication for MANET [7]. Bio-inspired Ant Colony Optimization based Clustering Algorithm (CACONET) is implemented to enhance the eminence of clustering by generating the optimal clustering with maximum performance. The CACONET obtains better outcomes on the basis of number of clusters, and intracluster distance against the Multi Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning PSO (CLPSO) [8]. Another bio-inspired clustering algorithm dragonfly optimizer (CAVDO) [9] is developed to obtain better results as scalability and efficiency against CLPSO and ACO in VANET. Grey Wolf Optimization based Clustering Network (GWOCNET) is specific approach which is developed on the basis of grey wolf`s communal activities and hunting device to obtain optimal clusters by utilizing node`s velocity in VANET. GWOCNET generates better outcomes than CLPSO and MOPSO [10]. In the above study, several clustering approaches are developed on various networks to enhance the quality of clustering and data restoration. Multiple bio-inspired optimization algorithms like CLPSO, MOPSO, CACONET, GWOCNET etc. are developed for optimal clustering, but no one can solve all optimization issues. So we implemented a new Grasshopper Optimization based Clustering Algorithm (GOCA) enthused by grasshoppers. It obtains quick searching exploration and rapid junction exploitation. Proposed GOCA is developed for clustering of UAVs and results are analyzed with other clustering approaches CLPSO, GWOCNET, and CACONET in terms of the packet delivery ratio (PDR), cluster lifetime, end to

end delay and consumed energy.

2. P

ROPOSED

G

RASSHOPPER

O

PTIMIZATION BASED

C

LUSTERING

A

LGORITHM

(GOCA)

Grasshopper Optimization based Clustering Algorithm (GOCA) evaluates the cluster head UAV`s and their member UAV`s in terms of UAV`s Participation which is a combination of neighbourhood involvement, velocity and energy of UAVs. The position coordinates of UAVs are taken through GPS. Cluster is generated by following steps:

________________________

 Ankur Pandey, Research Scholar, Department of Computer Science & Engineering, University Institute of Technology, RGPV, Bhopal 462023, India: [email protected]

 Dr. Piyush Kumar Shukla, Department of Computer Science & Engineering, University Institute of Technology, RGPV, Bhopal 462023, India; [email protected]

 Dr. Ratish Agrawal,Department of Information Technology, University Institute of Technology, RGPV, Bhopal 462023, India, [email protected]  Ankur Khare, Computer Science, Institute for Excellence in Higher

Education, Kaliyasot Dam, Kolar Road, Bhopal 462016, India;

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3732

2.1. UAV`s Neighbourhood Involvement

A UAV is obsessed as cluster head having maximum number of neighbours in FANET. Therefore, energy consumption in information interchange is decreased with maximum neighbours. Every UAV transmits a HELLO packet to initiate itself to the neighbours, and then each UAV obtains its neighbourhood involvement (number of

neighbours in its transmission range,

N

(

u

)

) after sending all the packets. UAV`s neighbourhood involvement is obtained by using eq. 1.







        u u U u

U N u dis ceuu transmission range

I , _ ) , ( tan ) ( (1)

2.2. UAV`s Velocity

A UAV is obsessed as cluster head having minimum velocity as contrast with neighbours and moving with similar speed and direction as neighbours in FANET. UAV`s velocity



_

,



₁

_



_

N y x x

UAVs

Velocity

UAVs

is obtained

by eq. 2. (x = 1 to number of nodes and y = 1 to total time)

)

1 )

4 .

0 (

*

2 (

*

))

1 ,

(

_

*

(

)

1 ,

(

_

)

,

(

_

Velocity

x

y



UAVs

velocity

x

y





UAVs

velocity

x

y



rand





UAVs



(2)

2.3. UAV`s Energy

A UAV is obsessed as cluster head having greatest remaining energy and smallest addicted energy among neighbour UAVs in FANET. The UAV`s energy



_

,1



₁

_

Energy

_x



UAVs

Energy

_x _N_

UAVs

is obtained

by eq. 3.

x u x x

N

Energy

UAVs

_

,1



(3)

Here

Energy

_x= energy of

x

th UAV and

x

u

N

= number of

neighbours of

x

th UAV. 2.4. UAV`s Participation

UAV`s Participation is calculated for every UAV by utilizing

all three factors (

I

U_{, UAV`s Velocity and UAV`s Energy) by} eq. 4.





_





























Energy

UAVs

w

Velocity

UAVs

w

I

w

ion

Participat

s

UAV

U

_

1 *

_

1 *

*

`

1 2 3

(4)

Where

w

₁,

w

₂ and

w

₃are weights of factors,

therefore

w

₁



w

₂



w

₃



1

.

2.5. Generation of cluster head and their member UAVs The UAV`s Participation of all UAVs are analyzed against a threshold value and If it is to be found that UAV`s Participation value of UAV is higher than a threshold value, then that UAV is obsessed as a cluster head. After that GOCA is initiated to find out the optimal members of cluster head UAVs on the basis of UAV`s position (obtains from GPS) and UAV`s participation.

Grasshopper Optimization (GO) algorithm is a nature-inspired approach which performs optimization using grasshopper swarms behavior and utilizes for increasing the harvest generation and cultivation in both nymph (eat

plants in their path by moving like to progress cylinder) and adulthood (rapidly move long distance for eating the crops). The exploration and exploitation are two search processes utilized by GO to move quickly in local environment. GOCA is performed by using following steps.

Start

Step1. Mathematical initiation of GOA by defining initial values of generation, population, fitness function, and location of all grasshoppers (denotes clusters in FANET), and search agents (denotes the UAV`s participation in FANET) to capriciously value.

Step2: The grasshopper swarming nature is obtained by using arithmetical model to generate the location of every grasshopper by eq.5.

a a

a

SOI

GTF

WNA

LOC





(5)

Here, LOCa = a th

grasshopper location, SOIa = social interface, GTFa = a

th

grasshopper gravitational force, WNAa = advection of wind.

Step 3: To improve the uncertainty of the eq. 5, it can be modified

as

LOC

_a



n

1 *

SOI

_a



n

2 *

GTF

_a



n

3 *

WNA

_a,

where

n

1

,

n

2

, and

n

3

are capricious numbers in [0, 1].



  



N a b b ab ab

a

soi

d

SOI

1

)

(

(6) Here, dab = distance between the a

th

and bth grasshopper, obtained as dab=|locb−loca|, soi is a social interface power

function ab a b ab

d

lo

d







is a unit vector from the ath

to bth grasshopper.

Step 4: The soi social interface power function is obtained by eq. 7.

r r

A

e

I

r

soi

 







)

(

(7)

Here,

I

_A= intensity of attraction and



= length magnitude of attraction. The function soi denotes the attraction or repulsion social interaction of grasshoppers.

Step 5: The gravitational force GTFa is obtained by eq. 8.





g

a

g

e

GTF

(8)

Here, g=universal constant of gravitation and



g

e

= unit vector (in earth center direction).

Step 6: The advection of wind WNAa is obtained by eq. 9.

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3733 Here



= drift constant and



wn

e

= unit vector near the wind.

Step 7: Putting the values of SOIa, GTFa, and WNAa in eq. (5), then equation can be modified as follows.

  

 









_g _wn

ab a b N

a b b

a b

a

g

e

d

loc

soi

LOC



1

)

(

(10)

Here, r

r

A

e

I

r

soi







)

(

, N represents number of

grasshoppers. If the exploitation of swarm is taken in free space, then eq. (10) is modified to imitate the grasshoppers interface.



 

      

   

 







N _d

a b

b ab

a b d a d b d

d

a TR

d loc loc loc loc soi LWB UPB

c c LOC

1

) (

2

(11)

Here, UPBd = upper bound and LWBd = lower bound in the

Dth magnitude,



d

TR

= target Value of the Dth dimension. Until now, the gravitational force component (GTF) is not utilized and let the advection of wind (WNA) is constantly in

the path of a target



d

TR

. Adaptive parameter c is utilized two times in eq. 11 (first c denotes the inertial weight and used for exploration and exploitation of absolute swarm in the order of the target and second c is utilized to reduce the desirability, relieve and revolting region of grasshoppers. Eq. 11 is obtained to generate the next position of grasshoppers on the basis of the existing position.

i

N

c

max min

max





(12)

Here

c

_max= maximum value,

c

_min = minimum value, and

i

N

= maximum number of iterations. The best target position is modified by eq. 11 and distances among grasshoppers are normalized in every iteration and c is calculated by eq. 12. Position modifying is continued yet the gratification of an end situation and at last provided the best value of position and fitness of global optimal of the target in a search space.

In GOCA, every cluster is defined as grasshoppers and each UAV`s Participation is defined as search agents. So, every grasshopper is modifying their positions (locations) based on fitness value. The flow chart of proposed GOCA is shown in Fig. 1.

GOCA Clustering Algorithm

INPUT:

N

_r=number of iterations,

K

=Number of cluster

heads,

P

_GA= grass hopper population,

N

G=number of

grasshoppers,

N

sa =number of search agents,

N

=number of UAV`s,

N

UP= UAV`s Participation.

OUTPUT: cluster centroids (

TB

_sa) with target best optimum value.

Start

Evaluate

I

_U using eq. 1

Evaluate UAV`s velocity using eq. 2

Obtain UAV`s energy using eq. 3

Obtain UAV`s Participation by combining

I

_U , UAV`s velocity

and UAV`s energy using eq. 4

Select K cluster head UAVs having maximum UAV`s Participation values

higher than a threshold value

WHILE an end situation is not delighted

FOR each cluster

Provided initial values of the grasshopper Swarm location

a

loc

(a=1,2,3,...n)

Provided initial values for

c

_max

,

c

min_, and

N

r. Calculate the fitness of all search agents

sa

TB

=best target search agent

WHILE (a <

N

_r)

Update c by eq. 12.

FOR all search agents

Calculated normalized distance among grasshoppers

Update the position of existing search agent by eq. 11.

If existing search agent jumps outside the limitations, then take it back

END FOR

Modify

TB

_sa if better position generates a=a+1

END WHILE

Return

TB

_sa

END FOR

Choose

TB

_sa as the new cluster centroid

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3734 Start

Select K cluster head UAVs having maximum UAV`s Participation Values

Initialize GOCA to find cluster member UAVs

End

Obtain UAV`s Participation by combining Iv, UAV`s velocity and UAV`s energy

Evaluate UAV`s velocity and UAV`s energy Evaluate Iv using eq. 1

Fig1. Flow Chart of Proposed GOCA

3. Result and Analysis

The proposed GOCA is implemented and analyzed in terms of several parameters (table 1) with MATLAB 2018a environment.

Table1: Parameters for Experiment

Parameters Values

Number of UAV`s 20, 30, 40, 50, 60 Size of Network 2 km * 2 km

UAV`s Velocity 0 – 30 m/s (randomly) Transmission Range 100 – 600 m Weights (W1, W2, W3) 0.33, 0.33, 0.34

Population Size 100 Number of iterations 100

We analyzed the performance of the GOCA based on packet delivery ratio (PDR), cluster lifetime, end to end delay and consumed energy and shown that the better performance of GOCA against other approaches like CLPSO, CACONET, and GWOCNET.

3.1. Packet Delivery Ratio (PDR)

PDR is the fraction of sending information taken from the base station to the all information transmitted by total cluster member UAVs by their cluster head UAVs in a FANET.

Fig.2. Packet Delivery Ratio versus number of nodes for

network size 2 km X 2 km

Fig 3. Packet Delivery Ratio versus Transmission Ranges

for network size 2 km X 2 km

It describes that the proposed GOCA generates the maximum PDR value of 93% for 2 km X 2 km network (fig. 2 & 3) in terms of the number of UAVs and transmission ranges of UAVs against the CLPSO (79% & 77%), CACONET (81% & 79%), and GWOCNET (83% & 81%), PDR value is enhanced with enhancing the number of UAVs because of more UAVs send the packets. PDR value is also enhanced by increasing the transmission ranges due to less chance of packet drop with minimum UAVs.

3.2. End to End Delay

End to end delay uses the time to discover a route and transmit the information in FANET. Optimal clustering has minimum value of it.

Fig 4. End to End Delay versus number of nodes for

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3735

Fig 5. End to End Delay versus Transmission Ranges for

It shows that the proposed GOCA calculates the minimum end to end delay (seconds) value of 0.2712 for 2 km X 2 km network (fig. 4 & 5) in terms of the number of UAVs and transmission ranges of UAVs against the CLPSO (0.4235 & 0.4515), CACONET (0.3891 & 0.4033), and GWOCNET (0.3934 & 0.4344). End to end delay value is enhanced with enhancing the number of UAVs because of more UAVs to send the packets. Its value is also reduced with increasing the transmission ranges due to packet transmission by minimum numbers of UAVs.

3.3. Cluster Lifetime

The time from generation to destruction of a cluster is known as cluster lifetime. Optimal clustering has maximum cluster lifetime to decrease the overhead of maintenance in FANET.

Fig 6. Cluster Lifetime versus number of nodes for network

size 2 km X 2 km

Fig 7. Cluster Lifetime versus Transmission Ranges for

It represents that the proposed GOCA calculates the maximum cluster lifetime (seconds) value of 1925 for 2 km X 2 km network (fig. 6 & 7) in terms of the number of UAVs and transmission ranges of UAVs against CLPSO (795 & 867), CACONET (1027 & 1164), GWOCNET (893 & 984), and ECRNET (1243 and 1375). Its value is reduced by enhancing the number of UAVs because of more UAVs to modify FANET topology. Its value is also enhanced by increasing the transmission ranges because of increase the number of neighbours UAVs.

3.4. Consumed Energy

Consumed energy is explained as the UAV`s energy utilize for transferring the data during a specific time period over FANET. Optimal clustering has Minimum energy consumption for increasing the stability and lifetime of cluster of UAVs in FANET.

Fig 8. Consumed Energy versus number of nodes for

Fig 9. Consumed Energy versus Transmission Ranges for

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4. Conclusion

Unmanned aerial vehicles (UAVs) can be necessarily utilized in rescue and disaster operations with limited battery power in FANET. The issues of small flying time and infertile routing because of high velocity will be shortened by using efficient Grasshopper Optimization Clustering Algorithm (GOCA). GOCA is implemented for clustering and outcomes are compared with other optimization approaches like CLPSO, CACONET, and GWOCNET in terms of several performance parameters. The experimental outcomes are illustrated that the GOCA has been obtained better performance than CLPSO, CACONET, and GWOCNET based on packet delivery ratio (PDR), cluster lifetime, end to end delay and consumed energy. In future, the clustering approach in FANET is also developed by utilizing other bio-inspired techniques like Harris-hawk Optimizer, Fruit Fly optimization, and Salp Swarm Algorithm for logical studies.

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