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ANALYSIS OF OPPORTUNISTIC ROUTING IN WIRELESS

AD-HOC NETWORKS USING FIREFLY WITH D-ADAPT-OR

AND ARTIFICIAL FISH-SWARM ALGORITHMS

P.Balamurugan#1, Dr.S.Dhanalakshmi*2,

#1Research Scholar, PG and Research Department of Computer Science, Vivekanandha College of Arts and

Science for Women (Autonomous), Tiruchengode,India. *2Professor, PG and Research Department of Computer Science, Vivekanandha College of Arts and Science for

Women (Autonomous), Tiruchengode,India.

Abstract: Opportunistic routing network carries in opportunistic data forwarding that permits multiple

candidate nodes in the forwarding region to perform on the broadcasted data packet. Novel routing paradigm for

opportunistic routing protocols has been proposed to overcome routing protocol limitations and to provide

efficient delivery of data in these highly dynamic ad hoc networks. This increases the reliability of data delivery

in the network with reduced delay. If the network congestion, hence delay, were to be replaced by time-invariant

quantities, the heuristics in would become a special case of d-AdaptOR in a network with deterministic channels

and with no receiver diversity. In this paper, analyze performance between the Hybrid Firefly Algorithm with

d-adaptOR and Artificial Fish-Swarm Algorithm approach to test shortest route for transferring the packets

of data from source node to the destination node for increase the transmission reliability of sensor networks, life

time of the network, throughput, and minimize the delay. An experimental result shows that when compared to

the existing method, best algorithm to provides better result.

Keywords: Wireless ad hoc networks, Opportunistic Routing, ExOR Protocol, d-adaptOR, Firefly Algorithm,

Meta heuristic technique, Artificial Fish-Swarm Algorithm

I.INTRODUCTION

The opportunistic routing defeats the negative

aspect of unreliable wireless transmission through

broadcasting one transmission that can be

overheard by multiple neighbors. MANET is to

make wireless links as better as wired ones, so as to

developments in the field of routing. Opportunistic

routing enhances network throughput rate and the

transmission reliability of networks. Therefore, OR

is utilized different possible routes or pathways to

the each node and transmit packets toward the

destination. Opportunistic routing includes a

candidate set that has a group of nodes selected

while the next-hop forwarders, if any of the

candidates of a node is already received from the

transmitted packet that may forward it again.

The next forwarder is completed the decision of

choosing via coordination between candidates and

successfully received the transmitted packet. Only

one of the node is very close to the target

destination to perform and to handle the forwarding

data as the rest can just drop the packet when they

have successfully received it. Hence, opportunistic

routing acquires several benefits, up till now

unreliable wireless links in the network while they

actually received. In Opportunistic Routing, the

transmission reliability of sensor networks and

network throughput rate know how to be

automatically increased by using a dynamic relay

node to forward the packet. This paper analyses

and compares the performance of the Firefly with

AdaptOR algorithms and Artificial Fish Swarm

Algorithm in opportunistic routing protocols in

wireless ad hoc networks.

II. ALGORITHMS FOR

OPPORTUNISTIC ROUTING

The meta-heuristic method built to resolve

optimization problems utilizing the simulation of

behavior of the fireflies. A number of research

works have proven the high standard and the

accuracy of the outputs of the optimization

methods solved by FA and AFSA. This exact factor

can be very useful in the establishment of neighbor

tables in MANETs where node can be identified

based on the intensity, but may ends into a

Journal of Information and Computational Science

Volume 9 Issue 12 - 2019

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deadlock situation if FA is used solely. In this

paper, the two algorithms, hybridization of Firefly

and d-daptOR and Artificial Fish-Swarm

Algorithm (AFSA) is discussed to optimize the

route discovery in the deployment of MANETs

where efficiencies of both the algorithms are

utilized for enhancing the routing algorithm then

compare with other opportunistic algorithms.

2.2. d-AdaptOR Methods

The operation of d-AdaptOR can be described

in terms of initialization and four stages of

transmission, reception and acknowledgement,

relay, and adaptive computation as shown in Figure

2. For simplicity of presentation assume a

sequential timing for each of the stages. Use n+ to

denote some (small) time after the start of nth slot

and (n + 1) - to denote some (small) time before

the end of nth slot such that n < n+ < (n + 1) - < n +

1.

Initialization:

For all i € Ɵ, S € Ϭi , a € A(S), initialize

∧0 (i, S, a) = 𝝂-1 (i, S, a) = N-1 (i,

S)=0, ∧imax = 0, while ∧T

max = -R.

Table 1: Notations used in the

description of the algorithm

Symbol Definition

Sin

Nodes receiving the transmission from

node i at time n

ain

Decision taken by node i at time n

A(S) Set of variable actions when nodes in S

receive a packet

N(i) Neighbors of node I including node i

g(S,a) Reward obtained by taking decision a

when the set A of nodes receive a packet

νn (i, S, a) Number of times up to time n, nodes S

have received a packet from node I and

decision a is taken

Nn(i, S) Number of times up to time n, node S

have received a packet from node i

Λn(i, S, a) Score for node I at time n, when nodes S

have received the packet and decision a is

taken

Λmaxi Estimated best score for node i

Transmission Stage: In this stage occurs at

any time n wherein node i transmit if it has a

packet.

Reception and acknowledgement Stage: In

the reception and acknowledgement stage,

successful reception of the packet transmitted

by node i is acknowledged by all the nodes in

Sin. Let Si

n denote the (random) set of nodes

that have received the packet transmitted by

node i. Assume that the delay for the

acknowledgement stage is small enough (not

more than the duration of the time slot) such

that node i infers Sin by time n+.

In support of all nodes k € Sin, the ACK

packet of node k to node i includes the EBS

message ∧k max. The counting random variable Nn is

incremented depend upon acknowledgement and

reception as follows:

Nn(i,S) = Nn-1(i,S) + 1 if S = Sin

N

n 1(i,S)

if S

≠ Sin

Relay Stage: Node i chooses a routing

action ain ∈ A (Si

n) corresponding to the

following (randomized) rule

parameterized by ∈n (i ,S) = 1

𝑁𝑛(𝑖,𝑆)+1 :

With probability (1- ∈n (i ,S)),

ain ∈

𝑎𝑟𝑔 𝑚𝑎𝑥𝑗∈𝐴(𝑆𝑛

𝑖 )

∧𝑛 (𝑖, 𝑆𝑛𝑖 , 𝑗)

is selected,

With probability ∈n (i , Sin )

ain ∈ A (Si

n)

is selected uniformly with probability,

∈𝒏(𝒊,𝑺𝒏𝒊 )

|𝑨 𝑺𝒏𝒊 |

.

Node i broadcasts FO, a control packet that

contains information about routing decision ai n at

some time strictly between n+ and (n + 1)-. If ain ≠

T, then node ain prepares for forwarding in next

time slot, while nodes j ∈ Sin , j ≠ ai

n remove the

packet. When termination action is selected, i.e. ain

= T, all nodes in Sin expunge the packet.

Furthermore, the counting variable 𝜈n is updated

depend upon selection of a routing action.



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𝜈n(i,S) = 𝜈n-1(i,S, a) + 1

if (S, a) = ( Sin, ai

n)

𝜈n-1(i,S, a)

if (S,a) ≠ ( Sin, ai

n)

Adaptive Computation Stage: On time

(n+1)-, after being done the process with

transmission and relaying, node i updates

score vector 𝜈n(i, ., .) as follows:

For all S = Sin , a = ai

n ,

𝛬n+1 (i, S ,a) = 𝛬n (i, S ,a) + 𝛼𝜈𝑛 (i, S ,a)

× ( - 𝛬n (i, S ,a) + g (S , a) + 𝛬𝑚𝑎𝑥𝑎 ) ,

Otherwise,

𝛬n+1 (i, S ,a) = 𝛬n (i, S ,a).

Moreover, node i updates current EBS

message 𝛬𝑚𝑎𝑥𝑖 for future acknowledgements and

reception as:

𝛬𝑚𝑎𝑥𝑖 =

𝑚𝑎𝑥𝑗∈𝐴(𝑆𝑛

𝑖 )∧𝑛+1 (𝑖, 𝑆𝑛

𝑖 , 𝑗).

2.2. Firefly Algorithm

Firefly Algorithm is an emerging swarm

intelligent algorithm that exploits bioluminescence

behavior of fireflies in nature [15, 16]. A firefly in

the search space communicates with the

neighboring fireflies/prey through its brightness,

that influences mate selection and prey attraction.

The parameters of FA used in this algorithm are

given below.

γ - Absorption coefficient

of light

α’ - Randomization

parameter

Bi - Brightness of the

firefly i

Atij - Attractiveness of the

link (i, j)

The Firefly Algorithm is implemented to

solve MANET routing problem. The attractiveness

of the links is initially initialized. The firefly swarm

is placed in the source node with the initial

brightness assigned. RREP packets mimic the

fireflies here. The additional parameters associated

with the algorithm viz., the brightness and

attractiveness are included in the header of RREQ

packet as shown given below. Brightness is

updated after every move and accordingly the

attractiveness of the current node and the receiver

node (next-hop node from the neighbor list) is

updated [17]. RREQ packets move across the

network and once the destination node is reached,

RREP packet is sent to the source node after which

the final selected route is obtained. The RREQ and

RREP packet format for FA is ,

Source Address -> Destination

Address -> Brightness-> Attractiveness -

> Hop Distance -> Cost -> Delay -> Load -

> Reliability

Algorithm:

If node i == S, then

//Source node

For all nodes j in |NHi|

Send RREQ in broadcast mode

Initialize Brightness of RREQ

randomly, 𝐵𝑅𝑅𝐸𝑄0 = 𝑅𝑎𝑛𝑑𝑜𝑚

Fp=0

End For

If RREQ received

Drop RREQ

// Sender received the packet

End If

If RREP received

Start the CBR traffic session

End If

Else

If RREQ received

k = RREQ_SRC;

//Sender of RREQ -

source/intermediate node

i =Receiver of RREQ

// not the

source node

If check_neighbor_list(k)==0

NHi=NHi ∪ {k}

End If

Calculate

𝑤𝑘𝑖 = 𝑑𝑒̅̅ ̅𝑖𝑘 + 𝑐�̅�𝑘 + 𝑙�̅�𝑘 + �̅�𝑖𝑘 − �̅�𝑖𝑘

𝐴𝑡𝑘𝑖 = 𝑤𝑘𝑖 + 𝐵𝑅𝑅𝐸𝑄 + 𝛼′(𝑟𝑎𝑛𝑑 − 0.5)

If , 𝐴𝑡𝑘𝑖 > 𝐴𝑡𝑖𝑗,



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j=k //replace the

new node k with the existing next-hop node j

𝐵𝑅𝑅𝐸𝑄 = 𝐵𝑅𝑅𝐸𝑄𝑒

−𝛾𝑤𝑘𝑖2

0

𝐹𝑝 += 𝑤𝑘𝑖

Else

𝐹𝑝 += 𝑤𝑖𝑗

If i==D

//Destination reached

Send RREP in unicast mode to S

to its next-hop j

Else Forward RREQ to all j in NHi

// Intermediate node

End If

End If

If RREP received and i ≠ 𝐷

// Intermediate node

Forward RREP to next-hop node of i

End If

End If

2.3 Hybrid algorithm for Routing (Firefly with

d-adaptOR)

Hybrid Firefly with d-adaptOR algorithm is

used to selecting the best path for data

transmission, initial populations are considered as a

route discovered by Firefly algorithm [18]. In this

method, the best path is selected by utilizing the

concept of firefly algorithm. The firefly

optimization algorithm is used to identify the best

transmission range over the networks. The fireflies

moves one place to another place in order to

estimate nodes and evaluates more transmission

range within less time of the interval. After

identifying the optimal path, the Unilateral-(Uni-)

scheme is applied for the mobile adhoc networks.

By using the unilateral wakeup scheme the nodes

with slower moving speed to sleep more without

losing the network performance. This scheme is

used in both entity mobility and group mobility of

nodes. The main intent of this hybrid algorithm is

to enhance the energy efficiency with less

computation overhead in the mobile adhoc

networks [19]. The distance between the nodes is

considered as r in the algorithm. The paths found in

the first phase are considered as a firefly algorithm.

The parameters such as energy, bandwidths are

considered as parameters of the algorithm. The

fitness value of the algorithm is considered as a

residual energy. The light intensity is considered as

a total energy cost for particular path or route from

source to destination. The light intensity of the

proposed system is directly proportional to the

fitness of the algorithm (𝑥) ∝ (𝑥).based on the light

intensity the path’s are selected and given as input

to the attractiveness. There are four components of

the algorithm: Initialization, Attractiveness

Function, Movement of Firefly, Transmission

Stage, Reception and acknowledgment stage, Relay

Stage and Adaptive Computation Stage, Fitness

value, Light intensity calculation.

Initialization: Initialize all the nodes.

Find the shortest path using firefly

functions.

• Attractiveness Function: The

attractiveness of fireflies is proportional to

their brightness or light intensity. The

brightness or light intensity is decreased as

the distance between fireflies increase.

Therefore, the attractiveness of a firefly is

determined by the following [13, 14].

𝛽 = 𝛽0𝑥 𝑒−𝛾𝑟2

where the distance between any two

fireflies denotes by r, the attractiveness at

r = 0 is β0 and a light absorption

coefficient is γ. The distance between any

two fireflies 𝑥𝑖 and 𝑥𝑗 is expressed as

follows [13]:

𝑟𝑖𝑗 ||𝑥𝑖 − 𝑥𝑗|| = √∑ (xi,k − xj,k)2d

𝑘=1

where xi,k is the k-th component of the

spatial coordinate xi of i-th firefly and d is

the number of dimensions.

• Movement of Firefly: Firefly i is attracted

toward the other brighter firefly j, then its

movement is defined as below equation

[20]

𝑥𝑖 = 𝑥𝑖 + 𝛽0𝑥 𝑒−𝛾𝑟𝑖𝑗 2

𝑥 (𝑥𝑗 − 𝑥𝑖)

+ 𝑎𝑥 (𝑟𝑎𝑛𝑑 −1

2 )

In equation (3), 𝑥𝑖 is current position,

𝛽0𝑥 𝑒−𝛾𝑟𝑖𝑗 2

𝑥 (𝑥𝑗 − 𝑥𝑖) is the brightness of

the firefly and the last term represent

random motion. The example results of

firefly algorithm developed by Yang, and

the location of fireflies [21].

Transmission Stage: This stage occurs at

time n in which node i transmit if it has

packet for transmission

Reception and acknowledgment Stage: Si

denotes the random set of nodes that have



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received the packet transmitted by node i.

In this stage, successful reception of the

packet transmitted by node i is

acknowledged to it by all the nodes in the

Si. The delay for the acknowledgment

stage is small enough (not more than the

duration of the time slot) such that node i

infers Si by time n. For all the nodes the

ACK packet of node j to node i includes

the EBS message. Upon reception and

acknowledgment the counting random

variable Nn is incremented.

Relay Stage: In this stage node i select a

routing action according to the

randomized rule parameterized by

forwarding probability. The node i

transmit control packet that contains

information about routing decision at

some time strictly between two intervals.

Upon selection of routing action, the

counting variable is updated.

Adaptive Computation Stage: At time

(n+1), after being done with transmission

and relaying, node i updates score vector.

Node i updates its EBS message for future

acknowledgments.

Fitness value: The residual energy of a

node by calculating the total energy

consumed by a node whenever it is in one

of the following states [22]:

IDLE: During this state the node

is idle

CCA_BUSY: During this state

the node is busy

TX: During this state the node

only transmits packets

RX: During this state the node

only receives packets

SWITCHING: During this state

the nodes switches from one channel to

another.

For each node 𝑛𝑖 the residual

energy (𝑛𝑖) is computed as:

(𝑛𝑖) = 𝐸curent(𝑛𝑖) − 𝐸con(𝑛𝑖)

where, 𝐸curent(𝑛𝑖) and 𝐸con(𝑛𝑖)

denote the current energy of the node 𝑛𝑖

and the energy consumed by the node 𝑛𝑖,

respectively. On the verge, the current

energy is set to the initial energy of the

node. (𝑛𝑖) is considered as a attractiveness

of the path. Since the consumed energy of

each node 𝑛𝑖 is influenced by the node is

(i.e., IDLE, TX, RX, CCA BUSY,

SWITCHING), the following equation is

used to determine its consumed energy as:

𝐸con (𝑛𝑖) = Current (𝑛𝑖) ∗ Voltage

(𝑛𝑖) ∗ Duration where, Current (𝑛𝑖) is the

current in Ampere and it depends on the

state in which the node 𝑛𝑖 is, Voltage (𝑛𝑖)

voltage is the supply voltage in Volts and

Duration is the interval that passed since

the last energy update.

• Light intensity calculation: For each node

𝑛𝑖 an energy cost function 𝐶(𝑛𝑖) is

assigned that is given by:

𝐶 (𝑛𝑖) =𝐸𝑖𝑛𝑖𝑡(𝑛𝑖)

𝑅(𝑛𝑖)

Therefore, the total energy cost for a route

p commencing source node 𝑛𝑆 to

destination node 𝑛𝐷, is given by:

𝐸𝑝 = ∑ 𝐶(𝑛𝑖)𝑛𝑖∈𝑝,𝑛𝑖≠𝑛𝐷

The selected route 𝑙 will be the one that

satisfies the following property:

𝐸𝑙 = min{𝐸𝑝: 𝑃 ∈ 𝑉}

Where, 𝑉 is the set of all the possible

routes.

2.4. Artificial Fish-Swarm Algorithm

AFSA (artificial fish-swarm algorithm) is

one of the best methods of optimization among the

swarm intelligence algorithms. This algorithm is

inspired by the collective movement of the fish and

their various social behaviors. Based on a series of

instinctive behaviors, the fish always try to

maintain their colonies and accordingly

demonstrate intelligent behaviors. Searching for

food, immigration and dealing with dangers all

happen in a social form and interactions between

all fish in a group will result in an intelligent social

behavior. This algorithm has many advantages

including high convergence speed, flexibility, fault

tolerance and high accuracy.

Artificial fish (AF) is a fictitious entity of true fish,

which is used to carry on the analysis and

explanation of problem, and can be realized by

using animal ecology concept. With the aid of the

object-oriented analytical method, we can regard

the artificial fish as an entity encapsulated with

one’s own data and a series of behaviors, which

can accepts amazing information of environment

by sense organs, and do stimulant reaction by the

control of tail and fin. The environment in which



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the artificial fish lives are mainly the solution space

and the states of other artificial fish. Its next

behavior depends on its current state and its

environmental state (including the quality of the

question solutions at present and the states of other

companions), and it influences the environment via

its own activities and other companions’ activities

(Jiang et al. 2007).

The AF realizes external perception by its vision

showed in Fig. 1. X is the current state of a AF,

Visual is the visual distance, and Xv is the visual

position at some moment. If the state at the visual

position is better than the current state, it goes

forward a step in this direction, and arrives the

Xnext state; otherwise, continues an inspecting tour

in the vision. The greater number of inspecting tour

the AF does, the more knowledge about overall

states of the vision the AF obtains. Certainly, it

does not need to travel throughout complex or

infinite states, which is helpful to find the global

optimum by allowing certain local optimum with

some uncertainty.

Algorithm 1 fish swarm intelligent

algorithm



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Let X = (x1, x2, . . . ., xn) and Xv = (xv1,

xv2, . . . ., xvn) then this process can be expressed

as follows:

xiv = xi + Visual.rand () , i ∈ (0, n]

(1)

Xnext = X +( Xv - X) / (|Xv – X|).Step.rand().

(2)

Where Rand () produces random numbers

between zero and 1, Step is the step length, and xi

is the optimizing variable, n is the number of

variables. The AF model includes two parts

(variables and functions). The variables include: X

is the current position of the AF, Step is the moving

step length, Visual represents the visual distance,

try_number is the try number and δ is the crowd

factor (0 < δ <1).

Algorithm 2 Random

The functions include the behaviors of the AF:

AF_Prey, AF_Swarm, AF_Follow, AF_Move,

AF_Leap and AF_Evaluate.

Random behavior - in general, fish looks

at random for food and other companion;

Searching behavior - when the fish

discovers a region with more food, it will

go directly and quickly to that region;

Swarming behavior - when swimming,

fish will swarm naturally in order to avoid

danger;

Chasing behavior - when a fish in the

swarm discovers food, the others will find

the food dangling after it;

Leaping behavior - when fish stagnates in

a region, a leap is required to look for food

in other regions.

Fish usually stay in the place with a lot of food, so

we simulate the behaviors of fish based on this

characteristic to find the global optimum, which is

the basic idea of the AFSO. The basic behaviors of

AF are defined (9, 10) as follow for maximum:

(1) AF_Prey: This is a basic biological behavior

that tends to the food; generally the fish perceives

the concentration of food in water to determine the

movement by vision or sense and then chooses the

tendency. Behavior description: Let Xi be the AF

current state and select a state X j randomly in its

visual distance, Y is the food concentration

(objective function value), the greater Visual is, the

more easily the AF finds the global extreme value

and converges.

X j = Xi + Visual.rand () (3)

If Yi < Yj in the maximum problem, it goes forward

a step in this direction;

Xi(t+1) = Xi

(t) +( X j − Xi(t)) / |(X j − Xi

(t)|.Step.rand()

(4)

Otherwise, select a state X j randomly again and

judge whether it satisfies the forward condition. If

it cannot satisfy after try_number times, it moves a

step randomly. When the try_number is small in

AF_Prey, the AF can swim randomly, which makes

it flee from the local extreme value field.

Xi(t+1) = Xi

(t) + Visual.rand ()

(5)

(2) AF_Swarm: The fish will assembles in groups

naturally in the moving process, which is a kind of

living habits to guarantee the existence of the

colony and avoid dangers. Behavior description:

Let Xi be the AF current state, Xc be the center

position and n f be the number of its companions in

the current neighborhood (d ij < Visual), n is total

fish number. If Yc > Yi and n f

n < δ, which means that the companion center has

more food (higher fitness function value) and is not

very crowded, it goes forward a step to the

companion center;



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X(t+1) i= Xi

(t) +( X c − Xi(t)) / |(X c − Xi

(t)|.Step.rand()

(6)

Otherwise, executes the preying behavior. The

crowd factor limits the scale of swarms, and more

AF only cluster at the best area, which ensures that

AF move to optimum in a wide field.

Algorithm 3 (Movement along a particular

direction)

(3) AF_Follow: In the moving process of the fish

swarm, when a single fish or several ones find

food, the neighborhood partners will trail and reach

the food quickly. Behavior description: Let Xi be

the AF current state, and it explores the companion

X j in the neighborhood (d ij <Visual), which has

the greatest Y j. If Y j > Yi and n fn < δ, which

means that the companion X j state has higher food

concentration (higher fitness function value) and

the surrounding is not very crowded, it goes

forward a step to the companion X j,

X (t+1) i

= Xi(t) +( X j − Xi

(t)) / |(X j − Xi(t)|.Step.rand()

(7)

Otherwise, executes the preying behavior.

(4) AF_Move: Fish swim randomly in water; in

fact, they are seeking food or companions in larger

ranges. Behavior description: Chooses a state at

random in the vision, then it moves towards this

state, in fact, it is a default behavior of AF_Prey.

Xi(t+1) = Xi

(t) + Visual.rand()

(8)

(5) AF_Leap: Fish stop somewhere in water, every

AF’s behavior result will gradually be the same, the

difference of objective values (food concentration,

FC) become smaller within some iterations, it

might fall into local extremum change the

parameters randomly to the still states for leaping

out current state.

Algorithm : (Leaping behavior)

input: x, l, u

rand ~ U{1,…..,m}

for k = 1,….., n do

Y1 ~ U[0; 1]; Y2 ~ U[0; 1]

if ¸Y1 > 0.5 then

yk = xrandk + Y2 (uk -

xrandk )

else

yk = xrand k -Y2 (xrand k -

lk)

end if

end for

Behavior description: If the objective function is

almost the same or difference of the objective

functions are smaller than a proportion during the

given (m−n) iterations, Chooses some fish

randomly in the whole fish swarm, and set

parameters randomly to the selected AF. The β is a

parameter or a function that can makes some fish

have other abnormal actions (values), eps is a

smaller constant.

if (BestFC(m) −

BestFC(n)) < eps

X(t+1) some = X(t) some + β.Visual.rand ()

(9)

AF_Swarm makes few fish confined in local

extreme values move in the direction of a few

fishes tending to global extreme value, which

results in AF fleeing from the local extreme values.

AF_Follow accelerates AF moving to better states,

and at the same time, accelerates AF moving to the

global extreme value field from the local extreme

values.

III. EXPEREMENTAL RESULT

In this simulation process, compares the

performance of the Hybrid Firefly with AdaptOR

algorithms and Artificial Fish Swarm Algorithm

(AFSA) in opportunistic routing protocols in

wireless ad hoc networks. This routing analysis has

been completed in order to route the number of

packets more effectively. To be taken into

consideration and comparing various performance



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parameters. By using OMNET++ simulator

analyzed the CBR (Constant Bit Rate) as a traffic

source with 100 mobile nodes are randomly

distributed within 1000 Sq.ft network coverage; the

network topology may possibly random

modification during the nodes’ distribution and

their movement are random performance. The

communication range of the nodes was set into 200

m, for each node, the initial energy level was set to

100 joules of the network. For analyzing the

simulation performance of the two-ray ground

propagation model is utilized. The simulation time

is set at 1000 seconds. To check the performance of

the routing protocols are highly dynamic in ad hoc

networks, the optimization of the protocols perform

with highly mobile nodes. The transmission speed

of the nodes is differed from a minimum of 1 m/s

to various maximum limits in the topology setup to

optimize Mobility is initiated in the network with

the Random Way Point mobility model. Parameters

have been performed for analyzing the algorithm

results based on End-to-end delay, Packet delivery

ratio, Throughput, Network Lifetime,Routing

Overhead Ratio and Energy Consumption.

End-To-End Delay:

The comparison of delay demonstrates in Figure 1.

The Hybrid Firefly with AdaptOR algorithms and

Artificial Fish Swarm Algorithm (AFSA) are

compared and analyzed in below figure clearly.

When the packet size increases as 64, 128, 256,

512, 1024 bytes, the end-to-end delay also

enhances. The E2E delay in Hybrid FF with d-

adaptOR routing protocol enhances from 0.78 ms

to 1.39 ms, in the AFSA it increases from 0.24 ms

to 1.1 ms. The AFSA routing algorithm has

performed better as it compared than Hybrid FF

with d-adaptOR in terms of end-to-end delay.

Figure 1: End to end delay (MS)

Packet Delivery Ratio (PDR)

PDR of the Hybrid Firefly with AdaptOR


(AFSA) results are shown in Figure 2. The AFSA

depicts the high packet delivery ratio even if

compare and analyze to other approaches Hybrid

Firefly with AdaptOR. It illustrated that even if the

number of nodes enhances the PDR of the AFSA

algorithm is furthermore increases.

Figure 2: Packet Delivery Ratio

Throughput

The graph between Throughput and Simulation

Time of the network clearly illustrates in Figure 3.

The graphical representation compares Hybrid

Firefly with d-AdaptOR algorithms and Artificial

Fish Swarm Algorithm (AFSA). In case of AFSA

algorithm the throughput of the network is constant

state and more than the throughput evaluated using

the FF with d-adaptOR algorithm.

Figure 3: Throughput of the Network

Network Lifetime

The main comparison of network lifetime depends

upon simulation time that has shown in Figure 4

shows. In this figure x axis show the simulation



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time and y axis shows the number of exhausted

nodes for FF with d-adaptOR and AFSA, when

unreliable the simulation time. The AFSA and FF

with d-adaptOR maximizes its network lifetime as

it routes the traffic to the nodes having higher

energy in the network. In such case, if the energy

of these nodes get exhausted the topology has the

property of storing information about various

energy efficient routes and therefore it transfers the

traffic to next energy efficient shortest path, thus

enhancing the network lifetime.

Figure 4: Graph of network lifetime

Routing overhead

The major variation of routing overhead ratio

performs over Hybrid Firefly with AdaptOR


(AFSA) where shown in Figure 5. When the node

speed maximizes as (0, 2.5, 5, 7.5, 10) m/s, the

routing overhead ratio enhances too. The FF with

d-adaptOR raises from 9.25% to 15.55% and The

AFSA raises from 4.12% to 10.14%. The AFSA

Algorithm has better performance than FF with d-

adaptOR in terms of routing overhead ratio

because, it establishes stable routes, strong and the

possibility of route failure becomes almost minimal

with less route discovery process.

Figure 5: Routing Overhead

Energy consumption

In Figure 6, the mobility speed ranges from 5 to 25

are plotted in the x-axis with the unit of interval 5,

in which that the y-axis is plotted energy

consumption ranging from 20 to 120 joules with

the unit interval of 10. Figure 6 compares and

analyzes energy consumed by AFSA to deliver the

packet to the receiver, with FF with d-adaptOR.

The AFSA routing protocol designed and choose to

find the most stable route toward the destination

using the available multiple paths. The route

chosen by AFSA could be the best routing pathway

and consumes less energy than other routes, with

the shortest route and storage optimization and time

consuming, where the other protocols simply select

the shortest path without check whether its quality

of results so that the issues occurs retransmission

and route failure. Because of retransmission and

route failure vast amount of energy is wasted by

AFSA. The algorithm ASFA consumes less energy

for delivering data packets to the destination than

FF with d-adaptOR where illustrates clearly in

Figure 6.



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Figure 6: Energy consumption over node speed

IV. CONCLUSION

In this paper analyzed the behavior and

performance of opportunistic routing methods in ad

hoc networks. To determine the optimal path for

traffic flow with minimum resource utilization in

shortest path routing is a significant network

optimization issue was occurred during the

transmission. Results from simulations showed

the variations in behavior and performance of these

opportunistic protocols. By this simulation result,

the Artificial Fish Swarm Algorithm results display

the improvement in PDR, E2E, energy

consumption, throughput and routing overhead and

better network lifetime those parameters are

analyzed in an efficient way. The graphs verify

implementation and display much better result as

compared Firefly with d-adaptOR. This Analyze

would further help in designing next level

optimized opportunistic protocols that would

guarantee very high quality of service in highly

dynamic ad hoc networks.

V.REFERENCES

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[14]. Mehdi Neshat, Ghodrat Sepidnam,

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