CHAPTER 4 ENERGY AND DELAY AWARE MULTIPATH ROUTING …shodhganga.inflibnet.ac.in/bitstream/10603/17900/13/13_chapter 4.p… · delay, packet delivery ratio, bandwidth, routing overhead,

140

CHAPTER 4 ENERGY AND DELAY AWARE MULTIPATH ROUTING

USING BAT ALGORITHM

4.1 INTRODUCTION

Most of the on-demand routing protocols of MANET uses the

shortest path method during the path establishment process for data

transmission and stick to the path till it break. The incessant usage of a single

path will deplete the battery power. The power failure of a mobile node not

only affects the node itself but also the overall network lifetime.The mobile

nodes in the MANET are typically powered by limited energy reservoir.

Reducing the number of hops will not be the aim of a routing algorithm,

instead the optimization of multiple parameters such as energy consumption,

delay, packet delivery ratio, bandwidth, routing overhead, multipath

establishment etc. Among these issues, decisive issue is constrained energy

which raises the need of energy efficient routing protocol for MANET. For

this reason, many research efforts have been devoted to developing energy

aware routing protocols.

The delay is the total latency experienced by a packet to traverse the

network from the source to the destination. At the network layer, the end-to-

end packet latency is the sum of processing delay, packetization delay,

transmission delay, queuing delay and propagation delay. The end-to-end

delay of a path is the summation of the node delay at each node, plus the link

delay at each link on the path. The end-to-end delay is an additive metric,

Print to PDF without this message by purchasing novaPDF (http://www.novapdf.com/)

http://www.novapdf.com/


141 because it is the accumulation of all delays of the links along the path. To find

a QoS feasible path for a concave metric, the available resource on each link

should be atleast equal to the required value of the metric.

The high mobility of nodes in MANET causes frequent topological

changes in the network which may make routes invalid. This in turn leads to

the frequent route discovery process and high overhead. The multipath routing

addresses this problem by providing more than one route to a destination node.

Multipath routing appears to be a promising technique for ad hoc routing

protocols. The source and intermediate nodes can use these routes as primary

and backup routes. Alternatively, traffic can be distributed among multiple

routes to enhance transmission reliability, provide load balancing, and secure

data transmission. The multipath routing reduces the frequency of route

discovery effectively and therefore the latency for discovering another route is

reduced when currently used route is broken. Multiple paths can be useful in

improving the effective bandwidth of communication, responding to

congestion and heavy traffic, and in increasing delivery ratio. The

establishment of multiple paths between source and destination in a single

route discovery will drastically reduce the frequency of route establishment

process which in turn increases the overall network performance.

In addition, metaheuristic algorithms start to emerge as major

thespians for multi objective global optimization; they often mimic the best

characteristics in nature, especially biological systems. Many new algorithms

are emerging with many important applications [25, 29, 39]. Recently, a new

meta heuristic search algorithm, called BAT algorithm (BA), has been

developed by Xin-She Yang [56]. Initial studies reveal that the Bat algorithm




142 is very potential and could outperform existing algorithms. This optimization

algorithm is applied with the traditional AODV routing protocol for finding

effective energy and delay aware routing in MANET.

In this chapter, a novel Energy and Delay aware Dynamic Multipath

Routing Algorithm using BAT algorithm (BAT-EDMR) is proposed. This

algorithm modifies and extends AODV to establish dynamic multiple energy

and delay aware node-disjoint routing paths with the usage of BAT

optimization technique that provides better packet delivery ratio, minimum

end-to-end delay and reduced energy consumption and ensures load balancing

and improved network throughput.

The remainder of this chapter is organized as follows: Section 4.2

describes the characteristics of the Bat optimization algorithm. In Section 4.3,

the mechanism of the proposed novel Energy and Delay Aware Dynamic

Multipath Routing using BAT Algorithm (BAT-EDMR) is discussed in detail.

In Section 4.4, the performance of the algorithm BAT-EDMR is analyzed and

compared with the existing unipath routing protocol AODV and with the

existing multipath protocol ENDMR. The results are summarised in Section

4.5.

4.2 BAT ALGORITHM – AN OVERVIEW

BAT algorithm is a recently developed metaheuristic algorithm by

Xin-She Yang that adopts the behaviour of the natural bats for solving

optimization problems [56, 57].




143 4.2.1 Echolocation of Microbats

Bats are fascinating animals. They are the only mammals with

wings and they also have advanced capability of echolocation. It is estimated

that there are about 1000 different species which account for up to about one

fifth of all mammal species [2]. Their size ranges from the tiny bumblebee bat

(of about 1.5 to 2 g) to the giant bats with wingspan of about 2 m and weight

up to about 1 kg. Microbats typically have forearm length of about 2.2 to 11

cm. Most bats uses echolocation to a certain degree; among all the species,

microbats are a famous example as microbats use echolocation extensively

while megabats do not.

Most microbats are insectivores. Microbats use a type of sonar,

called, echolocation, to detect prey, avoid obstacles, and locate their roosting

crevices in the dark. These bats emit a very loud sound pulse and listen for the

echo that bounces back from the surrounding objects. Their pulses vary in

properties and can be correlated with their hunting strategies, depending on the

species.

Most bats use short, frequency-modulated signals to sweep through

about an octave, while others more often use constant-frequency signals for

echolocation. Their signal bandwidth varies depends on the species, and often

increased by using more harmonics.

Though each pulse only lasts a few thousandths of a second (up to

about 8 to 10 ms); however, it has a constant frequency which is usually in the

region of 25 kHz to 150 kHz. The typical range of frequencies for most bat




144 species are in the region between 25 kHz and 100 kHz, though some species

can emit higher frequencies up to 150 kHz. Each ultrasonic burst may last

typically 5 to 20 ms, and microbats emit about 10 to 20 such sound bursts

every second. When hunting for prey, the rate of pulse emission can be sped

up to about 200 pulses per second when they fly near their prey. Such short

sound bursts imply the fantastic ability of the signal processing power of bats.

In fact, studies show the integration time of the bat ear is typically about 300

to 400 μs.

As the speed of sound in air is typically v = 340 m/s, the

wavelength λ of the ultrasonic sound bursts with a constant frequency f is

given by λ = v/f, which is in the range of 2 mm to 14 mm for the typical

frequency range from 25 kHz to 150 kHz. Such wavelengths are in the same

order of their prey sizes.

Amazingly, the emitted pulse could be as loud as 110 dB, and,

fortunately, they are in the ultrasonic region. The loudness also varies from the

loudest when searching for prey and to a quieter base when homing towards

the prey. The travelling range of such short pulses is typically a few meters,

depending on the actual frequencies. Microbats can manage to avoid obstacles

as small as thin human hairs.

Studies show that microbats use the time delay from the emission

and detection of the echo, the time difference between their two ears, and the

loudness variations of the echoes to build up three dimensional scenario of the

surrounding. They can detect the distance and orientation of the target, the

type of prey, and even the moving speed of the prey such as small insects.




145 Indeed, studies suggested that bats seem to be able to discriminate targets by

the variations of the Doppler Effect induced by the wing-flutter rates of the

target insects [2].

Obviously, some bats have good eyesight, and most bats also have

very sensitive smell sense. In reality, they will use all the senses as a

combination to maximize the efficient detection of prey and smooth

navigation. Here, the echolocation and the associated behavior are considered.

Such echolocation behavior of microbats can be formulated in such

a way that it can be associated with the objective function to be optimized, and

this makes it possible to formulate new optimization algorithms. The basic

formulation of the Bat Algorithm (BA) followed by the implementation details

are outlined in the forth coming sections.

4.2.2 Bat Algorithm

If some of the echolocation characteristics of microbats are

idealized, various bat inspired algorithms or bat algorithms can be developed. Some of the approximate or idealized rules: 1. All bats use echolocation to sense distance, and they also `know' the

difference between food/prey and background barriers in some magical way; 2. Bats fly randomly with velocity vi at position xi with a fixed frequency fmin,

varying wavelength λ and loudness A0 to search for prey. They can




146 automatically adjust the wavelength (or frequency) of their emitted pulses and

adjust the rate of pulse emission r in the range of [0, 1], depending on the

proximity of their target; 3. Although the loudness can vary in many ways, it is assumed that the

loudness varies from a large (positive) A0 to a minimum constant value Amin.

Another obvious simplification is that no ray tracing is used in

estimating the time delay and three dimensional topography.

In addition to these simplified assumptions, the following

approximations are used for simplicity. In general the frequency f in a range

[fmin, fmax] corresponds to a range of wavelengths [λmin, λmax]. For example a

frequency range of [20 kHz, 500 kHz] corresponds to a range of wavelengths

from 0.7 mm to 17 mm. The ranges can be chosen freely to suit different

applications. The pseudo code of Bat Algorithm (BA) is as follows.

1. define objective function

2. initialize the population of the bats

3. define and initialize parameters

4. while(Termination criterion not met)

{

generate the new solutions randomly

if (Pulse rate (rand) >current)

select a solution among the best solution




147

generate the local solution around the selected

best ones.

end if

generate a new solution by flying randomly

if (Pulse rate (rand) >current)

select a solution among the best solution

generate the local solution around the selected best ones

end if

generate a new solution by flying randomly

if ( loudness & pulse frequency (rand) < current )

accept the new solutions

increase pulse rate and reduce

loudness end if

rank the bats and find the current best

} 5. Results and visualization 4.2.3 Bat Motion

For the bats in simulations, the rules have to be defined about their

positions xi and velocities vi in a d-dimensional search space are updated. The new solutions xt

i and velocities vti at time step t are given by

, (1)

(2)

(3)

where [ ] is a random vector drawn from a uniform distribution. Here x*

is the current global best location (solution) which is located after comparing




148 all the solutions among all the n bats at each iteration t. As the product f

i is

i

the velocity increment, f i

(or can be used to adjust the velocity change while

i)

fixing the other factor (or f ), depending on the type of the problem of

i i

interest. The values and can be taken as initial

assumptions, depending on the domain size of the problem of interest.

Initially, each bat is randomly assigned a frequency which is drawn uniformly from [ , ].

For the local search part, once a solution is selected among the

current best solutions, a new solution for each bat is generated locally using

random walk

(4) where is a random number vector drawn from [−1, 1], while ⟨ ⟩is the average loudness of all the bats at this time step.

The update of the velocities and positions of bats have some

similarity to the procedure in the standard particle swarm optimization, as fi

essentially controls the pace and range of the movement of the swarming

particles. To a degree, BA can be considered as a balanced combination of the

standard particle swarm optimization and the intensive local search controlled

by the loudness and pulse rate.




149 4.2.4 Loudness and Pulse Emission

Furthermore, the loudness Ai and the rate ri of pulse emission have

to be updated accordingly as the iterations proceed. As the loudness usually

decreases once a bat has found its prey, while the rate of pulse emission

increases, the loudness can be chosen as any value of convenience. For

example, the values A0 = 100 and Amin = 1 can be used. For simplicity, the

values A0 = 1 and Amin = 0 can also be used, with the assumption that Amin = 0 means that a bat has just found the prey and temporarily stop

emitting any sound. Now we have

[ ] (5) where and are constants. In fact, is similar to the cooling factor of a

cooling schedule in the simulated annealing. For any 0 < < 1 and > 0,

Now

(6)

In the simplest case, it can be used as = , and also = = 0.9 in the

simulations.

Preliminary studies by [57] suggested that bat algorithm is very

promising for solving nonlinear global optimization problems. Here it is used

for establishing effective routing for MANET.




150 4.3 BAT INSPIRED ENERGY AND DELAY AWARE

DYNAMIC ROUTING ALGORITHM (BAT-EDMR)

The new proposed algorithm BAT-EDMR is an Energy and Delay

Aware Dynamic Multipath Routing scheme that uses the behavior of the real

Bats to find dynamic multiple optimal paths between source and destination

nodes.

4.3.1 Route Discovery Phase

The steps involved in the new BAT-EDMR algorithm to establish

paths on-request using a reliable reverse path establishment method as follows.

1. Whenever the source node S wants to send data to the destination node

D, it floods the RREQ bats in the network. The bats which are small

control packets carrying the D-id, travel in all available paths to D, and

use their behaviours echolocation and loudness to collect details about

the path they travel to reach the D.

a) Echolocation behaviour calculates the time delay taken by the

bats to reach the destination.

b) Loudness behaviour measures the total energy level of all the

nodes in the path it travelled to reach D.

2. Upon receiving the RREQ Bats, the destination D inturn rebroadcasts

the Reverse RREQ Bats in all available paths to source S.




151 3. The Reverse RREQ (R-RREQ) Bats while travelling from D to S,

collect the time delay taken by each node to reach the source and the

total average residual energy of all nodes in the path they travelled. 4. In the BAT-EDMR, the intermediate node stores the incoming R-

RREQ bats during a particular time interval along with their energy and

delay taken to reach this node. The R-RREQ bat which has the

maximum energy level and minimum time delay is selected and

forwarded to the neighbours.

5. In order to reduce the number of R-RREQ bats flooding and to select

the best next hop node among the existing next hop nodes, the bat uses

its loudness and echo property. A hello packet which carries the D-id

will be sent to all the next neighbouring nodes for checking the

availability of paths to the destination D through them. The R-RREQ

bats will be forwarded to the neighbouring nodes that have paths to D

and responded for the hello messages within the stipulated time period.

6. Upon receiving multiple Reverse RREQ Bats from D, the source filters

only the node disjoint paths represented by the bats and discards the

other paths. 7. The selected paths(N) are ranked based on the higher residual energy

and minimum delay values.




152

8. The number of required paths which also satisfies the QoS

requirements of the application are selected as the best paths in the list

and data transmission is distributed among them. 4.3.2 Route Updation Process

As the loudness Ai (Energy level) of each bat usually decreases

when a bat(path) is involved in data transmission, it has to be updated as the

data transmission proceeds. In order to maintain an updated path list, the

source node floods RREQ Bats at regular intervals through the selected paths (N) to collect the current Energy level and time delay. Then the paths are again

ranked with the new collected values and the number of required best paths are

selected for data transmission.

4.3.3 Route Maintenance

In BAT-EDMR algorithm, the routes are maintained as follows: If a

node along a path moves or a route fails due to irregular circumstances, a link

failure message will be sent to the source through the intermediate nodes to

inform the erasure of the route. Upon receiving the link failure message, the

source node removes the broken route from the existing path list and redirects

the data packets in the remaining available paths in the path list. Thus the

overhead is drastically reduced due to the avoidance of frequent route

discoveries and uninterrupted data transmission.




153 4.4 PERFORMANCE EVALUATION 4.4.1 Analysis of Performance between AODV and BAT-

EDMR

The Network simulator (NS-2) is used to evaluate the performance

of the proposed algorithm [63]. In the simulation, the Distributed Coordination

Function (DCF) of IEEE 802.11 for wireless LANs is used as the MAC layer

protocol. It has the functionality to notify the network layer about link

breakage.

The simulation is carried out for 50 mobile nodes which move in a

1000 meter x 1000 meter rectangular region for 300 seconds simulation time.

It is assumed that each node in the network moves independently with the

same average speed. All nodes have the same transmission range of 250

meters. The simulated traffic is Constant Bit Rate (CBR).

The random waypoint model is used to simulate nodes movement.

The motion is characterized by two factors: the maximum speed and the pause

time. The pause time is defined as the period of time a node stays stationary

before heading for a new random location. Each node starts moving from its

initial position to a random target position selected inside the simulation area.

The node speed is uniformly distributed between zero and the maximum

speed. Table 4.1 lists the simulation parameters and environments used.




154

Table 4.1 Simulation Scenario

Simulation Terrain Dimension 1000 X 1000 meters

Transmission Range 250 m

Mobility model Random way point

Number of Nodes 50

Node Speed 0-30 m/s

Routing protocols AODV, BAT-EDMR

Traffic Source Model Constant Bit Rate

Channel Data Rate 2 Mbps

Initial energy 20 Joules

Performance Metrics

The following metrics are used to evaluate the performance of

BAT-EDMR: Packet Delivery Ratio: It represents the ratio of the number of data packets

delivered to the destination and the number of data packets sent by the source. End-to-End delay: It depicts the delay experienced by packets from source to

destination. This delay also includes processing and queuing delays in each

intermediate node. Remaining Energy: It is measured as the total amount (in Joules) of remaining

battery energy of the nodes at the end of the simulation period.




155 Normalized Routing Load: It can be measured by the number of routing

control packets transmitted per data packet delivered at the destination. Throughput: Throughput or network throughput is the average rate of

successful message delivery over a communication channel. This data may be

delivered over a physical or logical link, or pass through a certain network

node. The throughput is usually measured in bits per second (bit/s or bps), and

sometimes in data packets per second or data packets per time slot.

The system throughput or aggregate throughput is the sum of the

data rates that are delivered to all terminals in a network. Network Life Time: The lifetime of a network is defined as the duration of

time until the first node failure due to battery depletion. 4.4.1.1 Packet Delivery Ratio (a) Varying Number of Nodes

Table 4.2 shows the effect of Number of Nodes over Packet

Delivery Ratio of two protocols. BAT-EDMR shows a margin of 11.6 % to

22% improvement in Packet Delivery Ratio than AODV.




156

Table 4.2 Effect of Number of Nodes on Packet Delivery Ratio

Number of Packet Delivery Ratio (%)

Nodes

AODV BAT-EDMR

5 88.2 99.81

10 87.6 99.4

15 84.4 99.2

20 83.6 98.89

25 81 98.71

30 79.2 98.58

35 76.4 96.04

40 72.1 94.54

45 71.4 91.2

50 67.5 89.3

(b) Varying Velocity

Figure 4.1 portrays the performance of the proposed protocol BAT-

EDMR in terms of Packet Delivery Ratio over Velocity. The Packet Delivery

Ratio of BAT-EDMR is higher with a hike of 10% to 23% when compared to

the traditional AODV.




157

PACKET DELIVERY RATIO 100

(%)

90

RATI

O

80

70

DEL

IVER

Y

60

50

PACK

ET

40

30

20

0 5 10 15 20 25 30

MAXIMUM VELOCITY (m/s)

AODV

BAT-EDMR

Figure 4.1 Effect of Velocity on Packet Delivery Ratio 4.4.1.2 End-To-End Delay (a) Varying Number of Nodes

Figure 4.2 divulges the effect of Varying Number of Nodes over

End-to-End Delay of two protocols. The proposed algorithm BAT-EDMR

shows very minimum delay (up to 60%) when compared to that of AODV.

END TO END DELAY 600

(ms)

500

400

DEL

AY

300

AVER

AGE

200

100

0

5 10 15 20 25 30 35 40 45 50

NUMBER OF NODES

AODV BAT-EDMR

Figure 4.2 Effect of Number of Nodes over End-to-End Delay




158 (b) Varying Node Velocity

The effect of Varying Node Velocity over End-to-End Delay of

AODV and BAT-EDMR is shown in the figure 4.3 which proves that the

proposed algorithm BAT-EDMR shows minimum delay when compared to

that of AODV with a marginal decrease of 58% to 74%.

END-TO -END DELAY 600

(ms)

500

DEL

AY 400

300

AVER

AGE

200

100

0

0 5 10 15 20 25 30


AODV

BAT-EDMR

Figure 4.3 Effect of Node Velocity on End-to-End Delay 4.4.1.3 Average Remaining Energy

Figure 4.4 expounds the effect of number of nodes on average

remaining energy of nodes under two protocols. The remaining energy of the

nodes at the end of simulation is much higher for BAT-EDMR than AODV. In

BAT-EDMR, the reduction in energy consumption is about 44% to 66% than

the traditional AODV. This is because the nodes which have higher energy are

taken for data transmission.




159

REMAINING ENERGY

(J) 14

ENER

GY 12

10

REM

AIN

NG 8

6

4

AVER

AGE

2

0

5 10 15 20 25 30 35 40 45 50

NUMBER OF NODES

AODV

BAT-EDMR

Figure 4.4 Effect of Number of Nodes on Average Remaining Energy 4.4.1.4 Normalized Routing Load

Figure 4.5 shows the effect of node velocity on normalized routing

load under two protocols. The normalized routing load is much higher for

AODV because of the frequent re-route discoveries. In BAT-EDMR, the

energy and delay aware multipath routing mechanism avoids the frequent re-

route discoveries thus in turn reduces normalized routing load from 44% to

68% when compared to the traditional AODV.




160

NORMALIZED ROUTING LOAD

LOAD

8

7

6

ROU

TING

5

4

NO

RMA

LIZE

D

3

2

1

0

0 5 10 15 20 25 30


AODV BAT-

EDMR

Figure 4.5 Effect of Node Velocity on Normalized Routing Load 4.4.1.5 Throughput

The throughput over the varying number of nodes for the two protocols AODV and BAT-EDMR is depicted in the figure 4.6.

THROUGHPUT

4600

(bits

/sec

) 4400

4200

4000

THRO

UGHP

UT

3800

3600

3400

3200

5 10 15 20 25 30 35 40 45 50

NUMBER OF NODES

AODV

BAT-EDMR

Figure 4.6 Effect of Number of Nodes on Throughput




161

The multipath mechanism with energy and delay considerations provides the better throughput for BAT-EDMR up to 17.33 % over the protocol AODV. 4.4.2 Performance Analysis between BAT-EDMR and ENDMR

The evaluation of the performance between the protocols BAT-

EDMR and ENDMR is carried out using the simulation environment and

assumptions made in [6].

4.4.2.1 Total Residual Energy

Figure 4.7shows that the total residual energy of nodes in BAT-

EDMR is high with the marginal of 38% when compared to the protocol

ENDMR. Since BAT-EDMR selects nodes with higher residual energy and

minimum delay at each and every step of the routing process, the total residual

energy is high.

TOTAL RESIDUAL ENERGY Vs. TIME 4500

4000

(J) 3500

ENER

GY

3000

2500

RESI

DU

AL 2000

1500

1000

500

0

0 10 20 30 40 50 60 70 80 90 100

TIME(SEC)

ENDMR

BAT-EDMR

Figure 4.7 Effect of Simulation time on Residual Energy




162 4.4.2.2 Network Life Time

From the figure 4.8, it is found that the network life time is increased in BAT-EDMR than ENDMR.

SIMULATION TIME Vs. NUMBER OF DEAD NODES 8

EXPI

RED

7

6

NO

DE 5

4

OF

3

NUM

BER

2

1

0

0 10 20 30 40 50 60 70 80 90 100

TIME (SEC)

ENDMR

BAT-EDMR

Figure 4.8 Effect of Simulation time on No. of nodes expired 4.5 SUMMARY

The Energy and Delay aware multipath on-demand routing

algorithm using BAT optimization (BAT-EDMR) proposed in this chapter

improves the QoS parameters delay and energy. The proposed algorithm

selects the paths with the minimum delay and the maximum residual energy at

nodes. The BAT-EDMR produces better results than the existing AODV in

terms of packet delivery ratio, end-to-end delay, and residual energy at nodes

and normalized routing load. The performance of BAT-EDMR is also

compared with the existing multipath protocol ENDMR and it is observed that

BAT-EDMR outperforms ENDMR.




Documents

CHAPTER 4 ENERGY AND DELAY AWARE MULTIPATH ROUTING …shodhganga.inflibnet.ac.in/bitstream/10603/17900/13/13_chapter 4.p… · delay, packet delivery ratio, bandwidth, routing overhead,