GAACO : Metaheuristic Driven Approach for Routing in ... - IEEE Xplore

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Guru Gobind Singh. Indraprastha ... Research in the area of Delay Tolerant Networks. (DTN) has been ... ideas from social networking and contact patterns to.
GAACO : Metaheuristic Driven Approach for Routing in OppNet Rahul Johari

Dhari Ali Mahmood

USICT

Computer Engineering Department

Guru Gobind Singh

University of Technology,

Indraprastha University, Delhi

Baghdad,Iraq [email protected]

[email protected]

Over the period of time researchers have proposed Abstract

-Routing in delay tolerant network addresses the ability to transport or route bundles from a source to a destination, which is a primary responsibility that all communication networks must have. However, Opportunistic Networks (Opp-Nets) or Delay Tolerant Networks are characterized by the sparse connectivity resulting in a lack of instantaneous end-to-end path between source and destination. In such a challenging environment, popular ad hoc routing protocols such as AODV and DSR fail to establish routes. We introduce a new metaheuristic oriented routing protocol GAACO that utilizes the optimization techniques of Genetic Algorithm and Ant Colony Optimization to find the path between source and destination. GAACO has been simulated in Network Simulator (NS 2) and its performance has been compared with Epidemic routing Protocol on issues such as delay, number of hops traversed, energy consumed and throughput achieved.

two categories of schemes for routing in DTN. First one exploits the opportunistic contact and the other one uses the source based routing. For both the approaches, decision to select most suitable next hop node is to be made. Routing is a critical issue in DTN. Since popular MANET routing protocols such as AODV and DSR cannot be deployed [3] , so we propose a new metaheuristic oriented routing protocol GAACO that utilizes the optimization techniques of Genetic Algorithm and Ant Colony Optimization to find

the

optimized

path

between

source

and

destination. The paper is organized as follows: Section 2 describes the related work done in the area of routing in delay tolerant network. Section 3 describes the routing considerations. Section 4 presents the motivation to take

up

this

methodology.

work. Section

Section 6

5

presents

presents

the

our

proposed

solution using GAACO protocol : Genetic Algorithm and Ant Colony Optimization based protocol. Section 7 describes the algorithms proposed as a part of

Keywords-component; MANET, DTN, EPIDEMIC, GAACO.

routing using GAACO protocol. Section 8 presents the

experimental

simulation

setup.

Section

9

presents

the

scenario and snapshots. Section 10

presents the comparative result between GAACO and Epidemic Protocol. Section II presents the results and

I. INTRODUCTION Research in the area of Delay Tolerant Networks (DTN) has been receiving considerable attention in the

last

few

occurrence

in

years a

owing

variety

of

to

their

widespread

applications

such

as

communication between the nodes in terrestrial deep space, in under water acoustic network, in disaster hit areas and in mountainous hilly terrain where live connectivity between the nodes is not possible. Other prominent examples include networks such as those operating

in

mobile

or

extreme

terrestrial

environments, or planned networks in space. The DTN architecture [1,2]

provides a framework for

routing and forwarding at the bundle layer for unicast, any cast, and multicast information.

it's analysis. Section 12 presents the conclusion and future

work

followed

by

acknowledgements

and

references respectively.

II. RELATED WORK In [6, 7, 9] authors claim that the mobility is mostly not random and there is a pattern in encounters. The Probabilistic Routing Schema is based on individual probabilities of nodes of successful delivery of a message. The SimBet routing algorithm [4] borrows ideas from social networking and contact patterns to predict paths to destinations to improve delivery ratio and time. Bubble Rap

[5]

extend their work by

allocating nodes into social groups based on direct and indirect contacts. In [8,11] author(s) describes a

978-1-4799-5627-2/14/$31.00 ©2014

IEEE

utility function for a node to decide whether to

that we introduce brief section on Genetic Algorithm

forward the message to an opportunistic contact or to

and Ant Colony Optimization.

a scheduled contact. In [10] author(s) proposes a new approach RIMCA (Routing in MANET using Cluster Based Approach) which consists of mobile wireless nodes moving randomly within boundary of cluster. In [12,13] author(s) propose a metaheuristic based search technique termed as VAST(Volume Adaptive search technique) to determine an optimal path from source node to destination node in densely deployed mobile ad-hoc network.

on-Demand

intermittent-opportunistic

,intermittent-Scheduled, and intermittent-predicted

contacts [1] and which contact the node would exploit whether it is emergency driven or peaceful or warfare based. There are three major cases classifying the level of the mobility in the network [3]. First, it is possible that there are no mobile entities in the in

the

is possible that some, but not all,

network

(EA)

which

generate

solutions

to

optimization problems using techniques inspired by natural

evolution

such

as

selection,

crossover,

mutation and inheritance[I5, I6] . Four steps have to

starts

depends on the topo-graphical area and the situation

nodes

Genetic algorithm (GA) is a subclass of evolutionary algorithms

be considered to solve a problem with GA. The first

DTN provides various types of contacts such as

network, secondly

Genetic Algorithm[J 4]

step is initialization in which the evolution usually

III. ROUTING CONSIDERATIONS

persistent,

A.

are

mobile.

These

nodes,

sometimes referred to as data mules are exploited for their mobility. Thirdly, it is possible that the vast majority, if not all, nodes in the network are mobile.

from

a

population

of

randomly

generated

individuals. Each individual or chromosome is a set of genomes. In each generation, we need to evaluate the chromosomes by a fitness function and determine how suitable each of them, is to be chosen for the next generation. The population of subsequent generations is formed by selecting and modifying the chromosomes. These modifications are based on crossover and mutation. Crossover is a method to combine two chromosomes to produce new offspring. The idea behind crossover is that the new chromosome may be better than both of the parents if it takes the best characteristics from each of the parents. In mutation one or more genome values is altered in a particular chromosome. Mutation is done in order to prevent the population from stagnating at any local optima. The procedure of

IV. MOTIVATION The loopholes and anomalies existing in achieving the

breeding

offspring

continue

until

a

from previous generation termination

condition

has

will been

reached. This condition may be either finding a

routing in Delay Tolerant network motivated us to

solution that satisfies minimum criteria or reaching a

work in this direction. A brief review of the literature

fixed number of generations.

on routing describes the various types of approaches that have been adopted by the researchers but we have

B.

ANT Colony Optimization

used the metaheuristic driven approach to solve the routing problems. More specifically we adopted the principle(s) of biological science by using Genetic Algorithm and combined blended it with Ant colony optimization techniques. By mapping the biological ..

concept and real life principle we develop new routing

....

approach for DTN environment GAACO: Genetic

.....

Algorithm coupled with Ant Colony Optimization, to solve the routing problems in DTN. If the path get disconnected or intermediate nodes suffer drop in energy level or

run out of memory/buffer space

Fig l. Ant Colony Optimization[17]

during the data transmission then our newly proposed protocol

(GAACO)

will

select

another

most

appropriate route by selecting those nodes which have more energy as well as huge memory buffer.

The ant takes different routes to search the food, after it detect the food, it keep the path by depositing chemical material called pheromone then it takes some food and return back to the colony. All the ants

V. OUR METHODOLOGY

when sniff pheromone, they start converging towards a common path and after some time all ants will take same consolidated path. Ant colonies are distributed

As a part of the GAACO protocol we have postulated

systems that,

routing algorithms to illustrate it's working but before

individuals,

in spite of the simplicity of their present

a

highly

structured

social

organization. As a result of this organization, ant

colonies can accomplish complex tasks that in some

f) E transfer the F within R4 10.2 m then deposit P+2.2.

cases far exceed the individual capabilities of a single

populations of artificial agents that collaborate to

Triggerwhile F !=o do 3: for i=A;i