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
