04-09-2012, 03:14 PM
Ant Colony based Routing for Mobile Ad-Hoc Networks towards Improved Quality of Services
Ant Colony.pdf (Size: 301.58 KB / Downloads: 53)
ABSTRACT
Mobile Ad Hoc Network (MANET) is a dynamic multihop wireless network which is established by a set of mobile nodes on a
shared wireless channel. One of the major issues in MANET is routing due to the mobility of the nodes. Routing means the act
of moving information across an internet work from a source to a destination. When it comes to MANET, the complexity
increases due to various characteristics like dynamic topology, time varying QoS requirements, limited resources and energy
etc. QoS routing plays an important role for providing QoS in wireless ad hoc networks. The biggest challenge in this kind of
networks is to find a path between the communication end points satisfying user’s QoS requirement. Nature-inspired
algorithms (swarm intelligence) such as ant colony optimization (ACO) algorithms have shown to be a good technique for
developing routing algorithms for MANETs. In this paper, a new QoS algorithm for mobile ad hoc network has been proposed.
The proposed algorithm combines the idea of Ant Colony Optimization (ACO) with Optimized Link State Routing (OLSR)
protocol to identify multiple stable paths between source and destination nodes.
INTRODUCTION
A mobile ad hoc network (MANET) is a
decentralized group of mobile nodes which exchange
information temporarily by means of wireless transmission
[1]. Since the nodes are mobile, the network topology may
change rapidly and unpredictably over time. The network
topology is unstructured and nodes may enter or leave at
their will. A node can communicate to other nodes which are
within its transmission range. This kind of network promises
many advantages in terms of cost and flexibility compared to
network with infrastructures. MANETs are very suitable for
a great variety of applications such as data collection,
seismic activities, and medical applications
PREVIOUS WORKS
Some works related to ACO and OLSR are found
in the literature. In [1], the authors described a hybrid
routing algorithm for MANETs based on ACO and zone
routing framework of bordercasting. A new QoS routing
protocol combined with the flow control mechanism has
been done in [2]. This proposed routing solution is modeled
by ant systems. The proposed routing protocol in [2] uses a
new metric to find the route with higher transmission rate,
less latency and better stability. P.Deepalakshmi. et.al [4]
proposed a new on demand QoS routing algorithm based on
ant colony metaheuristic. An algorithm of ant colony
optimization for mobile ad hoc networks has been described
in [5]. But the QoS issues end-to-end delay, available
bandwidth, cost, loss probability, and error rate is not
considered in [5]. A hybrid QoS routing algorithm has been
proposed in [6]. In [6], the authors used ant’s pheromone
update process approach for improving QoS. But the authors
described only bandwidth. Other QoS issues are not
considered in [6]. Shahab Kamali. et.al [7] implemented a
new ant colony based routing algorithm that uses the
information about the location of nodes.
OLSR ROUTING PROTOCOL
The Optimized Link State Routing Protocol
(OLSR) [3] is a proactive routing protocol. It is introduced
by the IETF MANET working group for mobile ad-hoc
networks for accuracy and stability. OLSR protocol is the
enhanced version of pure link state routing protocol that
chooses the optimal path during a flooding process for route
setup and route maintenance. In OLSR, only symmetric
links are used for route setup process.
ANT COLONY OPTIMIZATION
The ACO metaheuristic is based on generic
problem representation and the definition of the ant’s
behavior. ACO adopts the foraging behavior of real ants.
When multiple paths are available from nest to food, ants do
random walk initially. During their trip to food as well as
their return trip to nest, they lay a chemical substance called
pheromone, which serves as a route mark that the ants have
taken [4]. Subsequently, the newer ants will take a path
which has higher pheromone concentration and also will
reinforce the path they have taken. As a result of this
autocatalytic effect, the solution emerges rapidly.
To illustrate this behavior, let us consider the
experiment shown in Figure 1. A set of ants moves along a
straight line from their nest A to a food source B (Figure 1a).
At a given moment, an obstacle is put across this way so that
side © is longer than side (D) (Figure 1b). The ants will
thus have to decide which direction they will take: either C
or D. The first ones will choose a random direction and will
deposit pheromone along their way. Those taking the way
ADB (or BDA), will arrive at the end of the obstacle
(depositing more pheromone on their way) before those that
take the way ACB (or BCA). The following ants’ choice is
then influenced by the pheromone intensity which stimulates
them to choose the path ADB rather than the way ACB
(Figure 1c). The ants will then find the shortest way between
their nest and the food source.
CONCLUSIONS
This proposed routing strategy can be optimized to
support multimedia communications in mobile ad hoc
networks based on Ant Colony framework. The major
complexity in mobile ad hoc network is to maintain the QoS
features in the presence of dynamic topology, absence of
centralized authority, time varying QoS Requirements etc.
The challenges reside in ad hoc networks is to find a path
between the communication end points satisfying user’s QoS
requirement which need to be maintain consistency. The
algorithm consists of both reactive and proactive
components. In a reactive path setup phase, an option of
multiple paths selection can be used to build the link
between the source and destination during a data session.
For multimedia data to be sent, we need stable, failure-free
paths and to achieve that the paths are continuously
monitored and improved in a proactive way. Our previous
work [8] also guaranteed QoS based proactive routing using
flooding technique by best utilization of network resources.