Routing swarm intelligence based multipath routing protocols for

Routing is a challenging task in Mobile AdHoc Network due to its nodal mobility, unstable
links and limited resources. Swarm intelligence, as demonstrated by biological
swarm, such as ant colony has found to be catering to the characteristics of
MANET. Recently, swarm intelligence based multipath routing has received a lot
of attention as muitpath routing further increases reliability and minimizes
end-to-end delay. However existing swarm intelligence based multipath routing
protocols find an optimal paths by considering only one or two route selection
metrics, such as hop count or time delay making them unsuitable for routing
multimedia traffic or real time applications which requires optimization of
several QOS parameters. Also they don’t consider correlations among multiple
route selection parameters. This paper proposes a novel approach called fuzzy
ant colony based multipath routing protocol (FACO-MR) using fuzzy logic and
swarm intelligence to select optimal paths by considering optimization of
multiple objectives while retaining the advantages of swarm based intelligence
algorithm. Simulation results show that the proposed protocol is superior over
existing swarm intelligence based multipath routing protocols for routing in
MANET.A real ant colony is able to find food and
follow the shortest path from the nest to the food. As a real ant moves, it
deposits a substance called pheromone on the ground. When an ant reaches a
point that has more than outgoing branch, the probability that a branch will be
selected by an ant is dependent on the amount of pheromone deposited on each
branch. An ant will select a branch and deposit more pheromone on this branch;
as a result, the probability of selecting this branch will increase. The
pheromone on the branches of the shortest path to the food will grow faster
than pheromone on other branches. The pheromone is evaporated over time,
allowing the system to forget old paths and helping to avoid quick convergence
to a sub-optimal solution. A single ant is not intelligent, but the ant colony
can find the shortest path. As the ants search for the

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font-family:”Times New Roman”,”serif”;}Routing is a challenging task in Mobile AdHoc Network due to its nodal mobility, unstable
links and limited resources. Swarm intelligence, as demonstrated by biological
swarm, such as ant colony has found to be catering to the characteristics of
MANET. Recently, swarm intelligence based multipath routing has received a lot
of attention as muitpath routing further increases reliability and minimizes
end-to-end delay. However existing swarm intelligence based multipath routing
protocols find an optimal paths by considering only one or two route selection
metrics, such as hop count or time delay making them unsuitable for routing
multimedia traffic or real time applications which requires optimization of
several QOS parameters. Also they don’t consider correlations among multiple
route selection parameters. This paper proposes a novel approach called fuzzy
ant colony based multipath routing protocol (FACO-MR) using fuzzy logic and
swarm intelligence to select optimal paths by considering optimization of
multiple objectives while retaining the advantages of swarm based intelligence
algorithm. Simulation results show that the proposed protocol is superior over
existing swarm intelligence based multipath routing protocols for routing in
MANET.shortest
path, they explore many paths. The longest paths and unexplored paths still
have a probability to be visited. If the shortest path fails, the ants will
follow a recently explored path. Even if, the first ants used the longer path,
the ant colony is able to find the shortest one as the pheromone evaporates
with time and the shortest path still has a probability to be visited. The real
ant colony is a dynamic self-built and self-configured system, which is capable
of solving its problems efficiently. These features of real ant colony system
cater to the requirements of the MANETs. The proposed routing protocol is built
on the top of this basic routing protocol.

                                                                                                                             
III.       
Design Of 
Fuzzy Inference System

We know that
fuzzy set theory models the interpretation of imprecise and incomplete sensory
information as perceived by human brain. Thus, it represents and numerically
manipulates such linguistic information in a natural way via membership
functions and fuzzy rules. For proper decision making by controller, heuristics
or theory need to be incorporated into it. However the success of right
decision making by controller depends upon the valid and accurate model.
However in MANET, because of uncertainty due to nodal mobility, unstable links,
and limited resources, a precise model is not available. Therefore, fuzzy set
theory has been applied in a control decision system either to improve the
performance or to handle the problem that conventional theory cannot approach
successfully because latter relies on a valid and accurate model, which does
not always exist. Fuzzy representations of control algorithms, as linguistic
rules per se, offer a number of advantages over the conventional approach to
the specification of control algorithms as algebraic formulas, particularly in
ill-structured situations. The key concept is that linguistic rules describe
the operation of the process of interest from the standpoint of some (human)
operator of the process and capture the empirical knowledge of operation of
that process that has been acquired through direct experience with the actual
operation of the process. Clearly, this knowledge can be reflected in the rule
set only to the extent that the operator can articulate the control action in
linguistic form. It is this empirical knowledge, nonetheless, that a fuzzy
controller effectively embodies, and which enables it to control the process as
if it were the human operator.  The
inputs to the fuzzy controller to be designed for routing are: (i) buffer
occupancy,(ii) remaining battery power and (iii) signal stability. These
three selection parameters make the pheromone to reflect the network status and
the node’s ability to reliably deliver network packets. The steps
involved in calculation of fuzzy cost are elaborated as follows:

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