04-05-2012, 10:45 AM
Improvement of AODV Routing on MANETs Using Fuzzy Systems
Improvement of AODV Routing on MANETs Using Fuzzy Systems.pdf (Size: 314.52 KB / Downloads: 160)
INTRODUCTION
In recent years, the progress of
communication technology has made wireless
devices smaller, less expensive and more
powerful. Such rapid technology advance has
provoked great growth in mobile devices
connected to the Internet. There are two
variations of wireless network, which are
infrastructure networks and ad hoc networks.
In an infrastructure network, a mobile station
must find the nearest base station within its
communication range before it communicates
with another, whereas an ad hoc network is a
collection of mobile nodes that are capable of
communicating with each other without the
aid of any established infrastructure or
centralized administration. They are selforganized,
dynamically changing multi-hop
networks as illustrated in figure (1).
FUZZY SYSTEM FOR FAODV
Fuzzy logic theory [11][12] was first
introduced by Zadeh in the 1960s as a tool for
modeling the uncertainty of natural language.
Fuzzy systems are suitable for uncertain or
approximate reasoning, especially for the
system with a mathematical model that is
difficult to derive, and allow decision making
with estimated values under incomplete or
uncertain information. A fuzzy system as
represented in figure (2) basically consists of
basically three steps :
PROPOSED FAODV
During the route discovery process of the
AODV routing protocol, the Route Request
Message (Rreq Msg.) carries the required
fuzzy input parameters: Number of Hops and
Delay taken along that path, each node
embedded a fuzzy system which calculates the
fuzzy cost each time it receives the Rreq Msg.
and updates the reverse route entry to the
source of the Rreq. Msg. if the new fuzzy cost
less than the stored one, while the
dissemination of the Rreq. Msg. is continued
until getting the destination. In addition to
reducing the routing time by this scheme, it
also reduces the memory overhead required
to store all routes until reaching the
destination.
4. SIMULATION MODEL
Our simulation modeled a network of 20 / 30
mobile nodes placed randomly within 700 ×
700 meter area. Each node had channel
capacity of 54 Mbps. The IEEE 802.11g was
used as the medium access control protocol. A
random waypoint mobility model was used
with a speed ranging from 0 m/s to 10 m/s. A
traffic generator was developed to simulate
CBR (Constant Bit Rate), UDP application. The
size of the data payload was 512 bytes.