1. BRA A Bidirectional Routing Abstraction
for Asymmetric Mobile Ad Hoc Networks
(Synopsis)

2. ABSTRACT
Wireless links are often asymmetric due to heterogeneity in the
transmission power of devices, non-uniform environmental noise, and
other
signal
propagation
phenomenons.
Unfortunately,
routing
protocols for mobile ad hoc networks typically work well only in
bidirectional networks. This paper first presents a simulation study
quantifying the impact of asymmetric links on network connectivity
and routing performance. It then presents a framework called BRA
that provides a bidirectional abstraction of the asymmetric network to
routing protocols. BRA works by maintaining
multi-hop reverse routes for unidirectional links and provides three
new abilities: improved connectivity by taking advantage of the
unidirectional links, reverse route forwarding of control packets to
enable off-the-shelf routing protocols, and detection packet loss on
unidirectional links. Extensive simulations of AODV layered on BRA
show that packet delivery increases substantially (two-fold in some
instances) in asymmetric networks compared to regular AODV, which
only routes on bidirectional links.
INTRODUCTION
Most job-scheduling approaches for parallel machines apply
space sharing which
means allocating CPUs/nodes to jobs in a dedicated manner and
sharing the machine among multiple jobs by allocation on different
subsets of nodes. Some approaches apply time sharing (or better to

3. say a combination of time and space sharing), i.e. use multiple time
slices per CPU/node. Job scheduling determines when and where to
execute the job, given a stream of parallel jobs and set of computing
resources. In a standard working model, when a parallel job arrives to
the system, the scheduler tries to allocate required number of
processors for the duration of runtime to the job and, if available,
starts the job immediately. If the requested processors are currently
unavailable, the job is queued and scheduled to start at a later time.
The most common metrics evaluated include system metrics such as
the system utilization, throughput, etc. And users metrics such as
turnaround time, wait time, etc. The typical charging model is based
on the amount of total resources used (resources $times$ runtime) by
any job.
Data mining, the extraction of hidden predictive information from
large databases, is a powerful new technology with great potential to
help companies focus on the most important information in their data
warehouses. Data mining tools predict future trends and behaviors,
allowing businesses to make proactive, knowledge-driven decisions.
The automated, prospective analyses offered by data mining move
beyond the analyses of past events provided by retrospective tools
typical of decision support systems. Data mining tools can answer
business questions that traditionally were too time consuming to

4. resolve. They scour databases for hidden patterns, finding predictive
information that experts may miss because it lies outside their
expectations.
Most companies already collect and refine massive quantities of
data. Data mining techniques can be implemented rapidly on existing
software and hardware platforms to enhance the value of existing
information resources, and can be integrated with new products and
systems as they are brought on-line. When implemented on high
performance client/server or parallel processing computers, data
mining tools can analyze massive databases to deliver answers to
questions such as, "Which clients are most likely to respond to my
next promotional mailing, and why?"
Data mining (DM), also called Bi-Directional Routing (BRA) Data
Mining, is the process of automatically searching large volumes of data
for patterns using tools such as classification, association rule mining,
clustering, etc.. Data mining is a complex topic and has links with
multiple core fields such as computer science and adds value to rich
seminal computational techniques from statistics, information retrieval,
machine learning and pattern recognition.
Data mining techniques are the result of a long process of research
and product development. This evolution began when business data
was first stored on computers, continued with improvements in data

5. access, and more recently, generated technologies that allow users to
navigate through their data in real time. Data mining takes this
evolutionary process beyond retrospective data access and navigation
to prospective and proactive information delivery. Data mining is ready
for application in the business community because it is supported by
three technologies that are now sufficiently mature:
o Massive data collection
o Powerful multiprocessor computers
o Data mining algorithms
Commercial databases are growing at unprecedented rates. A recent
META Group survey of data warehouse projects found that 19% of
respondents are beyond the 50-gigabyte level, while 59% expect to be
there by second quarter of 1996.1 in some industries, such as retail,
these numbers can be much larger. The accompanying need for
improved computational engines can now be met in a cost-effective
manner with parallel multiprocessor computer technology. Data mining
algorithms embody techniques that have existed for at least 10 years,
but have only recently been implemented as mature, reliable,
understandable tools that consistently outperform older statistical
methods.

6. Overview of the System
There are mainly two types of scheduling namely the system level
scheduling and the application level scheduling. The scheduling system
will analyze the load situation of every node and select one node to
run the job. The scheduling policy is to optimize the total performance
of the whole system. If the system is heavily loaded, the scheduling
system has to realize the load balancing and increase the throughput
and resource utilization under restricted conditions. This kind of
scheduling is known as the system level scheduling.
If multiple jobs arrive within a unit scheduling time slot, the
scheduling system shall allocate an appropriate number of jobs to
every node in order to finish these jobs under a defined objective.
Obviously, the objective is usually the minimal average execution
time. This scheduling policy is application-oriented so we call it
application-level scheduling.
A Bi-Directional Routing Algorithm is a search technique used in
computing to find true or approximate solutions to optimization and
search problems. Bi-Directional Routing Algorithm is categorized as
global search heuristics. Bi-Directional Routing Algorithm are a
particular class of evolutionary algorithms that use techniques inspired
by evolutionary biology such as inheritance, mutation, selection, and
crossover (also called recombination).

7. Bi-Directional Routing Algorithm are implemented as a computer
simulation in which a population of abstract representations (called
chromosomes or the genotype or the genome) of candidate solutions
(called individuals, creatures, or phenotypes) to an optimization
problem evolves toward better solutions. Traditionally, solutions are
represented in binary as strings of 0s and 1s, but other encodings are
also possible. The evolution usually starts from a population of
randomly generated individuals and happens in generations. In each
generation, the fitness of every individual in the population is
evaluated, multiple individuals are stochastically selected from the
current population (based on their fitness), and modified (recombined
and possibly mutated) to form a new population. The new population
is then used in the next iteration of the algorithm. Commonly, the
algorithm terminates when either a maximum number of generations
has been produced, or a satisfactory fitness level has been reached for
the population. If the algorithm has terminated due to a maximum
number of generations, a satisfactory solution may or may not have
been reached.
A typical Bi-Directional Routing Algorithm requires two things to be
defined:

8. 1. a Bi-Directional representation of the solution domain,
2. a fitness function to evaluate the solution domain.
A standard representation of the solution is as an array of bits. Arrays
of other types and structures can be used in essentially the same way.
The main property that makes these Bi-Directional representations
convenient is that their parts are easily aligned due to their fixed size
that
facilitates
simple
crossover
operation.
Variable
length
representations may also be used, but crossover implementation is
more complex in this case. Tree-like representations are explored in
Bi-Directional
programming
and
free
form
representations
are
explored in HBGA.
The fitness function is defined over the Bi-Directional representation
and measures the quality of the represented solution. The fitness
function is always problem dependent. For instance, in the knapsack
problem we want to maximize the total value of objects that we can
put in a knapsack of some fixed capacity. A representation of a
solution might be an array of bits, where each bit represents a
different object, and the value of the bit (0 or 1) represents whether or
not the object is in the knapsack. Not every such representation is

9. valid, as the size of objects may exceed the capacity of the knapsack.
The fitness of the solution is the sum of values of all objects in the
knapsack if the representation is valid, or 0 otherwise. In some
problems, it is hard or even impossible to define the fitness
expression; in these cases, interactive BRA Routing algorithm is used.
Once we have the Bi-Directional representation and the fitness
function defined, GA proceeds to initialize a population of solutions
randomly, and then improve it through repetitive application of
mutation, crossover, and selection operators.

10. Description of Problem
The similar system is already available are non-predictive and
employs greedy based algorithms or a variant of it. That is the existing
system will not predict in advance regarding the situation. So we
cannot schedule the jobs in network in such a way that the resources
are utilized at the optimal level. The problem is to reduce the
processing overhead during scheduling. The proposed system work to
data transfer between computers of two networks. Generally, during
data transfer between pc of two different networks.
Existing Method
The Data mining Algorithms can be categorized into the
following:

AODVAlgorithm

Classification

Clustering Algorithm
Classification:
The process of dividing a dataset into mutually exclusive groups
such that the members of each group are as "close" as possible to one
another, and different groups are as "far" as possible from one

11. another,
where
variable(s)
you
distance
are
is
trying
measured
to
predict.
with
For
respect
example,
to
a
specific
typical
classification problem is to divide a database of companies into groups
that
are
as
homogeneous
as
possible
with
respect
to
a
creditworthiness variable with values "Good" and "Bad."
Clustering:
The process of dividing a dataset into mutually exclusive groups
such that the members of each group are as "close" as possible to one
another, and different groups are as "far" as possible from one
another, where distance is measured with respect to all available
variables.
Given databases of sufficient size and quality, data mining technology
can
generate
new
business
opportunities
by
providing
these
capabilities:
•
Automated prediction of trends and behaviors. Data mining
automates the process of finding predictive information in large
databases. Questions that traditionally required extensive handson analysis can now be answered directly from the data —
quickly. A typical example of a predictive problem is targeted
marketing. Data mining uses data on past promotional mailings

12. to identify the targets most likely to maximize return on
investment in future mailings. Other predictive problems include
forecasting
bankruptcy
and
other
forms
of
default,
and
identifying segments of a population likely to respond similarly to
given events.
•
Automated discovery of previously unknown patterns.
Data
mining
tools
sweep
through
databases
and
identify
previously hidden patterns in one step. An example of pattern
discovery is the analysis of retail sales data to identify seemingly
unrelated products that are often purchased together. Other
pattern discovery problems include detecting fraudulent credit
card transactions and identifying anomalous data that could
represent data entry keying errors.
Proposed System
Job scheduling is the key feature of any computing environment
and the efficiency of computing depends largely on the scheduling
technique
used.
Popular
algorithm
called
Bi-Directional
Routing
concept is used in the systems across the network and scheduling the
job according to predicting the load.

13. Here the system will take care of the scheduling of data packets
between the source and destination computers.
•
Job scheduling to route the packets at all the ports in the router
•
Maintaining queue of data packets and scheduling algorithm is
implemented
•
First Come First Serve scheduling and Bi-Directional Routing
Algorithm scheduling is called for source and destination
•
Comparison of two algorithm is shown in this proposed system

14. System Requirement
Hardware specifications:
Processor
RAM
:
:
Intel Processor IV
128 MB
Hard disk
:
20 GB
CD drive
:
40 x Samsung
Floppy drive
:
1.44 MB
Monitor
:
15’ Samtron color
Keyboard
Mouse
:
:
108 mercury keyboard
Logitech mouse
Software Specification
Operating System – Windows XP/2000
Language used – J2sdk1.4.0, JCreator