Modified LA to Optimize the Performance of Mobile Ad Hoc

DOI 10.4010/2015.340
ISSN-2321 -3361 © 2015 IJESC
Research Article
May 2015 Issue
Modified LA to Optimize the Performance of Mobile Ad Hoc Network
Monisha Rani1, Er. Amit Chhabra2
PG Student1, Associate Professor 2
Department of Computer Science and Engineering
Chandigarh University, Gharuan, Mohali
[email protected], [email protected]
Abstract:
The fault tolerance in mobile ad hoc networks is one of the most important aspects which must be taken into consideration for
efficient and reliable routing of the data packets from source to destination node. The fault in the network results in loss of
packets and information in the network. In past many research is been done to increase the lifetime of the nodes and to tolerate
the fault in the nodes. In [13] learning automata is used to make the mobile ad hoc network more fault tolerant where the packet
delivery of the nodes is taken as the measure of fault in the network. However, the packet delivery across the node may be
attributed to mobility of the nodes. The proposed scheme has been comprehensively compared with the existing scheme and it
outperforms the existing scheme.
Keywords: mobile ad hoc network, fault tolerance, learning automata
I.
INTRODUCTION
Mobile Ad Hoc Network (MANET) is a collection of two or
more devices or nodes with wireless communications and
networking capability that communicate with each other
without the aid of any centralized administrator also the
wireless nodes that can dynamically form a network to
exchange information without using any existing fixed
network infrastructure. These nodes are often energy
constrained- that is, battery-powered- devices with a great
diversity in their capabilities. Furthermore, devices are free to
join or leave the network and they may move randomly,
possibly resulting in rapid and unpredictable topology
changes.
An ad hoc mobile network is a collection of mobile nodes that
are dynamically and arbitrarily located in such a manner that
the interconnections between nodes are capable of changing
on a continual basis. In order to facilitate communication
within the network, a routing protocol is used to discover
routes between nodes. The primary goal of such an ad hoc
network routing protocol is correct and efficient route
establishment between a pair of nodes so that messages may
be delivered in a timely manner. Route construction should be
done with a minimum of overhead and bandwidth
consumption.[10]
Dynamic topologies, Bandwidth-constrained, variable
capacity links, Energy-constrained operation, Limited
physical security are several salient characteristics of
MANET. The infrastructure less and the dynamic nature of
these networks demands new set of networking strategies to
be implemented in order to provide efficient end-to-end
communication.
This along with the diverse application of these networks in
many different scenarios such as battlefield and disaster
recovery, etc. MANETs employ the traditional TCP/IP
structure to provide end-to-end communication between
nodes. However, due to their mobility and the limited resource
in wireless networks, each layer in the TCP/IP model require
redefinition or modifications to function efficiently in
MANETs.
In a Mobile Ad Hoc Network reliable routing of packets has
always been a major concern. The susceptibility and open
medium of the nodes of being fault-prone make the design of
protocols for these networks a challenging task. The faults in
these networks, which occur either due to the failure of nodes
or due to reorganization, can eventuate to packet loss. Such
losses degrade the performance of the routing protocols
running on them. Ubiquitous communications using MANETs
require support of reliable routing protocols. The situation
gets worse when there are faulty nodes in the network, as they
increase the data loss and degrade the performance of
protocols. To set up an efficient communication mechanism
between the nodes, strong routing fabrics between the
intermediate nodes are required, if the intermediate nodes
misbehave, then performance of the network is significantly
affected.
II.
RELATED WORK
Xue et al. [1] proposed End-to-End Fault-tolerant (E2FT)
routing algorithm [1] in which routing is based on end-to-end
computation using a route estimation and route selection
process. Route selection is done via two procedures:
Confirmation and Dropping. Confirmation is a procedure
1283
http://ijesc.org/
which selects a path from a given set of paths with specific
packet delivery ratio. Dropping is a procedure which drops a
given path if the packet delivery ratio of that path falls below
a given threshold value. In this manner, using the route
estimation process, the path probability estimation for the
available paths is done, and thereafter, using the route
selection process, a reduced set of paths can be selected in
very short duration of time interval based on the constraint.
Zhou et al. [2] proposed location based fault tolerant
routing algorithm (FTRA) .In this algorithm based on
geographical location information networks divided in to grid.
Fault may occur and can select alternate route from unused at
hop in normal routing path, the route selection depends upon
location information of its neighbors grids.
Roberts et al. [3] presents a solution to the capacity
assignment problem for prioritized networks, that is based on
the use of discretized learning automata. The proposed
solution finds the best possible set of capacities for the links
that satisfy the traffic requirements of the network while
minimizing the cost.
Oommen and Misra [4] proposed a weak-estimation
learning-based fault-tolerant routing algorithm (WEFTR),
which used a similar model as proposed by Xue et al. [1], was
to minimize the overhead by sending the least possible
number of redundant packets, while guaranteeing a certain
rate for the delivery of packets. There is a tradeoff between
the rate of delivery of packets and the overhead. It is possible
to achieve a very high packet delivery rate if the number of
packets sent is not a concern (e.g., by using the multipath
routing scheme). On the other hand, it is possible to achieve a
very low overhead, if we do not care about the number of
packets that are successfully delivered. So a weak estimationbased learning process gets better judgment of the multiple
routes between source and destination nodes as compare to
E2FT .
the face of anomalous delivery of routing control messages),
avoiding problems (such as "counting to infinity") associated
with classical distance vector protocols. But suffers from the
disadvantage of stale entries causing the route to be
inconsistent.
G.I. Papadimitriou et al. [8] describes S-model absorbing
learning automaton (LA) which is based on the use of a
stochastic estimator is introduced. In stochastic estimator
scheme, the estimates of the mean rewards of actions are
computed stochastically. Actions that have not been selected
many times have the opportunity to be estimated as optimal,
to increase their choice probabilities, and consequently, to be
selected. In which the input to the automaton from the
environment can be completely favorable, completely
unfavorable, or some continuous intermediate value depicting
partially favorable or partially unfavorable cases. The S-model
approach helps to model the environmental feedback for
partially favorable/ unfavorable cases. This helps to deal with
nodes which are not completely faulty nodes. This way can
efficiently deal with partially faulty nodes and nodes where
radio wave signals are weak, for instance.
Misra S. et al. [9] presents a new solution to the dynamic
all-pairs shortest-path routing problem using a fast-converging
pursuit automata learning approach. The particular instance of
the problem that we have investigated concerns finding the
all-pairs shortest paths in a stochastic graph, where there are
continuous probabilistically based updates in edge-weights.
We present the details of the algorithm with an illustrative
example. The algorithm can be used to find the all-pairs
shortest paths for the ‗statistical‘ average graph, and the
solution converges irrespective of whether there are new
changes in edge-weights or not. On the other hand, the
existing popular algorithms will fail to exhibit such a behavior
and would recalculate the affected all-pairs shortest paths after
each edge-weight update.
III.
Misra et al. [5] used an ant colony optimization (ACO)based framework [5,6] for finding out the suitable path for
routing packets. An algorithm FTAR which uses control
packets called ants for acquiring routing information and are
generated continuously by nodes in the network. These
control packets deposit pheromone (control information) on
each node, similar to pheromone deposited by real ants on the
path they travel which is used for routing of packets.
Zarei et al. [6] proposed an approach for routing stability
estimation in MANETs using the concepts of Learning
Automata, where the penalty scheme is based on the
consideration of the stability in links/fitness of the routes
between the nodes. In this scheme, the nature of the node is
not considered, but is considered for the links. By which node
failure get increases and faults in a network enlarge.
Perkins C et al. [7] offers quick adaptation to dynamic link
conditions, low processing and memory overhead, low
network utilization, and determines unicast routes to
destinations within the ad hoc network. It uses destination
sequence numbers to ensure loop freedom at all times (even in
LEARNING AUTOMATA
The theory of LA can be applied in problems aiming at
finding the optimal action, taking the random environment
into account. The learning cycle involves two components, the
Random Environment (RE) and a Learning Automaton. The
process of learning is performed by interacting with the RE,
and computing its responses to choose the best (closest to
optimum) action.
Learning Automata (LA) [10] is a self-operating learning
model, where ―learning‖ refers to the process of gaining
knowledge during the execution of a simple machine/code
(automaton), and using the gained knowledge to decide on
actions to be taken in the future. This model has three main
components—the Automaton, the Environment, and the
Reward/Penalty structure.
The Automaton continuously performs actions on the
Environment and the Environment responds to these actions.
This response may be either positive or negative and it serves
as the feedback to the Automaton, which in effect, leads to the
Automaton either getting rewarded or penalized. Over a period
of time, the Automaton learns the characteristics of the
1284
http://ijesc.org/
Environment and identifies ―optimal‖ actions that can be
performed on the Environment.
The Automaton refers to the self-learning machine. The
medium in which this machine functions is called the
Environment. A learning automaton is a finite state machine
that interacts with a stochastic environment trying to learn the
optimal action offered by the environment, via a learning
process. The automaton chooses one of the offered actions
according to a probability vector which at any time instant
contains the probability of choosing each action. The chosen
action triggers the environment, which responds with an
answer (reward or penalty), according to the reward probability
of the chosen action. The automaton takes into account this
answer and modifies the probability vector by means of a
learning algorithm. A learning automaton is one that learns the
action that has the maximum probability to be rewarded and
that ultimately chooses this action more frequently than other
actions.[11] [12]
Learning Automata (LA) for optimizing the selection of paths,
reducing the overhead in the network, and for learning about
the faulty nodes present in the network.
Fig 1: Routing Overhead Comparison
IV.
PROPOSED SCHEME
In the existing scheme[13], the authors implemented the
learning automata into the network by considering the packet
delivery across the node as the main parameter which decides
whether the node will be granted reward points or the penalty
points. In the proposed scheme we have incorporated more
factors into account.
The above figure shows the comparison of the routing
overhead between the proposed and the existing scheme. The
routing overhead reflects how many routing packets needs to
be sent in the network to receive a unit of the data packet.
It is calculated by the formula:
Routing Overhead= Number of routing packets sent /
Number of data packets received
A. Proposed Method
In the proposed method we have taken the reason for
packet drop across a node into account. In mobile ad hoc
networks the packet drop across a node may be contributed to
the mobility of the nodes. The nodes moving with higher
mobility is more likely to drop a packet which is being
forwarded to it. Also to achieve the more fault tolerance into
network the nodes in the network which are chosen to
forwards the data to the destination node must be having more
energy so that it can function longer in the network. These
factors have been incorporated into account to optimize the
performance of the network.
Lesser the routing overhead, better is the performance of
the network.
V. SIMULATION RESULTS
In this section, the proposed method has been simulated in
NS2.35 and the simulation results are presented.
Fig 2. Throughput Comparison
1285
http://ijesc.org/
Above graph shows the throughput comparison between
proposed and existing scheme. Throughput is amount of the
data that is received at the destination in the network. More
the throughput better is the performance of the network.
Proposed scheme outperformed the existing scheme in terms
of throughput.
[2] Jipeng Zhou and Chao Xia, ―A Location-Based
Fault-Tolerant Routing Algorithm for Mobile Ad Hoc
Networks ―, WRI International Conference on
Communications and Mobile Computing, Volume 2
,Page(s) 92 – 96,Jan 2009.
[3] B. J. Oommen and T. D. Roberts, ―Continuous
learning automata solutions to the capacity assignment
problem,‖ IEEE Trans. Comput., vol. 49, pp. 608–620,
June 2000.
[4] Oommen BJ, Misra S (2006) A fault-tolerant routing
algorithm for mobile ad hoc networks using a stochastic
learning-based weak estimation procedure. In: IEEE
international conference on wireless and mobile computing,
networking and communications, (WiMob‘2006), 19–21
June, pp 31–37.
[5] Misra S, Dhurandher SK, Obaidat MS, Verma K,
Gupta P (2009) Using ant-like agents for fault tolerant
routing in mobile ad-hoc networks. In: IEEE international
conference on communications ICC‘09, 14–18 June, pp 1–
5.
[6] Zarei M, Faez K, Nya JM, Meinagh MA (2008)
Route stability estimation in mobile ad hoc networks using
learning automata. In: 16th telecommunications forum
TELFOR 2008, Belgrade, Serbia, Nov 25–27, pp 76–79.
Fig 3: Packet Delivery Ratio
Above graph shows the packet delivery ratio. Packet
Delivery Ratio is the ratio between number of packets
received and number of packets sent. The packet delivery ratio
for the proposed scheme is better than existing scheme.
VI.
CONCLUSIONS
The exceedingly rapid topology of Ad Hoc systems and
their restricted bandwidth makes the routing task more
troublesome. The work reflects the idea that by taking into
consideration the factors like mobility and energy of the nodes
the performance of the network can be optimized. The
proposed work showed better results than the existing scheme
in terms of routing overhead, throughput and packet delivery
ratio. However, in future we would like to take network
security into account and check the performance of the
network.
ACKNOWLEDGMENT
The paper has been written with the kind assistance,
guidance and active support of my department who have
helped me in this work. I would like to thank all the
individuals whose encouragement and support has made the
completion of this work possible.
REFERENCES
[1] Xue Y, Nahrstedt K (2003) Fault-tolerant routing in
mobile ad hoc networks. In: Wireless communications and
networking, 2003, WCNC, 20–20 March, vol 2, pp 1174–
1179.
[7] Perkins C, Belding-Royer E, Das S (2003) Ad hoc ondemand distance vector (AODV) routing. IETF, RFC 3561,
July.
[8] G.I. Papadimitriou , A.S. Pomportsis, S. Kiritsi, E.
Talahoupi, Absorbing stochastic estimator learning
automata for S-model stationary environments ,
Information Sciences (2002) 193–199.
[9] Misra S, Oommen BJ (2009) An efficient pursuit
automata approach for estimating stable all-pairs shortest
paths in stochastic network environments. Int J Commun
Syst 22(4):441–468.
[10] K.S. Narendra, S. Lakshmivarahan, Learning
automata––a critique, Journal of Cybernetics and
Information Sciences 1 (1977) 53–66.
[11] Anuj K. Gupta, Harsh Sadawarti , Anil K. Verma
"Performance Analysis of MANET Routing Protocols in
Different Mobility Models" I.J. Information Technology
and Computer Science, 2013, 73-82.
[12] Narendra KS, Thathachar MAL (1989) Learning
automata, IEEE Transactions on Systems, Man and
Cybernetics SMC-4 (8) (1974) 323–334.
[13] Sudip Misra·P. Venkata Krishna·Akhil Bhiwal·
Amardeep Singh Chawla·Bernd E. Wolfinger· Changhoon
Le, "A learning automata-based fault-tolerant routing
algorithm for mobile ad hoc networks" J Supercomput
(2012) 62:4–23 DOI 10.1007/s11227-011-0639-8
1286
http://ijesc.org/