M.Tech Student Rohit Kumar Das Research on Load Balancing using Learning Automata in Wireless Mesh Networks

Rohit Kumar Das
M.Tech (IT), 3rd Sem
Roll- 031312 No36320137
Assam University

Outline
Introduction
 Literature Survey
 Related Work
 Motivation
 Backbone of Project
 Proposed Method
 Experimental Result
 Conclusion
 References


3/3/2014

Wireless Networks


Collection of nodes where each mesh node is also a router.



WMN is dynamically
 self-organized,
 self-configured,
 self-healing,
 easy maintenance,
 high scalability and
 reliable service with the nodes in the network



Implemented with various wireless technology including
802.11(WiFi), 802.15(Wireless PAN ), 802.16 (Wireless
Broadband standards), cellular technologies or combinations
of more than one type.
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Ad-hoc Networks


Communication

done

without

any

available.


Discover their own path for transmission.



Relay on the intermediate nodes.



Types of Ad-hoc networks:
 Wireless Mesh Network (WMN)
 Wireless Sensor Network (WSN)
 Mobile Ad-hoc Network (MANET)



Mesh Networks

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fixed

infrastructure

Introduction (Conti.)


Load Balancing
◦ Increase in network traffic cause load imbalance and
leading to network degradation.



Routing Protocol
◦ AODV routing protocol because it use less memory space
helping to achieve the goal.



Learning Automata
◦ Works well with stochastic environment.

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Literature Survey
1.

Gateway Discovery Protocol through message
notification [11].

At a IGW:
If the average Q_length > Max_Permissible_Threshold
Identify all the active sources
For each active source
Send a Congest_Notify message to switch the gateway, if
possible
End for
End if
If a GW_REQ message arrives from a node
If the average Q_length < Max_Permissible_Threshold
Admit this node and send a GW_REP to it
End if
End if

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Conti…
At a source node:
Record the gateway information (GW IDs) in the gateway table
When a notification message from IGW arrives:
For each gateway ID in the gateway table
Send a GW_REQ with the node’s estimated traffic

End for
When a GW_REP message arrives from a gateway:
Make the nearest gateway as the primary gateway

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2.

The authors of [12] mentioned about three
different load balancing scheme using IEEE 802.
11k



Admission control and


3.

Client driven

Cell breathing

Balancing of load by using nodes nearer to
gateway node. Have low bandwidth blocking

rate. Boundary nodes get un-notified [13].

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4.

In [14], load balancing is performed by dividing
domain into clusters then selecting gateway by
G_value.
Parameter for selecting Gateway:
a)

Power supply

b)

Velocity of node

c)

Distance to center of cluster and

d)

Processing power of node

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5.

Learning automaton for routing incoming
calls [18].




Virtual link length
Combination of packets
Reduce packet delay

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Related Works
1.

LALB (Learning Automata Based Load Balancing)
Algorithm proposed by the authors of [5] is an approach for
load balancing in Gateway level.

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2.

SARA (Stochastic Automata Rate Adaptation) Algorithm[15]
for selecting the transmission rate.





3.

Randomly selects.
R : x = 1, 2, . . . . . , k (bps)
Updates from feedback.
R (x) should be best possible rate.

Multicasting – major problem for MANET.
Authors of [17] proposed a weighted LA based multicasting
protocol





most stable multicast route.
packets are forwarded along the edges of Steiner tree.
Used LA to find the node with less mobility.
Routes composed of long duration link are consider –
weights are assign.

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4.

Mehdi Zarei proposed Reverse AODV with Learning
Automata (ROADVA) [25] works in similar way with
Reverse AODV

Reverse route is available.

Route is selected based on stability factor.

Updates the choice probability of routes stability
according to the feedback information form network.

5.

A routing protocol for Ad-hoc mobile network (AAODV)
Learning Automata AODV Routing protocol was projected
by authors of [26]

Operates with energy restriction.

Packet are routed through best path.

Saves energy.
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Problems Domain
 Route

 Just

flapping

consider their load

 Associate

each node to

its nearest gateway
 Switching

to another

domain
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Figure 1: Problem Layer By Layer [20]
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Motivation


Wireless Mesh Network (WMN) emerging topic for

research.



Problem with balancing of load.
Learning Automata (LA) working ability with
stochastic environment like WMN.



Ad-hoc On demand Distance Vector (AODV)
routing protocol.

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Wireless Mesh Network (WMN)

Figure 2: Wireless Mess Network [6]
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Characteristic of WMN


Multiple type of Network access.



Two types of nodes:
 Access Points (APs)/ Mesh Routers (MRs)
 Mobile Clients / Nodes (MNs)



Mobility dependence on the type of mesh nodes
 Mesh routers usually have minimal mobility
 Mesh clients can be stationary or mobile nodes



Multi-hop wireless network



Compatibility and interoperability with existing wireless networks

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Load Balancing
 Traffic

volume very high

 Makes scalability and load balancing
becomes important issues.

 Load

balancing

 Optimization of usage of network
resources

 Moving traffic from congested links to
less loaded part.
 Traffic aggregation occurs in paths.
 Due to the limited wireless link
capacity.
 Potential bottleneck
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Why Load Balancing ???


Avoiding congestion



Increasing network throughput



Providing reliability in case of any failure



Three categories:
◦ Path-based load balancing
◦ Mesh-router-based load balancing and
◦ Internet gateway load balancing
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Learning Automata (LA)
 Systems
 Select

possess incomplete knowledge

current action based on past

experiences from the environment


Adaptive decision-making unit

 probability

distribution

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Learning Automata in Network


Does not require prior
knowledge about traffic
characteristic



Utilized online in different
networks



Doesn't not require to complex
analyze of network during
learning phase



Keep just one action probability
vector



Exhibits less memory demands
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Integrating LA
Stochastic automaton:
◦ Six tuple
◦ { x, Ø, α, p, A, G}

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Integrating LA (Conti….)
Environment:
 i/p -> α(n) = {α1,…….,αr}
 o/p -> x
 Response [0,1]
 Penalty Probability
 Ci (i = 1,……r)

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Integrating LA (Conti….)
Learning automaton:
• Operates in a random environment

Figure 5: Learning
automaton

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Learning Automata Models
 P-Model:

The output can take only two

values, 0 or 1
 Q-Model:

Finite output set with more than

two values, between 0 and 1
 S-Model:

The output is a continuous

random variable in the range [0,1]

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Operation of LA
Four Stages:

1.

Sequences of repetitive cycles

2. Chooses action
3. Receives environmental response
4. Based on response from earlier action, next
action is determined.

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Operation (Conti…)
 During each cycle: αi is chosen with

probability pi
 Environment



response with Ci , update p.

Next action chosen according p(n+1)

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Learning Automata
Feedback Connection of Automaton and
Environment

Figure 6: Feedback mechanism of LA
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Reinforcement Scheme
 Choosing the best response based on the rewards or punishments
token from environment
 Lower the β(n) the more favorable the response.
 General Scheme:
Pi(n) - ( 1-β(n) ) gi( P(n) ) + β(n) hi( P(n) ), if a(n)≠ai
◦ Pi(n+1) =

Pi (n) + ( 1- β(n) ) Ʃj≠i gj( P(n) ) - β(n) Ʃj≠i hj( P(n) ), if
a(n)=ai

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Reinforcement Schemes
Different Scheme according to selection made
from functions are :
1.

The linear Reward–Penalty (LR–P) scheme

2.

The linear Reward–Inaction (LR–I) scheme

3.

Nonlinear schemes

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Application of LA in Layers


Physical Layer:
 Transmission power
 Distributed power control problem



Network Layer:
 Multicasting
 Routing



Transport Layer:
 Congestion window updation
 Control mechanisms

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Routing Protocol


Ad-hoc On-Demand Distance Vector Routing Protocol
(AODV)



Both unicast and multicast routing



Builds routes between nodes only as desired



It is
◦ loop-free,
◦ self-starting,
◦ low network utilization,
◦ no memory overhead,
◦ and scales to large numbers of mobile nodes

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AODV Properties


The route table stores:
<destination addr, next-hop addr, destination
sequence number, life_time>



The basic message set consists of:
 RREQ – Route Request
 RREP – Route Reply
 RERR – Route Error
 HELLO – For link status monitoring

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Re-active routing AODV(RFC3561)
A wants to communicate with
B

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A floods a route request

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A route reply is unicasted
back

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Route Requests in AODV
Y
Z
S

E
F

B

C

M

L

J
A

G
H

D
K
I

N

Represents a node that has received RREQ for D from S
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Y

Broadcast transmission

Z
S

E
F

B

C

M

J

A

L

G
H

K

D

N

I

Represents transmission of RREQ
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Y
Z
S

E
F

B

C

M

J

A

L

G
H

K

D
N

I

Represents links on Reverse Path
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Reverse Path Setup in AODV
Y
Z
S

E
F

B

C

M

J

A

L

G
H

K

D
N

I

•Node C receives RREQ from G and H, but does not forward
it again, because node C has already forwarded RREQ once
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Y

Z
S

E
F

B

C

J

A

L

M

G
H

K

D
N

I

3/3/2014

Y
Z
S

E
F

B

C

M

J

A

L

G
H

K

D
N

I

•Node D does not forward RREQ, because node D is the intended
target of the RREQ
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Forward Path Setup in AODV
(contd…)
Y
Z
S

E
F

B

C

J

A

L

M

G
H

K

D
N

I

Forward links are setup when RREP travels along the reverse p
Represents a link on the forward path
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Proposed Method


Learning Automata – Ad-hoc On Demand

Distance Vector (LA-AODV) routing protocol


Integrating LA with AODV



Find the best available path for packet
delivery.



Each routers will be employed with LAAODV
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Algorithm for Proposed Method


Step 1: (Path Discovery)
Start Route Discovery Phase by sending
RREQ packet.
If reach destination
initiate RTL phase

Else
Forward to next node

For each RREQ packet, check for same packet
Same packet then discard or forward to next

End for

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Reverse Path Establishment

Fig: Reverse Path
Formation

Fig: Forward Path
Formation

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

Step 2: (Route Table Management by
Learning)
◦ Receive feedback from neighbors.
◦ Construct local forwarding table using Learning
Algorithm.

Forwarding Table:
 check for RREQ entry in routing table.
 If present check
 RREQ seq_no > Dest seq_no
 Else
 Use recorded route for RREQ

 Create RREP
 Forward to intermediate nodes

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

Step 3: (Routing Phase using
Learning)
◦ Node activates LA
 Obtain best route from RLT phase.
 Check for constraint
 If between 50% to 100%
 Positive feedback (rewarded)
 Else
 Negative feedback (penalized)

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Flow Chart

Figure 7: Flow chart for Proposed Model
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Experimental Results

Figure 8: Basic AODV with Performance
measurement
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Figure 9: Modified AODV with Performance
measurement

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Conclusion & Future Works

Relatively new technology
 Significant advantages for many
applications
 Load balancing is one of the important area
of research in WMN
 Load can be balanced using different
techniques like Learning Automata
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

Conclusion (Conti.)
Collaborating LA with AODV
 Learning Automata AODV routing
protocol (LA-AODV) for WMN
 LA agent keep running on each node.
 Provide best available path
 Lead to the goal – Load Balancing


3/3/2014

References
[1] Subir Kumar Sarkar, T G Basavaraju, C Puttamadappa, “Ad-hoc Mobile Wireless
Networks Principles, Protocol and Applications” Auerbach Publications, ISBN
978-1-4200-6221-2
[2] Ram Ramanathan and Jason Redi, “A Brief Overview of Ad-hoc Networks: Challenges
and Directions”, IEEE Communication Magazine 50th Anniversary Commemorative
Issue/May 2002
[3] Bing He, Dongmei Sun, Dharma P. Agrawal “Diffusion based Distributed Internet
Gateway Load Balancing in a Wireless Mesh Network,” In proceedings of IEEE
"GLOBECOM" 2009
[4] Ashish Raniwala, Tzi-cker Chiueh. “Architecture and algorithms for an IEEE 802.11based multi-channel wireless mesh network” In: Infocom 2005.
[5] Maryam Kashanaki, Zia Beheshti, Mohammad Reza Meybodi, “A Distributed Learning
Automata based Gateway Load Balancing Algorithm in Wireless Mesh Networks”,
Proceedings of IEEE for GLOBECOM 2009
[6] Akyildiz, Ian F., “A Survey on Wireless Mesh Networks”, Georgia Institute of Technology
Xudong Wang, Kiyon, Inc., IEEE Radio Communications, 2005.
[7] firetide.com “An Introduction to Wireless Mesh Networking”, 16795 Lark Avenue, Suite
200
[8] Kumpati S. Narendra, And M. A. L. Thathachar, “Learning Automata - A Survey”, IEEE
Transactions On Systems, Man, And Cybernetics, Vol. Smc-4, No. 4, July 1974
[9] M.S. Obaidat, G.I. Papadimitriou, A.S. Pomportsis,“Efficient fast learning automata”,
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International journal of Information Science, June 2002.

References
[11] Deepti Nandiraju, Lakshmi Santhanam, Nagesh Nandiraju, and Dharma P. Agrawal,
“Achieving Load Balancing in Wireless Mesh Networks through Multiple Gateways”,
Proceeding of IEEE in 2006.
[12] E.Garcia Villegas, R. Vidal Ferré, J. Paradells Aspas, “Load Balancing in WLANs
through IEEE 802.11k Mechanisms”, Proceeding of the 11th IEEE Symposium on
Computers and Communications (ISCC'06).
[13] P. Hsiao, A. Hwang, H. Kung, D. Vlah, “Load-Balancing Routing for Wireless Access
Networks”, Proceeding of IEEE INFOCOM '01.
[14] Mohammad Shahverdy, Misagh Behnami & Mahmood Fathy, “A New Paradigm for
Load Balancing in WMNs” International Journal of Computer Networks (IJCN), Volume
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[15] Tarun Joshi, Disha Ahuja, Damanjit Singh, and Dharma P. Agrawal, “SARA: Stochastic
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Automata for Adaptively Adjusting the Downlink-to-Uplink Ratio in IEEE 802.16e
Wireless Networks”
[17] Vinodha K, Joydipa Sen, “A Weighted Learning Automata-Based Multicast Routing
Protocol for Wireless MANET” International Journal of Engineering Reasearch &
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1995
[19] Anastasios A. Economides, “Real-Time Traffic Allocation Using Learning Automata”,
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[20] Fry, Michael, et al. “Challenge identification for network resilience.” Next Generation

References
[21] Nicopolitidis, Petros, et al. “Adaptive wireless networks using learning
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[23] Usop, Nor Surayati Mohamad, Azizol Abdullah, and Ahmad Faisal Amri Abidin.
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[24] Prashant Kumar Maurya, Gaurav Sharma, Vaishali Sahu, Ashish Roberts, Mahendra
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[25] Zarei, Mehdi. “Reverse AODV routing protocol extension using learning Automata in ad
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3/3/2014

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