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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
 Experimenta...
Outline
Introduction
 Literature Survey
 Related Work
 Motivation
 Backbone of Project
 Proposed Method
 Experimenta...
Wireless Networks


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



WMN is dynamically
 self-organized,
...
Ad-hoc Networks


Communication

done

without

any

available.


Discover their own path for transmission.



Relay on...
Introduction (Conti.)


Load Balancing
◦ Increase in network traffic cause load imbalance and
leading to network degradat...
Outline
Introduction
 Literature Survey
 Related Work
 Motivation
 Backbone of Project
 Proposed Method
 Experimenta...
Literature Survey
1.

Gateway Discovery Protocol through message
notification [11].

At a IGW:
If the average Q_length > M...
Conti…
At a source node:
Record the gateway information (GW IDs) in the gateway table
When a notification message from IGW...
2.

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

Admission control a...
4.

In [14], load balancing is performed by dividing
domain into clusters then selecting gateway by
G_value.
Parameter for...
5.

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

Virtual link length
Combination of packets
Reduce packet de...
Outline
Introduction
 Literature Survey
 Related Work
 Motivation
 Backbone of Project
 Proposed Method
 Experimenta...
Related Works
1.

LALB (Learning Automata Based Load Balancing)
Algorithm proposed by the authors of [5] is an approach fo...
2.

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

3.

Randomly se...
4.

Mehdi Zarei proposed Reverse AODV with Learning
Automata (ROADVA) [25] works in similar way with
Reverse AODV

Revers...
Problems Domain
 Route

 Just

flapping

consider their load

 Associate

each node to

its nearest gateway
 Switching...
Figure 1: Problem Layer By Layer [20]
3/3/2014
Outline
Introduction
 Literature Survey
 Related Work
 Motivation
 Backbone of Project
 Proposed Method
 Experimenta...
Motivation


Wireless Mesh Network (WMN) emerging topic for

research.



Problem with balancing of load.
Learning Auto...
Outline
Introduction
 Literature Survey
 Related Work
 Motivation
 Backbone of Project
 Proposed Method
 Experimenta...
Wireless Mesh Network (WMN)

Figure 2: Wireless Mess Network [6]
3/3/2014
Characteristic of WMN


Multiple type of Network access.



Two types of nodes:
 Access Points (APs)/ Mesh Routers (MRs...
Load Balancing
 Traffic

volume very high

 Makes scalability and load balancing
becomes important issues.

 Load

bala...
Why Load Balancing ???


Avoiding congestion



Increasing network throughput



Providing reliability in case of any f...
Learning Automata (LA)
 Systems
 Select

possess incomplete knowledge

current action based on past

experiences from th...
Learning Automata in Network


Does not require prior
knowledge about traffic
characteristic



Utilized online in diffe...
Integrating LA
Stochastic automaton:
◦ Six tuple
◦ { x, Ø, α, p, A, G}

3/3/2014
Integrating LA (Conti….)
Environment:
 i/p -> α(n) = {α1,…….,αr}
 o/p -> x
 Response [0,1]
 Penalty Probability
 Ci (...
Integrating LA (Conti….)
Learning automaton:
• Operates in a random environment

Figure 5: Learning
automaton

3/3/2014
Learning Automata Models
 P-Model:

The output can take only two

values, 0 or 1
 Q-Model:

Finite output set with more ...
Operation of LA
Four Stages:

1.

Sequences of repetitive cycles

2. Chooses action
3. Receives environmental response
4. ...
Operation (Conti…)
 During each cycle: αi is chosen with

probability pi
 Environment



response with Ci , update p.

...
Learning Automata
Feedback Connection of Automaton and
Environment

Figure 6: Feedback mechanism of LA
3/3/2014
Reinforcement Scheme
 Choosing the best response based on the rewards or punishments
token from environment
 Lower the β...
Reinforcement Schemes
Different Scheme according to selection made
from functions are :
1.

The linear Reward–Penalty (LR–...
Application of LA in Layers


Physical Layer:
 Transmission power
 Distributed power control problem



Network Layer:...
Routing Protocol


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



Both unicast and multicast routing



Bu...
AODV Properties


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



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

3/3/2014
Re-active routing AODV(RFC3561)
A floods a route request

3/3/2014
Re-active routing AODV(RFC3561)
A route reply is unicasted
back

3/3/2014
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
3...
Route Requests in AODV
Y

Broadcast transmission

Z
S

E
F

B

C

M

J

A

L

G
H

K

D

N

I

Represents transmission of ...
Route Requests in AODV
Y
Z
S

E
F

B

C

M

J

A

L

G
H

K

D
N

I

Represents links on Reverse Path
3/3/2014
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 ...
Reverse Path Setup in AODV
Y

Z
S

E
F

B

C

J

A

L

M

G
H

K

D
N

I

3/3/2014
Reverse Path Setup in AODV
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 ...
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 travel...
Outline
Introduction
 Literature Survey
 Related Work
 Motivation
 Backbone of Project
 Proposed Method
 Experimenta...
Proposed Method


Learning Automata – Ad-hoc On Demand

Distance Vector (LA-AODV) routing protocol


Integrating LA with...
Algorithm for Proposed Method


Step 1: (Path Discovery)
Start Route Discovery Phase by sending
RREQ packet.
If reach des...
Reverse Path Establishment

Fig: Reverse Path
Formation

Fig: Forward Path
Formation

3/3/2014


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

Step 3: (Routing Phase using
Learning)
◦ Node activates LA
 Obtain best route from RLT phase.
 Check for constraint
...
Flow Chart

Figure 7: Flow chart for Proposed Model
3/3/2014
Outline
Introduction
 Literature Survey
 Related Work
 Motivation
 Backbone of Project
 Proposed Method
 Experimenta...
Experimental Results

Figure 8: Basic AODV with Performance
measurement
3/3/2014
Figure 9: Modified AODV with Performance
measurement

3/3/2014
Outline
Introduction
 Literature Survey
 Related Work
 Motivation
 Backbone of Project
 Proposed Method
 Experimenta...
Conclusion & Future Works

Relatively new technology
 Significant advantages for many
applications
 Load balancing is on...
Conclusion (Conti.)
Collaborating LA with AODV
 Learning Automata AODV routing
protocol (LA-AODV) for WMN
 LA agent keep...
Outline
Introduction
 Literature Survey
 Related Work
 Motivation
 Backbone of Project
 Proposed Method
 Experimenta...
References
[1] Subir Kumar Sarkar, T G Basavaraju, C Puttamadappa, “Ad-hoc Mobile Wireless
Networks Principles, Protocol a...
References
[11] Deepti Nandiraju, Lakshmi Santhanam, Nagesh Nandiraju, and Dharma P. Agrawal,
“Achieving Load Balancing in...
References
[21] Nicopolitidis, Petros, et al. “Adaptive wireless networks using learning
automata.” Wireless Communication...
3/3/2014
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Learning Automata with Wireless Mesh Network

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Intergrating Learing Automata (LA) with Wireless Mesh Network (WMN). How LA can be incorporated in different layer of network

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  • The Ad hoc On Demand Distance Vector (AODV) routing algorithm is a routing protocol designed for ad-hoc mobile networks2. AODV is capable of both unicast and multicast routing3. It is an on demand algorithm, meaning that it builds routes between nodes only as desired by source nodes. It maintains these routesas long as they are needed by the sources4.
  • The basic message set includes a route request message, route reply message, route error message, and a hello message.The mechanics of each of these messages will be covered in detail later in the presentation.Briefly, however, a host (node) multicasts a RREQ message when it needs to find a route to a destination (either not already contained in its routing table, or one whose status is invalid).
  • Transcript of "Learning Automata with Wireless Mesh Network"

    1. 1. Rohit Kumar Das M.Tech (IT), 3rd Sem Roll- 031312 No36320137 Assam University
    2. 2. Outline Introduction  Literature Survey  Related Work  Motivation  Backbone of Project  Proposed Method  Experimental Result  Conclusion  References  3/3/2014
    3. 3. Outline Introduction  Literature Survey  Related Work  Motivation  Backbone of Project  Proposed Method  Experimental Result  Conclusion  References  3/3/2014
    4. 4. 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. 3/3/2014
    5. 5. 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 3/3/2014 fixed infrastructure
    6. 6. 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. 3/3/2014
    7. 7. Outline Introduction  Literature Survey  Related Work  Motivation  Backbone of Project  Proposed Method  Experimental Result  Conclusion  References  3/3/2014
    8. 8. 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 3/3/2014
    9. 9. 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 3/3/2014
    10. 10. 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]. 3/3/2014
    11. 11. 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 3/3/2014
    12. 12. 5. Learning automaton for routing incoming calls [18].    Virtual link length Combination of packets Reduce packet delay 3/3/2014
    13. 13. Outline Introduction  Literature Survey  Related Work  Motivation  Backbone of Project  Proposed Method  Experimental Result  Conclusion  References  3/3/2014
    14. 14. 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. 3/3/2014
    15. 15. 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. 3/3/2014
    16. 16. 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. 3/3/2014
    17. 17. Problems Domain  Route  Just flapping consider their load  Associate each node to its nearest gateway  Switching to another domain 3/3/2014
    18. 18. Figure 1: Problem Layer By Layer [20] 3/3/2014
    19. 19. Outline Introduction  Literature Survey  Related Work  Motivation  Backbone of Project  Proposed Method  Experimental Result  Conclusion  References  3/3/2014
    20. 20. 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. 3/3/2014
    21. 21. Outline Introduction  Literature Survey  Related Work  Motivation  Backbone of Project  Proposed Method  Experimental Result  Conclusion  References  3/3/2014
    22. 22. Wireless Mesh Network (WMN) Figure 2: Wireless Mess Network [6] 3/3/2014
    23. 23. 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 3/3/2014
    24. 24. 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 3/3/2014
    25. 25. 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 3/3/2014
    26. 26. Learning Automata (LA)  Systems  Select possess incomplete knowledge current action based on past experiences from the environment  Adaptive decision-making unit  probability distribution 3/3/2014
    27. 27. 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 3/3/2014
    28. 28. Integrating LA Stochastic automaton: ◦ Six tuple ◦ { x, Ø, α, p, A, G} 3/3/2014
    29. 29. Integrating LA (Conti….) Environment:  i/p -> α(n) = {α1,…….,αr}  o/p -> x  Response [0,1]  Penalty Probability  Ci (i = 1,……r) 3/3/2014
    30. 30. Integrating LA (Conti….) Learning automaton: • Operates in a random environment Figure 5: Learning automaton 3/3/2014
    31. 31. 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] 3/3/2014
    32. 32. 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. 3/3/2014
    33. 33. Operation (Conti…)  During each cycle: αi is chosen with probability pi  Environment  response with Ci , update p. Next action chosen according p(n+1) 3/3/2014
    34. 34. Learning Automata Feedback Connection of Automaton and Environment Figure 6: Feedback mechanism of LA 3/3/2014
    35. 35. 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 3/3/2014
    36. 36. 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 3/3/2014
    37. 37. Application of LA in Layers  Physical Layer:  Transmission power  Distributed power control problem  Network Layer:  Multicasting  Routing  Transport Layer:  Congestion window updation  Control mechanisms 3/3/2014
    38. 38. 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 3/3/2014
    39. 39. 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 3/3/2014
    40. 40. Re-active routing AODV(RFC3561) A wants to communicate with B 3/3/2014
    41. 41. Re-active routing AODV(RFC3561) A floods a route request 3/3/2014
    42. 42. Re-active routing AODV(RFC3561) A route reply is unicasted back 3/3/2014
    43. 43. 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 3/3/2014
    44. 44. Route Requests in AODV Y Broadcast transmission Z S E F B C M J A L G H K D N I Represents transmission of RREQ 3/3/2014
    45. 45. Route Requests in AODV Y Z S E F B C M J A L G H K D N I Represents links on Reverse Path 3/3/2014
    46. 46. 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 3/3/2014
    47. 47. Reverse Path Setup in AODV Y Z S E F B C J A L M G H K D N I 3/3/2014
    48. 48. Reverse Path Setup in AODV 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 3/3/2014
    49. 49. 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 3/3/2014
    50. 50. Outline Introduction  Literature Survey  Related Work  Motivation  Backbone of Project  Proposed Method  Experimental Result  Conclusion  References  3/3/2014
    51. 51. 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 3/3/2014
    52. 52. 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 3/3/2014
    53. 53. Reverse Path Establishment Fig: Reverse Path Formation Fig: Forward Path Formation 3/3/2014
    54. 54.  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 3/3/2014
    55. 55.  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) 3/3/2014
    56. 56. Flow Chart Figure 7: Flow chart for Proposed Model 3/3/2014
    57. 57. Outline Introduction  Literature Survey  Related Work  Motivation  Backbone of Project  Proposed Method  Experimental Result  Conclusion  References  3/3/2014
    58. 58. Experimental Results Figure 8: Basic AODV with Performance measurement 3/3/2014
    59. 59. Figure 9: Modified AODV with Performance measurement 3/3/2014
    60. 60. Outline Introduction  Literature Survey  Related Work  Motivation  Backbone of Project  Proposed Method  Experimental Result  Conclusion  References  3/3/2014
    61. 61. 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 3/3/2014 
    62. 62. 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
    63. 63. Outline Introduction  Literature Survey  Related Work  Motivation  Backbone of Project  Proposed Method  Experimental Result  Conclusion  References  3/3/2014
    64. 64. 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”, 3/3/2014 International journal of Information Science, June 2002.
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    66. 66. References [21] Nicopolitidis, Petros, et al. “Adaptive wireless networks using learning automata.” Wireless Communications, IEEE 18.2 (2011): 75-81. [22] S. Das, C. Perkins, and E. Royer, "Ad Hoc On Demand Distance Vector (AODV) Routing," in IETF. RFC 3561, 2003. [23] Usop, Nor Surayati Mohamad, Azizol Abdullah, and Ahmad Faisal Amri Abidin. “Performance evaluation of AODV, DSDV & DSR routing protocol in grid environment.” IJCSNS International Journal of Computer Science and Network Security 9.7 (2009): 261-268. [24] Prashant Kumar Maurya, Gaurav Sharma, Vaishali Sahu, Ashish Roberts, Mahendra Srivastava, “An Overview of AODV Routing Protocol”, International Journal of Modern Engineering Research (IJMER), Vol.2, Issue.3, May-June 2012 pp-728-732. [25] Zarei, Mehdi. “Reverse AODV routing protocol extension using learning Automata in ad hoc networks.” Computer, Control and Communication, 2009. IC4 2009. 2nd International Conference on. IEEE, 2009. [26] Vahid Hosseini, Majid Taghipoor, “A Novel Method of Routing for MANETs with Considering the Energy by Learning Automata” World Applied Sciences Journal 17 (1): 113-118, 2012, ISSN 1818-4952, IDOSI Publications, 2012 [27] Arnrita Bose Paul, Shantanu Konwar,Upola Gogoi, Angshuman Chakraborty, Nilufar Yeshrnin, Sukurnar Nandi, “Implementation and Performance Evaluation of AODV in Wireless Mesh Networks using NS-3”, 2010 2nd International Conforence on Education Technology and Computer (ICETC) [28] Ghorbani, Mahdi, Ali Mohammad Saghiri, and Mohammad Reza Meybodi. “A novel adaptive version of AODV routing protocol based on learning automata utilizing cognitive networks concept.”, Technical Journal of Engineering and Applied Sciences, ISSN 20510853, 2013. 3/3/2014
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