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.
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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
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fixed
infrastructure
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.
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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
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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
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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].
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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
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12. 5.
Learning automaton for routing incoming
calls [18].
Virtual link length
Combination of packets
Reduce packet delay
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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.
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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.
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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.
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17. Problems Domain
Route
Just
flapping
consider their load
Associate
each node to
its nearest gateway
Switching
to another
domain
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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.
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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
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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
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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
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26. 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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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
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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]
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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.
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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)
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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)
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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
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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
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64. References
[1] Subir Kumar Sarkar, T G Basavaraju, C Puttamadappa, “Ad-hoc Mobile Wireless
Networks Principles, Protocol and Applications” Auerbach Publications, ISBN
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[2] Ram Ramanathan and Jason Redi, “A Brief Overview of Ad-hoc Networks: Challenges
and Directions”, IEEE Communication Magazine 50th Anniversary Commemorative
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[3] Bing He, Dongmei Sun, Dharma P. Agrawal “Diffusion based Distributed Internet
Gateway Load Balancing in a Wireless Mesh Network,” In proceedings of IEEE
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[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
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[8] Kumpati S. Narendra, And M. A. L. Thathachar, “Learning Automata - A Survey”, IEEE
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[9] M.S. Obaidat, G.I. Papadimitriou, A.S. Pomportsis,“Efficient fast learning automata”,
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65. References
[11] Deepti Nandiraju, Lakshmi Santhanam, Nagesh Nandiraju, and Dharma P. Agrawal,
“Achieving Load Balancing in Wireless Mesh Networks through Multiple Gateways”,
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[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
Automata Rate Adaptation for IEEE 802.11 Networks” IEEE Transactions On Parallel
and Distributed Systems, Vol. 19, No. 11, November 2008
[16] Antonios Sarigiannidis, Petros Nicopolitidis, Georgios Papadimitriou, “Using Learning
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 &
Technology (IJERT) ISSN: 2278-0181, Vol. 2 Issue 6, June – 2013
[18] Anastasios A. Economides, “Learning Automata Routing In Connection-Oriented
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[19] Anastasios A. Economides, “Real-Time Traffic Allocation Using Learning Automata”,
International Conference on Systems, Man and Cybernetics, pp. 3307- 3312, IEEE,
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[20] Fry, Michael, et al. “Challenge identification for network resilience.” Next Generation
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[21] Nicopolitidis, Petros, et al. “Adaptive wireless networks using learning
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[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.
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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).