Intergrating Learing Automata (LA) with Wireless Mesh Network (WMN). How LA can be incorporated in different layer of network
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An Adaptive Method for Load Balancing using Learning Automata of Wireless Mesh Network Rohit Kumar Das M.Tech (IT), 3 rd Sem Roll- 031312 No- 36320137 Assam University
Transcript
1. Rohit Kumar Das M.Tech (IT), 3rd Sem Roll- 031312 No36320137
Assam University
2. Outline Introduction Literature Survey Related Work
Motivation Backbone of Project Proposed Method Experimental Result
Conclusion References 3/3/2014
3. Outline Introduction Literature Survey Related Work
Motivation Backbone of Project Proposed Method Experimental Result
Conclusion References 3/3/2014
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. 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. 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. Outline Introduction Literature Survey Related Work
Motivation Backbone of Project Proposed Method Experimental Result
Conclusion References 3/3/2014
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. 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 nodes 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. 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. 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. 5. Learning automaton for routing incoming calls [18].
Virtual link length Combination of packets Reduce packet delay
3/3/2014
13. Outline Introduction Literature Survey Related Work
Motivation Backbone of Project Proposed Method Experimental Result
Conclusion References 3/3/2014
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. 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. 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. Problems Domain Route Just flapping consider their load
Associate each node to its nearest gateway Switching to another
domain 3/3/2014
18. Figure 1: Problem Layer By Layer [20] 3/3/2014
19. Outline Introduction Literature Survey Related Work
Motivation Backbone of Project Proposed Method Experimental Result
Conclusion References 3/3/2014
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. Outline Introduction Literature Survey Related Work
Motivation Backbone of Project Proposed Method Experimental Result
Conclusion References 3/3/2014
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. 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. 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. 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. 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. Integrating LA Stochastic automaton: Six tuple { x, , , p,
A, G} 3/3/2014
29. Integrating LA (Conti.) Environment: i/p -> (n) =
{1,.,r} o/p -> x Response [0,1] Penalty Probability Ci (i = 1,r)
3/3/2014
30. Integrating LA (Conti.) Learning automaton: Operates in a
random environment Figure 5: Learning automaton 3/3/2014
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. 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. Operation (Conti) During each cycle: i is chosen with
probability pi Environment response with Ci , update p. Next action
chosen according p(n+1) 3/3/2014
34. Learning Automata Feedback Connection of Automaton and
Environment Figure 6: Feedback mechanism of LA 3/3/2014
35. Reinforcement Scheme Choosing the best response based on
the rewards or punishments token from environment Lower the (n) the
more favorable the response. General Scheme: Pi(n) - ( 1-(n) ) gi(
P(n) ) + (n) hi( P(n) ), if a(n)ai Pi(n+1) = Pi (n) + ( 1- (n) ) ji
gj( P(n) ) - (n) ji hj( P(n) ), if a(n)=ai 3/3/2014
36. Reinforcement Schemes Different Scheme according to
selection made from functions are : 1. The linear RewardPenalty
(LRP) scheme 2. The linear RewardInaction (LRI) scheme 3. Nonlinear
schemes 3/3/2014
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. 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. AODV Properties The route table stores: The basic message
set consists of: RREQ Route Request RREP Route Reply RERR Route
Error HELLO For link status monitoring 3/3/2014
40. Re-active routing AODV(RFC3561) A wants to communicate with
B 3/3/2014
41. Re-active routing AODV(RFC3561) A floods a route request
3/3/2014
42. Re-active routing AODV(RFC3561) A route reply is unicasted
back 3/3/2014
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. 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. 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. 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. 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. 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. 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. Outline Introduction Literature Survey Related Work
Motivation Backbone of Project Proposed Method Experimental Result
Conclusion References 3/3/2014
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. 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
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. 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. Flow Chart Figure 7: Flow chart for Proposed Model
3/3/2014
57. Outline Introduction Literature Survey Related Work
Motivation Backbone of Project Proposed Method Experimental Result
Conclusion References 3/3/2014
59. Figure 9: Modified AODV with Performance measurement
3/3/2014
60. Outline Introduction Literature Survey Related Work
Motivation Backbone of Project Proposed Method Experimental Result
Conclusion References 3/3/2014
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. 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. Outline Introduction Literature Survey Related Work
Motivation Backbone of Project Proposed Method Experimental Result
Conclusion References 3/3/2014
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