Adjusted Counter-Based Broadcast for Wireless Mobile Ad hoc Networks

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Adjusted Counter-Based Broadcast for Wireless Mobile Ad hoc Networks

Sara Omar al-Humoud

Department of Computing Science

University of Glasgow

Supervisors: Dr. L.M. Mackenzie and Dr. M. Ould-Khaoua

First Year Viva

Outline

2.Related work

3.Motivations and objectives

4.Thesis Statement

6.Tentative work plan

7.Thesis structure

MANETs

Routing

Broadcasting

Characteristics & Limitations

Applications

1.Introduction Proactive

Reactive

Hybrid

Probabilistic

Algorithms

Methodology

Deterministic

5.Contributions

Counter Related

Simulation study

Measures

Assumptions

ACB

HACB

4

Introduction

• MANETs?

• Decentralized

• Dynamic topology

• Radio communication

• Energy constrained

MANETs Characteristics and Limitations

5

Introduction

• Military applications

• Collaborative and distributed computing

• Emergency operations

• Inter-Vehicle Communications

• Hybrid wireless networks

MANET Applications

Outline

2.Related work


4.Thesis Statement


7.Thesis structure

MANETs

Routing

Broadcasting


Applications


Reactive

Hybrid

Probabilistic

Algorithms

Methodology

Deterministic

5.Contributions

Counter Related

Simulation study

Measures

Assumptions

ACB

HACB

7

Introduction

• Table Driven Routing Protocols (Proactive)– Destination-Sequenced Distance-Vector Routing (DSDV)

– Clusterhead Gateway Switch Routing (CGSR)

– Global state routing (GSR)

– Source-tree adaptive routing (STAR)

– Fisheye state routing (FSR)

– Distance routing effect algorithm for mobility (DREAM)

– Optimised link state routing (OLSR)

– Topology broadcast reverse path forwarding (TBRPF)

– Wireless Routing Protocol (WRP)

Routing in MANET

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Introduction

• Source-initiated On-demand Routing (reactive)– Ad-hoc On-Demand Distance Vectoring (AODV)– Dynamic Source Routing (DSR) – Temporally-Ordered Routing Algorithm (TORA)– Associativity Based Routing (ABR) – Light-weight mobile routing (LMR) – Routing on-demand acyclic multi-path (ROAM) – Relative distance micro-discovery ad hoc routing (RDMAR) – Location-aided routing (LAR) – Ant-colony-based routing algorithm (ARA) – Flow oriented routing protocol (FORP) – Cluster-based routing protocol (CBRP) – Signal Stability Routing (SSR)

Routing in MANET

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Introduction

• Hybrid routing protocols– Zone routing protocol (ZRP)

– Zone-based hierarchical link state (ZHLS)

– Scalable location update routing protocol (SLURP)

– Distributed spanning trees based routing protocol (DST)

– Distributed dynamic routing (DDR)

Routing in MANET

Outline

2.Related work


4.Thesis Statement


7.Thesis structure

MANETs

Routing

Broadcasting


Applications


Reactive

Hybrid

Probabilistic

Algorithms

Methodology

Deterministic

5.Contributions

Counter Related

Simulation study

Measures

Assumptions

ACB

HACB

11

Introduction

• Discovering neighbours

• Collecting global information

• Addressing

• Helping in multicasting and Unicast

– Route discovery, route reply

– in on-demand routing protocols like DSR, AODV to broadcast

control messages.

• Conventionally broadcast is done through flooding

Broadcasting Applications

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Introduction

• Flooding may lead to– Redundancy

x Consume limited bandwidth

– Contentionx Increase in delay

– Collisionx High packet loss rate

– Broadcast storm problem!

Broadcasting Applications

0

10

20

30

40

50

60

70

80

90

0 1 2 3 4 5 6 7 8 9 10

Number of Nodes

Nu

mb

er o

f M

essa

ges

f(n) = n2 – 2n + 1

Outline


4.Thesis Statement


7.Thesis structure

MANETs

Routing

Broadcasting


Applications


Reactive

Hybrid

Probabilistic

Algorithms

Methodology

Deterministic

5.Contributions

Counter Related

Simulation study

Measures

Assumptions

ACB

HACB

2.Related work

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Related work

• Probability-based– Rebroadcast with probability P

• Counter-based– Rebroadcast if the node received less

than Cth copies of the msg

• Location-based– Rebroadcast if the area within the

node’s range that is yet to be covered by the broadcast > Ath

• Distance-based– Rebroadcast if the node did not receive

the msg from another node at a distance less than Dth

Probabilistic Broadcasting Methods

Receiver rebroadcast

decision

Simple Implementation RD based on instantaneous information from broadcast msgs

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Related work

• Reliable Broadcast

• Self-pruning

• Scalable broadcasting

• Dominant Pruning

• Cluster-based

Deterministic Broadcasting Methods

Sender rebroadcast

decision

Elaborate Implementation Rebroadcast decision based on neighbourhood study

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Related work

1. Counter-based broadcast

– Adaptive Counter-based broadcast

2. Color-based broadcast

3. Distance-aware counter-based broadcast

Counter-Based related Broadcasting Methods

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Related work

1- Counter-based broadcast• Scheme:

– When receiving a message: • a counter c is set to keep track of number of duplicate messages

received. • Random Assessment Delay (RAD) timer is set.

• When the RAD timer expires the counter is tested against a fixed threshold value C, broadcast is inhibited if c ≥ C.

• Remarks:– The threshold is fixed: scores high efficiency only when used with homogeneous density networks; when the network is sparse a high

threshold is used and when dense low threshold value.

Adaptive Counter-based broadcast• Threshold = C(n) where n is the number of neighbors

• The function C(n) is undefined yet


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Related work

2- Color-based broadcast• Scheme:

– each broadcast node selects a color from a set of η colors which it writes to a color-field present in the broadcast message.

– all nodes which hear the message rebroadcast it unless they have heard all η colors by the time a random timer expires.

• Remarks: – With the added overhead, we may end with a bad case: – E.g. a node receive 3 messages with only c1 and this node will still

rebroadcast the message.


c1 c2 c3

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Related work

3- Distance-aware counter-based broadcast• Scheme:

– Similar to the counter-based scheme in addition to:• Two distinct RADs are applied to the border and interior nodes• SRAD to border nodes• LRAD to interior nodes

• Remarks: – The use of distance as an enhancement factor to the original counter-based

may be degraded knowing that real networks transmissions will be affected by obstacles.


Outline

2.Related work

4.Thesis Statement


7.Thesis structure

MANETs

Routing

Broadcasting


Applications


Reactive

Hybrid

Probabilistic

Algorithms

Methodology

Deterministic

5.Contributions

Counter Related

Simulation study

Measures

Assumptions

ACB

HACB


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Motivations and objectives

• Area-based scheme – Rely on GPS

• Deterministic approaches – High time overhead– High number of control messages exchanged to broadcast

one packet– it demands accurate neighbourhood information and cannot

ensure the coverage with outdated topology information.

Related work limitations - overhead

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• Counter-based schemes– Fixed counter-based

• Threshold = c

– Adaptive counter-based• Threshold = C(n) where n is the number of neighbors• The function C(n) is undefined yet

– Color-based• Used with homogeneous density networks• Rebroadcast when many duplicates received by the a partial set

of colors

– Distance-aware counter-based• Distance estimated by signal strength• Not considering obstacle existence

Related work limitations - overhead

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• Adjusted Counter-Based (ACB)

• Highly Adjusted Counter-Based (HACB).

• Counter-Based AODV

• Adjusted Counter-Based AODV

• Highly Adjusted Counter-Based AODV

• Study the superiority of our proposed schemes to

the probabilistic broadcasting

Objectives

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• Adjusted Counter-Based (ACB) Broadcast– Based on the original counter-based scheme

– Add the ability to decide the counter according to neighbourhood density

– Neighbourhood density is divided according to the Average number of neighbours into:

• Density1: Sparse

• Density2: Dense

Objectives

Avg

Sparse DenseNeighbourhoodDensity

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• Highly Adjusted Counter-Based (HACB) Broadcast– Neighbourhood density is divided according to the Max and

Min number of neighbours into: • Density1: Sparse

• Density2: Medium

• Density3: Dense

– Adding the average as a discriminator will divide the neighbourhood density into four groups and will reveal a better adjustment

Objectives

Sparse DenseMedium

Min Max

NeighbourhoodDensity

Outline

2.Related work



7.Thesis structure

MANETs

Routing

Broadcasting


Applications


Reactive

Hybrid

Probabilistic

Algorithms

Methodology

Deterministic

5.Contributions

Counter Related

Simulation study

Measures

Assumptions

ACB

HACB

4.Thesis Statement

Thesis Statement

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Go to Thesis Statement

Cont

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Thesis Statement

• T1. While most previous studies have used a fixed counter threshold for rebroadcasting irrespective of the node status, this research proposes two new counter-based algorithms that dynamically adjust the counter threshold as per the node’s neighbourhood distribution and node movement using one-hop neighbourhood information. Employing neighbourhood information in counter threshold decision will enhance the existing fixed counter-based flooding in terms of reachability, saved rebroadcast and delay.

T1

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Thesis Statement

• T2. The Adjusted Counter-Based (ACB) uses the average number of neighbour to dynamically adjust the threshold value to adapt to either sparse or dense network. Moreover, when incorporated in the Ad hoc On-Demand Distance Vector (AODV) routing protocol; one of the well-known and widely studied routing protocols over that past few years, ACB will perform better than both standard and fixed counter-based AODV protocols.

T2

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Thesis Statement

• T3. The Highly Adjusted Counter-Based (HACB) uses three items of derived neighbourhood information the maximum, the minimum in addition to the average number of neighbours to dynamically adjust the threshold value. HACB is better than ACB however, perhaps with an added complexity. Moreover, when incorporated in the AODV routing protocol; HACB will perform better than both standard and fixed counter-based AODV protocols. Additionally, it will perform better than probabilistic and adjusted probabilistic AODV routing protocols.

T3

Outline

2.Related work


4.Thesis Statement


7.Thesis structure

MANETs

Routing

Broadcasting


Applications


Reactive

Hybrid

Probabilistic

Algorithms

Methodology

Deterministic

5.Contributions

Counter Related

Simulation study

Measures

Assumptions

ACB

HACB

Contributions

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Algorithms

Cont

Adjusted_Counter_Based_Broadcast_Algorithm

Pre: avg is average number of neighborsa broadcast packet m at node X is heard

Post: rebroadcast the packet or drop it, according to the algorithm

Get the Broadcast IDGet degree n of node XSet RADc = 1If n < avg then

Sparse network

threshold = c1;

Else

Dense network

threshold = c2;

End if While (RAD) Do

If (same packet heard)

Increment c

End whileIf (counter > threshold)

drop packet

exit algorithm

End IfSubmit the packet for transmission End Adjusted_Counter_Based_Broadcast_Algorithm

Highly_Adjusted_Counter_Based_Broadcast_Algorithm

Pre: avg is average number of neighborsmin is minimum number of neighborsmax is maximum number of neighborsa broadcast packet m at node X is heard

Post: rebroadcast the packet or drop it, according to the algorithm

Get the Broadcast IDGet degree n of node XSet RADc = 1

If n < min then threshold = c1;

Else

If n < max then

threshold = c2;

Else

threshold = c3;

End if

End if While (RAD) Do

If (same packet heard)

Increment c

End whileIf (counter > threshold)

drop packet

exit algorithm

End If

Submit the packet for transmission

End Highly_Adjusted_Counter_Based_Broadcast_Algorithm

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Contributions

• Simulation study

– First study considers a static network, using a Null MAC to evaluate and compare our proposed algorithms to simple flooding, the worst case.

– Second study considers the network under two sources of instability:

• Mobility: changeable node speed.

• Congestion: variable quantities of packets originated per second.

– Third study considers a combination of variable node density, node speed, and congestion.

Methodology

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Contributions

• Reachability

– r/e, where r is the number of hosts receiving the broadcast packet and e is the number of mobile hosts that are reachable, directly or indirectly, from the source host .

• Saved Rebroadcast

– (r − t)/r, where r is the number of hosts receiving the broadcast message, and t is the number of hosts that actually transmitted the message.

• Average latency

– the interval from the time the broadcast was initiated to the time the last host finished its rebroadcasting.

Performance measures

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Contributions

• Simulate a university campus with the following assumptions: – Existence of pedestrians and vehicles equipped with

IEEE 802.11 wireless transceivers– Speed:

• walk speed of 1 m/sec with appropriate pose times to vehicles having a maximum speed of 70 km/hour

– Area:• First study: open unobstructed • Second study: open with obstacles

– Mobility: • First study: Random way point (RWP) mobility model• Second study: Realistic Mobility Model

– We assume that a host can detect duplicate broadcast messages.

– Assume that nodes have sufficient power to function properly throughout the simulation time

Assumptions

Contributions

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Simulation parameters

Simulation parameter Value

Simulator ns-2 version (2.31)

Network Area 1500 x 1500 meter

Transmission range 100 meter

Data Packet Size 64 bytes

Node Max. IFQ Length 50

Simulation Time 500 sec

Pause Times 0, 10, 20, 40 sec

Number of Trials 10

MAC layer protocol IEEE 802.11

Mobility model Random waypoint model, Realistic Mobility Model

Traffic Type CBR (Constant Bit Rate)

Channel Bandwidth 2Mb/sec

Confidence Interval 95%

Number of Nodes 20, 40, 50, 60, 80, 100

Packet Rate 10, 20, 40, 60, 80 100, 150 packets per sec

Counter threshold pairs ACB: [2,3], [2,4], [3,4], HACB [2,3,4]

Node Max. Speed 1, 5, 10, 15, 20 m/sec

Outline

2.Related work


4.Thesis Statement

7.Thesis structure

MANETs

Routing

Broadcasting


Applications


Reactive

Hybrid

Probabilistic

Algorithms

Methodology

Deterministic

5.Contributions

Counter Related

Simulation study

Measures

Assumptions

ACB

HACB


Tentative work plan

40

Assumptions

ID Task End Date

1 Submit my first year report July /2007

2 Experiment with ns2 simulator August /2007

3 Develop adaptive counter-based (ACB) scheme Nov /2007

4 Compare ACB with counter-based (CB) scheme Dec /2007

5 Develop Highly adaptive counter-based (HACB) scheme Jan /2008

6 Develop Adaptive counter-based AODV Mar /2008

7 Develop Highly Adaptive counter-based AODV May /2008

8 Develop a realistic mobility model for ACB and HACB June /2008

9 Submit my second year report July /2008

10 Write up for my Ph.D. dissertation August /2008

11 Submit my dissertation July /2009

12 Do my final year Viva August /2009

14 Thesis correction and final submission Oct /2009

Outline

2.Related work


4.Thesis Statement


7.Thesis structure

MANETs

Routing

Broadcasting


Applications


Reactive

Hybrid

Probabilistic

Algorithms

Methodology

Deterministic

5.Contributions

Counter Related

Simulation study

Measures

Assumptions

ACB

HACB

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Thesis structure

Chapter 1: IntroductionMANET

Broadcasting

Related work

Motivation

Contribution

Thesis statement

Chapter 2: Background and related workIntroduction

Fixed counter-based broadcasting

Chapter 3: Adjusted Counter-based BroadcastingIntroduction

Adjusted counter-based broadcasting

Analysis on Adjusted counter-based

Comparison between Fixed and Adjusted Counter-based

Chapter 4: Highly Adjusted Counter-based BroadcastingIntroduction

Highly Adjusted counter-based broadcasting

Analysis on Highly Adjusted counter-based

Comparison between Adjusted and Highly Adjusted Counter-based

Chapter 6: Performance Evaluation of AODV with Adjusted Counter-based Route Discovery

Chapter 7: Performance evaluation of Counter-Based with Real mobility model

Chapter 8: Conclusions and Future Work

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Questions

EAC

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(a) (b)

Adjusted Counter-Based Broadcast for Wireless Mobile Ad hoc Networks

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