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Improving the Performance of Probabilistic

Flooding in Mobile Ad Hoc Networks (MANETs)

- Muneer Bani Yassein
- Department of Computer Science
- masadeh_at_just.edu.jo
- muneer_at_dcs.gla.ac.uk

Outline

- Mobile Ad Hoc Networks (MANETs)
- Broadcasting and its Importance
- Common Problems of Broadcasting in MANETs
- Related Work on and Limitations
- Motivation
- Proposed Contributions
- Plan of Work and Structure of the Thesis
- Conclusions

Mobile Ad Hoc Networks (MANETs)

- A set of wireless mobile nodes, which communicate

without relying on any pre-existing

infrastructure. - self-organizing and self-administrating without

deploying any infrastructure. - mobile nodes communicate with each other using

multi-hop wireless links. - Topology changes could occur randomly, rapidly

and frequently

Potential use communication in battlefield,

home networking, temporary local area networks,

disaster recovery operations, group

communication.

Important Issues

- What is Broadcasting
- Broadcasting is a fundamental operation in

MANETs, a source sends the same message to all

the network nodes. In the one-to-all model, a

transmission by a given node reach all nodes

that are within its transmission radius. - Characteristics
- Spontaneous
- Unreliable
- No ACK required . ACK may cause additional medium

contention.

Why Broadcasting?

- Broadcasting has many important uses, and several

MANET protocols assume the availability of an

underlying broadcast service. - Applications which make use of broadcasting

include - Paging a particular host
- Finding a route to particular host, It can also

be used for route discovery in routing protocols.

E.g., a number of MANET routing protocols such as

Dynamic Source Routing (DSR), Ad Hoc on Demand

Distance Vector (AODV), Zone Routing Protocol

(ZRP), and Location Aided Routing (LAR) use

broadcasting to establish routes - One of the first proposed mechanisms is blind

flooding.

What is Blind Flooding ?

- Blind Flooding
- Node transmits a message to all neighbours. Each

node then re-transmits the message until the

message has been propagated to the entire

network. - Straightforward flooding is usually costly and

results in serious redundancy and collisions in

the network. Such a scenario is often referred to

as the broadcast storm problem.

Common Problems

- Redundant retransmission
- Host rebroadcasts packet although neighbors may

already have it. - Contention
- Simultaneous rebroadcast attempts by neighbours.
- Rather obvious the more crowded the area, the

more the contention - Collision
- No Request to Send/Clear to send (RTS/CTS)

scheme - No CD, entire packet transmitted anyways

Related Work and Limitations

- Ni et al. have classified broadcasting schemes

into - Probabilistic scheme
- Rebroadcast the packet with the fixed chosen

probability - Counter-based scheme
- Rebroadcast if the number of received duplicate

packets is less than a threshold - Distance-based scheme
- Uses the relative distance between nodes to make

the decision - Location-based scheme
- Based on pre-acquired location information of

neighbors - 5. Neighbor Based scheme
- a) Cluster-based.
- Only cluster heads and gateways forward

again - b) selecting forwarding

neighbours - S.-Y. Ni, Y.-C. Tseng, Y.-S. Chen, and J.-P.

Sheu, The broadcast storm problem in a mobile ad

hoc network, - Wireless Networks, vol. 8, no. 2, pp.153-167,

2002

Related Work and Limitations

- The counter-based scheme does provide significant

savings when a small threshold C (such as 2) is

used. Unfortunately, the reachability degrades

sharply in a sparse network when this parameter

is used. Increasing the value of C will improve

reachability, but, saved rebroadcasts suffer.

Tseng et al have proposed an adaptive counter

based scheme in which each node can dynamically

adjust its threshold C based on neighbourhood

status. - In the distance-based scheme and location-based

scheme, it is assumed that each node is equipped

with a positioning device such as GPS which is

another overhead - In selecting forwarding neighbours, the goal is

to minimize the number of relay points. The

computation of a multipoint relay set with

minimal size is NP-complete problem, - Y.-C. Tseng, S.-Y. Ni, E.-Y. Shih, Adaptive

approaches to relieving broadcast storm in a

wireless multihop mobile - ad hoc network, IEEE Transactions on Computers,

vol. 52, no 5, 2003.

Related Work and Limitations

- Tseng et al. have proposed a simple probabilistic

flooding scheme. - ? This scheme has poor reachability and is

inefficient, especially in topologies with a low

density. In fact, this approach is static as

each mobile node has the same rebroadcast

probability, regardless of its number of

neighbours. - S.-Y. Ni, Y.-C. Tseng, Y.-S. Chen, and J.-P.

Sheu, The broadcast storm problem in a mobile ad

hoc - network, Wireless Networks, vol. 8, no. 2,

pp.153-167, 2002

Related Work and Limitations

- ? Cartigny and Simplot have described a

probabilistic scheme and the probability p of a

node retransmitting a message is computed from

the local density n (i.e. the number of

neighbours) and a fixed value k for the

efficiency parameter to achieve the reachability

of the broadcast - Zhang and Dharma have described dynamic

probabilistic scheme. They use a combination of

probabilistic and counter-based approaches. - J. Cartigny and D. Simplot. Border node

retransmission based probabilistic broadcast

protocols in ad-hoc networks.

Telecommunication Systems, vol. 22, no 14, pp.

189204, 2003. - Qi Zhang and Dharma P. Agrawal , Dynamic

probabilistic broadcasting in MANETs, J. Parallel

Distrib. Comput. Vol 65, pp. 220-233, 2005

Motivation

- The broadcast storm problem can be avoided by

providing efficient broadcast algorithms that aim

to reduce the number of nodes that retransmit the

broadcast packet while still guaranteeing all

nodes receive the packet. My research work

focuses on providing some efficient probabilistic

broadcast algorithms that can dynamically adjust

the broadcast probability to take into account

the current state of the node in one and two

hopes in order to ensure a certain level of

control over re-broadcasting, and thus helps to

improve reachability and saved rebroadcasts to

reduce the broadcast redundancy in MANETs.

Motivation

- There has not been so far any attempt to analyse

its performance behaviour in a MANET environment.

For example, The effects of a number of

important system parameters in a MANETs,

including node speed, pause time, traffic load,

and node density on the performance of

probabilistic flooding.

Proposed Contributions

- Performance Analysis of Probabilistic Flooding
- Analysis of Topological Characteristic
- The Adjusted Probabilistic Flooding Algorithm
- The Highly Adjusted Probabilistic Flooding

Algorithm

Ch3 Proposed Contributions

- Analysis of Probabilistic Flooding
- There has not been so far any attempt to analyse

the performance probabilistic - flooding behaviour in MANETs. We are the first

who investigates the effects - of a number of important parameters in a MANET on

the performance of - probabilistic flooding using extensive ns-2

simulations - Speed and Node Pause Time
- Mobility and Density
- Mobility and Traffic Load

M. Bani Yassein, M. Ould-Khaoua, S.

Papanastasiou, On the Performance of

Probabilistic Flooding in Mobile Ad Hoc Networks,

to appear in the Proc. of International Workshop

on Performance Modelling in Wired, Wireless,

Mobile Networking and Computing in conjunction

with 11th (ICPADS-2005),IEEE Computer Society

Press, 20 - 22 July 2005.

Simulation Experiments

1- We have studied the effects of mean node speed

and pause time of the random waypoint model on

the probabilistic flooding in MANETs. We have

done this through simulation by using NS-2

packet level simulator v.2.27. Assumptions Eac

h mobile node is equipped with CSMA/CA (carrier

sense multiple access with collision avoidance)

which can access the air medium following the

802.11 protocol.

Simulation Experiments

Input parameters Transmitter range

250 m Bandwidth

2Mbits Interface queue length

50 packets Simulation time

900 sec No of node

25,50,75,100 Max.

Speed 1,5,10,20

m/sec Packet size

512 bytes Topology size

600X600 m2

Pause time 0 ,20

,40sec

Simulation Experiments

- Performance metrics
- Saved Rebroadcasts (SRB) is computed as (r -

t)/r where r is the number of nodes receiving the

- broadcast message, and t the number of nodes that

actually transmitted the message. - Reachability (RE) is the percentage of mobile

nodes receiving the broadcast message divided by

the - total number of mobile nodes that are reachable,

directly or indirectly.

Simulation Experiments

Fig. 1 Effects of speed on saved rebroadcast

using probabilistic flooding with pause time 0 .

Fig. 2 Impact of speed on reachability with

with pause time 0 .

done.

Simulation Experiments

Fig. 3 Effects of pause time on saved

rebroadcast using probabilistic flooding with

speed 1m/s.

Fig. 4 Effects of pause time on saved

rebroadcast using Probabilistic flooding with

speed 5 m/s

don1

Mobility and Density

2- Density is the number of network nodes per

unit area for a given transmission range. In this

work, we investigate the effect of density under

different mobility and effectiveness of

probabilistic flooding. In particular, using the

popular random waypoint model we study through

simulation the effects of varying node density

with different mean node speed parameters on two

important flooding metrics, namely reachability

and saved rebroadcasts.

Simulation Experiments

Fig. 5 Impact of density on reachability for

different network densities with node speed of

10 m/s..

Fig. 6 Impact of density on reachability for

different network densities with node speed 1

m/s..

done.

Simulation Experiments

Fig. 7 Impact of density on saved rebroadcast

for different Network densities with node speed

of 10 m/s..

Fig. 8 Impact of density on saved rebroadcast

for different network densities with node speed

1 m/s.

done.

Mobility and Traffic Load

3- Traffic load is the number of broadcast

request injected into the network per second , we

investigate the effect of traffic load under

different mobility and effectiveness of

probabilistic flooding. In particular, using the

popular random waypoint model we study through

simulation the effects of varying traffic load

with different mean node speed parameters on two

important flooding metrics, namely reachability

and saved rebroadcasts.

Simulation Experiments

Figure 9 The impact of traffic load on

reachability at three broadcasts/second for

different node speeds

Figure 10 The impact of load on reachability at

one broadcast/ second for different node

speedtime.

done.

Simulation Experiments

Fig. 11 Impact of load on saved rebroadcast 3

messages/s for node speeds 1, 5, 10, and 20 m/s.

Figure 12 The impact of load on reachability at

one broadcast/ second for different node

speedtime.

done.

Proposed Contributions

- Analysis of Topological Characteristic
- We present the analysis of average number of

neighbour to provide the basis - for the selection of the value of p. Figures

13-14 show the minimum, average and - maximum number of neighbours for different node

number with the network - area of 600 m 600 m, 800 m 800 m, and 1000 m

1000 m, respectively. - The higher is the maximum number of neighbours,

the denser the network is. - Lower the minimum number of neighbours is sparser

the network is. From - the minimum, average and maximum number of

neighbours, we can estimate - the value of rebroadcast probability.

M. Bani Yassein, M. Ould-Khaoua, S.

Papanastasiou, On the Performance of

Probabilistic Flooding in Mobile Ad Hoc Networks,

to appear in the Proc. of International Workshop

on Performance Modelling in Wired, Wireless,

Mobile Networking and Computing in conjunction

with 11th (ICPADS-2005),IEEE Computer Society

Press, 20 - 22 July 2005.

Simulation Experiments

Figure 13 Maximum number of neighbors vs.

number of nodes

Figure14 Average number of neighbors vs. number

of nodes .

done.

New Proposed Algorithms

Dynamic Probabilistic Flooding Using One Hop

Neighbours

The Adjusted Probabilistic Flooding Algorithm

- The adjusted probabilistic flooding algorithm

operates as follows. On hearing a broadcast

message m at node X, the node rebroadcast a

message according to a high probability if the

message is received for the first time, and the

number of neighbours of node X is less than

average number of neighbours typical of its

surrounding environment. Hence, if node X has a

low degree (in terms of the number of

neighbours), retransmission should be likely.

Otherwise, if X has a high degree its rebroadcast

probability is set low

Adjusted Probabilistic Flooding

- Protocol receiving ()
- On hearing a broadcast packet m at node X
- Get the Broadcast ID from the message n3

average number of neighbour - Get degree n of a node X (number of neighbours of

node X) - If packet m received for the first time then
- If n lt n3 then
- Node X has a low degree the

high rebroadcast probability pp1 - Else If ngt n3 then
- Node X has a high degree

the low rebroadcast probability pp2 - End if
- Generate a random number RN over 0, 1.
- If RN lt p rebroadcast the received message

otherwise, drop it

Simulation Experiments

Figure 15 saved rebroadcast of three broadcast

schemes against network density with node speed

10m/s.

Figure 16 The reachability of three broadcast

algorithms

done.

New Proposed Algorithms

Dynamic Probabilistic Flooding Using One Hope

Neighbours

Highly Adjusted Probabilistic Flooding

- The highly adjusted probabilistic flooding

algorithm operates as follows when a broadcast

message is received for the first time by a node,

it is rebroadcast according to a probability

distribution which depends on the nodes degree.

The message is re-broadcast with probability

which depends on the nodes degree if the node

is inside a sparse node population. Similarly, it

is re-broadcast with the probability is if the

degree denotes a medium density node population.

Finally, in dense node populations the node will

rebroadcast the message with a lower probability.

Sparse, medium and dense populations correspond

to minimum, average and maximum threshold values

which we will determine through simulation..

Highly Adjusted Probabilistic Flooding

- Protocol receiving ()
- On hearing a broadcast packet m at node X
- Get the Broadcast ID from the message n1

minimum numbers of neighbour,n2 maximum number of

neighbour and n3 average number of neighbour all

are threshold values - Get degree n of a node X (number of neighbours of

node X) - If packet m received for the first time then
- If n lt n1 then
- Node X has a low degree the

high rebroadcast probability pp1 - Else If n gt n1 and n lt n2

or ngt n3 and n ltn2 then - Node X has a medium

degree the medium rebroadcast - probability pp2
- Else If ngt n2 then
- Node X has a high degree

the low rebroadcast probability pp3 - End if
- Generate a random number RN over 0, 1.
- If RN lt p rebroadcast the received message

otherwise, drop it

Dynamic Probabilistic Flooding Using two Hope

Neighbours

Dynamic Probabilistic Flooding Using two Hope

Neighbours

The Adjusted Probabilistic Flooding Algorithm

Highly Adjusted Probabilistic Flooding