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Improving the Performance of Probabilistic Flooding in Mobile Ad Hoc Networks (MANETs)

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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 – PowerPoint PPT presentation

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Title: Improving the Performance of Probabilistic Flooding in Mobile Ad Hoc Networks (MANETs)


1
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

2
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

3
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.
4
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.

5
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.

6
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.

7
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

8
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

9
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.

10
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

11
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

12
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.

13
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.

14
Proposed Contributions
  • Performance Analysis of Probabilistic Flooding
  • Analysis of Topological Characteristic
  • The Adjusted Probabilistic Flooding Algorithm
  • The Highly Adjusted Probabilistic Flooding
    Algorithm

15
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.
16
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.
17
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
18
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.

19
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.
20
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
21
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.
22
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.
23
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.
24
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.
25
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.
26
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.
27
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.
28
Simulation Experiments
Figure 13 Maximum number of neighbors vs.
number of nodes
Figure14 Average number of neighbors vs. number
of nodes .
done.
29
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

30
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

31
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.
32
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..

33
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

34
Dynamic Probabilistic Flooding Using two Hope
Neighbours
Dynamic Probabilistic Flooding Using two Hope
Neighbours
The Adjusted Probabilistic Flooding Algorithm
Highly Adjusted Probabilistic Flooding
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