Improving the Performance of Broadcast Flooding in
Mobile Ad Hoc Networks (MANETs)
Muneer Bani Yassein
Department of Computer Science
masadeh@just.edu.jo
7/26/2016
1
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 Project.
 Conclusions
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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.
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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.
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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.
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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.
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I.
Flooding is a common mechanism that is used to discover
routes and disseminate data throughout the network.
 Pros:
 Simplicity.
 High delivery ratio.
 Cons :
 Resources consumption.
 Broadcast storm problem:
 Redundancy.
 Contention.
 Collision.
7
Algorithm: Blind Flooding
Protocol receiving ()
On receiving a broadcast packet m at node X do the following:
If packet m received for the first time Then
broadcast (m)
End if
End Algorithm.
Figure 2.3: A description of the blind flooding algorithm.
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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
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Redundant Rebroadcasts
Optimal schedule: 2 transmissions
flooding: 7 transmissions
II.
Mobility is a major factor in MANET.
 The nodes can move anytime, in any direction and at any
speed.
 Mobility of nodes leads to frequent link breakage.
 A breakages of routes need to be reinitiated.
 Routes reinitiations consume network resources.
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Related Work and Limitations
•
Ni et al. have classified broadcasting schemes into
1. Probabilistic scheme
 Rebroadcast the packet with the fixed chosen probability
2. Counter-based scheme
 Rebroadcast if the number of received duplicate packets is less than a threshold
3. Distance-based scheme
 Uses the relative distance between nodes to make the decision
4. 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
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Categorization of Protocols
• Simple Flooding
• Probability Based Methods
 Probabilistic Scheme
 Counter-Based Scheme
• Area Based Methods
 Distance-Based Scheme
 Location-Based Scheme
• Neighbor Based Methods
Neighbor Based Methods
•
•
•
•
•
•
•
Flooding with self-Pruning
Scalable Broadcast Algorithm, SBA
Dominant Pruning
Multipoint Relaying
Ad Hoc Broadcast Protocol, AHBP
CDS-Based Broadcast Algorithm
Lightweight and Efficient Network-Wide
Protocol, LENWB
Related Works
• Another approach : exploit topological
information
– Self-pruning
• Each forwarding node piggybacks the list of neighbors of
itself on outgoing packet
– Dominant-pruning
• Extends the range of neighbor information to two-hop away
neighbors
• Still depend on the periodic hello messages to
collect topological information
– Extra hello messages consume resources and drop
the network throughput in MANETs
MPR (Multipoint Relays)
• Reduce the flooding of broadcast messages
• Set of one-hop neighbors and two-hop neighbors
• To get the information about the one-hop neighbors, most protocols
use some form of HELLO messages periodically
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.
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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
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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 1–4, pp. 189–204, 2003.
Qi Zhang and Dharma P. Agrawal , Dynamic probabilistic broadcasting in MANETs, J. Parallel Distrib. Comput. Vol 65, pp. 220233, 2005
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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 rebroadcasting, and thus helps to improve reachability and saved rebroadcasts to
reduce the broadcast redundancy in MANETs.
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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.
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Proposed Contributions
•
Performance Analysis of Probabilistic Flooding
•
Analysis of Topological Characteristic
•
The Adjusted Probabilistic Flooding Algorithm
•
The Highly Adjusted Probabilistic Flooding Algorithm
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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:
1.
2.
3.
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.
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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:
Each 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.
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Simulation Experiments
Input parameters
Transmitter range
Bandwidth
Interface queue length
Simulation time
No of node
Max. Speed
Packet size
Topology size
Pause time
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250 m
2Mbits
50 packets
900 sec
25,50,75,100
1,5,10,20 m/sec
512 bytes
600X600 m2
0 ,20 ,40sec
25
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.
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Simulation Experiments
RE at 1 m/s
Probability Vs Reachability
Probability Vs Saved Re broadcast
RE at 5 m/s
100
RE at 20 m/s
90
85
80
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Probability
Fig. 2: Impact of speed on reachability
with with pause time 0 .
SRB
RE
SRB at 5 m/s
RE at 10 m/s
95
SRB at 1 m/s
0.97
0.96
0.95
0.94
0.93
0.92
0.91
SRB at 10 m/s
SRB at 20 m/s
0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Probability
Fig. 1: Effects of speed on saved rebroadcast using
probabilistic flooding with pause time 0
.
done.
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Simulation Experiments
Probability Vs Saved broadcast at 1m/s
Probability Vs Saved Broadcasd at 5 m/s
SRB at 20 sec
SRB at 20 sec
0.96
0.95
SRB
SRB
SRB at 0 sec
0.95
0.95
0.94
0.94
0.93
0.93
0.92
0.92
SRB at 0 sec
0.94
0.93
0.92
0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Probability
Fig. 4: Effects of pause time on saved rebroadcast using
Probabilistic flooding with speed 5 m/s
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Probability
Fig. 3: Effects of pause time on saved rebroadcast using
probabilistic flooding with speed 1m/s.
don1
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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.
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Simulation Experiments
Re at 25
Re at 25
100
100
Re at 50
RE at 50
80
RE at 75
60
RE at 100
40
RE
RE
80
Re at 75
60
Re at 100
40
20
20
0
0
0
0.1
0.2 0.3
0.4
0.5 0.6
0.7 0.8
0.9
1
P r obabi l i ty
Fig. 6: Impact of density on reachability for different network
densities with node speed 1 m/s..
0
0.1
0.2
0.3
0.4
0.5 0.6
0.7
0.8
0.9
1
P r obabi l i ty
Fig. 5: Impact of density on reachability for different network
densities with node speed of 10 m/s..
done.
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Simulation Experiments
SRB at 50
0.98
0.96
SRB st 75
0.94
SRB at 100
0.92
0.9
0.88
SRB at 25
1
SRB at 50
0.98
SRB
SRB
SRB at 25
1
SRB at 75
0.96
SRB at 100
0.94
0.92
0.9
0.86
0.88
0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
1
P r obabi l i ty
Fig. 8: Impact of density on saved rebroadcast for different
network densities with node speed 1 m/s.
0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
1
P r obabi l i ty
Fig. 7: Impact of density on saved rebroadcast for different
Network densities with node speed of 10 m/s..
done.
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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.
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Simulation Experiments
RE at 1m/ s
RE at 5m/ s
RE at 5m/ s
RE at 10m/ s
100
40
RE at 20m/ s
80
RE
60
RE at 10m/ s
100
RE at 20m/ s
80
RE
RE at 1m/ s
60
40
20
20
0
0
0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
1
Pro b ab ilit y
Figure 10 : The impact of load on reachability at one
broadcast/ second for different node speedtime.
0
0.1 0.2 0.3 0.4
0.5 0.6 0.7 0.8 0.9
1
Pro b ab ilit y
Figure 9: The impact of traffic load on reachability
at three broadcasts/second for different node speeds
done.
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Simulation Experiments
Figure 12 : The impact of load on reachability at one
broadcast/ second for different node speedtime.
Fig. 11: Impact of load on saved rebroadcast
3 messages/s for node speeds 1, 5, 10, and 20 m/s.
done.
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 Overhead:
Overhead # of control packet sent
=
# of data packet received
 Delivery ratio:
PDR =
# of packets received
# of packets sent
 Saved Rebroadcast
SRB =
r-t
* 100%
r
Where r is the number of RREQ packets received, and t is the number of RREQ
packets retransmitted
35
Algorithm: Blind Flooding
Protocol receiving ()
On receiving a broadcast packet m at node X do the following:
If packet m received for the first time Then
broadcast (m)
End if
End Algorithm.
Figure 2.3: A description of the blind flooding algorithm.
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Algorithm: Probabilistic Flooding
Protocol receiving ()
On receiving a broadcast packet m at node X do the following:
If packet m received for the first time Then
broadcast (m) with fixed probability p
End if
End Algorithm
Figure 2.4: A description of the probabilistic flooding algorithm.
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The Random Waypoint Model
 The Random Waypoint Model
 The random waypoint mobility model [39] is one of the most popular
mobility models in MANET research and in itself a focal point of much
research activity [13, 38, 50, 53]. The model defines a collection of
nodes which are placed randomly within a confined simulation space.
Then, each node selects a destination inside the simulation area and
travels towards it with some speed, s meter/second. Once it has
reached the destination, the node pauses for some time, pause, before
it chooses another destination and repeats the process. The node speed
of each node is specified according to a uniform distribution between 0
and Vmax, where Vmax is the maximum speed parameter. Pause time
is a constant, e.g. 0 secs. It has been suggested in [43] that simulations
should be left to run for some period of time before collecting data. In
the initial use of the random waypoint model for evaluation [43], an
increase in mobility was simulated by increasing the maximum speed
parameter or decreasing the pause time.
38
0.9
0.8
0.7
SRB
0.6
0.5
FP
0.4
0.3
0.2
0.1
0
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
Rebroadcas Probability
39
 Figure 3.1 explores SRB at low mobility conditions of maximum
speeds of 2 m/sec and 0 pause time. The rebroadcast
probabilities have been varied from 0.1 to 1.0 percent with 0.1
percent increment when 5 broadcast packets/sec are injected
into the network. Examining the results reveals that SRB
decreases as the rebroadcast probability increases. For instance,
when p=0.1 SRB is around 90% and when p is increased to 0.7
SRB decreases to 30%. When p=1 (blind flooding) SRB is 0%.
This is because as the probability of the transmission increases
for every node, this implies that there are more candidates for
broadcast re-transmissions in a given area, and as a result the
number of nodes that transmit the packet increases which
increases the number of redundant rebroadcast packets and that
leads to a higher chance of collision and contention due to the
increases in redundant rebroadcast packets.
40
120
100
RE
80
60
FP
40
20
0
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
Rebroadcast Probability
41
 Figure 3.2 explores reachability (RE) of fixed probabilistic
flooding for low mobility conditions of maximum speeds
of 2 m/sec and 0 pause time. The rebroadcast probabilities
have been varied from 0.1 to 1.0 percent with 0.1 percent
increment. The figure shows that RE increases as the
rebroadcast probability increases. For instance when p=0.1
RE is close to 45% and when p is increased to 1.0 RE is close
to 100%. This is because as the probability of the
transmission increases for every node, this implies that
there are more candidates for broadcast re-transmissions in
a given area, and as a result the number of nodes which
really transmit the packet increases which increases the
number of nodes receiving the broadcast packet over the
total number of mobile nodes that are reachable
42
120
100
80
RE
2 m/s
60
8 m/s
20 m/s
40
20
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
Rebroadcast Probability
43
 Figure 3.4 shows RE against the rebroadcast probability for three
different node speeds and continuous mobility. Overall, across
the different rebroadcast probabilities, RE increases as the node
speed increases. For example RE is 100% when the rebroadcast
probability p=0.6 and when the nodes move with a high speed of
20 m/sec. However, to achieve the same level of RE when nodes
move at a lower speed 2 m/sec, the rebroadcast probability has to
be over 0.9. This is due to the fact that as the node speed
increases network connectivity increases resulting in a larger
number of nodes receiving the broadcast packet which causes RE
to increases. However at a low speed and a rebroadcast
probability p=0.6, the number of nodes receiving the broadcast
packet decreases, and thus so does RE. When the node speed is
low, the rebroadcast probability has to be set higher (e.g. p=0.9)
in order to maintain a good reachability level.
44

 We have varied the traffic load in the network from
light traffic through moderate to heavy traffic. To do
so, the following rates of broadcast packets generated
at the source node are considered:
 -Light traffic load: 1 packet/sec;
 - Medium traffic load: 5 packets/sec;
 - Heavy traffic load: 10 packets/sec.
45
120
100
80
RE
1 B/S
60
5 B/S
10 B/S
40
20
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
Rebroadcast Probability
46
 RE results for a varying rebroadcast probability when the
traffic is varied under continuous node mobility and a
speed of 2 m/sec. Figure 3.7 reveals that the achieved RE
increases as rebroadcast probability increases when the
traffic load is light. Moreover when the rebroadcast
probability is over 0.7, RE is over 95%. However, as the
traffic load increases the rate of increase in RE slows down.
 Figure 3.8 shows that in general RE is not affected that
much when the node speed increases, especially as the
traffic load becomes heavy. This is due to the same reason
given above; i.e. due to the increased number of collisions
as well as reduced channel access.
47
 Effects of Network Density
 To study the performance effects of varying network
density, i.e. the number of network nodes per unit area
for a given transmission range, the following three
relative levels of network density are examined:
 - Low density: 25 nodes;
 - Medium density: 50 nodes;
 - High density: 100 nodes.
48
120
100
80
RE
N=25
60
N=50
N=100
40
20
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
Rebroadcast Probability
49
 Figures 3.11 and 3.12 depict the results for RE considering
the three different network densities and two different
node speeds. The figures suggest that RE increases with a
higher network density. The trend in the figures also
suggests that the reachability increases as the node speed
increases. RE improves with higher density and faster
moving nodes for the following reasons. As the density of
the nodes increases, the number of nodes covering a
particular area also increases. As the probability of rebroadcast is fixed for every node, this implies that there are
more candidates for transmission in each “coverage “area.
Hence, there is a greater chance that a broadcast retransmission occurs, resulting in increased RE.
50
 ‘Hello’ Packets
 ‘Hello’ packets are a special control packet that is sent
out periodically from a node to establish and confirm
network adjacency relationships and responsible for
establishing and maintaining neighbor relationships.
When a node receives a ‘Hello’ packet from its
neighbour, it creates or refreshes the routing table
entry to the neighbour.
51
 To maintain connectivity, if a node has not sent any
broadcast control packet within a specified interval, a
‘Hello’ packet is locally broadcast (over one hop
radius). This results in at least one ‘Hello’ packet
transmission during every time period. Failure to
receive any ‘Hello’ packet from a given neighbour for
several time intervals indicate that neighbour is no
longer within transmission range, and connectivity is
assumed to have been lost.
52
 The information contained in the ‘Hello’ packet varies
depending on its intended usage. Thus it is necessary
to quantitatively compare the size of the ‘Hello’
packets when analysing overhead and performance
tradeoffs. A common element of the ‘Hello’ packet is
the ID (four bytes) of the node that is broadcasting the
packet. The node ID is sufficient for neighbour
discovery and link detection. However, if nodes use
their neighbour table for forwarding packets, then the
position of the node (typically two integers) might be
necessary
53
 In order to construct a local view of a given node’s
locality, 1-hop information based on, for instance, the
minimum, average, maximum number of neighbours
can be used. The selection of the time interval for the
exchange of ‘Hello’ packets is usually set at 1 second as
recommended in the AODV protocol ,A node assumes
that a particular neighbour has moved away and is
currently outside transmission range if no a ‘Hello’
packet not has been received from that neighbour for
two seconds, as is suggested in the AODV
54
 . Each node in the network has a constant
transmission range of 250 meter. The MAC layer
scheme follows the IEEE 802.11 MAC specification. We
have used the broadcast mode with no RTS/CTS/ACK
mechanisms for all packet transmissions, including
Hello, DATA and ACK packets.
 The movement pattern of each node follows the
random way-point model. Each node moves to a
randomly selected destination with a constant speed
between 0 and the maximum speed. When it reaches
the destination, it stays there for a random period and
starts moving to a new destination.
55
 We have varied the network density (i.e., the number of
nodes on a given terrain size) and have measured the
minimum, average and maximum number of neighbours
over the whole nodes in the network. For each
configuration,
 we have gathered statistics for 30 arbitrary topologies
where nodes are initially placed randomly over the terrain.
 The results represent the average over the 30 different
topologies in order to achieve a 95% confidence interval in
the collected statistics. For a given number of nodes, three
terrain sizes have been considered: 600m × 600m, 800m ×
800m and 1000m ×1000m.
56
 Figures 4.1, 4.2 and 4.3 depict the minimum, average,
and maximum number of neighbours after averaging
over the whole network nodes when the nodes move at
the max. speed of 2m/sec. Various network densities
resulting from a combination of different network
sizes (from 25 to 125 nodes) and terrain sizes
(600m×600m, 800m×800m, and 1000m×1000m) have
been examined
57
 . A summary of the minimum, average and maximum
number of neighbours is listed in Table 4.2. Also a
summary of confidence intervals, margin errors for the
minimum, average and maximum number of
neighbours of a given node (averaged over the whole
network) is shown in Table 4.3. The results show that
as expected the denser the network is, the higher the
maximum number of neighbours is at a given node.
On the other hand, the sparser the network is, the
lower is the minimum number of neighbours at a given
node.
58
 As the network size increases so does the minimum,
average, and maximum number of neighbours. For
example, in a terrain size of 1000m × 1000m when the
network size is 50 nodes, a typical node has the
minimum number of neighbours equals to 4, the
average number of neighbour to 11, the maximum
number of neighbour to 17. When the network size is
doubled to 100 nodes, a typical node has the minimum
number of neighbours equals to 7, the average number
of neighbour to 22, the maximum number of
neighbour to 34.
59
Minimum Number of Neighbors
20
18
16
14
Area 600x600
12
10
Area 800X800
8
Area 1000X1000
6
4
2
0
0
25
50
75
100
125
150
Number of Nodes
60
Average number of nodes
60
50
40
Area 600x600
30
Area 800X800
Area 1000X1000
20
10
0
0
25
50
75
100
125
150
Number of nodes
61
Maximum Number of Neighbors
90
80
70
60
Area 600x600
50
Area 800X800
40
Area 1000X1000
30
20
10
0
0
25
50
75
100
125
150
Number of nodes
62

 In MANETs, due to node mobility, neighbourhood
relationship changes frequently. In order to cope with
mobility and have up-to-date neighbourhood
information, nodes advertise ‘Hello’ packets
periodically. In this work, we have conducted a set of
simulation experiments in order to characterise node
neighbourhood in MANETs using ‘Hello’ packet
exchange
63
7/26/2016
64
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
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65
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 < n3 then
•
•
Node X has a low degree: the high rebroadcast probability p=p1;
•
Else If n> = n3 then
•
Node X has a high degree: the low rebroadcast probability p=p2;
•
•
•
End if
Generate a random number RN over [0, 1].
If RN <= p rebroadcast the received message; otherwise, drop it
7/26/2016
66
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 node’s degree. The message is re-broadcast with
probability which depends on the node’s 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..
7/26/2016
67
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 < n1 then
Node X has a low degree: the high rebroadcast probability p=p1;
Else If n >= n1 and n <= n2 or n>= n3 and n <=n2 then
Node X has a medium degree: the medium rebroadcast
probability p=p2;
•
•
•
•
•
Else If n> n2 then
Node X has a high degree: the low rebroadcast probability p=p3;
End if
Generate a random number RN over [0, 1].
If RN <= p rebroadcast the received message; otherwise, drop it
7/26/2016
68
Dynamic Probabilistic Flooding Using two Hope Neighbours
Dynamic Probabilistic Flooding Using two Hope Neighbours
The Adjusted Probabilistic Flooding Algorithm
7/26/2016
Highly Adjusted Probabilistic Flooding
69