1. Muneer Bani Yassein Department of Computer Science masadeh@just.edu.jo
Improving the Performance of Broadcast Flooding in Mobile Ad Hoc Networks (MANETs)
Muneer Bani Yassein
Department of Computer Science
9/17/2018

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 Project.
Conclusions
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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.
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4. Important Issues What is Broadcasting Characteristics
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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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.
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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.
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7. Problem statement Cons :
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.

8. Algorithm: Blind Flooding
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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9. Common Problems Contention Collision 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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10. Redundant Rebroadcasts
Optimal schedule: 2 transmissions
flooding: 7 transmissions

11. Problem statement 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.

12. 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 , 2002
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13. 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

14. 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

15. 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

16. 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

17. 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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18. 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 , 2002
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19. 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 , 2005
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20. 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.
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21. 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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22. 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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23. 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, July 2005.
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24. 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 protocol.
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25. Simulation Experiments
Input parameters
Transmitter range m
Bandwidth Mbits
Interface queue length packets
Simulation time sec
No of node ,50,75,100
Max. Speed ,5,10,20 m/sec
Packet size bytes
Topology size X600 m2
Pause time ,20 ,40sec
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26. 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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27. 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.
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28. 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
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29. 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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30. 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.
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31. 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.
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32. 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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33. 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.
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34. 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.
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35. Performance metrics Overhead: # of control packet sent Delivery ratio:
# of data packet received
Delivery ratio:
# of packets received
# of packets sent
Saved Rebroadcast
r - t r
Where r is the number of RREQ packets received, and t is the number of RREQ packets retransmitted
Overhead =
PDR =
SRB =
100% *

36. Algorithm: Blind Flooding
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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37. Algorithm: Probabilistic Flooding
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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38. 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.

39. SRB vs. rebroadcast probability for a network size of 50 nodes and a node speed 2 m/sec.

40. 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.

41. Figure 3.2: RE vs. rebroadcast probability for a network size of 50 nodes and node speed 2 m/sec.

42. 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

43. Figure 3.4: RE vs. rebroadcast probability for different node speeds 2, 8, and 20 m/sec

44. 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.

45. Effects of Traffic Load
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.

46. Figure 3.8: RE vs. rebroadcast probability for different traffic loads 1, 5 and 10 packets/sec with a node speed of 20 m/sec.

47. 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.

48. 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.

49. : RE vs. rebroadcast probability for different network densities 25, 50, and 100 nodes and a node speed 2 m/sec.

50. 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 re- broadcast 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 re- transmission occurs, resulting in increased RE.

51. Neighbourhood Characteristics in MANETs
‘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.

52. 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.

53. 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

54. 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

55. Performance Evaluation
. Each node in the network has a constant transmission range of 250 meter. The MAC layer scheme follows the IEEE 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.

56. Performance Evaluation
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.

57. Performance Evaluation
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

58. . 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.

59. 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.

60. Figure 4.1: Minimum numbers of neighbours (averaged over the whole network) vs. network size with a node speed of 2 m/sec.

61. Figure 4.2: Average number of neighbours (averaged over the whole network) vs. network size with a node speed of 2 m/sec.

62. Figure 4.3: Maximum number of neighbours (averaged over the whole network) vs. network size with a node speed of 2 m/sec.

63. Conclusions
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

64. 9/17/2018

65. 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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66. 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
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67. 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..
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68. 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
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69. Dynamic Probabilistic Flooding Using two Hope Neighbours
The Adjusted Probabilistic Flooding Algorithm
Highly Adjusted Probabilistic Flooding
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