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@just.edu.jo muneer@dcs.gla.ac.uk

2. Outline Mobile Ad Hoc Networks (MANETs) Broadcasting and its Importance CommonProblems 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? BlindFlooding • 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 • Redundantretransmission • 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. RelatedWorkandLimitations • 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. RelatedWorkandLimitations • 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. RelatedWorkandLimitations  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 havedescribed 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. 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. ProposedContributions • Performance Analysis of Probabilistic Flooding • Analysis of Topological Characteristic • The Adjusted Probabilistic Flooding Algorithm • The Highly Adjusted Probabilistic FloodingAlgorithm

15. Ch3: ProposedContributions • 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 with11th(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: 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.

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. MobilityandDensity 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 rebroadcastfor 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. MobilityandTrafficLoad 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. ProposedContributions • AnalysisofTopologicalCharacteristic • 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 with11th(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 • 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 DynamicProbabilisticFloodingUsingOneHopNeighbours The Adjusted Probabilistic Flooding Algorithm

30. AdjustedProbabilisticFlooding • Protocol receiving () • On hearing a broadcast packet m at node X: • Get the Broadcast ID from the message; n3average 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 < n3then • 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

31. Simulation Experiments Figure 15: saved rebroadcastof three broadcast schemes against network density with node speed 10m/s. Figure 16: The reachability of three broadcast algorithms done.

32. New Proposed Algorithms • The highly adjusted probabilistic flooding algorithm operates as followswhen 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.. DynamicProbabilisticFloodingUsingOneHopeNeighbours HighlyAdjustedProbabilisticFlooding

33. HighlyAdjustedProbabilisticFlooding • 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 n3average 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 < n1then • Node X has a low degree: the high rebroadcast probability p=p1; • Else If n >= n1 and n <= n2or n>= n3and 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

34. DynamicProbabilisticFloodingUsingtwoHopeNeighbours DynamicProbabilisticFloodingUsingtwoHopeNeighbours The Adjusted Probabilistic Flooding Algorithm HighlyAdjustedProbabilisticFlooding