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1 Using Manhattan Mobility Model for the Counter-Base Broadcasting protocol in MANETs Sara Omar al-Humoud ENDs Talk.

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Presentation on theme: "1 Using Manhattan Mobility Model for the Counter-Base Broadcasting protocol in MANETs Sara Omar al-Humoud ENDs Talk."— Presentation transcript:

1 1 Using Manhattan Mobility Model for the Counter-Base Broadcasting protocol in MANETs Sara Omar al-Humoud ENDs Talk

2 Outline Introduction Cbase Mobility Models RWP MMM Results Future Direction

3 Research Outline Routing Broadcasting Wireless MANET Flooding ProbabilisticDeterministic ACBase1 Introduction Related work Contribution ACBase2

4 Counter Base Broadcast When receiving a message: –counter c is set to keep track of number of duplicate messages received. –Random Assessment Delay (RAD) timer is set. –When the RAD timer expires the counter is tested against a fixed threshold value C, broadcast is inhibited if c > C. Scheme

5 Get the Broadcast ID Get degree n of node X c = 1 n < avg C = c1 Tmax = Tmax1 C = c2 Tmax = Tmax2 Set RAD [0..Tmax] While (RAD) same packet heard c = c + 1 End while C < c drop packetTrans packet Get the Broadcast ID c = 1 Set RAD [0..Tmax] While (RAD) same packet heard c = c + 1 End while C < c drop packetTrans packet Get the Broadcast ID same packet heard drop packetTrans packet Adjusted Counter-based1 Comparison Flow charts between Counter-based Flooding

6 6 Adjusted Counter-Based Broadcast –Based on the original counter-based scheme –Add the ability to decide the counter and the RAD according to neighbourhood density –Neighbourhood density is divided according to the Average number of neighbours into: Density1: Sparse Density2: Dense ACBase1 Scheme Avg SparseDense Neighbourho od Density

7 Outline Introduction Cbase Mobility Models RWP MMM Results Future Direction

8 Mobility Models Traces Synthetic Model –Entity –Group MobiLib Dartmouth

9 Random Way Point Mobility Model How it works: –at every instant, a node randomly chooses a destination and moves towards it with a velocity chosen randomly from [0, V max ], where V max is the maximum allowable velocity for every mobile node. 9 RWP 25 nodes

10 Manhattan Mobility Model How it works: –A node is allowed to move along the grid of horizontal and vertical streets on the map. –At an intersection the node can turn left, right or go straight. –P of same street = 0.5 –P of turning left = 0.25 –P of turning right = 0.25 10 MMM 25 nodes (3x3 street) MANHATTAN HOR_STREET_NUM 3 VER_STREET_NUM 3 LANE_NUM 12

11 Outline Introduction Cbase Mobility Models RWP MMM Results Future Direction

12 Prameters 12 Simulation parameters Simulation parameterValue Simulatorns-2 version (2.33) Network Area1000 x 1000 meter Transmission range250 meter Data Packet Size 256 bytes Node Max. IFQ Length50 Simulation Time900 sec Pause Times0 sec Number of Trials30 MAC layer protocolIEEE 802.11 Mobility modelRandom waypoint model, Manhattan Mobility Model Channel Bandwidth2Mb/sec Confidence Interval 95% Packet Rate2 packets per sec Node Speed Max = 30 km_per_hour Min = 5 km_per_hour

13 13 Performance metrics Saved Rebroadcast (SRB) (r t)/r r = number of hosts receiving the broadcast message t = number of hosts that actually transmitted the message. Reachability r/e r = number of hosts receiving the broadcast packet e = number of mobile hosts that are reachable, directly or indirectly, from the source host. Average latency –the interval from the time the broadcast was initiated to the time the last host finished its rebroadcasting.

14 Results 14 SRB

15 Results 15 Reachability

16 Results 16 Average Latency

17 17 Future Directions MMM –Limiting the number of nodes (cars) in a lane –Building a bigger map (Glasgow cc) Scripting a mobility map generator Develop the ACBase2 that calculates the threshold value according to a function of the number of neighbours

18 18 Questions

19 19 Introduction Discovering neighbours Collecting global information Addressing Helping in multicasting and Unicast –Route discovery, route reply –in on-demand routing protocols like DSR, AODV to broadcast control messages. Conventionally broadcast is done through flooding Broadcasting Applications

20 20 Introduction Flooding may lead to –Redundancy x Consume limited bandwidth –Contention x Increase in delay –Collision x High packet loss rate –Broadcast storm problem! Broadcasting Applications f(n) = n 2 – 2n + 1

21 21 Related work Probability-based –R–Rebroadcast with probability P Counter-based –R–Rebroadcast if the node received less than C th copies of the msg Location-based –R–Rebroadcast if the area within the nodes range that is yet to be covered by the broadcast > A th Distance-based –R–Rebroadcast if the node did not receive the msg from another node at a distance less than D th Probabilistic Broadcasting Methods Receiver rebroadcast decision Simple Implementation RD based on instantaneous information from broadcast msgs

22 22 Related work Reliable Broadcast Self-pruning Scalable broadcasting Dominant Pruning Cluster-based Deterministic Broadcasting Methods Sender rebroadcast decision Elaborate Implementation Rebroadcast decision based on neighbourhood study

23 23 Related work 1.Counter-based broadcast –Adaptive Counter-based broadcast [Tseng2003] –Adjusted Counter-Based [Aminu2007] 2.Color-based broadcast [Haddad 2006] 3.Distance-aware counter-based broadcast [Chen 2005] Counter-Based related Broadcasting Methods

24 24 Related work 2- Color-based broadcast Scheme: –each broadcast node selects a color from a set of η colors which it writes to a color-field present in the broadcast message. –all nodes which hear the message rebroadcast it unless they have heard all η colors by the time a random timer expires. Remarks: –With the added overhead, we may end with a bad case: –E.g. a node receive 3 messages with only c1 and this node will still rebroadcast the message. Counter-Based related Broadcasting Methods c1 c2 c3

25 25 Related work 3- Distance-aware counter-based broadcast Scheme: –Similar to the counter-based scheme in addition to: Two distinct RADs are applied to the border and interior nodes SRAD to border nodes LRAD to interior nodes Remarks: –The use of distance as an enhancement factor to the original counter-based may be degraded knowing that real networks transmissions will be affected by obstacles. Counter-Based related Broadcasting Methods

26 26 Motivations and objectives Area-based scheme –Rely on GPS Deterministic approaches –High time overhead –High number of control messages exchanged to broadcast one packet –it demands accurate neighbourhood information and cannot ensure the coverage with outdated topology information. Related work limitations - overhead

27 27 Motivations and objectives Counter-based schemes –Fixed counter-based Threshold = c –Adaptive counter-based Threshold = C(n) where n is the number of neighbors The function C(n) is undefined yet –Color-based Used with homogeneous density networks Rebroadcast when many duplicates received by the a partial set of colors –Distance-aware counter-based Distance estimated by signal strength Not considering obstacle existence Related work limitations - overhead

28 28 Contributions Simulate a university campus with the following assumptions: –Existence of pedestrians and vehicles equipped with IEEE 802.11 wireless transceivers –Speed: walk speed of 1 m/sec with appropriate pose times to vehicles having a maximum speed of 70 km/hour –Area: First study: open unobstructed Second study: open with obstacles –Mobility: First study: Random way point (RWP) mobility model Second study: Realistic Mobility Model –We assume that a host can detect duplicate broadcast messages. –Assume that nodes have sufficient power to function properly throughout the simulation time Assumptions

29 Contributions 29 Simulation parameters Simulation parameterValue Simulatorns-2 version (2.31) Network Area1500 x 500 meter Transmission range250 meter Data Packet Size 256 bytes Node Max. IFQ Length50 Simulation Time500 sec Pause Times0, 10, 20, 40 sec Number of Trials10 MAC layer protocolIEEE 802.11 Mobility modelRandom waypoint model, Realistic Mobility Model Channel Bandwidth2Mb/sec Confidence Interval 95% Number of Nodes20, 40, 50, 60, 80, 100 Packet Rate10, 20, 40, 60, 80 100, 150 packets per sec Counter threshold pairs [2,3], [2,4], [3,4] Max RADs[0.005, 0.01], [0.05, 0.01] Node Max. Speed1, 5, 10, 15, 20 m/sec

30 30 Questions Is there a realistic mobility model? –Obstacle Mobility Model Project 2005 Ns2, GlomoSim the Mobility Management and Networking (MOMENT) Lab, the Networking and Multimedia Systems Lab (NMSL) and the Geometric Computing Lab (GCL). University of Califorrnia at Santa Barbara –RealMobGen 2008 Ns2 Dartmouth's and University of Southern California's C. Walsh, A. Doci, and T. Camp, A Call to Arms: Its Time for REAL Mobility Models, ACM's Mobile Computing and Communications Review, to appear 2008 Towards a better simulation

31 31 Questions Is there a visualisation tool to view network topology? –iNSpect Towards a better simulation iNSpect NS-2 Mobility files Trace files OpenGL animation

32 32 Questions How to validate and compare scenarios? –SCORES tool (SCenariO characteRizEr for Simulation) Towards a better simulation SCORES Num nodes Node coverage Simulation area Transmission range Nw diameter Neighbor count Foot print … Mobility file topology change rate Delivery ratio, end-to-end delay, throughput, overhead Metamodels

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34 34 Questions


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