1. © Neeraj Suri EU-NSF ICT March 2006 Dependable Embedded Systems & SW Group www.deeds.informatik.tu-darmstadt.de Gossiping: Adaptive and Reliable Broadcasting in MANETs Abdelmajid Khelil & Neeraj Suri LADC’07, Morelia, Mexico

2. © A. Khelil 2 Motivation  Ad hoc communication  WLAN, Bluetooth, ZigBee, WiMax.. 802.11{a,b,g,p} 802.15.{1,3,4} 802.16{a,e} IEEE 802.11{a,b,g,p} 802.15.{1,3,4} 802.16{a,e} IEEE  Main characteristics  Hop-by-hop communication  Node mobility  Limited resources (energy, processing, storage etc.)  Mobile Ad Hoc Networks (MANET)  Diversity of application scenarios  Rescue, military scenarios  Vehicle ad hoc network, and many others. 1 0 1 0 1 1 0 0 1 1 1 0 1 0 1 1 0 0 1 1 1 0 1 0 1 1 0 0 1 1

3. © A. Khelil 3 Motivation (cont.)  A MANET may show  Frequent perturbations Continuously changing network topologyContinuously changing network topology Comm. failures, power...Comm. failures, power...  Strong heterogeneity Node spatial distributionNode spatial distribution Node movementNode movement  Evolving properties Temporal (daytime..)Temporal (daytime..) Technological (deployment stages..)Technological (deployment stages..)

4. © A. Khelil 4 Outline  Problem Statement  Related Work  System and Fault Model  Epidemic Model for Gossiping  Adaptation of Gossiping  Evaluation

5. © A. Khelil 5  Flooding encounters one main problem:  Broadcast storms, i.e., Collision,Collision, Contention, andContention, and Unnecessary forwards.Unnecessary forwards. Problem Statement  Restrict Forwarding  Gossiping: Nodes forward messages with a certain probability p  How should nodes select the forwarding probability p? com. range A B  Broadcasting is widely used in MANETs  Flooding is a common approach Nodes forward messages to all neighbors, using MAC broadcastNodes forward messages to all neighbors, using MAC broadcast source Plain flooding p(A) low! p(B) high!

6. © A. Khelil 6 Related Work - Classification

7. © A. Khelil 7 Related Work – in Density-Mobility-Space DENSITY MOBILITY restrict forwarding Energy-efficient Topology-based Heuristic-based Adaptive counter-based comm. range ACB Adaptive probabilistic STOCH-FLOOD Adaptation purely relies on simulations! Two comparative studies: - Gerla et al.: Efficient flooding in ad hoc networks: A comparative performance study. In ICC’03. - Williams et al.: Comparison of broadcasting techniques for mobile ad hoc networks. In Mobihoc’02.

8. © A. Khelil 8 System and Fault Model  A generalized MANET scenario  N mobile nodes populating a fixed area A (node density: d=N/A)  Heterogeneous and evolving Node spatial distributionNode spatial distribution Node mobilityNode mobility  Nodes do not need Location / velocity informationLocation / velocity information  HELLO beaconing to acquire neighborhood information  Messages are uniquely identified  Failures  Communication: Collision, contention and frequent link breakage.  Topology: Continuous change.

9. © A. Khelil 9 Epidemic Model for Gossiping Broadcast in MANETs Broadcast protocol Susceptible Infective SI fitting Fittin g time [s] #Reached/N - Protocol: SPIN - Random waypoint - N=100 “Infection” rate a simulation Spread of infectious disease analytical Infection rate a #Individuals: N Contact pattern Susceptible Infective SI Infection transmission #Nodes: N Movement pattern

10. © A. Khelil 10 a(d,p) Adaptation of Gossiping to Node Density  Compute infection rate a(d,p) for  Different node densities d in [50,800] km -2 Uniform node distributionUniform node distribution Fixed comm. range (100m)Fixed comm. range (100m)  Different probabilities p in ]0,1] All nodes use the same pAll nodes use the same p Localization & Interpolation Infection rate maximization  Determination of optimal probability: For a given node density d 0, find p such that a(d 0,p) is maximal  Nodes set p depending on #Neighbors  Adaptive gossiping STEP 1 STEP 2 STEP 3 #Neighbors Node density d (km -2 ) Optimal p

11. © A. Khelil 11 Simulation Parameters ns-2 simulator ValuesParameter Area1km x 1km Number of nodesN = 50.. 500 Communication range100 m Bandwidthr = 1 Mbps Message size280 Bytes Mobility modelRandom waypoint - Max speedv max = 3.. 30 m/s - Pause0.. 2 s HELLO beaconing interval Random in [0.75, 1.25] s Number of senders25 Packet rate0.001 pkt/s Forwarding delayRandom in [0, 10] ms Simulation runs10 Group- & graph-based mobility also considered CSMA/CA MAC layer - Collision - Contention - Frequent link breakage - Continuous topology change

12. © A. Khelil 12 Reachability = #Reached_Nodes / #Total_Nodes High reachability

13. © A. Khelil 13 Average Number of Partitions Network partitioning

14. © A. Khelil 14 Reliability of Adaptive Gossiping (1) Comparison to the optimal case

15. © A. Khelil 15 Reliability of Adaptive Gossiping (2) Gossip reaches either almost all nodes or only the source

16. © A. Khelil 16 MNF: Mean Number of Forwards per Node & per Msg High efficiency Max MNF: 1.0

17. © A. Khelil 17 Comparison to Related Work: Tunable Thresholds - ACB stops to adapt after 12 neigh -Gossiping saves more forwards till 30 neigh

18. © A. Khelil 18 Comparison to Related Work: Reachability Comparably high reachability Node speed: 3 m/s

19. © A. Khelil 19 Comparison to Related Work: MNF Plain flooding Highest efficiency Node speed: 3 m/s

20. © A. Khelil 20 Comparison to Related Work: End2End Delay Lowest delay Node speed: 3 m/s

21. © A. Khelil 21 Conclusions  Adaptive Gossiping provides for efficient, scalable and reliable broadcast for a wide range of node densities and mobilities:  Easy to use on a wide range of resource-limited devices  Adaptation of forward probability is independent from cause of changes in node density: Application scenarios,Application scenarios, Node mobility,Node mobility, Deployment stages,Deployment stages, Technology penetration rate,Technology penetration rate, On-off usage, etc.On-off usage, etc.  Extensions  Broadcast repetition to cope with network disconnections Broadcast extinction at the source,Broadcast extinction at the source, Network partitioning,Network partitioning, Reboot, etc.Reboot, etc.

22. © Neeraj Suri EU-NSF ICT March 2006 Dependable Embedded Systems & SW Group www.deeds.informatik.tu-darmstadt.de Thanks for your attention! Abdelmajid Khelil and Neeraj Suri Department of Computer Science TU Darmstadt, Germany {khelil, suri}@informatik.tu-darmstadt.de