Gossiping: Adaptive and Reliable Broadcasting in MANETs

1. Gossiping: Adaptive and Reliable Broadcasting in MANETs Abdelmajid Khelil & Neeraj Suri LADC’07, Morelia, Mexico

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

3. com. range velocity Motivation (cont.) • A MANET may show • Frequent perturbations • Continuously changing network topology • Comm. failures, power ... • Strong heterogeneity • Node spatial distribution • Node movement • Evolving properties • Temporal (daytime ..) • Technological (deployment stages ..)

4. Outline • Problem Statement • Related Work • System and Fault Model • Epidemic Model for Gossiping • Adaptation of Gossiping • Evaluation

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

6. Related Work - Classification

7. comm. range Adaptive probabilistic ACB STOCH-FLOOD Adaptation purely relies on simulations! Related Work – in Density-Mobility-Space DENSITY Topology-based Adaptive counter-based Energy-efficient Heuristic-based MOBILITY restrict forwarding • 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 comm. range velocity 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 distribution • Node mobility • Nodes do not need • Location / velocity information • HELLO beaconing to acquire neighborhood information • Messages are uniquely identified • Failures • Communication: Collision, contention and frequent link breakage. • Topology: Continuous change.

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

10. a(d,p) #Neighbors Optimal p Node density d (km-2) Adaptation of Gossiping to Node Density • Compute infection rate a(d,p) for • Different node densities d in [50,800] km-2 • Uniform node distribution • Fixed comm. range (100m) • Differentprobabilitiesp in ]0,1] • All nodes use the same p STEP 1 Infection rate maximization • Determination of optimal probability: For a given node density d0 , find p such that a(d0,p) is maximal STEP 2 Localization & Interpolation • Nodes set p depending on #Neighbors  Adaptive gossiping STEP 3

11. Area 1km x 1km Number of nodes N = 50 .. 500 Communication range CSMA/CA 100 m Bandwidth r = 1 Mbps 0.001 pkt/s Random in [0 , 10] ms 10 Message size 280 Bytes Mobility model Random waypoint Random in [0.75 , 1.25] s - Max speed vmax = 3 .. 30 m/s - Pause 0 .. 2 s HELLO beaconing interval Simulation runs Packet rate Forwarding delay Number of senders 25 Simulation Parameters Parameter Values Group- & graph-based mobility also considered MAC layer ns-2 simulator - Collision - Contention - Frequent link breakage - Continuous topology change

12. Reachability = #Reached_Nodes / #Total_Nodes High reachability

13. Average Number of Partitions Network partitioning

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

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

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

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

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

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

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

21. Conclusions • Adaptive Gossipingprovides 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, • Node mobility, • Deployment stages, • Technology penetration rate, • On-off usage, etc. • Extensions • Broadcast repetition to cope with network disconnections • Broadcast extinction at the source, • Network partitioning, • Reboot, etc.

22. Thanks for your attention! Abdelmajid Khelil and Neeraj Suri Department of Computer Science TU Darmstadt, Germany {khelil, suri}@informatik.tu-darmstadt.de