Universität Stuttgart Institute of Parallel and Distributed Systems (IPVS) Universitätsstraße 38 D Stuttgart Hypergossiping: A Generalized Broadcast Strategy for Mobile Ad Hoc Networks A. Khelil, P.J. Marrón, C. Becker, K. Rothermel

Universität Stuttgart IPVS Research Group “Distributed Systems” 2 Overview Motivation Related Work System Model Hypergossiping Evaluation Conclusion and Future Work

Universität Stuttgart IPVS Research Group “Distributed Systems” 3 Motivation (1) Ad hoc communication ◦ WLAN, Bluetooth, UMTS (UTRA-TDD)  Mobile Ad Hoc Networks (MANET) Examples of applications ◦ Vehicle ad hoc network ◦ Rescue scenarios MANETs may show ◦ significant variation in node spatial distribution ◦ significant variation in node movement Broadcasting is widely used in MANETs ◦ Flooding is a common approach

Universität Stuttgart IPVS Research Group “Distributed Systems” 4 Motivation (2) Flooding encounters two main problems: ◦ In dense MANETs: broadcast storms ▪ Collision, contention and redundancy ◦ In sparse MANETs: network partitioning ▪ Flooding reaches only nodes of one partition Gossiping is probabilistic flooding ◦ Nodes forward messages with a certain probability to all neighbors, using MAC broadcast ◦ Variation in node density  we adapted gossip probability to number of neighbors  reduces broadcast storms ◦ Gossip still reaches only nodes of one partition  Broadcast repetition strategy is needed

Universität Stuttgart IPVS Research Group “Distributed Systems” 5 Overview Motivation Related Work System Model Hypergossiping ◦ Partition Join Detection ◦ Rebroadcasting Evaluation Conclusion and Future Work

Universität Stuttgart IPVS Research Group “Distributed Systems” 6 Related Work density mobility repeat forwarding restrict forwarding sparse (partitioned) dense low mobile (e.g. pedestrians) highly mobile (e.g. vehicles) Integrated Flooding (IF) scoped flooding hyper flooding plain flooding non-partition-aware protocols, e.g. adaptive gossiping negotiation-based protocols Goal: a generalized strategy that supports a wide range of densities and mobilities

Universität Stuttgart IPVS Research Group “Distributed Systems” 7 System Model MANET ◦ N mobile nodes populating a fixed area A (density: d=N/A) ◦ Mobility is required to overcome partitioning Assumptions ◦ Fixed communication range R ◦ Nodes do not need ▪ Location information ▪ Velocity information Hello beaconing to acquire neighborhood information Broadcast data is relevant up to lifetime ◦ Source sets the initial lifetime ◦ Nodes decrement lifetime Messages are uniquely identified by “source.seqNum” R A + +

Universität Stuttgart IPVS Research Group “Distributed Systems” 8 Overview Motivation Related Work System Model Hypergossiping ◦ Partition Join Detection ◦ Rebroadcasting Evaluation Conclusion and Future Work

Universität Stuttgart IPVS Research Group “Distributed Systems” 9 Our Approach: Hyper-Gossiping (HG) Goal: maximize reachability efficiently within the given max delay (lifetime) MANET:= set of partitions that split or join over time. Approach: we combine two strategies ◦ Gossiping for intra-partition forwarding ◦ Broadcast Repetition Gossiping (forwarding) Repetition (rebroadcasting) Gossiping (rebroadcasting)

Universität Stuttgart IPVS Research Group “Distributed Systems” 10 5 Broadcast Repetition: Basic Idea partition join detectionMANET is partitioned rebroadcasting m1 m5 m m5 m1 m5 m1 m5 m1 m5 m1 m5 m1 m5 m1 m5 m1 1 broadcast repetition

Universität Stuttgart IPVS Research Group “Distributed Systems” 11 Partition Join Detection Heuristic LBR_own ID1 ID2.. IDk LBR_recv Nodes maintain a list of the IDs of Last Broadcast packets Received (  LBR) Nodes share LBRs with neighbors using existing HELLO beacons Detection heuristic If then partition join is detected Heuristic parameters ◦ Max LBR list size: maxLBRlength ◦ Max tolerated intersection of LBR lists: IS_threshold AB

Universität Stuttgart IPVS Research Group “Distributed Systems” 12 Rebroadcasting If a node detects a partition join, it sends the IDs of all (still relevant) received packets Receiver sends missed packets A DATA Buffer (node A) P4 P5 P6 P7 time B Node ANode B P1 P2 P3 P4 P5 P6 P7 P1 P2 P3 P4 P5

Universität Stuttgart IPVS Research Group “Distributed Systems” 13 Overview Motivation Related Work System Model Hypergossiping ◦ Partition Join Detection ◦ Rebroadcasting Evaluation Conclusion and Future Work

Universität Stuttgart IPVS Research Group “Distributed Systems” 14 Simulation Parameters Area1Km x 1Km Number of nodesN = Communication rangeR = 100 m Bandwidthr = 1 Mbps Data size280 Bytes Mobility modelRandom waypoint - Max speedv in {3, 12.5, 20, 30} m/s - Pause2 s HELLO beaconingRandom in [0.75, 1.25] s Wide density range Wide mobility range Lifetime600 s Buffer_sizeinfinity Simulation time650 s Simulation runs10 ns-2 simulator

Universität Stuttgart IPVS Research Group “Distributed Systems” 15 Hypergossiping Reachability Reachability = number_of_reached_nodes / total_number_of_nodes

Universität Stuttgart IPVS Research Group “Distributed Systems” 16 Hypergossiping MNFR MNFR: Mean Number of Forwards and Rebroadcasts per node and per message

Universität Stuttgart IPVS Research Group “Distributed Systems” 17 Integrated Flooding (IF) IMAHN project Integration of ◦ Plain flooding: every node forwards a newly received message once ◦ Scoped flooding: nodes forward a newly received message, only if a certain ratio of neighbors is not covered by the sender ◦ Hyper flooding: Nodes buffer all packets for a fixed time (=60s), and on discovering new neighbor rebroadcast all buffered packets Switch depending on relative speed relative speed to node‘s neighbors low_thresholdhigh_threshold Hyper Flooding Plain Flooding Scoped Flooding (10 m/s)(25 m/s)

Universität Stuttgart IPVS Research Group “Distributed Systems” 18 Comparison to Integrated Flooding (IF): Reachability Reachability = number_of_reached_nodes / total_number_of_nodes

Universität Stuttgart IPVS Research Group “Distributed Systems” 19 Comparison to Integrated Flooding (IF): MNFR MNFR: Mean Number of Forwards and Rebroadcasts per node and per message

Universität Stuttgart IPVS Research Group “Distributed Systems” 20 Conclusion and Future Work Hypergossiping is a generalized broadcast strategy for MANETs ◦ Adaptive gossiping for intra-partition forwarding ◦ Efficient broadcast repetition strategy on partition join Hypergossiping covers ◦ a wide range of node densities, and ◦ a wide range of node mobility levels Future Work ◦ Investigate different buffering strategies ◦ Adapt buffering parameters to node mobility

Universität Stuttgart Institute of Parallel and Distributed Systems (IPVS) Universitätsstraße 38 D Stuttgart Q&A {khelil, marron, becker,