1 Using Directionality in Mobile Routing Bow-Nan Cheng (MIT LL) Murat Yuksel (Univ Nevada - Reno) Shivkumar Kalyanaraman (IBM IRL) (Work done at Rensselaer Polytechnic Institute)

2 Motivation Main Issue: Scalability Infrastructure / Wireless Mesh Networks Characteristics: Fixed, unlimited energy, virtually unlimited processing power Dynamism – Link Quality Optimize – High throughput, low latency, balanced load Mobile Adhoc Networks (MANET) Characteristics: Mobile, limited energy Dynamism – Node mobility + Link Quality Optimize – Reachability Sensor Networks Characteristics: Data-Centric, extreme limited energy Dynamism – Node State/Status (on/off) Optimize – Power consumption Introduction MORRP Key Concepts Simulation Results Conclusion Scalability  Layer 3: Network Layer

3 Scaling Networks: Trends in Layer 3 Flood-basedHierarchy/StructuredUnstructured/Flat Scalable Mobile Ad hoc / Fixed Wireless Networks DSR, AODV, TORA, DSDV Partial Flood: OLSR, HSLS LGF, VRR, GPSR+GLS Hierarchical Routing, Peer to Peer / Overlay Networks Wired Networks Gnutella Kazaa, DHT Approaches: CHORD, CAN OSPF, IEGRP, RIP OSPF Areas WSR (Mobicom 07) ORRP (ICNP 06) BubbleStorm (Sigcomm 07) LMS (PODC 05) Introduction MORRP Key Concepts Simulation Results Conclusion

4 Trends: Directional Communications Directional Antennas – Capacity Benefits  Theoretical Capacity Improvements - factor of 4  2 /sqrt(  ) where  and  are the spreads of the sending and receiving transceiver ~ 50x capacity with 8 Interfaces (Yi et al., 2005)  Sector Antennas in Cell Base Stations – Even only 3 sectors increases capacity by (Rappaport, 2006) A’ B’ C’ D’ A B C D Omni-directional A’ B’ C’ D’ A B C D Directional Directional/Directive AntennasHybrid FSO / RF MANETS Current RF-based Ad Hoc Networks:  omni-directional RF antennas  High-power – typically the most power consuming parts of laptops  Low bandwidth  Error-prone, high losses  Free Space Optics:  High bandwidth  Low Power  Dense Spatial Reuse  License-free band of operation Introduction MORRP Key Concepts Simulation Results Conclusion

5 ORRP Big Picture Up to 69% A 98% B 180 o Orthogonal Rendezvous Routing Protocol S T ORRP Primitive 1: Local sense of direction leads to ability to forward packets in opposite directions 2: Forwarding along Orthogonal lines has a high chance of intersection in area Introduction Wireless Mesh Networks Mobile Ad-Hoc Networks Overlay Networks ORRP High reach (98%), O(N 3/2 ) State complexity, Low path stretch (~1.2), high goodput, unstructured BUT.. What happens with mobility? 65% 55% 42% Increasing Mobility

6 A B What can we do? Replace intersection point with intersection region. Shift directions of send based on local movement information Route packets probabilistically rather than based on rigid next- hop paths. (No need for route maintenance!) Solution: a NEW kind of routing table: Directional Routing Table (DRT)  R Mobile-ORRP (MORRP) Introduction Introduction MORRP Key Concepts Simulation Results Conclusion

7 J K L M I H OP S N R Q F C G E A B MORRP Basic Example Original Path Original Direction (   ) New Direction (   ) R: Near Field DRT Region of Influence D: Near Field DRT Region of Influence S: Near Field DRT Region of Influence D D’ D R R’ R S 1.Proactive Element – Generates Rendezvous to Dest Paths 2.Reactive Element – Generates Source to Rendezvous Paths Introduction MORRP Key Concepts Simulation Results Conclusion

8 The Directional Routing Table Dest ID Next Hop Dest ID Next Hop Beam ID Dest IDs (% of Certainty) Beam ID BCD:ZBCD:Z BBZ:ZBBZ:Z BCD:ZBCD:Z BBZ:ZBBZ:Z 113:3113:3 B(90%), C(30%). Z(90%), D(40%) B C Z D A Routing TableRT w/ Beam IDDirectional RT (DRT) ID ID set of IDsSet of IDs set of IDs Routing Tables viewed from Node A Soft State – Traditional routing tables have a hard timeout for routing entries. Soft State decreases the level of certainty with time. Uncertainty with Distance – Nodes closer to a source will have increasingly more information about the location of the source than nodes farther away Uncertainty with Time – As time goes on, without updates, one will have lesser amount of information about the location of a node Uncertainty with Mobility – Neighbors can potentially be “covered” by different interfaces based on mobility speed and direction Use Decaying Bloom Filter (DBF) Introduction MORRP Key Concepts Simulation Results Conclusion

9 DRT Intra-node Decay Time Decay with Mobility Spread Decay with Mobility 7 8 x As node moves in direction +x, the certainty of being able to reach nodes covered by region 8 should decay faster than of region 7 depending on speed. This information is DROPPED. As node moves in direction +x, the certainty of being able to reach nodes covered by region 2 should be SPREAD to region 1 and 3 faster than the opposite direction. The information about a node in region 2 should be SPREAD to regions 1 and 3.    a a       x Introduction MORRP Key Concepts Simulation Results Conclusion

10 N N N N N N N N N N N N N N N N N N N MORRP Fields of Operation Near Field Operation  Uses “Near Field DRT” to match for nodes 2-3 hops away Far Field Operation  RREQ/RREP much like ORRP except nodes along path store info in “Far- Field DRT” SR D Introduction MORRP Key Concepts Simulation Results Conclusion

11 Performance Evaluation of MORRP Metrics Evaluated  Reachability – Percentage of nodes reachable by each node in network (Hypothesis: high reachability)  Delivery Success – Percentage of packets successfully delivered network-wide  Scalability – The total state control packets flooding the network (Hypothesis: higher than ORRP but lower than current protocols out there)  Average Path Length  End to End Delay (Latency)  Aggregate Network Goodput Scenarios Evaluated (NS2)  Evaluation of metrics vs. AODV (reactive), OLSR (proactive), GPSR with GLS (position-based), and ORRP under various node velocities, densities, topology-sizes, transmission rates.  Evaluation of metrics vs. AODV and OLSR modified to support beam- switched directional antennas. Introduction MORRP Key Concepts Simulation Results Conclusion

12 MORRP: Aggregate Goodput Results Aggregate Network Goodput vs. Traditional Routing Protocols  MORRP achieves from 10-14X the goodput of AODV, OLSR, and GPSR w/ GLS with an omni-directional antenna  Gains come from the move toward directional antennas (more efficient medium usage) Aggregate Network Goodput vs. AODV and OLSR modified with directional antennas  MORRP achieves about 15-20% increase in goodput vs. OLSR with multiple directional antennas  Gains come from using directionality more efficiently Introduction MORRP Key Concepts Simulation Results Conclusion

13 MORRP: Simulations Summary MORRP achieves high reachability (93% in mid-sized, 1300x1300m 2 and 87% in large-sized, 2000x2000 m 2 topologies) with high mobility (30m/s). With sparser and larger networks, MORRP performs fairly poorly (83% reach) suggesting additional research into proper DRT tuning is required. In lightly loaded networks, MORRP end-to-end latency is double of OLSR and about 7x smaller than AODV and 40x less than GPSR w/ GLS MORRP scales well by minimizing control packets sent MORRP yields over 10-14X the aggregate network throughput compared to traditional routing protocols with one omnidirectional interface  gains from using directional interfaces MORRP yields over 15-20% the aggregate network goodput compared to traditional routing protocols modified with 8 directional interfaces  gains from using directionality constructively Introduction MORRP Key Concepts Simulation Results Conclusion

14 MORRP: Key Contributions The Directional Routing Table  A replacement for traditional routing tables that routes based on probabilistic hints  Gives a basic building block for using directionality to overcome issues with high mobility in MANET and DTNs Using directionality in layer 3 to solve the issues caused by high mobility in MANETs MORRP achieves high reachability (87% - 93%) in high mobility (30m/s) MORRP scales well by minimizing control packets sent MORRP shows that high reach can be achieved in probabilistic routing without the need to frequently disseminate node position information. MORRP yields high aggregate network goodput with the gains coming not only from utilizing directional antennas, but utilizing the concept of directionality itself. MORRP is scalable and routes successfully with more relaxed requirements (No need for coordinate space embedding) Introduction MORRP Key Concepts Simulation Results Conclusion

15 Thank You! Questions and Comments? Papers / Posters / Slides / NS2 Code (MORRP, ORRP, OLSR + AODV with Beam switched directional antennas) [ ] Introduction MORRP Key Concepts Simulation Results Conclusion

EXTRA SLIDES 16

17 The Directional Routing Table Dest ID Next Hop Dest ID Next Hop Beam ID Dest IDs (% of Certainty) Beam ID BCD:ZBCD:Z BBZ:ZBBZ:Z BCD:ZBCD:Z BBZ:ZBBZ:Z 113:3113:3 B(90%), C(30%). Z(90%), D(40%) B C Z D A Routing TableRT w/ Beam IDDirectional RT (DRT) ID ID set of IDsSet of IDs set of IDs Routing Tables viewed from Node A Destination ID % of Certainties for each Beam ID stored within a Decaying Bloom Filter Bloom Filter – A space-efficient probabilistic data structure that is used to test whether an element is a member of a set.  Consist of a bit array and a set of k linearly independent hash functions  Storage: IDs are hashed to each of the k hash functions  stores a ``1’’ in position in the bit array for each hash function.  Search: IDs are hashed through each of the k hash functions  if all positions have a “1”, then the ID is in the set. Otherwise, the ID is not in the set Introduction Wireless Mesh Networks Mobile Ad-Hoc Networks Overlay Networks

18 DRT: Decaying Bloom Filter Primer h 1 (x) = (x ) % 32h 2 (x) = x % 32h 3 (x) = (x + 5) % 32h 4 (x) = (x ) % 32 h 1 (1) = 21 h 2 (1) = 1h 3 (1) = 6h 4 (1) = ID: 1ID: 2 h 1 (2) = 24h 2 (2) = 2h 3 (2) = 7h 4 (2) = ID: 6 h 1 (6) = 24h 2 (6) = 6h 3 (6) = 11h 4 (6) = 17 Search ID 1 – 4 of 4 bits match (IN set) Search ID 6 – 2 of 4 bit match (Not in set) Traditional Bloom Filter Decaying Bloom Filter (DBF) Search ID 1 – 4 of 4 bits match (100% chance in set) Search ID 6 – 2 of 4 bit match (50% chance in set) Introduction Wireless Mesh Networks Mobile Ad-Hoc Networks Overlay Networks 32 Bit Array: 4 Hash Funcs: A Dest Prob. (DBF) Beam ID DRT What policies For decaying bits can we employ?

DRT Inter-Node Decay Decay 50% of Bits D Noise C Low Info B Med Info A Strong Info S … … … … DRT at Node A BEAM ID: 1 BEAM ID: 2 BEAM ID: 3 BEAM ID: 4 Bitwise-OR … Merged DBF (Update DBF) … Decayed DBF (50% bits dropped) C B A … My ID (A) h 1 (x), h 2 (x), …, h n (x) Broadcasted by A to all Neighbors B is now 100% sure A is 1 hop away while only 50% sure C can be reached through sending out interface 1 Introduction Wireless Mesh Networks Mobile Ad-Hoc Networks Overlay Networks

20 DRT Intra-node Decay Time Decay with MobilitySpread Decay with Mobility 7 8 x As node moves in direction +x, bits in DBF of region 8 should decay faster than of region 7 depending on speed As node moves in direction +x, bits in DBF of region 2 should be SPREAD to region 1 and 3 faster than the opposite direction    a a       x Beam ID 1 Beam ID 2 Beam ID 3 Introduction Wireless Mesh Networks Mobile Ad-Hoc Networks Overlay Networks

21 Conclusion / Future Work Used Directionality to scale wireless networks (ORRP, MORRP) Used concept of Virtual Directions to scale overlay networks (VDR) Future Work: Extensions  Virtual direction abstraction analysis  Hybrid ORRP (that works with omnidirectional and directional antennas)  Analysis of Effect of knobs in MORRP New Directions with Directionality  Multi-path / multi-interface diversity  Directional Network Coding  Destination-based routing based on local directions Introduction Wireless Mesh Networks Mobile Ad-Hoc Networks Overlay Networks

22 Scaling Networks: OSI Model 1: Physical Layer 4: Transport Layer 3: Network Layer 2: Link Layer Layers 5-7 Z C E F H G A B A Z Physical Layer – Handles transmission of bits through a medium Link Layer – Manages node-to-node transmissions Network Layer – Manages routing from end-to-end Transport Layer – Handles reliable transmissions end-to-end Introduction Wireless Mesh Networks Mobile Ad-Hoc Networks Overlay Networks Application/Presentation/Session Layers – Deal with the actual programs/data

23 Research Objectives Wireless Mesh Context  Can directionality be used to address issues with scalability at higher throughput in layer 3 routing? Mobile Ad Hoc Context  Can directionality be used to address issues with high mobility and throughput in layer 3 routing? Overlay Network Context  Can directionality be used to scale flat, unstructured networks? Introduction Wireless Mesh Networks Mobile Ad-Hoc Networks Overlay Networks

24 By removing position information, can we still efficiently route packets? Orthogonal Rendezvous Routing Protocol L3: Geographic Routing using Node IDs (eg. GPSR, TBF etc.) L2: ID to Location Mapping (eg. GHT, GLS etc.) L1: Node Localization ORRP N/A Issues in Position-based Schemes S N WE (0,4) (4,6) (5,1) (8,5) (12,3) (15,5)S D D(X,Y)? ? Introduction Wireless Mesh Networks Mobile Ad-Hoc Networks Overlay Networks

25 ORRP Design Considerations Considerations:  High probability of connectivity without position information [Reachability]  Scalability O(N 3/2 ) total state information maintained. (O(N 1/2 ) per node state information)  Even distribution of state information leading to no single point of failure [State Complexity]  Handles voids and sparse networks Trade-offs  Path Stretch  Probabilistic Reachability Introduction Wireless Mesh Networks Mobile Ad-Hoc Networks Overlay Networks

26 ORRP Proactive and Reactive Elements Node B Fwd Table DestNextHops AA1 120 o North Node F Fwd Table DestNextHops AB2 Node C Fwd Table DestNextHopsDir AF3120 o DD1230 o 1.ORRP Announcements (Proactive) – Generates Rendezvous-to-Destination Routes 2.ORRP Route Request (RREQ) Packets (Reactive) – Generates Source-to-Rendezvous Rts 3.ORRP Route Reply (RREP) Packets (Reactive) 4.Data path after route generation D C F B A Introduction Wireless Mesh Networks Mobile Ad-Hoc Networks Overlay Networks A to D

27 Reachability Numerical Analysis P{unreachable} = P{intersections not in rectangle} 4 Possible Intersection Points %99.75% 57% 67.7% Probability of Unreach highest at perimeters and corners NS2 Simulations with MAM show around 92% reachability Introduction Wireless Mesh Networks Mobile Ad-Hoc Networks Overlay Networks

28 Path Stretch Analysis Average Stretch for various topologies Square Topology – Circular Topology – X 4 Rectangular – 3.24 Expected Stretch – x = 1.255x = 1.15 x = 3.24 Introduction Wireless Mesh Networks Mobile Ad-Hoc Networks Overlay Networks

29 State Complexity Analysis/Simulations GPSRDSDVXYLSORRP Node StateO(1)O(n 2 )O(n 3/2 ) ReachabilityHigh 100%High (99%) Name ResolutionO(n log n)O(1) InvariantsGeographyNoneGlobal Comp.Local Comp. ORRP state scales with Order N 3/2 ORRP states are distributed fairly evenly in an unstructured manner (no single point of failure) Introduction Wireless Mesh Networks Mobile Ad-Hoc Networks Overlay Networks

30 ORRP: Simulation Results Summary Base Case  Reach – 99% for Square topologies, 92% for Rectangular topologies (MAM helped)  Path Stretch – Roughly 1.2  Goodput – About 30x more aggregate network goodput than AODV, 10x more aggregate network goodput than OLSR and 35x more aggregate network goodput than GPSR with GLS (due to better usage of medium) Network Voids  Average path length fairly constant (Reach and State not different) Additional Lines  Reach/Path Stretch – All showed large gains from 1 to 2 lines but diminishing returns thereafter  Goodput – Higher average network throughput with additional lines (better paths and higher reach) but not by much Varying Number of Interfaces  Significant increase in reachability from 4 to 8 interfaces, but gains trail off Introduction Wireless Mesh Networks Mobile Ad-Hoc Networks Overlay Networks

31 ORRP: Summary ORRP achieves high reachability in random topologies ORRP achieves O(N 3/2 ) state maintenance – scalable even with flat, unstructured routing ORRP achieves low path stretch (Tradeoff for connectivity under relaxed information is very small!) ORRP achieves roughly 30X in aggregate network goodput compared to AODV, 10X the aggregate network goodput compared to OLSR, and 35X the aggregate network goodput compared to GPSR with GLS. Relevant Papers B. Cheng, M. Yuksel, and S. Kalyanaraman, Rendezvous-based Directional Routing: A Performance Analysis, In Proceedings of IEEE International Conference on Broadband Communications, Networks, and Systems (BROADNETS), Raleigh, NC, September (invited paper) B. Cheng, M. Yuksel, and S. Kalyanaraman, Directional Routing for Wireless Mesh Networks: A Performance Evaluation, Proceedings of IEEE Workshop on Local and Metropolitan Area Networks (LANMAN), Princeton, NJ, June B. Cheng, M. Yuksel, and S. Kalyanaraman, Orthogonal Rendezvous Routing Protocol for Wireless Mesh Networks, Proceedings of IEEE International Conference on Network Protocols (ICNP), pages , Santa Barbara, Nov Introduction Wireless Mesh Networks Mobile Ad-Hoc Networks Overlay Networks

32 Wireless Nets: Key Concepts to Abstract Directionality CAN be used to provide high reach, high goodput, low latency routing in wireless mesh (ORRP) and highly mobile adhoc networks (MORRP) Primitives:  Local directionality is enough to maintain forwarding along a straight line  Two sets of orthogonal lines intersect with a high probability in a bounded region Overlay Networks:  Can we take these concepts to scale unstructured, flat, overlay networks? Introduction Wireless Mesh Networks Mobile Ad-Hoc Networks Overlay Networks

33 Virtual Direction Routing Introduction Structured vs. Unstructured Overlay Networks  Unstructured P2P systems make little or no requirement on how overlay topologies are established and are easy to build and robust to churn Typical Search Technique (Unstructured Networks)  Flooding / Normalized Flooding High Reach Low path stretch Not scalable  Random Walk Need high TTL for high reach Long paths Scalable, but hard to find rare objects Virtual Direction Routing  Globally consistent sense of direction (west is always west)  Scalable interface to neighbor mapping  Routing can be done similarly to ORRP Focus (for now)  Small world approximations Random Walk Virtual Direction Routing Flooding Normalized Flooding Introduction Wireless Mesh Networks Mobile Ad-Hoc Networks Overlay Networks

34 VDR: Neighbor to Virtual Interface Map Neighbors are either physical neighbors connected by interfaces or neighbors under a certain RTT latency away (logical neighbors) Neighbor to Virtual Interface Mapping  Each neighbor ID is hashed to 160 bit IDs using SHA-1 (to standardize small or large IDs)  The virtual interface assigned to the neighbor is a function of its hashed ID (Hashed ID % number of virtual interfaces) Virtual Interfaces 30 % 8 = 6 15 % 8 = 7 10 % 8 = 2 26 % 8 = 2 68 % 8 = Introduction Wireless Mesh Networks Mobile Ad-Hoc Networks Overlay Networks Example: Neighbor IDs used Instead Of SHA-1 Hashes

35 VDR: State Seeding and Route Request |10 – 1| = 9 |26 – 1| = 25 |5 – 1| = 4 |13 – 1| = 12 |14 – 1| = 13 |22 – 1| = 21 Ex: Seed Source: Node 1 State Seeding – State info forwarded in orthogonal directions, biasing packets toward IDs that are closer to SOURCE ID. Packets are forwarded in virtual straight lines |10 – 12| = 2 |26 – 12| = 15 |5 – 12| = 7 |13 – 12| = 1 |6 – 12| = 6 |38 – 12| = 26 Ex: Route Request: Node 12 RREQ Source: Node 1 Route Request – RREQ packets are forwarded in orthogonal directions, biasing packets towards REQUESTED ID Introduction Wireless Mesh Networks Mobile Ad-Hoc Networks Overlay Networks

36 VDR: Simulation Parameters RREQ: Node 12 Rendezvous Node VDR Route Request Virtual View Seed Path RREQ Path RREP Path Flooding Random Walk VDR – Random NB Send (VDR-R) Virtual Direction Routing Normalized Flooding Random Walk Routing (RWR) Simulation of VDR vs. RWR, VDR-R  VDR-R: VDR with random neighbor forwarding (no biasing)  RWR: Data is seeded in 4 random walks and 4 walkers are sent for search PeerSim – 50,000 Nodes, Static + Dynamic Network  Reach Probability – High (98% w/ TTL of 100)  Average Path Stretch – High (16)  State and Load Spread – Not evenly distributed Introduction Wireless Mesh Networks Mobile Ad-Hoc Networks Overlay Networks

37 VDR: Robustness Results 5% drop 15% drop 12% drop State Distribution Network-wide  Average States maintained relatively equal for VDR, VDR-R and RWR at  VDR States are not very evenly distributed, with some nodes having more state than others. This is due to the sending bias Robustness to Network Churn  VDR drops only 5% compared to VDR-R and RWR which drop % reach when going from 0% to 50% network churn  Even with a TTL of 50, VDR reaches a good amount of the network Introduction Wireless Mesh Networks Mobile Ad-Hoc Networks Overlay Networks

38 VDR: Key Contributions Introduction of the concept of Virtual Directions to eliminate need for structure (coordinate space, DHT structures) to scale flat, unstructured overlay networks A flat, highly scalable, and resilient to churn routing algorithm for overlay networks VDR provides high reach (98% even only for a TTL of 100 in a 50,000 node network) VDR drops only 2-5% going from 0% churn to 50% churn Relevant Papers B. Cheng, M. Yuksel, and S. Kalyanaraman, Virtual Direction Routing for Overlay Networks, In preparation for submission to IEEE Peer to Peer Computing (P2P) Introduction Wireless Mesh Networks Mobile Ad-Hoc Networks Overlay Networks