1. 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. 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. 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. 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 1.714 (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. 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. 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. 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. 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%). 12341234 B C Z D A 4 1 2 3 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. 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. 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. 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. 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. 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. 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. 15 Thank You! Questions and Comments? Papers / Posters / Slides / NS2 Code (MORRP, ORRP, OLSR + AODV with Beam switched directional antennas) [ http://networks.ecse.rpi.edu/~bownan ]http://networks.ecse.rpi.edu/~bownan bownan@gmail.com Introduction MORRP Key Concepts Simulation Results Conclusion