Towards Scale-Free Routing in MANETs J.J. Garcia-Luna-Aceves, Stephen Dabideen, Rolando Menchcaca- Mendez, Dhananjay Sampath, Brad Smith University of California Santa Cruz (UCSC)

2 2 2 Proactive RoutingD a S e c f h b Too many nodes are forced to know about how to reach each destination! Does not work well with random partitions Path first, then data forwarding D Information about D propagates away from D in a circle of radius r

3 3 3 On-Demand RoutingD a S e c f h b Too many nodes are forced to help find or repair ways to reach a few destinations! (RREQ flooding). Does not work with partitioned networks! S Too few nodes keep state for D. So too many nodes try to fix broken paths Information from S propagates away from S in a circle of radius r Nodes with paths to D reply to S. Path first, then data forwarding

4 4 Approaches  Exploit temporal and spatial locality of reference of information flows u Nodes need not know about all links, nodes or clusters in the network.  Establish pre-ordering of nodes using dynamic addresses to reduce route signaling u Time and effort to establish routes is more important than route optimality. u Keep overhead increase sub-linear with number of nodes.  Establish ordering over multiple dimensions to provide more alternatives for routing

5 5 Two Approaches Today  PRIME: Protocol for Routing in Interest-defined Mesh Enclaves u Exploit temporal and spatial locality of reference u Nodes need not know about all links, nodes or clusters in the network.  PROSE: Positional Routing Over Searched Elements u Establish pre-ordering of nodes to reduce signaling u Time and effort to establish routes is more important than route optimality. u Keep overhead increase sub-linear with number of nodes.

6 6 PRIME  Nodes state their interest in certain destinations persistently.  Destinations with interest announce their presence.  Only those relays between source-destination pairs of interest incur signaling overhead.  Destinations can be anything (individual nodes, groups, content objects, roles, etc.) and any node can be a source.  Establish regions of interest (“enclaves”) for the dissemination of routing information between sources and destinations.

7 7 PRIME: Meshes and Enclaves

8 8 PRIME Signaling  First source with interest in (unicast or multicast) sends first data packet piggybacked in a mesh-activation request (MR) u MR specifies, among other fields, a horizon threshold and the persistence of the interest  Once a destination is activated with MR, it starts advertising its existence using mesh announcements (MA). u MA states: Dest ID, core ID, Dist, next hop, Seq #, and membership Destinations, interested sources, and relays needed between them remain active for as long as there is interest in the connected component of the network. Destinations, interested sources, and relays needed between them remain active for as long as there is interest in the connected component of the network. MAs and MRs sent in HELLOs MAs and MRs sent in HELLOs

9 9 PRIME: Opportunistic Signaling

10 Performance Comparison  PRIME vs ODMRP+OLSR and ODMRP+AODV  Assume infinite horizon and persistence for PRIME  Metrics: Packet delivery ratio, Group delivery ratio, end-to- end delay, and total overhead  CBR sources at 10 pps, a packet is 256 bytes  Infinite horizon and persistence!  TDMA and as MAC u Timers in ODMRP tailored to TDMA

11 Delivery Ratio for Multicast and Unicast Combined ( MAC) Delivery vs. number of multicast groups: Group area 900x900, 15-node groups, 3 sources per group, and 5 unicast flows.

12 Overhead for Multicast and Unicast Combined ( MAC) Overhead vs. number of multicast groups: Group area 900x900, 15-node groups, 3 sources per group, and 5 unicast flows.

13 Delivery Ratio for Multicast (TDMA MAC) Group delivery vs. number of multicast groups: Group area 900x900, 15-node groups, 3 sources per group.

14 End-to-End Delay for Multicast (TDMA MAC) Delay vs. number of multicast groups: Group area 900x900, 15-node groups, 3 sources per group.

15 PROSE  “Prosa” Latin for straightforward, simple  Two components u Positional Labels F Simple HELLO mechanism labels all nodes with positional labels relative to one ore more elected “roots.” u Using the right DHT F “Link” sources to destinations F Distributed and self organizing  Routing is automatic from the positional label  Overhead scalability is only O(log d N), where d = node degree, N = number of nodes u mostly due to maintaining the DHT

16 PROSE: Positional Labels Define Routes Root node (A) is elected in a distributed fashion using HELLOs Each node is given a label relative to node A with same HELLOs Positional labels of source and destination define the route (prefix routing) How does node K know that node J’s label is 0210?

17 Global ID Positional Label Hash DHT in PROSE D routes its mapping to its anchor’s positional label (A D ) S sends request for positional label of D to A D Anchors store the global ID to positional label mappings. These entries form the DHT

18 DHT in PROSE S learns of D’s current label and routes directly to it. Anchor forwards packet/request to known label for D Hashing distributes the load of anchoring D replies to S

19 PROSE Order Performance  Signaling overhead: u Establishing labels at each node: F Complexity is O(1), because each node sends HELLO to state its own label. u Publish and subscribe: F Communicating ID-to-label mapping from destinations to anchors is publishing F Obtaining label for destinations from anchors is subscribing F Complexity is O(log d (N)), because longest path from destination to its anchor is 2 log d (N) and mappings are aggregated as they traverse the network.  Route stretch: u Bounded by the amount of neighborhood routing information and the worst prefix route u Order stretch with two-hop routing information is O(log d (N+1))  Routing table complexity: u Labels have length d h, with h = height of DAG u Each node stores O(d 2 ) + O(1) entries (i.e., two-hop neighbors and destinations of interest)

20 PROSE Performance  Qualnet Simulator  500 nodes  250 active flows  Flows distributed exponentially with mean of 1/20 th the simulation duration  Simulation time = 1200s  10 Random seeds  Random Waypoint mobility  Pause Times varying between 1 to 10m/s  Protocols compared: AODV, OLSR, FSR Simulation Setup 900 m 600 m

21 PROSE Performance  OLSR has heavy control overhead and tanks under high mobility  AODV suffers from constant flooding as nodes move around  FSR performs worse than AODV but better than OLSR as the scoped floods reduce interference  PROSE performs better as there is lesser interference and packets are delivered even when nodes are highly mobile. Delivery Ratio vs. Pause Time PROSE

22 PROSE Performance Control Overhead vs. Pause Time PROSE

23 Degree Label Resets PROSE Overhead: Decreases with Density

24 PROSE Overhead: Orders of Magnitude Smaller than Traditional Proactive and Reactive Routing

25 Next Steps  Integrate PRIME with a schedule-based MAC  Provide multicast support in PROSE  Compare PRIME and PROSE  Develop integrated PRIME and PROSE mechanisms.  Complete multi-root PROSE  Apply PRIME and PROSE mechanisms to content-basedrouting  QoS and multi-dimensional routing  Integrate PROSE with MAC  Provide Linux implementations of PROSE and PRIME  Make QualNet and Linux implementations available topublic

26 Publications over Past Year Routing in Wireless Networks: 1. PRIME: Menchaca-Mendez and J.J. Garcia-Luna-Aceves, “An Interest-Driven Approach to Integrated Unicast and Multicast Routing in MANETs,” The 16th IEEE International Conference on Network Protocols (ICNP 08), Oct , 2008, Orlando, Florida 2. D. Sampath and J.J. Garcia-Luna-Aceves. “Proactive Path Maintenance in Regions of Interest,” Proc. LOCAN 2008: 4th International Workshop on Localized Communication and Topology Protocols for Ad hoc Networks, September 29, 2008, Atlanta, Georgia. 3. BEST PAPER AWARD : 3. BEST PAPER AWARD : R. Menchaca-Mendez and J.J. Garcia-Luna-Aceves, “Scalable Multicast Routing in MANETs Using Sender-Initiated Multicast Meshes,” Proc. IEEE MASS 2008: Fifth IEEE International Conference on Mobile Ad hoc and Sensor Systems, September 29 - October 2, 2008, Atlanta, Georgia. 4. S. Dabideen and J.J. Garcia-Luna-Aceves, “Multi-Dimensional Routing,” Proc. ANC 08: IEEE Workshop on Advanced Networking and Communications 2008, August 3–7, 2008, St. Thomas U.S. Virgin Islands. 5. B. Smith and J.J. Garcia-Luna-Aceves, “ Best-Effort Quality-of-Service,” Proc. IEEE ICCCN 2008, August 3–7, 2008, St. Thomas U.S. Virgin Islands. 6. X. Wu, H. Xu, H. Sadjadpour, and J.J. Garcia-Luna-Aceves, “Proactive or Reactive Routing: A Unified Analytical Framework in MANETs,” Proc. IEEE ICCCN 2008, August 3–7, 2008, St. Thomas U.S. Virgin Islands. 7. X. Wang and J.J. Garcia-Luna-Aceves, "Distributed Joint Channel Assignment, Routing, and Scheduling for Wireless Mesh Networks," Computer Communications, Elsevier. Accepted for publication, X. Wang and J.J. Garcia-Luna-Aceves, ``Embracing Interference in Ad Hoc Networks Using Joint Routing and Scheduling with Multiple Packet Reception,'' Ad Hoc Networks, Elsevier. Accepted for publication, May X. Wu, H. Sadjadpour, and J.J. Garcia-Luna-Aceves, ``A Hybrid View of Mobility in MANETs: Analytical Models and Simulation Study,'' Computer Communication, Elsevier. Invited Paper, Best Paper Series. Accepted for publication, 2008.

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