1. ASWP – Ad-hoc Routing with Interference Consideration June 28, 2005

2. Scenarios Deploy troops into field Goals QoS Traffic classes, flow requirements Scalable Difficulty Interference

3. Outline Problem description Interference model Possible solutions Ad-hoc shortest widest path ASWP problem Proposed algorithm Simulations Conclusion

4. Interference is critical Wired networks Independent links Ad-hoc networks Neighbor links interfere Interference range > Transmission range For simulations Tx range = 500 m Ix range = 1 km

5. Interference Model Conflict graph G(X,A )  CG(A,I ) Undirected graph Violate Bellman’s Principle of Optimality Clique Constraint Node 1  3: path A (c) Node 1  5: path A-D-E (c/3) path B-C-D-E (c/2)

6. Routing solutions CG-based methods Ideal solution Clique constraint Row constraint Two-hops interference model AQOR MAC scheduling SEEDEX, TDM/CDM Connectivity only DSR, AODV

7. Ideal solution Goals Solve routing/scheduling simultaneously Maximize concurrent transmissions Solution Identify sets of non-interfering links, I.e., Independent Sets in CG Schedule Independent Sets s.t. QoS requirements are met for flows Very hard problem, even if centralized Finding Independent Sets is NP-complete Scheduling is also difficult

8. Outline Problem description Interference model Possible solutions Ad-hoc shortest widest path ASWP problem Proposed algorithm Simulations Conclusion

9. Ad-Hoc Shortest Widest Path Path metrics Width Length Shortest widest path between (s,d ) Want to find the widest path; If more than one, take the shortest. NP-complete

10. ASWP Design Separate scheduling and routing Finding the widest path Distributed algorithm Clique computation Path computation Minimize overhead Localized cliques

11. ASWP Heuristic Bellman approach Key step Compute path width for one-hop extension Bottleneck clique Unchanged A maximal clique that the extending link belongs to Can be done locally K-shortest-path approach

12. Outline Problem description Interference model Possible solutions Ad-hoc shortest widest path ASWP problem Proposed algorithm Simulations Conclusion

13. Simulations – path width 50-node network Distant s/d pair 7 hops away X axis: load = average clique utilization Y axis: path width

14. Simulations – path width 50-node network Load = 0.32 All pairs performance X axis: distance between s/d pair Y axis (upper): ratio of improved s/d pair Y axis (lower): average improvement

15. Simulations – admission ratio 50-node network Dynamic simulation 5 s/d pairs Randomly chosen Given distance Traffic model Flow requests: 4Kb/s, 10,000 flow requests Incoming rate: 0.32 flows per second Duration: uniform distribution between 400 and 2800 seconds Load = 0.32  (400+2800)/2  4 = 2048 Kb/s = 2 Mb/s Results: admission ratio (%) distanceSPASWP2ASWP4ASWP 2 hops99.4100 4 hops47.954.8 54.7 7 hops31.844.143.443.9 Mixed66.571.471.070.9

16. More on ASWP Optimal path = shortest widest path Complexity Polynomial, but … Running time (sec): Optimal SWP necessary? Wide path = long path Long term behavior: bad SPASWP2ASWP4ASWP 5.327.950.480.0

17. Outline Problem description Interference model Possible solutions Ad-hoc shortest widest path ASWP problem Proposed algorithm Simulations Conclusion

18. Overall goals Bandwidth guaranteed path Long-term admission ratio Interference model Conflict constraints ASWP solution Find shortest widest path Distributed algorithm Bellman-Ford architecture + k-shortest-path approach A small k value is the good trade-off