ASWP – Ad-hoc Routing with Interference Consideration

June 28, 2005

Scenarios Deploy troops into field Goals

QoS Traffic classes, flow requirements

Scalable Difficulty

Interference

Outline Problem description

Interference model Possible solutions

Ad-hoc shortest widest path ASWP problem Proposed algorithm

Simulations Conclusion

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

InterferenceRange

TransmissionRange

Node A

Node D

Node C

Node B

Link 2

Link 1

Interference Model Conflict graph

G(X,A ) CG(A,I ) Undirected graph

Violate Bellman’s Principle of Optimality

Clique Constraint • Node 13: path A (c)• Node 15: path A-D-E (c/3)

path B-C-D-E (c/2)

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

Outline Problem description

Interference model Possible solutions

Ad-hoc shortest widest path ASWP problem Proposed algorithm

Simulations Conclusion

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

ASWP Design Separate scheduling and routing

Finding the widest path Distributed algorithm

Clique computation Path computation

Minimize overhead Localized cliques

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

Outline Problem description

Interference model Possible solutions

Ad-hoc shortest widest path ASWP problem Proposed algorithm

Simulations Conclusion

Simulations – path width

50-node network Distant s/d pair

7 hops away X axis: load =

average clique utilization

Y axis: path width

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

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)/24 = 2048 Kb/s = 2 Mb/s

Results: admission ratio (%)

distance

SP ASWP 2ASWP

4ASWP

2 hops 99.4 100 100 100

4 hops 47.9 54.8 54.8 54.7

7 hops 31.8 44.1 43.4 43.9

Mixed 66.5 71.4 71.0 70.9

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

SP ASWP 2ASWP

4ASWP

5.3 27.9 50.4 80.0

Outline Problem description

Interference model Possible solutions

Ad-hoc shortest widest path ASWP problem Proposed algorithm

Simulations Conclusion

Conclusion 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