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