minimizing multi-hop wireless routing state under application-based accuracy constraints
DESCRIPTION
Minimizing Multi-Hop Wireless Routing State under Application-based Accuracy Constraints. Mustafa Kilavuz & Murat Yuksel University of Nevada, Reno. Motivation. Need of application-specific routings Flexibility, more control Expressiveness of the routing interface must be at sufficient level - PowerPoint PPT PresentationTRANSCRIPT
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Minimizing Multi-Hop Wireless Routing State under Application-based Accuracy Constraints
Mustafa Kilavuz & Murat YukselUniversity of Nevada, Reno
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Motivation
•Need of application-specific routings▫Flexibility, more control▫Expressiveness of the routing interface
must be at sufficient level▫Send(src, dst, data, option)▫Constraints
Path quality Path accuracy Path cost
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Our focus
•Minimizing routing state under application specific constraints▫Trajectory-based Routing (TBR)
Geographic routing Application-specific routing
Path accuracy: follow a trajectory Very small state information
▫State cost – Path accuracy
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Trajectory-based Routing (TBR)
TBR Model User Application
Trajectory Approximator
Trajectory-based
Forwarding (TBF)
Actual Trajector
y
Ideal Trajector
y
Constraints
Approximation Error
Destination
Source
Approximate Trajectory
y = ax3 + bx2 + cx + d
y = ax2 + bx + c
y = ax + b
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Error• The area between the ideal and approximate
trajectories is called error.• Error is a measure of how accurate the
approximate trajectory is.• Accuracy constraint is an error tolerance
percentage that the total error should not exceed this limit. e.g. 30% or 40%. Otherwise it is considered as an infeasible solution.
• To calculate this we need to define what 100% error is. We can define it▫ Intuitively, by giving it a reasonable quantity.▫ Or considering the error of a single line from
source to destination 100% error assuming that any solution would be better than this approximation.
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TBR Demonstration
Ideal Trajectory Actual
Trajectory
Data
Approximate Trajectory
Source
Destination
Intermediate Nodes
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Cost Calculations• Aggregate cost = +
Source
Destination
Data
DataData
Packet Header Cost
Network state cost
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Solving the problem
•Trajectory approximation is NP-hard▫Weight Constrained Shortest Path Problem
•Methods▫Exhaustive (slow, optimum)▫Genetic Algorithm▫Heuristics
Equal Error Heuristic Longest Representation Heuristic
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1. Exhaustive Search
Possible Split Points
Approximate Trajectory
(curve + line + curve)
Ideal Trajectory
SelectedSplit Points
0 0 0 0 0 1 0 0 0 0 0 1 0 00 0 0 01 1
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2. Genetic Algorithm•The first N+2 bits represent possible split
points•Next bit couples chooses which
representation is used starting from the corresponding split point
1 0 1 0 0 1 0 1 1 0 0 0 1 1…… 1 1……
N 2(N+1)
2nd Degree Curve line
3rd Degree Curve
Source Destination
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3. Equal Error•First find the best fit to the whole trajectory•Calculate the error• If it is higher than the error tolerance
▫Divide the trajectory into two equal pieces and repeat the process for each piece
30% errorError Tolerance
= 20%
5% error
7% error
Ideal Trajectory
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4. Longest Representation•Fit a representation to the shortest interval• Increase the interval and find the best fit until
we cannot find one under the error tolerance•Repeat the process for the rest of the trajectory
1% error
Error Tolerance = 5%1%
error
4% error
9% error 0%
error
1% error
4% error
2% error
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Performance evaluation
•Comparison of algorithms▫Cost▫Time
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Error tolerance %5
GA performs pretty close to the exhaustive
search
Longest representation heuristic is not
bad
Exhaustive Search
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Error tolerance %50
GA performs pretty close to the exhaustive
search
Longest representation heuristic is not
bad
Exhaustive Search
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Error tolerance %5
Equal Error heuristic runs
in no time
Exhaustive search takes
too much time
These run in reasonable
amount of time
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Error tolerance %50
Equal Error heuristic runs
in no time
These run in reasonable
amount of time
Exhaustive search takes
too much time
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Customization to the packet header and network state cost trade-off
Ideal TrajectoryApproximate Trajectory
High Network State CostLow Transmission Cost
Low Network State CostHigh Transmission Cost
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Summary?
•Presented an optimization framework minimizing routing state under application-specific constraints
•Applied on TBR, minimizing the state cost under path accuracy constraint
•Proposed four methods to solve the approximation problem which is NP-hard
•Showed that the problem is customizable for different specifications
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Future Work?
•User application input needs to be more defined
•The whole framework is to be tested together
•New representations for trajectories•Multiple connections•Mobility
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Questions?