minimizing recovery state in geographic ad-hoc routing noa arad school of electrical engineering tel...
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Minimizing Recovery State In Geographic Ad-Hoc Routing
Noa Arad
School of Electrical Engineering
Tel Aviv University
Yuval Shavitt
School of Electrical Engineering
Tel Aviv University
MobiHoc ‘06
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Outline
Introduction The NEAR (Node Elevation Ad-hoc Routing)
Algorithm Simulation Conclusion
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Introduction_background
Ad-Hoc network is a network without AP, and they have mobile ability in general
Routing schemes of mobile Ad-Hoc networks– Topology-based routing– Position-based routing
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Introduction_motivations
Most position-based routing protocols can’t prevent the packet from reaching a concave node
sourcedestinationconcave node
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Introduction_motivations
Most position-based routing protocols can’t prevent the packet from reaching a concave node
Recovery Algorithm may choose a long path
sourcedestination
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Introduction_goals
To prevent the routing algorithm from entering concave node
To minimize the recovery state
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Concave Node
A node that has no neighbor that can make a greedy progress towards the destination
A concave node can not be predicted in advance, based on the position of its neighbor nodes
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The NEAR Algorithm
Repositioning Algorithm
Routing Algorithm
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Repositioning Algorithm_goals
To identify and mark concave node
To improve the greedy routing
To improve the recovery process
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Repositioning Algorithm_identify and mark concave node
A
B
C
D
α = 180°
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A
B
Cα = 180°
A’floating nodez = z+1
Repositioning Algorithm_repositioning
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Repositioning Algorithm_repositioning
A’(x1, y1, 1)
A B C
A B C
B’(x2, y2, 1)
A”(x1, y1, 2)
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Repositioning Algorithm_threshold angle
A minimal angle of 180° is simply too low, and almost all nodes will float
α = 210° − 230° was found to be best for various scenarios
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Repositioning Algorithm_an example
Before repositioning After repositioning
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Routing Algorithm_three states
Descending
source destination
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Routing Algorithm_three states
Descending
Ground to ground
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Routing Algorithm_three states
Descending
Ground to ground
Climbing
sourcedestination
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Routing Algorithm_descending
A’(x1, y1, 2)
A B
B’(x2, y2, 1)
C
A B C Zmax = 1Zmax
Zmax = 0
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Routing Algorithm_ground to ground
Protocol– GPSR
Zmax
– Always 0
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Routing Algorithm_climbing
A’(x1, y1, 2)
A B
B’(x2, y2, 1)
C
A B C
Zmax = 2
Zmax = 2
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Routing Algorithm_recovery state
Environment– Ground to ground
Protocol– GPSR
Minimizing the recovery state
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Simulation_environment 1
Field: 2000m x 2000m Variable network density
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Simulation_elimination of concave nodes
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Simulation_routing hops
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Simulation_routing distance
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Simulation_routing success
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Simulation_environment 2
Mobile node 1– 90Km/h – Updating messages per second
Mobile node 2– 4.5Km/h – Updating messages every 20 seconds
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Simulation_average numbers of iterations
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Simulation_average numbers of iterations per node
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Simulation_change of physical links per node
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Conclusions
Smoothing the shape of voids and concave nodes can be predicted by their added virtual height
Improving greedy routing and minimizing the recovery state
NEAR is believed to improve ad-hoc networks’ ability to deal with voids and concave nodes
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Thank You !