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

Outline

Introduction The NEAR (Node Elevation Ad-hoc Routing)

Algorithm Simulation Conclusion

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

Introduction_motivations

Most position-based routing protocols can’t prevent the packet from reaching a concave node

sourcedestinationconcave node

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

Introduction_goals

To prevent the routing algorithm from entering concave node

To minimize the recovery state

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

The NEAR Algorithm

Repositioning Algorithm

Routing Algorithm

Repositioning Algorithm_goals

To identify and mark concave node

To improve the greedy routing

To improve the recovery process

Repositioning Algorithm_identify and mark concave node

A

B

C

D

α = 180°

A

B

Cα = 180°

A’floating nodez = z+1

Repositioning Algorithm_repositioning

Repositioning Algorithm_repositioning

A’(x1, y1, 1)

A B C

A B C

B’(x2, y2, 1)

A”(x1, y1, 2)

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

Repositioning Algorithm_an example

Before repositioning After repositioning

Routing Algorithm_three states

Descending

source destination

Routing Algorithm_three states

Descending

Ground to ground

Routing Algorithm_three states

Descending

Ground to ground

Climbing

sourcedestination

Routing Algorithm_descending

A’(x1, y1, 2)

A B

B’(x2, y2, 1)

C

A B C Zmax = 1Zmax

Zmax = 0

Routing Algorithm_ground to ground

Protocol– GPSR

Zmax

– Always 0

Routing Algorithm_climbing

A’(x1, y1, 2)

A B

B’(x2, y2, 1)

C

A B C

Zmax = 2

Zmax = 2

Routing Algorithm_recovery state

Environment– Ground to ground

Protocol– GPSR

Minimizing the recovery state

Simulation_environment 1

Field: 2000m x 2000m Variable network density

Simulation_elimination of concave nodes

Simulation_routing hops

Simulation_routing distance

Simulation_routing success

Simulation_environment 2

Mobile node 1– 90Km/h – Updating messages per second

Mobile node 2– 4.5Km/h – Updating messages every 20 seconds

Simulation_average numbers of iterations

Simulation_average numbers of iterations per node

Simulation_change of physical links per node

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

Thank You !