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A Novel Mechanism for Flooding Based Route Discovery in Ad hoc Networks

Jian Li and Prasant Mohapatra

Networks Lab, UC Davis

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Agenda

Introduction

Our Proposal: PANDA

PANDA Algorithms

Performance Evaluation

Conclusion

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What is a Mobile Ad hoc Network (MANET)? A set of wireless devices that can move around freely Form a dynamic topology via ad hoc wireless links No fixed infrastructure, no central administration Limited bandwidth, battery, etc

MANET applications Military tactical communication Disaster relief Conferencing ...

Intro (1): MANET

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Intro (2): Routing in MANET

Why routing is different in MANETs? Unpredictable moving pattern

Dynamic link availability, limited bandwidth

Fast changing topology

Other concerns: battery source, security issues, etc

Proposed routing protocols Proactive v.s. Reactive (On-Demand)

Topology-based v.s. Position-based

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Intro (3): Flooding based Routing Flooding technique is often used by on demand

protocols, such as Dynamic Source Routing(DSR) and Ad hoc On-demand Distance Vector(AODV) routing

S

D Route Request

Route Reply

Broadcast Storm Problem: for example, node C may receive broadcast messages from nodes A and B almost simultaneously, which results in a collision.

CA

B

Proposed Solution: each node applies a random delay before rebroadcasting a message or responding to a broadcast message.

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Intro(4): Random Rebroadcast Delay

Random Rebroadcast Delay (RRD) approach can solve the problem of “broadcast storm” effectively

But, the “Randomness” also introduces a new problem: “Next-hop Racing” behavior

S

I

J

K

M

L

D

At nodes L and M, node I “wins” over node J,

even if J is better than I in term of link lifetime

Suppose:Node I moves much faster than Node J,so the link S-I will be broken sooner than the link S-J.

It happens that:Node I rebroadcast earlier than Node J.

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Agenda

Introduction

Our Proposal: PANDA

PANDA Algorithms

Performance Evaluation

Conclusion

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Our Proposal: PANDA (1)

PANDA Positional Attributes based Next-hop Determination Approach

Basic idea of PANDA Classify neighboring nodes into different classes, each of which

uses a different delay range such that better candidates go first

“Good” or “Bad” candidates Utilize geographical location, velocity, energy, etc., to determine

the rebroadcast delay

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Our Proposal: PANDA (2)

Greedy approach selecting better link at each hop hopefully leads to better end-to-

end routes

Fully distributed an intermediate node makes local decision without any

communications with its neighboring nodes

Versatile capabilities e.g., search for a route with smallest number of hops, or with

minimal transmission power consumption, etc

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Agenda

Introduction

Our Proposal: PANDA

PANDA Algorithms

Performance Evaluation

Conclusion

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PANDA Algorithms

Different Variants PANDA-LO (Location Only)

PANDA-LV (Location & Velocity)

PANDA-TP (Transmission Power)

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PANDA-LO (Location Only)

Determine rebroadcast delay according to link distance Attempt to make a big jump at each hop

At node A

If |SA| > L1

delay = t1 + uniform(0, t1)

else if |SA| > L2

delay = 2*t1 + uniform(0, t1)

else if |SA| > L3

delay = 3*t1 + uniform(0, t1)

else

delay = 4*t1 + uniform(0, t1)

Note: L1 > L2 > L3 and T1 > T2 > T3.

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PANDA-LV (Location & Velocity)

Determine rebroadcast delay according to link distance and lifetime Attempt to select more stable link with a BIG jump

At node A

If |SA| > L1 && Lifetime > T1

delay = t1 + uniform(0, t1)

else if |SA| > L2 && Lifetime > T2

delay = 2*t1 + uniform(0, t1)

else if |SA| > L3 && Lifetime > T3

delay = 3*t1 + uniform(0, t1)

else

delay = 4*t1 + uniform(0, t1)

Note: L1 > L2 > L3 and T1 > T2 > T3. This figure shows how to estimate the link lifetime.

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PANDA-TP (Transmission Power)

Motivation: multiple small hops can save transmission power over a big single hop

Assuming the path loss is a simple function of the transmission distance:

The path energy ratio:

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Similar to PANDA-LO, only consider link distance Attempt to make a big jump at each hop

PANDA-TP (2)

At node A

If |SA| < L3

delay = t1 + uniform(0, t1)

else if |SA| < L2

delay = 2*t1 + uniform(0, t1)

else if |SA| < L1

delay = 3*t1 + uniform(0, t1)

else

delay = 4*t1 + uniform(0, t1)

Note: L1 > L2 > L3 and T1 > T2 > T3.

smaller

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Agenda

Introduction

Our Proposal: PANDA

PANDA Algorithms

Performance Evaluation

Conclusion

Networks Lab @ UC Davis Jian Li, Prasant Mohapatra 17 / 22

Simulation Setup

NS-2 simulator

simulation area 1500m x 300m

250m transmission range

100 nodes

30 connections

speed (0, 20) m/sec

“random waypoint” mobility model pause time: 0, 30, 60, 150, 300, and 500 sec

simulation time 500 sec

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Simulation Results (1)

Path optimality ratio = length of actual path / optimal path

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Simulation Results (2)

End to end delay

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Simulation Results (3)

Energy conserving route discovery

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Agenda

Introduction

Our Proposal: PANDA

PANDA Algorithms

Performance Evaluation

Conclusion

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Conclusion

PANDA approach outperforms RRD approach

Both PANDA-LO and PANDA-LV can improve path optimality

PANDA-LV can improve end-to-end delay

PANDA-TP can discover routes with much less power consumption than RRD approach