Real Time Flow Handoff in Ad Hoc Wireless Networks using Mobility Prediction William Su Mario Gerla...

Real Time Flow Handoff in Ad Hoc Wireless Networks using Mobility Prediction

William Su

Mario Gerla

Comp Science Dept, UCLA

Challenges in Ad Hoc Wireless Network

• Topology is constantly changing• Key requirements

– dynamic route reconfiguration– minimize impact on multimedia connections (voice/video/data)– minimize control overhead (bandwidth is very limited)

source

destination

data route

On Demand Routing

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query(0)

query(0)

query(0)

query(0)

query(0)

query(0)

query(0)

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reply(0)

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Destination

On Demand Approach

• PROS:– No periodic routing table broadcast (routing table maintained

only when a node has data to send)

• CONS:– Initial route acquisition delay and route rebuild delay

– Overhead goes up as number of active connections in the network increases (broadcast storm!)

– Requires mechanisms to detect route break and perform route reconstruction

• beacons, passive acknowledgements

Mobility Prediction Enhancements

• The motivation:– Mobility patterns often exhibit predictable behavior (i.e., cars

traveling on freeway)– Reacting to topology changes only after they occur can

seriously degrade real-time (voice, video) performance

Mobility Prediction Enhancements

• The goal:– Minimize disruptions due to topology changes by performing

re-route ahead of time– Reduce the transmission of unnecessary control overhead

by using more stable routes (bandwidth efficient)

Prediction of link connectivity

mobile

BA

TXB

TXA TX Transmission Range

• For mobiles A and B, we compute the link expiration time (LET) of the radio link– Approach 1: use GPS position information exchange – Approach 2: use Transmission power information

Other Schemes that use GPS

• Location Aided Routing (LAR) by Ko-Vaidya at Texas A&M University– an On Demand scheme that uses location information

obtained from GPS to limit the propagation region of Route Requests packets

• Distance Routing Effect Algorithm for Mobility (DREAM) by Basagni-Chlamtac at UT Dallas– Performs routing (location) table updates periodically,

however data is flooded in the general direction of the destination

On Demand Mobility Prediction (OD-MP) Protocol

• Initial route discovery– as the ROUTE-REQ message is flooded, intermediate nodes

also append their ID and LET for last hop of the ROUTE-REQ

– destination receives ROUTE-REQ with different paths and the link expiration times

• Destination computes the Route Expiration Time (RET) for each route and selects the most stable one (maximum RET) for data delivery– ROUTE-SETUP message is sent back to the source to setup

the route

Initial Route Construction

Route Discovery

source

A B

C

D

E

destination

RouteSetup A B

C

D

E

mobile

ROUTE-SETUP

ROUTE-REQ

4.1

5.0

3.04.0

4.5LET

RET for route A-B-C-E= 4.1RET for route A-B-D-E= 3.0

Predictive Route Reconstruction

• Data packets carry current RET in their header; thus, RET is refreshed at the destination

• When RET is approaching, destination floods ROUTE-REQ messages in similar fashion as initial route construction

• source receives ROUTE-REQ messages and chooses the best route for the data delivery

Connection reroute example

beforereroute

A B

C

D

E Fafter

reroute

mobile

data route

source

A B

C

D

E F

destination

current time= 4.9

6.3

5.0

6.0

5.0

7.0

RET = 5.0

6.5

RET = 6.06.3

7.0

5.05.0

6.5

6.0

Simulation Experiment environment

multihop network environment 100 mobile nodes, radio bandwidth = 2Mbps, roaming square =

500x500m, transmission range = 120m

routing protocols evaluated OD-MP DSDV (Destination Sequence Distance Vector) LMR (Lightweight Mobile Routing)

UDP traffic, single source/destination pair; constant bit rate = 40 packets/sec; packet size = 10kbits

Mobility varying between 18 km/hr to 180 km/hr; mobility pattern = straight trajectory

Performance Parameters

• Packet Delivery Ratio : Fraction of original packets delivered to destination

• End to End Delay• Control Traffic Overhead (Kbits/s)

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0.1

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15 30 45 60 75 90 105 120 135 150 165 180Mobility Speed (km/hr)

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ck

et

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live

ry R

ati

o

OD-MPLMRDSDV

Packet Delivery Ratio vs. Mobility Speed

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15 30 45 60 75 90 105 120 135 150 165 180Mobility Speed (km/hr)

Av

g. P

ac

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ela

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s)

OD-MPLMRDSDV

Avg. Packet Delay vs. Mobility Speed

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Mobility Speed (km/hr)

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ntr

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rhe

ad

(K

bit

s/s

)

OD-MPLMRDSDV

Control Overhead vs. Mobility Speed

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Mobility Speed (km/hr)

OD-MP

LMR

Future Directions

• Impacts of prediction errors on performance– Location and speed errors

– Mobility pattern randomness

• Hybrid distance vector and on demand routing using mobility prediction

• Performance improvements with prediction for non-realtime applications (TCP)

Prediction of connectivity• Approach 1: GPS

– Assuming a free space propagation model– let the mobility info for mobile i be (xi,yi,vi,i,TXi,), where (xi,yi) =

position, vi = speed, i = heading, and TXi = transmission power for mobile i

– assume we have mobiles 1 and 2 and TX1 = TX2 = TX, then Dt, the amount of time mobiles 1 and 2 will stay connected is given by

)(2

))((4)(4)(222

222222

ca

TXdbcacdabcdabDt

where

21

2211

21

2211

sinsin

coscos

yyd

vvc

xxb

vva

– We can obtain mobility information using Differential GPS

Prediction of connectivity

• Approach 2: Transmission Power Measurements– Transmission power samples are measured from a mobile’s

neighbor

– From the samples we can obtain the rate of change for the neighbor’s transmission power level

– the time that the neighbor’s power level drops below the accepted level for a connection (e.g. hysterisis region) can be computed

Introduction

Wireless Mobile Networks Single hop (cellular) : fixed base stations Multihop (ad hoc) : no fixed base stations, mobile stations

act as routers

IPv6 Flow Supports real time flows (i.e., voice, video) Designed to replace existing IPv4 protocol

Approach 2

Example: Transmission power level measured by mobile 1 for mobile 2 (free space model)

Distance (m)

Power level (dB)

T1= current

hysterisis region

Texp = ?

Minimumacceptable power level

• We can determine Texp by measuring rate of power change at T1

• A low pass filter can also be applied to the measured samples to filter out short term power level fluctuations

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Example of Clustering