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

0

5

1

2

4

3

query(0)

query(0)

query(0)

query(0)

query(0)

query(0)

query(0)

Destination

reply(0)

reply(0)

0

5

1

2

4

3

reply(0)

Source Source

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)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

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

Pa

ck

et

De

live

ry R

ati

o

OD-MPLMRDSDV

Packet Delivery Ratio vs. Mobility Speed

0

20

40

60

80

100

120

140

160

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

Av

g. P

ac

ke

t D

ela

y (m

s)

OD-MPLMRDSDV

Avg. Packet Delay vs. Mobility Speed

0

5000

10000

15000

20000

25000

30000

35000

15 30 45 60 75 90 105 120 135 150 165 180

Mobility Speed (km/hr)

Co

ntr

ol

Ove

rhe

ad

(K

bit

s/s

)

OD-MPLMRDSDV

Control Overhead vs. Mobility Speed

0

200

400

600

800

1000

1200

1400

1600

1800

2000

15 30 45 60 75 90 105 120 135 150 165 180

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

1

2

3

4

5

67

8

9

9

10

11

1213

14 1516 17

18 19

20

21

2223

2425

26

27

28

29

30

31

32

33

34

35

36

Example of Clustering