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)
reply(0)
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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)
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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)
Pa
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)
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g. P
ac
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t D
ela
y (m
s)
OD-MPLMRDSDV
Avg. Packet Delay vs. Mobility Speed
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15000
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15 30 45 60 75 90 105 120 135 150 165 180
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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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
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Example of Clustering