1. Real Time Flow Handoff in Ad Hoc Wireless Networks using Mobility Prediction William Su Mario Gerla Comp Science Dept, UCLA

2. 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

3. On Demand Routing 0 5 1 2 4 3 query(0) Destination reply(0) 0 5 1 2 4 3 Source Destination

4. 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

5. 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

6. 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)

7. Prediction of link connectivity mobile B A TX B TX A 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

8. 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

9. 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

10. Initial Route Construction Route Discovery source AB C D E destination Route Setup AB C D E mobile ROUTE-SETUP ROUTE-REQ 4.1 5.0 3.0 4.0 4.5 LET RET for route A-B-C-E= 4.1 RET for route A-B-D-E= 3.0

11. 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

12. Connection reroute example before reroute AB C D EF after reroute mobile data route source AB C D EF destination current time= 4.9 6.3 5.0 6.0 5.0 7.0 RET = 5.0 6.5 RET = 6.0 6.3 7.0 5.0 6.5 6.0

13. 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

14. Performance Parameters Packet Delivery Ratio : Fraction of original packets delivered to destination End to End Delay Control Traffic Overhead (Kbits/s)

15. Packet Delivery Ratio vs. Mobility Speed

16. Avg. Packet Delay vs. Mobility Speed

17. Control Overhead vs. Mobility Speed

18. 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)

19. Prediction of connectivity Approach 1: GPS –Assuming a free space propagation model –let the mobility info for mobile i be (x i,y i,v i,  i,TX i,), where (x i,y i ) = position, v i = speed,  i = heading, and TX i = transmission power for mobile i –assume we have mobiles 1 and 2 and TX 1 = TX 2 = TX, then D t, the amount of time mobiles 1 and 2 will stay connected is given by )(2 ))((4)(4)(2 22 222222 ca TXdbcacdabcdab D t    where 21 2211 21 2211 sin cos yyd vvc xxb vva       –We can obtain mobility information using Differential GPS

20. 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

21. 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

22. Approach 2 Example: Transmission power level measured by mobile 1 for mobile 2 (free space model) Distance (m) Power level (dB) T 1 = current hysterisis region T exp = ? Minimum acceptable power level We can determine T exp by measuring rate of power change at T 1 A low pass filter can also be applied to the measured samples to filter out short term power level fluctuations

23. Example of Clustering