Exploring Maximum Velocity Transportation Models on Handovers

1. Exploring the Effect of Maximum VelocityTransportation Models on Handovers inHeterogeneous Environments using Exit Times Glenford Mapp Principal Lecturer, Middlesex University

2. Supporting Cast Brian Ondiege Ferdinand Katsriku David Silcott Jonathan Loo Haris Pervaiz Qiang Ni

3. Outline of the Talk • Motivation for the work • Handover Classification • Proactive Handover • Analysis of Urban/Suburban context • Results for Urban/Suburban context • Analysis of Motorway context • Results for Motorway context • Implications for future networking infrastructure (VANETs, etc) • Future Plans

4. The Complete Y-Comm Framework PERIPHERAL NETWORK SECURITY LAYERS CORE NETWORK APPLICATION ENVIRONMENTS SAS SERVICE PLATFORM QBS QOS LAYER NETWORK QOS LAYER CORE TRANSPORT END SYSTEM TRANSPORT NTS NAS NETWORK MANAGEMENT POLICY MANAGEMENT CONFIGURATION LAYER VERTICAL HANDOVER NETWORK ABSTRACTION (MOBILE NODE) NETWORK ABSTRACTION (BASE STATION) HARDWARE PLATFORM (MOBILE NODE) HARDWARE PLATFORM (BASE STATION)

5. This talk • Can’t explain everything about Y-Comm • It’s too big • Several institutions work on Y-Comm • Including Middlesex, Cambridge, USP and Lancaster University • See Y-Comm Research Webpage: • http://www.mdx.ac.uk/research/areas/software/ycomm_research.aspx • This talk looks at handover issues • In particular we are trying to understand the relationship between handover, the velocity of the mobile node and mobile infrastructure

6. Handover Terms • Hard vs Soft Handovers • Hard - break before make • Soft – make before break • Network vs Client Handovers • Network – network in control (current) • Client – future (Apple’s patent) • Upward vs Downward • Upward – smaller to bigger coverage • Downward – bigger to smaller

7. Advanced Handover Classification HANDOVER ALTERNATIVE IMPERATIVE SERVICES NETPREF CONTEXT REACTIVE USERPREF PROACTIVE UNANTICIPATED ANTICIPATED MODEL-BASED KNOWLEDGE-BASED

8. Proactive Policies • Benefits: • Allows us to minimize disruption due to packet loss or service degradation during handover by signalling to the higher layers that a handover is about to happen • Interested in 2 main parameters • Time Before Vertical Handover (TBVH) • Network Dwell Time (NDT) – the time a mobile spends in a given network due to mobility

9. Proactive Scenario REQ (Time , TBVH, NDT) A A TBVH NDT WIRELESS NETWORK

10. Proactive Policies • Proactive policies can themselves be divided into 2 types • Proactive knowledge-based systems • Knowledge of which local wireless networks are operating at a given location and their strengths at that point • We also need a system to maintain the integrity, accessibility and security of that data

11. Proactive Policies Knowledge-based approach Gather a database of the field strengths for each network around a city Need to maintain the database and also know how the results might be affected by seasonal effects

12. Knowledge-Based Policy Management (Cambridge)

13. Proactive Policies – Modelling Approach (Fatema Shaikh) • Uses a simple mathematical model • Defines a radius at which handover should occur • Finds out how much time I have before I hit that circle (TBVH), given my velocity and direction • Used simulation (OPNET) • Can be used in the real world as well as in simulation

14. The Model-Based Handover

15. Start of Analysis • Exit Time (ET) is defined as how much time a mobile node can be in a given network before it must begin handing over to another network • ET is primary dependent on NDT which is in turn dependent on the velocity • TEH – the time taken to handover to the next network

16. Key Observation • If ET is less than or equal to zero, then the handover to the first network should not take place as no work will be done because the interface will be forced to immediately begin handing over to the next network • This work looks at the effect of this observation on heterogeneous environments • Need to avoid useless handovers

17. Example of Exit Time Scenario X NETWORK B NETWORK A

18. Looking at Time-to-Handover • This was part of David Cottingham’s PhD work. Handover is dependent on 4 delays: • Td is the detection time – time to discover that you are on a new network • Tc is the configuration time – time to get and configure your network interface with a new IP address called the Care-of-Address (COA) • Tr is called the registration time – time taken to register the new COA with the Home Agent and Corresponding Nodes • Ta is called the adaptation time – the time it takes for the higher layers, such as TCP, to make use of the bandwidth of the new network

19. Slow Adaptation of TCP After LAN->GPRS Handover

20. Handover times are Type dependent • For Reactive Handover we need to add all 4 delays • Because the device is reacting to information from its interfaces, it is not planning ahead • For proactive handover, we may avoid the need to add all 4 delays • Because of TBVH, we can signal to the upper layers that handover will occur after a certain time, so they could take evasive action, especially at the transport level

21. Getting real values • If we assume the use of low-level triggers and IPv6 auto-configuration techniques • Td and Tc are effectively zero • So for reactive and proactive handovers we have:

22. Measured Results from the Cambridge Wireless Testbed

23. Need to look at NDT in detail NDT in a wireless network is given by the reciprocal of the mobility leave rate. In the literature, the mobility leave rate is given by:

24. NDT in detail (cont’d) Assuming circular coverage, we use propagation models to tell us the handover radius for different networks.

25. Estimating the Expected Velocity • This is highly dependent on the transportation model observed by a population • Must be realistic to get good results • Two main contexts • Urban/Suburban context • Motorway context

26. Transportation contexts • Urban/Suburban context • Mobile users are everywhere, both pedestrians, people in cars (not the driver, of course!) • Cars observe a maximum velocity or speed limit • Cars and people can mingle; traffic lights, people crossing the road, etc. • Motorway context • No pedestrians, mobile users are in cars • Motorways follow well-defined roads • We can work out the exact distance between two points on a motorway using GPS • Much higher speed limit compared to the urban/suburban case

27. Urban/Suburban context • Since pedestrians and cars are mingling and there is a speed limit, VMAX, it is reasonable to set the expected velocity to VMAX /2 • You cannot know every mobile user’s exact NDT so you will have to use a probability distribution • So if we plug this into our formula for NDT we get:

28. Generating results So we found outthe expected rate of NDT for different values of VMAX Used an exponential distribution, reasonable in the urban context Decided to use simulation to generate results HANDSIM is a simulation developed by myself and Eser Gemikonakli to study handover The team extended it to look at different velocities and different types of handovers

29. Generating Results (cont’d) • So the simulation generated handover requests for different users via a Poisson distribution • At a given maximum velocity, it generated handover requests with a given NDT using the expected value of NDT and the distribution • We then subtracted the handover time for the type of handover being considered from NDT to get the Exit Time. If the Exit Time was less than or equal to zero, that handover request was rejected • We plotted the % rejected handover requests against the maximum velocity

34. Key Conclusions • WLAN handovers do not do well • Much smaller handover radius • Also the time to handover is fairly long compared to 3G, i.e., 4 seconds for WLANs and 1 second for 3G • 3G handovers held their own • Fairly large radius • Handover times are fast for 3G compared to WLAN • Proactive handovers did improve the results • Needs further research

35. The Motorway Context Because mobile users are in cars and we know how to calculate the distance between two points, this means that we can use a different approach We define the Network Dwell Distance (NDD) as the distance travelled along a motorway that is in coverage of a given network NDT = NDD/E(vel)

36. Calculating the Expected Velocity • There are two instances: • The straight road: in this context we expect that the car will travel at or close to the maximum velocity • The other context is when there is a junction and the car has to slow down to negotiate the junction so the average velocity will fall and so NDT will increase

37. NET A F NET B C E H

38. NET A u Y F NET B T C R2 B E v G w H K Z

39. NET A C F E NET B S H T

40. NET A C F E NET B w v u Z T G B Y D S H R T

41. Expected velocity at junction At T-junctions, cross-roads or roundabouts we normally stop, so we have a expected velocity of VMAX/2 For other junctions we take the cosine of the angle of the two roads at the junction:

42. Scenario Three WLANs in a single UMTS cell NET A A NET B S NET C B C T

43. Analysis

44. Results

45. Calculating the Angles

50. Summary of Results Straight paths have lower NDD and mobile users travel at close to maximumspeed so these sections tend to have lower exit times Junction S had the greater exit time because it had the greater NDD as well as a lower average velocity Junction T did not have as much Exit Time as Junction S because Junction T had a shorter NDD and faster average velocity