Exploring the Effect of Maximum Velocity Transportation Models on Handovers in Heterogeneous...

SAT SEMINAR SERIES 2013

Exploring the Effect of Maximum Velocity

Transportation Models on Handovers in

Heterogeneous Environments using Exit Times

Glenford MappPrincipal Lecturer, Middlesex University


Brian Ondiege Ferdinand Katsriku David Silcott Jonathan Loo Haris Pervaiz Qiang Ni

Supporting Cast


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

Outline of the Talk


The Complete Y-Comm Framework

HARDWARE PLATFORM (MOBILE NODE)

HARDWARE PLATFORM (BASE STATION)

NETWORK ABSTRACTION (MOBILE NODE)

NETWORK ABSTRACTION (BASE STATION)

VERTICAL HANDOVER

POLICY MANAGEMENT

END SYSTEM TRANSPORT

QOS LAYER

APPLICATION ENVIRONMENTS

CONFIGURATION LAYER

NETWORK MANAGEMENT

CORE TRANSPORT

NETWORK QOS LAYER

SERVICE PLATFORM

CORE NETWORKPERIPHERAL NETWORK

SAS

NTS

NAS

QBS

SECURITY LAYERS


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/softwa

re/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

This talk

http://www.mdx.ac.uk/research/areas/software/ycomm_research.aspx

http://www.mdx.ac.uk/research/areas/software/ycomm_research.aspx


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

Handover Terms


Advanced Handover Classification

HANDOVER

IMPERATIVE ALTERNATIVE

REACTIVE PROACTIVE

KNOWLEDGE-BASED MODEL-BASED

NETPREF

USERPREF CONTEXT

SERVICES

UNANTICIPATED ANTICIPATED


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

Proactive Policies


Proactive ScenarioREQ (Time , TBVH, NDT)

A

WIRELESS NETWORK

TBVH

NDT

A


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

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

Proactive Policies


Knowledge-Based Policy Management (Cambridge)


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

Proactive Policies – Modelling Approach (Fatema Shaikh)


The Model-Based Handover

Threshold Circle coverage

Real coverage

Exit coverage

Exit threshold circle

Handover threshold circle


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

Start of Analysis


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

Key Observation


Example of Exit Time Scenario

X

NETWORK A

NETWORK B


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

Looking at Time-to-Handover


Slow Adaptation of TCP After LAN->GPRS Handover


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

Handover times are Type dependent


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:

Getting real values


Measured Results from the Cambridge Wireless Testbed


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:

Need to look at NDT in detail


Assuming circular coverage, we use propagation models to tell us the handover radius for different networks.

NDT in detail (cont’d)


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

Estimating the Expected Velocity


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

Transportation contexts


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:

Urban/Suburban context


So we found out the 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

Generating results


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

Generating Results (cont’d)


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

Key Conclusions


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)

The Motorway Context


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

Calculating the Expected Velocity


FC

E

H

NET B

NET A


FB

Z

C

E

G

HK

Y

R2T

w

u

NET B

NET A

v


NET A

F

C

T

NET B

S

E

H


NET A

FZ Y

C

E

S

TH

NET B

R

BGuvw

TD


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:

Expected velocity at junction


A

B

C

S

T

NET A

NET B

NET C

ScenarioThree WLANs in a single UMTS cell


Analysis


Results


Calculating the Angles


Straight paths have lower NDD and mobile users travel at close to maximum speed 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

Summary of Results


Make the radius of your communication cell larger◦ WLAN handover radius is too small

Intelligent Transport Systems VANET work, Roadside Units (RSU) Handover Radius is 1 Kilometre Modified form of 802.11a, higher transmission power Jonathan Loo and others are doing some research on

VANETs here at Middlesex So we wanted to see how well this setup

would respond to our methods.

How to address these issues


VANET Urban/Suburban Context


VANET Straight Motorway


Towards small cells Allows greater bandwidth But our work shows that there is an issue

with small cells and mobility Another way to deal with this is to look at

providing joint coverage along a road or highway

Industry is going in the other direction!


NET A NET B

URBAN ROAD P Q


NET A NET B

URBAN ROAD

NET C

P P QQ


Intersect distance for no loss of communication:◦PQ >= VMAX * TEH

If we want to support a row of intersecting cells along a straight road then:◦2R > 2(VMAX * TEH)◦R > (VMAX * TEH)

Back-of-Envelope Calculations


Results for other networks (LTE, etc)◦ What is the effect of velocity on these networks

Handover times and how we could improve them

Especially in 4G systems Why is the handover time in WLANs so long!

Smaller cell configuration, user mobility and networking infrastructure.◦ Issues of interference, QoS, etc.

Further work


THANK YOU!

Questions?

Exploring the Effect of Maximum Velocity Transportation Models on Handovers in Heterogeneous...

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