Urban/Suburban context

Report
Glenford Mapp
Principal Lecturer, Middlesex University
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Brian Ondiege
Ferdinand Katsriku
David Silcott
Jonathan Loo
Haris Pervaiz
Qiang Ni
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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
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PERIPHERAL NETWORK
SECURITY LAYERS
APPLICATION ENVIRONMENTS
QOS LAYER
END SYSTEM TRANSPORT
POLICY MANAGEMENT
VERTICAL HANDOVER
NETWORK ABSTRACTION
(MOBILE NODE)
HARDWARE PLATFORM
(MOBILE NODE)
SAS
QBS
NTS
NAS
CORE NETWORK
SERVICE PLATFORM
NETWORK QOS LAYER
CORE TRANSPORT
NETWORK MANAGEMENT
CONFIGURATION LAYER
NETWORK ABSTRACTION
(BASE STATION)
HARDWARE PLATFORM
(BASE STATION)
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Can’t explain everything about Y-Comm
 It’s too big
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Several institutions work on Y-Comm
◦ Including Middlesex, Cambridge, USP and Lancaster
University
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See Y-Comm Research Webpage:
http://www.mdx.ac.uk/research/areas/softw
are/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
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Hard vs Soft Handovers
◦ Hard - break before make
◦ Soft – make before break
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Network vs Client Handovers
◦ Network – network in control (current)
◦ Client – future (Apple’s patent)
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Upward vs Downward
◦ Upward – smaller to bigger coverage
◦ Downward – bigger to smaller
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HANDOVER
ALTERNATIVE
IMPERATIVE
NETPREF
REACTIVE
UNANTICIPATED
SERVICES
USERPREF CONTEXT
PROACTIVE
ANTICIPATED
KNOWLEDGE-BASED
MODEL-BASED
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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
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REQ (Time , TBVH, NDT)
A
A
TBVH
NDT
WIRELESS
NETWORK
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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
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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
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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
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Handover
threshold circle
Exit threshold
circle
Threshold Circle
coverage
Real coverage
Exit coverage
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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
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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
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X
NETWORK B
NETWORK A
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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
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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
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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
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If we assume the use of low-level triggers
and IPv6 auto-configuration techniques
◦ Td and Tc are effectively zero
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So for reactive and proactive handovers we
have:
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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:
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Assuming circular coverage, we use
propagation models to tell us the handover
radius for different networks.
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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
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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.
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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
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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
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So if we plug this into our formula for NDT we
get:
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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
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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
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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
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3G handovers held their own
◦ Fairly large radius
◦ Handover times are fast for 3G compared to WLAN
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Proactive handovers did improve the results
◦ Needs further research
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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)
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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
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NET A
NET B
C
F
E
H
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NET A
uY
NET B
T
C
R2
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v
G
w
K
H
Z
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NET A
C
E
NET B
F
S
H
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NET A
C
E
NET B
w
Z
v
T
G
u
F
B
Y
D
S
R
H
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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:
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Scenario
Three WLANs in a single UMTS cell
NET A
A
NET B
S
B
NET C
C
T
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Analysis
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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
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Make the radius of your communication cell
larger
◦ WLAN handover radius is too small
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Intelligent Transport Systems
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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.
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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
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NET A
P
NET B
Q
URBAN ROAD
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NET B
NET A
P
Q
NET C
P
Q
URBAN ROAD
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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)
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Results for other networks (LTE, etc)
◦ What is the effect of velocity on these networks
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Handover times and how we could improve
them
 Especially in 4G systems
 Why is the handover time in WLANs so long!
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Smaller cell configuration, user mobility and
networking infrastructure.
◦ Issues of interference, QoS, etc.
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