Slide 1

Report
Smart Road:
Wireless Networks for Intelligent Transport
system
Kun-chan Lan
NICTA
2015/7/17
NCTU
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About me
• Graduated from USC in 2004
• Currently working as a researcher at National ICT Australia
(NICTA)
• Past research
– Internet measurements, traffic modeling and simulations
• Current research
– Wireless mesh networks and vehicular ad-hoc networks
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About NICTA
• A national research institute funded by Australia
Government
• Our research staff includes regular full-time
researchers and contributed staff from major
universities such as Australian National Univ., Univ.
of Sydney, Univ. of Melbourn, New South Wales of
Univ. etc
• Our focus
– Research, commercialization, education, collaboration
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About NICTA
• A number of research labs
– located in Sydney, Canberra and Melbourn
• A variety of research programs
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Empirical Software Engineering;
Interfaces, Machines, And Graphic ENvironments
Networks and Pervasive Computing.
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Embedded, Real-Time, and Operating Systems
Formal Methods
Symbolic Machine Learning and Knowledge Acquisition
Statistical Machine Learning;
Systems Engineering and Complex Systems
Wireless Signal Processing
Logic and Computation;
Autonomous Systems and Sensing Technologies
Statistical Machine Learning.
Sensor Networks;
Network Information Processing.
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What is Intelligent Transportation System (ITS) ?
• Computer and communication technologies applied
to management of transportation systems
– To manage it in a safe and efficient manner
• To monitor traffic conditions (accident, incidents, construction
work, weather, major events)
• Control traffic flow
• To provide information to the traveling public about traffic
conditions
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Types of ITS implemented – infrastructure
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Types of ITS implemented – vehicles
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Why ITS?
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Improved safety to drivers
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Improved traffic efficiency
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e.g. reduced traffic congestion
Improved environmental quality
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e.g. reduce accident
e.g. reduced fuel/exhaust
Improved economic productivity
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e.g. broadband service on buses
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Improve safety
• highway deaths > 40K in 2003 for US alone
• Studies showed the use of ramp meters reduce accidents
15-50%.
• In-vehicle computer visioning cameras
– warn operators of drowsy driving behavior.
– Results showed the system improved safety and decreased fuel
consumption 15%.
• Radar sensors on trucks
– warn operators of obstacles in blind spots
– at-fault accidents decreased 34% in 1 year.
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Improve efficiency
• in-vehicle navigation systems
– a travel time savings of more than 10%
• intelligent cruise control vehicles (ICC)
– use road sensor data to optimize vehicle speeds and match signal
timing
– increase link capacity 3-6%.
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Improve environmental quality
• E-Zpass (an electronic toll collection) in NJ
– saves: 1.2 mil gallons of fuel/yr, 0.35 tons of CO/day, and 0.056
tons NOx/day.
• Similar study from Baltimore
– reduced hydrocarbons and Carbon monoxide emissions by 40-63%,
and reduced emissions of Nitrogen oxides by approximately 16%.
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ITS market
• Promising market
– US market for ITS is estimated to grow from $5 billion to
$35 billion by 2010.
– $700 billion is expected to be spent on transport
infrastructure in the Asia Pacific market
• In 2005, The Minister for ICT in Australia launched a new
industry cluster in Victoria for the ITS market
• the HK Government proposed to spend US$423 million on ITS in
the next decade
• In Japan, the annual market size has been estimated at 4 billion
ECU by 2010.
– The European standardized GSM-R
• cellular solutions in the transportation sector
• $5 billion new market in Europe within five years
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Today’s talk
• Part 1
– A brief talk about a project (STaR) we recently started at
NICTA
• A wireless mesh network for ITS
• Not much results at this point, only architecture overview for
today
• Part 2
– two vehicular-network applications
• MOBNET – A NEMO-based Network Mobility Testbed
• MOVE – A Mobility mOdel generator for VANET
– Only overview talk today, no discussion on math or
protocol
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About STaR (Smart Transports and Roads)
• A multi-million research project we recently started at NICTA
– Only a few months old
• Collaborating with New South Wales Road and Traffic
Authority (NSW RTA)
– NSW RTA is the creator of SCATS, a real-time traffic management
system
– SCATS is used in Sydney and ~80 other cities around the world
– It is expected that some outcome of STaR project can be integrated
into SCATS in the future
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SCATS (The Sydney Coordinated Adaptive Traffic System)
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• An adaptive traffic control system
• Goal
– Minimize vehicle travel time when traffic is light
– Maximize road capacities when traffic is heavy
• Components of SCAT
– Subsystem: 1-10 intersections
– Local controller: one at each intersection
– Critical intersection: need accurate timing control
• Non-critical intersections synchronize with the critical intersection
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Regional computer: control up to 64 subsystem
Regional computer
subsystem
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subsystem subsystem
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SCATS (The Sydney Coordinated Adaptive Traffic System)
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• Local controller: optimize local traffic flows
– On a phase-by-phase basis
– Phase length: time from one green to next green
• Regional computer: optimize subsystem capacity
– On a cycle-by-cycle basis
– One cycle contain multiple phases: typically 40s-150s
– All intersections in the same subsystem has the same cycle
Regional computer
subsystem
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Goal of STaR
• Improved traffic flow
• Improved public safety
• Improved performance, efficiency and running cost for
public transports
• Improved SCATS revenue in the multi-billion dollar ITS
market
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Benefit from working with RTA
• Access to real-time and historical road traffic data
• Access to public infrastructure (traffic controller,video
camera, road-side sensors, etc)
• Access to other RTA systems
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Problems with the existing RTA network
• Rely on a fixed communication infrastructure (ISDN and
dial-up)
– Costly to install, operate and maintain
– Easy to be damaged
– Low bandwidth (< 32KB/s): inflexible in its application
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Wireless mesh networks for ITS
• Replacement of current system is highly sought after
– By RTA and other traffic authorities elsewhere
– But commercial off-the-shelf systems (e.g. DSL, GPRS) don’t provide
required reliability and timeliness, and still incur costs
• NICTA proposes a multiple-hop wireless mesh network to
replace the dial-up network
– Easy deployment
– Infrastructureless: lower maintenance cost
– Initially, a 16-node testbed in Sydney CBD (Central Business District)
area
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Requirement for the test-bed
• Representative locations
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Foliages/trees
Pedestrians
Passing traffic
High-rise buildings
• Easy access
– Close to NICTA
• Cover at least one critical intersection
• Multiple paths available for each source/destination pairs
• can provide external source of power to mesh router
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Why wireless mesh networks?
• Wireless mesh networking is a promising technology to
replace current system
– ease and speed of installation, without reliance on a
telecommunications carrier or dependence on their time frames;
– lower on-going costs than the current system, with no annual or
monthly rental or service fees;
– flexibility to connect new locations, for new intersections or during
road works or emergencies;
• Others also recognise that the potentials of wireless
meshes
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Commercial product
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Meshnetworks Inc (now acquired by Motorola)
Tropos Networks
LocustWorld (MeshAP)
Intel
Nortel
Microsoft
Kiyon
Radiant Networks (Cambridge-based, work with BT)
Invisible Networks
Green Packet Inc. (M-Tapei)
SeattleWireless, NYCWireless,…
many others..
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Test-beds In academia
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MIT (Roofnet project)
Rice university (TFA project)
Berkeley (DGP project @ India)
Trinity College @ Dublin (WAND project)
University of Massachusetts @ Ahmerst (Diesel Net)
UC Santa Barbara
UC San Diego
State University of New York @ Stony Brook
UCLA
others…
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STaR network topology
public transport
Regional computer
Traffic controller
Mesh box
camera
road-side sensor
Internet
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Test-beds @ NICTA
• We are currently building two test-beds at NICTA
• Indoor – Linksys WRT54GS
– ~$100
– Linux-based firmware
• OpenWRT support
• For research above transport layer
• Outdoor – Soekris boards
– ~$300
– Use Compact Flash card for OS
• Customized MAC
– Can use any type of radio
• We’re only interested in research
Issues above MAC
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Current testbed activities
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Wireless survey
Building mesh routers with soekris boards
Integrate mesh routers with SCATS simulator
Integrate mesh routers with real SCATS traffic controller
Network management
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Wireless survey
• Understand the radio property in real word
• What to measure
– Signal strength quality
– Throughput
– Packet losses
• MAC layer re-transmission
• as a function of
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Distance
Transmission rate
Type/height of antenna
Number of MAC RETRY
• Two phases
– Open-space measurements
– Intersection measurements
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Multi-radio mesh router
• Soekris net4521
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133 Mhz AMD
64 Mbyte SDRAM
Use CF card for OS (pebble linux)
2 Ethernet ports
1 Serial port, DB9.
1 Mini-PCI slot
2 PC-Card slot for wireless adapters
Board size 9.2" x 5.7"
External power supply 11-56V DC
Operating temperature 0-60 °C
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Integrating with traffic simulator
• Test mesh router on the traffic simulator before integrating
with real traffic controller
• A micro-traffic simulator Paramics is used, located at UNSW
• One high speed link between UNSW and NICTA that allows
the mesh router to talk to the Paramics simulator remotely
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Integrating with SCATS traffic controllers
• SCATS system
– A large number of kerbside controllers
that control traffic signal
– A set of regional controllers that control
kerbside controllers
• Star topology (Masterlink mode)
• Currently connected by leased lines via a
Bell 103 modem at 300 baud
– Some kerbside controllers have a
special role for synchronizing signal
timing when the regional controller is
down (Flexilink mode)
– Each kerbside controller has a 25-pin
RS-232 connector for external access
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kerbside controller
regional controller
critical intersection
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Network management
• Graphic interface that shows
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Wireless connectivity
Network topology
Link latency
Link throughput
Routing path
Router status
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practical Issues for street deployment
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Waterproof/weatherproof
Power source
Antenna placement
Vandalism
Passing traffic
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Research Challenge
• Scalability
– Connecting numerous road-side devices to SCATS
– Need to Integrate video cameras: High throughput, low jitter
• Reliability
– Mission-critical data (e.g. accident detection, traffic signal control,
etc)
– Requires timely routing that is robust against faults in nodes or
links
• Low latency
– SCATS is a real-time traffic control system (< 1 sec)
• Heterogeneity
– Requires support for different radio types
• e.g. incrementally deploy new radio technologies
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Research focuses
• New multi-radio multi-channel MAC
– Scalability/reliability/latency
• Multi-path routing
– Reliability/latency
• Fault detection and recovery
– Reliability
• Network management
– Very difficult to physically to access the mesh nodes once they are
deployed
– Need to mechanism to node diagnostics, software upgrade, etc
• Communication between vehicles and road-side devices
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Current status of STaR
• 4 months old, not much technical results yet
– 6 months to deliver a pilot testbed that controls real traffic light
– 14 researchers/students working on this project
– International research collaboration
(U. Cal @ Davis and U. Texas @ Arlington)
– Communication with Australian startups
• We’ve developed a couple of applications for ‘vehicle to
road-side’ component though
– MOBNET
• Network mobility testbed (LANMAN 2005)
– MOVE
• Mobility model generator (poster in MOBICOM 2005)
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Today’s talk
• Part 1
– A brief talk about a project (STaR) we recently
started at NICTA
• Part 2
– two vehicular-network applications
•MOBNET – A NEMO-based Network Mobility Testbed
•MOVE – A Mobility model generator for VANET
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Mobile Network
• Providing broadband service for public transport passengers is
becoming a popular ITS service
– E.g. Connexion by Boeing
• Mobile Network (MN):a network that can move and attach arbitrary
points in the Internet
• Mobile Network
– On-board LAN
– Mobile Router:
• manage movement of MN and provide
Internet access to MNNs
– Mobile Network Node (MMN)
• MMN: a node in the MN
– Local Fixed Node (LFN)
– Visiting Mobile Node (VMN)
• Standardized protocol: NEMO
– Extension of MIPv6
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MOBNET - Network mobility testbed
• Ways to conduct network research
– Simulation
– Emulation
– Real implementation
• Physical layer models in wireless simulations are typically oversimplified
– Need realistic testbed
• Existing wireless testbed
– CMU (ad-hoc routing)
– TAP, Roofnet (mesh network)
– ORBIT (generic testbed)
• Existing work does not support testing of network mobility protocols
• Our contribution: a testbed for network mobility research
MOBNET: The Design and Implementation of a Network Mobility Testbed", Kun-chan Lan, Eranga
Perera, Henrik Petander, Christoph Dwertmann, Lavy Libman, Mahbub Hassan, IEEE LANMAN 05’
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Mobile Nework testbed functionality
• Emulation of a mobile network
• Experimental control
– Topology control
– Mobility control
• Management of the testbed
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Emulation of a Mobile Network
• A NEMO-based mobile router
– Extended from HUT MIPv6
– Built on Linux 2.4.26
• Support NEMO implicit mode
• Can use link layer information to trigger handoff
• Support Route Optimization
– Extension of MIPv6 RO
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Experimental control
• Topology control
– NIS NET network emulator
• Mobility control
– Mobility emulator (MobE)
– Emulate the movement of MR
– Emulate the variations of radio propagation by changing the
transmission power of the AP
– Can be driven used pre-made mobility patterns
• Markov Chains model
– For controllable experiments
• Signal strength traces
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Mobility emulator
• Architecture
– Input parser
– Graphical interface
– AP power level controller
• AP power level control
– web interface
– telnet
• Modeling signal strength variations
– Discrete markov chain
• e.g. frequency of changing from one power level to another, etc.
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Mobility Emulator interface
MR moving from ap1 toward ap2
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Testbed management
• We’d like to make our testbed available to other
researchers in the future
• Remote management server
– Remote users access
– Testbed monitoring
– Testbed maintenance
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Node generator - Virtual MMNs
• Currently each node is implemented via one single machine
– Not scalable: how to emulate a large number of MMNs
• Implement each MMN as a single process
• Virtual interface: a single wireless interface is abstracted
into multiple virtual interfaces
– Each MMN connects to MR via a virtual interface
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Effect of NEMO handoff on TCP and UDP
• Experiment setup
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1 home network and 2 foreign networks
Traffic is sent from CN to MNN
Using Iperf to generate TCP and UDP traffic
UDP: 200Bytes at 100Kbits/s
• Packet of a smaller size is more
sensitive to the effect of handoff
latency
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Effect of NEMO handoff on TCP and UDP
Use measurements on the Home network as a base line
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Performance analysis
• NEMO latency
– NEMO handoff latency
• Binding update and Binding Ack
– L2 handoff latency
– Duplicate Address Detection (DAD)
• Before acquiring a new CoA from foreign network
• Dominant factor: DAD
– Solution: Optimistic DAD
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Effect of NEMO handoff on TCP and UDP with ODAD
Without ODAD
With ODAD
Proxy DAD
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Future work
• Communication between Mobile router and mesh networks
– Understand NEMO performance on a mesh network
– Multi-homed MR
– Utilize Linksys routers test-bed
• Real world performance measurements
– On Sydney tour bus, collaborating with RTA
– Using service from Unwired network
– IPv4/IPv6 dual stack
• Releasing Mobile router software
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Today’s talk
• Part 1
– A brief talk about a project (STaR) we recently
started at NICTA
• Part 2
– two vehicular-network applications
•MOBNET – A NEMO-based Network Mobility Testbed
•MOVE – A Mobility model generator for VANET
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VANET for ITS
• Applications of Vehicular Ad-hoc network (VANET) for ITS
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Collision avoidance
Incident broadcasting
Traffic congestion avoidance
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MOVE – mobility model generator for VANET
• Motivation: a gap between transportation and networking
communities
– Many simulators are around to test/evaluate network protocols,
such as ns-2, Qualnet, OPNET
– In the transportation arena, many simulators such as PARAMICS,
CORSIM, VISSIM are developed to analyze transportation scenarios
at micro- or macro-scale level
– However, there is little effort in integrating these two types of
simulators together
– Realistic mobility models are important for VANET simulations
• Our contribution: MOVE
– A tool that allows users to rapidly generate realistic mobility models
for their simulations
Rapid Generation of Realistic Mobility Models for VANET, Feliz Kristianto Karnadi, Zhi Hai Mo,
Kun-chan Lan. Appeared in ACM MOBICOMM 2005 as a poster
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MOVE
• MObility generator for VANET
– Generation of realistic vehicle movement
patterns
– Based on an open source micro-traffic
simulator SUMO
– Output: realistic mobility traces for
vehicular ad-hoc network simulations
• Currently support ns-2/nam, qualnet
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Architecture of MOVE
• MAP editor
– Manual
– Automatic
– Import from real world maps
• Vehicle Movement Editor
– Trips of vehicles
– Routes for each trip
– Bus route
• Visualization of vehicle movements
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Map editor
• Manual Map
– Nodes: one particular point on the
map
• A junction or the dead end of the
road
– Junction node: normal road
junctions or traffic lights
– Edges: the road that connects two
points on a map
• Attributes: speed limit, number of
lanes, road priority, road length
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• Automatically generating Map
– Grid, spider, random
• Import from real world maps
– TIGER maps from U.S. Census
Bureau
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Vehicle movement editors
• Define the properties of vehicles routes
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Number of vehicles
Vehicles arrival time
Origin and destination of vehicles
Duration of the trip
Vehicle speed
• Acceleration, deceleration, speed
– Turning probability at each junction
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Bus routes generator
• The path of bus route
consists of a set of
intersections
• The coordinate of the
intersection is defined
in Map Editor
• Bus schedule is fixed
– Only start/end and
inter-departure time info
are needed
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Visualization of vehicles movements
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Automatically generate simulation scripts
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Conclusion and Future work
• Current implementation
– MOVE: a tool that can be used to rapidly generate realistic mobility models
for VANET simulations
– Mobility models are generated off-line and then used by ns-2/qualnet
simulator
• Next version of MOVE
– An interface that allow vehicle states can be fed into ns-2 in run time
– During the simulation the vehicles can dynamically adjust their movements
based on different traffic scenarios
– Export road info on Google Earth into MOVE
• Understand the effect of road traffic parameters in network simulations
– Multiple lanes, traffic light, etc
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That’s all, thanks!
Questions?
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