HOMELAND SECURITY IN THE STREETS
- THE VEHICLE GRID
Homeland Defense Workshop
Sorrento, Italy, Oct 18-21
Mario Gerla
Computer Science Dept
UCLA
Outline
• Urban Homeland Defense
– Cable TV installations vs mobile sensor platforms
• “Ad Hoc” Wireless Networks
– Conventional vs Opportunistic
• Vehicle Communications Standards
• V2V applications
– Car Torrent
– MobEyes
– Autonomous evacuation
• Beyond vehicles
– Health networks against bio attacks
– Under water networks against harbor attacks
Urban Homeland Security - CCTV
• In urban areas, the first line of defense has traditionally
been fixed video cameras
• Chicago, the leader in the US:
– 2,000 remote-control cameras and motion-sensing software are planned to
spot crimes or terrorist acts
– 1,000 already installed at O'Hare International Airport
• A few links below:
– 1. http://www.usatoday.com/news/nation/2004-09-09-chicagosurveillance_x.htm
– 2. http://www.securityinfowatch.com/online/The-Latest/Chicago-to-IncreasePresence-of-Surveillance-Cameras-on-Streets/9578SIW306
– 3. http://blog.publiceye.silkblogs.com/City-of-Chicago.1771.category
Emerging City Wide Surveillance Systems
With 4 millions CCTV cameras around the country, Britain is to become
the first country in the world where the movements of all vehicles on the
roads are recorded.
Jennifer Carlile, MSNBC
CHICAGO — A surveillance system that uses
2,000 remote-control cameras and motionsensing software to spot crimes or terrorist acts
as they happen is being planned for the city.
Debbie Howlett, USA TODAY
Urban Defense - Britain
• More than 4 million CCTV cameras operating around the
country:
–
–
–
–
–
Britain has more video surveillance than anywhere else in the world.
96 cameras at Heathrow airport, 1,800 in train stations,
6,000 on the London Underground,
260 around parliament,
230 used for license plate recognition in the city center, and the dozens
surveying West End streets.
• In London it's said that the average resident is viewed by
300 cameras a day.
•
References
http://www.msnbc.msn.com/id/5942513
http://news.independent.co.uk/uk/transport/
Urban surveillance by CCTV
• CCTV surveillance has benefits:
– Data collected in a data base via the very high speed urban wired
infrastucture
– High resolution video is good for criminal recognition
• However:
– Cameras cannot be installed at all locations
– Cameras can be taken out by terrorists
– The central data collection facility can be sabotaged
• Enter mobile video collection/storage platforms:
– Vehicles
– People
– Robots
• Mobile “eyes” are an excellent complement to CCTV
• In this talk we will focus on VEHICLES
Mobile Surveillance - Challenges
• New challenges:
– wireless communications medium
– wireless data protocols/architectures
– distributed storage strategy
– search of the distributed, mobile data base
• Let us begin with the wireless medium
challenge
The urban wireless “waves”
• Wave #1: cellular telephony (1980)
– Still, biggest profit maker
• Wave #2 : wireless Internet access (1995)
– Wireless LANs, WiFI, Mesh Nets, WIMAX
– Most Internet access on Campuses is wireless
– Urban Mesh Nets are rapidly proliferating in the US; Europe
and Asia to follow soon
– Cellular providers (2.5 G and 3G) are trying to keep up
• Wave #3: ad hoc wireless nets (now)
– Set up in an area with no infrastructure; to respond to a
specific, time limited need
The 3rd wave: Infrastructure vs Ad Hoc
Infrastructure Network (WiFI or 3G)
Ad Hoc, Multihop wireless Network
Ad Hoc Network Characteristics
• Instantly deployable, re-configurable (No fixed
infrastructure)
• Created to satisfy a “temporary” need
• Portable (eg sensors), mobile (eg, cars)
• Multi-hopping ( to save power, overcome
obstacles, etc.)
Typical Ad Hoc Network Applications
Military
– Automated battlefield
Civilian
–
–
–
–
–
–
Disaster Recovery (flood, fire, earthquakes etc)
Law enforcement (crowd control)
Homeland defense
Search and rescue in remote areas
Environment monitoring (sensors)
Space/planet exploration
SATELLITE
COMMS
SURVEILLANCE
MISSION
SURVEILLANCE
MISSION
UAV-UAV NETWORK
AIR-TO-AIR
MISSION
STRIKE
MISSION
COMM/TASKING
Unmanned
Control Platform
COMM/TASKING
COMM/TASKING
RESUPPLY
MISSION
UAV-UGV NETWORK
FRIENDLY
GROUND CONTROL
(MOBILE)
Manned
Control Platform
Typical Ad Hoc Network
Traditional ad hoc net architectures
• Tactical battlefield:
– no infrastructure
• Civilian emergency:
– infrastructure, if present, was destroyed
– Instant deployment
– Specialized missions (eg, UAV scouting)
– Critical: scalability, survivability, QoS, jam protection
– Non critical: Cost, Standards, Privacy
• These architectures are not suitable for “every day” urban
communications
• Enter: “Opportunistic” Ad Hoc Networks
New Trend: “Opportunistic” ad hoc nets
– Great for commercial applications
• Indoor W-LAN extended coverage
• Group of friends sharing 3G via Bluetooth
• Peer 2 peer networking in the vehicle grid
– Cost is a major issue
– Access to Internet:
– available, but;
– “bypass it” with “ad hoc” if too costly or
inadequate
– Critical: Standards -> cost reduction and
interoperability
– Critical: Privacy, security
Car to Car communications for Safe Driving
Vehicle type: Cadillac XLR
Curb weight: 3,547 lbs
Speed: 75 mph
Acceleration: + 20m/sec^2
Coefficient of friction: .65
Driver Attention: Yes
Etc.
Vehicle type: Cadillac XLR
Curb weight: 3,547 lbs
Speed: 65 mph
Acceleration: - 5m/sec^2
Coefficient of friction: .65
Driver Attention: Yes
Etc.
Alert Status: None
Alert Status: None
Alert Status: Inattentive Driver on Right
Alert Status: Slowing vehicle ahead
Alert Status: Passing vehicle on left
Vehicle type: Cadillac XLR
Curb weight: 3,547 lbs
Speed: 75 mph
Acceleration: + 10m/sec^2
Coefficient of friction: .65
Driver Attention: Yes
Etc.
Alert Status: Passing Vehicle on left
Vehicle type: Cadillac XLR
Curb weight: 3,547 lbs
Speed: 45 mph
Acceleration: - 20m/sec^2
Coefficient of friction: .65
Driver Attention: No
Etc.
Urban car to car communications:
the vehicle grid
New Vehicle Roles on the road
• Vehicle as a producer of geo-referenced
data about its environment
– Pavement condition
– Probe data for traffic management
– Weather data
– Physiological condition of passengers, ….
Vehicle Roles (cont)
• Vehicle & Vehicle, Vehicle & Roadway as
collaborators
– Cooperative Active Safety
• Forward Collision Warning, Blind Spot Warning,
Intersection Collision Warning…….
– In-Vehicle Advisories
• “Ice on bridge”, “Congestion ahead”,….
• Vehicle as Information Gateway (Telematics)
– Internet access, infotainment, dynamic route
guidance, ……
• These roles demand efficient communications
Car to Car/Curb communications
Transit Signal Priority
Transit Vehicle
up to 1000 ft
Transit Vehicle Stop
Traffic Signal
Grass Divider
Collision
Avoidance
E-Transaction: gas, movie, ….
Gas Pumps
Not to Scale
* Graphic created from Broady Cash (ARINC)
IDB Data
Transfer
Convergence to a Standard:
Government, Industry, Academia
•
•
•
•
•
•
ACM created Vehicular Ad-hoc Networks Workshop - VANET
IEEE created V2VCOM
Federal Communications Commission created DSRC
– The record in this proceeding overwhelmingly supports the
allocation of spectrum for DSRC based ITS applications to increase
traveler safety, reduce fuel consumption and pollution, and continue
to advance the nations economy.
• FCC Report and Order, October 22, 1999, FCC 99-305
• Amendment with licensing rules in December 2003
DSRC Standards
– ASTM E17.51, IEEE 802.11p
– http://grouper.ieee.org/groups/scc32/dsrc/
Automotive companies created Vehicle Safety Communications
Consortium (VSCC)
– Final Report Submitted January 2005
USDOT/CAMP have created Cooperative Intersection Collision
Avoidance (CICAS) Consortium
– http://www.its.dot.gov/cicas/cicas_workshop.htm
USDOT Vehicle Infrastructure Integration
Initiative
• http://www.itsa.org/vii.html
– The VII Initiative is a cooperative effort between
Federal and state departments of transportation
(DOTs) and vehicle manufacturers to evaluate the
technical, economic, and social/political feasibility
of deploying a communications system to be used
primarily for improving the safety and efficiency of
the nation's road transportation system.
The Standard: DSRC / IEEE 802.11p
• Car-Car communications at
5.9Ghz
• Derived from 802.11a
Event data recorder (EDR)
Forward radar
• three types of channels:
Vehicle-Vehicle service, a
Vehicle-Gateway service
and a control broadcast
channel .
• Ad hoc mode; and
infrastructure mode
• 802.11p: IEEE Task Group for
Car-Car communications
Positioning system
Communication
facility
Rear radar
Display
Computing platform
CarTorrent : Opportunistic Ad Hoc
networking to download large
multimedia files
Alok Nandan, Shirshanka Das
Giovanni Pau, Mario Gerla
WONS 2005
You are driving to Vegas
You hear of this new show on the radio
Video preview on the web (10MB)
One option: Highway Infostation download
Internet
file
Incentive for opportunistic “ad hoc
networking”
Problems:
Stopping at gas station for full download is a nuisance
Downloading from GPRS/3G too slow and quite
expensive
Observation: many other drivers are interested in download
sharing (like in the Internet)
Solution: Co-operative P2P Downloading via Car-Torrent
CarTorrent: Basic Idea
Internet
Download a piece
Outside Range of Gateway
Transferring Piece of File from Gateway
Co-operative Download: Car Torrent
Internet
Vehicle-Vehicle Communication
Exchanging Pieces of File Later
BitTorrent: Internet P2P file downloading
Uploader/downloader
Uploader/downloader
Tracker
Uploader/downloader
Uploader/downloader
Uploader/downloader
CarTorrent: Gossip protocol
A Gossip message containing Torrent ID, Chunk list
and Timestamp is “propagated” by each peer
Problem: how to select the peer for downloading
Selection Strategy Critical
CarTorrent with Network Coding
• Limitations of Car Torrent
– Piece selection critical
– Frequent failures due to loss, path breaks
• New Approach –network coding
– “Mix and encode” the packet contents at
intermediate nodes
– Random mixing (with arbitrary weights) will do
the job!
Network Coding
e = [e1 e2 e3 e4] encoding
vector tells how packet was
mixed (e.g. coded packet p =
∑eixi where xi is original packet)
buffer
Receiver
recovers
original
by
matrix
inversion
random
mixing
Intermediate nodes
CodeTorrent: Basic Idea
•
Single-hop pulling (instead of CarTorrent multihop)
Buffer
Internet
File: k blocks
Buffer
B1
B2
B3
*a1
*a2
*a3
*ak
+
“coded” block
Bk
Random Linear Combination
Buffer
Re-Encoding: Random Linear Comb.
OutsideBlocks
Rangeinofthe
APBuffer
of Encoded
Exchange Re-Encoded Blocks
Downloading Coded Blocks from AP
Meeting Other Vehicles with Coded Blocks
Simulation Results
• Avg. number of completion distribution
200 nodes
40% popularity
Time (seconds)
Simulation Results
Impact of mobility
– Speed helps disseminate from AP’s and C2C
– Speed hurts multihop routing (CarT)
– Car density+multihop promotes congestion (CarT)
Avg. Download Time (s)
•
40% popularity
Vehicular Sensor Network (VSN)
IEEE Wiress Communications 2006
Uichin Lee, Eugenio Magistretti (UCLA)
Roadside base station
Inter-vehicle
communications
Vehicle-to-roadside
communications
VSN-enabled vehicle
Sensors
Video
Chem.
Systems
Storage Proc.
Vehicular Sensor Applications
• Environment
– Traffic congestion monitoring
– Urban pollution monitoring
• Civic and Homeland security
– Forensic accident or crime site investigations
– Terrorist alerts
Infrastructure-Based Centralized Approach
- UK ANPR System
Vehicle passes ANPR Camera
ANPR s/w checks database
In Car System
CCTV
Decision taken to stop vehicle
Mobile Unit
Source: Automatic Number Plate Recognition (ANPR) - Driving Down Crime - Denying Criminals the Use of the Road
Accident Scenario: storage and retrieval
•
•
Designated Cars:
– Continuously collect images on the street (store data locally)
– Process the data and detect an event
– Classify the event as Meta-data (Type, Option, Location, Vehicle ID)
– Post it on distributed index
Police retrieve data from designated cars
- Sensing
- Processing
Summary
Harvesting
CRASH
Crash Summary
Reporting
Meta-data : Img, -. (10,10), V10
How to retrieve the data?
• “Epidemic diffusion” :
– Mobile nodes periodically broadcast meta-data of
events to their neighbors
– A mobile agent (the police) queries nodes and
harvests events
– Data dropped when stale and/or geographically
irrelevant
Epidemic Diffusion
- Idea: Mobility-Assist Meta-Data Diffusion
Epidemic Diffusion
- Idea: Mobility-Assist Meta-Data Diffusion
Keep “relaying”
its meta-data to
neighbors
1) “periodically” Relay (Broadcast)
its Event to Neighbors
2) Listen and store
other’s relayed events
into one’s storage
Epidemic Diffusion
- Idea: Mobility-Assist Meta-Data Harvesting
Meta-Data Rep
Meta-Data Req
1. Agent (Police) harvests
Meta-Data from its neighbors
2. Nodes return all the meta-data
they have collected so far
Simulation Experiment
•
Simulation Setup
–
–
–
–
NS-2 simulator
802.11: 11Mbps, 250m tx range
Average speed: 10 m/s
Mobility Models
• Random waypoint (RWP)
• Real-track model (RT) :
– Group mobility model
– merge and split at intersections
• Westwood map
Meta-data harvesting delay with RWP
Number of Harvested Summaries
• Higher mobility decreases harvesting delay
V=25m/s
V=5m/s
Time (seconds)
Harvesting Results with “Real Track”
Number of Harvested Summaries
• Restricted mobility results in larger delay
V=25m/s
V=5m/s
Time (seconds)
Protecting vehicles against road perils
Evacuation from a Tunnel after a Fire:
Emergency Video Streaming
• Multimedia type message propagation helps road
safety
– Precise situation awareness via video
– Drivers can make better informed decisions
Real-time Video Streaming
Fire inside the Tunnel
Source: http://www.landroverclub.net/Club/HTML/MontBlanc.htm
Emergency Video Streaming
• Problems
– Potential volume of multimedia traffic
– Unreliable wireless channel
• Multimedia data delivery service that is reliable
and efficient and real time
• Our Approach: Random network coding
Emergency Video Streaming
• Highway Data Mule: Data is store-carry-and-forwarded via
platoons in opposite direction
– Random network coding for delayed data delivery
405
Ramp
Pf -1
Pr -1
Ramp
Pf -2
Pr-2
Ramp
Simulation Results (Delivery Ratio)
1.01
Packet Delivery Ratio
1
0.99
0.98
0.97
0.96
0.95
0.94
Network Coding
0.93
Conventional Multicast
0.92
0
10
20
30
Max Node Speed (m/sec)
40
The vehicle grid as an emergency
network
Hot Spot
Hot Spot
Vehicular Grid as Opportunistic Ad Hoc Net
STOP
Power
Blackout
Hot Spot
Hot Spot
The Infrastructure Fails
STOP
Power
Blackout
Vehicular Grid as Emergency Net
Evacuation Scenario
•
•
•
•
A highly dense area of a town needs to be evacuated because of a
bomb threat, a chemical threat or an actual explosion
Evacuation plans that are in place today are static, do not adapt to a
highly dynamic scenario
Must be able to dynamically re-evaluate and readjust the strategy
The infrastructure may have failed - must rely on Car to Car only
Evacuation Scenario – Car to Car
communications
• Manage the evacuation of a town through the use of vehicular
networks
– Cars can sense and report local information (eg, radiation from a DIRTY Bomb
explosion)
– The information propagated by the cars can be used for safe evacuation
• Related project: RESCUE (Calit2) http://rescue.calit2.net
U-Ve T
Ucla - Vehicular Testbed
E. Giordano, A. Ghosh,
G. Marfia, S. Ho, J.S. Park, PhD
System Design: Giovanni Pau, PhD
Advisor: Mario Gerla, PhD
Project Goals
• Provide:
– A platform to support car-to-car experiments in various traffic
conditions and mobility patterns
– A shared virtualized environment to test new protocols and
applications
– Remote access to U-VeT through web interface
– Extendible to 1000’s of vehicles through WHYNET emulator
– potential integration in the GENI infrastructure
• Allow:
– Collection of mobility traces and network statistics
– Experiments on a real vehicular network
Big Picture
• We plan to install our node equipment in:
– 50 Campus operated vehicles (including shuttles and
facility
management trucks).
• Exploit “on a schedule” and “random” campus fleet mobility patterns
– 50 Communing Vans
• Measure freeway motion patterns (only tracking equipment installed
in this fleet).
– Hybrid cross campus connectivity using 10 WLAN Access Points .
The U-Box Node:
• In the final deployment:
–
–
–
–
–
Industrial PC (Linux OS)
2 x WLAN Interfaces
1 Software Defined Radio (FPGA based) Interface
1 Control Channel
1 GPS
• Current proof of concept:
–
–
–
–
1 Dell Latitude Laptop (Windows)
1 WLAN Interface
1 GPS
OLSR Used for the Demo
The Demo:
• Equipment:
–
–
–
–
–
6 Cars running in Campus
Clocks are in synch with the GPS
OLSR for the WLAN routing
1 EvDO interface in the Lead Car
1 Remote Monitor connected through the Internet
• Experiments:
– Connectivity map though OLSR
– Rough loss analysis though ping.
– On/OFF traffic using Iperf
The C2C testbed
Car 2 Car connectivity via OLSR
Beyond vehicular communications:
Defense from Bio-attacks
Previous Homeland Defense Work
– Portable sensors detect hazardous gas and identify fluids
through chemicals fingerprints
– Sensors track radioactive isotopes and explosives
– Small embedded cameras to sense movement
– Chemical sensors detect water borne species, airborne
substances, and cell-like structures
– Concrete Penetrating Radar sensor network uses micro
power impulse radars to identify structure’s contents (people
trapped in debris)
Airborne biohazards
Concrete penetrating radar
Implantable Sensors for Bio-terrorism
•
NEED: Early detection & rapid response after
bioterrorism attacks
–
–
–
–
Continuous monitoring, detection, and treatment for biochemical
agents and immunizations
Implantable sensors that wireless transmit data out of the body
Advances in MEMS research have provided ultra-small devices
Research needed on how to:
• Effectively get this information out of the body wirelessly
• Correlate the readings from various probes in order to
eliminate false positives
Implantable doppler probe
•
Proposed solution: Networked Health Belt
Implantable Sensors
MEMS pressure sensor
CardioMEMS sensor
Delivers medicine to
red blood cells
Implantable Drug Delivery
Pictures courtesy of CardioMems, Novosis, and Coneyl Jay Science Library
“Networking” the health belts
• A selected segment of the community (say,
police agents) wear the Health Belt:
–
–
–
–
–
Conventional Health probe monitors
Transducers from implants
PDA or Smart phone that collects/prepocesses/stores data
GPS
Communications:
• GSM (cellular phone); 802.11; Bluetooth; ZigBee
• Periodically, the belts are probed using SMS to
detect possible bio-attacks
Securing the Harbor:
Under Water Defenses
Underwater Persistent Surveillance
Monterey Bay, CA – Mobile and persistent
surveillance using new undersea vehicles
and deployment techniques.
MBARI project
Underwater Port Security
The Coast Guard is seeking to improve capability
to provide protection from underwater threats to
high value assets in domestic ports.
Detect, track, classify and intercept intruders and
terrorist threats
Anti-swimmer technology:
Swimmer or diver is covert delivery
method for explosives, sabotage or
chem/bio agent
Under Water Network Research at UCLA
• Efficient Dissemination of sensor data (ISCC 06)
– We show that conventional “directed diffusion” used in ground
sensors does not work under water
– A new technique called UW Diffusion greatly improves performance
• Under Water attacks and defenses (WISE 05):
– We show that low cost attacks are easy to launch Under Water
– We discuss possible protection measures
Why Large-scale UW Sensor networks?
• Various Scenarios
– (Homeland defense): 100’s of miles of coastline
– (Military) Anti-submarine warfare
• Submarines could be anywhere within 100 sq miles
– (Civilian) Marine pollution control
• Oil spill may have spread 100 sq miles
• Isolated probes (e.g., buoys, trailers) do not
work!
Sensor Equipped Aquatic Swarm
(SEA Swarm)
• SEA Swarm
–
–
–
–
Formed by air-dropping a large number of sensors
Moves as a group with water current and dispersion
Locally collect acoustic / chemical / temperature signatures
Report sensed data to command center in real-time
• Advantages
– 4D monitoring (space and time)
– Dynamic monitoring coverage
– Recoverable sensor nodes
• Triggerable air-bladder (to reduce cost)
• Goal: Efficient data collection from a SEA Swarm
Simulations
- Distinct-event delivery ratio
• Community-based forwarding improves delivery
Refresh period is important (15s vs. 45s)
Delivery Ratio
–
Network Size
U/W Defense Projects
• Monterey Bay 2006 field experiments, Underwater Persistent
Surveillence.
– http://www.mbari.org/MB2006/UPS/mb2006-ups-links.htm
• UnderWater Port Security
– http://www.trb.org/Conferences/MTS/1A%20WALKER%20UPSe
c.pdf
• Survaillance of inland waterways
• (Preventing the illegal crossing of the border, Protection of
ships).
– http://ieeexplore.ieee.org/iel5/9199/29174/01316409.pdf
• Underwater Robot Homeland Security Mission Inspecting Oil
Tanker
– http://www.videoray.com/Press_Room/propeller_collision.htm
Conclusions
• Vehicular Communications are critical for Homeland Defense:
–
–
–
–
Pervasive, mobile sensing: MobEyes
Autonomous Evacuation
Dynamic content sharing/delivery: Car Torrent
In summary, essential complement to CCTV
• Research Challenges:
– New routing/transport models: epidemic, P2P
– Searching massive mobile storage
– Security, privacy, incentives
• Future Research Directions:
– Vehicular tesbed experiments
– Health Networking
– Under Water defenses
The End
Thank You