HOMELAND SECURITY IN THE STREETS- THE VEHICLE GRID

Homeland Defense WorkshopSorrento, Italy, Oct 18-21

Mario GerlaComputer 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-chicago-

surveillance_x.htm– 2. http://www.securityinfowatch.com/online/The-Latest/Chicago-to-Increase-

Presence-of-Surveillance-Cameras-on-Streets/9578SIW306– 3. http://blog.publiceye.silkblogs.com/City-of-Chicago.1771.category

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.

CHICAGO — A surveillance system that uses 2,000 remote-control cameras and motion-sensing software to spot crimes or terrorist acts as they happen is being planned for the city.

Emerging City Wide Surveillance Systems

Jennifer Carlile, MSNBC

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/5942513http://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

SURVEILLANCE MISSION

SURVEILLANCE MISSION

AIR-TO-AIR MISSION

STRIKE MISSION

FRIENDLY GROUND CONTROL

(MOBILE)

RESUPPLY MISSION

SATELLITE COMMS

Unmanned Control Platform

COMM/TASKING

COMM/TASKING

MannedControl Platform

COMM/TASKING

UAV-UAV NETWORK

Typical Ad Hoc Network

UAV-UGV 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 XLRCurb weight: 3,547 lbsSpeed: 65 mphAcceleration: - 5m/sec^2Coefficient of friction: .65Driver Attention: YesEtc.

Vehicle type: Cadillac XLRCurb weight: 3,547 lbsSpeed: 45 mphAcceleration: - 20m/sec^2Coefficient of friction: .65Driver Attention: NoEtc.

Vehicle type: Cadillac XLRCurb weight: 3,547 lbsSpeed: 75 mphAcceleration: + 20m/sec^2Coefficient of friction: .65Driver Attention: YesEtc.

Vehicle type: Cadillac XLRCurb weight: 3,547 lbsSpeed: 75 mphAcceleration: + 10m/sec^2Coefficient of friction: .65Driver Attention: YesEtc.

Alert Status: None

Alert Status: Passing Vehicle on left

Alert Status: Inattentive Driver on Right

Alert Status: None

Alert Status: Slowing vehicle aheadAlert Status: Passing vehicle on left

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

Traffic Signal

Transit Vehicle

Transit Vehicle Stop

up to 1000 ft

Not to Scale

Grass DividerCollision Avoidance

E-Transaction: gas, movie, ….

Transit Signal Priority

Gas Pumps

IDB Data Transfer

Car to Car/Curb communications

* Graphic created from Broady Cash (ARINC)

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 • 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

Forward radar

Computing platform

Event data recorder (EDR)Positioning system

Rear radar

Communication facility

Display

CarTorrent : Opportunistic Ad Hoc networking to download

large multimedia files

Alok Nandan, Shirshanka DasGiovanni Pau, Mario Gerla

WONS 2005

You are driving to VegasYou 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

Download a piece

Internet

Transferring Piece of File from Gateway

Outside Range of Gateway

Co-operative Download: Car Torrent

Vehicle-Vehicle Communication

Internet

Exchanging Pieces of File Later

BitTorrent: Internet P2P file downloading

Uploader/downloader

Uploader/downloader

Uploader/downloader

Uploader/downloader

TrackerUploader/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

Receiverrecoversoriginal

by matrix

inversion

random mixing

buffer

Intermediate nodes

e = [e1 e2 e3 e4] encoding vector tells how packet was mixed (e.g. coded packet p = ∑eixi where xi is original packet)

CodeTorrent: Basic Idea

Internet

Downloading Coded Blocks from AP

Outside Range of AP

Buffer

BufferBuffer

Re-Encoding: Random Linear Comb.of Encoded Blocks in the Buffer

Exchange Re-Encoded Blocks

Meeting Other Vehicles with Coded Blocks

• Single-hop pulling (instead of CarTorrent multihop)

“coded” block

B1

File

: k b

lock

s

B2B3

Bk

+*a1

*a2*a3

*ak

Random Linear Combination

Simulation Results

• Avg. number of completion distribution

200 nodes40% 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)

40% popularity

Avg

. Dow

nloa

d T

ime

(s)

Vehicular Sensor Network (VSN)IEEE Wiress Communications 2006

Uichin Lee, Eugenio Magistretti (UCLA)

VSN-enabled vehicle

Inter -vehiclecommunications

Vehicle -to-roadsidecommunications

Roadside base station

Vid e o Ch e m.

Sensors

S to ra g e

Systems

P ro c.

Vehicular Sensor Applications

• Environment– Traffic congestion monitoring– Urban pollution monitoring

• Civic and Homeland security– Forensic accident or crime site investigations – Terrorist alerts

Vehicle passes ANPR Camera ANPR s/w checks database

Decision taken to stop vehicle

Source: Automatic Number Plate Recognition (ANPR) - Driving Down Crime - Denying Criminals the Use of the Road

Infrastructure-Based Centralized Approach- UK ANPR System

Mobile Unit

CCTV

In Car System

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

Meta-data : Img, -. (10,10), V10

CRASH

- Sensing - P rocessing

Crash Summary Report ing

Summary Harvesting

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

1) “periodically” Relay (Broadcast) its Event to Neighbors 2) Listen and store other’s relayed events into one’s storage

Keep “relaying” its meta-data to neighbors

Epidemic Diffusion - Idea: Mobility-Assist Meta-Data Harvesting

Meta-Data Req

1. Agent (Police) harvestsMeta-Data from its neighbors

2. Nodes return all the meta-datathey have collected so far

Meta-Data Rep

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

• Higher mobility decreases harvesting delay

Time (seconds)

Num

ber

of H

arve

sted

Sum

mar

ies V=25m/s

V=5m/s

Harvesting Results with “Real Track”

• Restricted mobility results in larger delay

Time (seconds)

Num

ber

of H

arve

sted

Sum

mar

ies V=25m/s

V=5m/s

Protecting vehicles against road perils

Evacuation from a Tunnel after a Fire: Emergency Video Streaming

Source: http://www.landroverclub.net/Club/HTML/MontBlanc.htm

Fire inside the Tunnel

• Multimedia type message propagation helps road safety– Precise situation awareness via video– Drivers can make better informed decisions

Real-time Video Streaming

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

RampRamp

Ramp

Pr -1

Pf -1 Pf -2

Pr-2

Simulation Results (Delivery Ratio)

0.92

0.93

0.94

0.95

0.96

0.97

0.98

0.99

1

1.01

0 10 20 30 40

Max Node Speed (m/sec)

Packet Delivery RatioNetwork Coding

Conventional Multicast

The vehicle grid as an emergency network

Hot Spot

Hot Spot

Vehicular Grid as Opportunistic Ad Hoc Net

Hot Spot

Hot Spot

PowerBlackout

STOPPower

Blackout

STOP

The Infrastructure Fails

PowerBlackout

STOPPower

Blackout

STOP

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

UU--VVee TTUcla - Vehicular TestbedUcla - 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)

Concrete penetrating radarAirborne biohazards

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

• Proposed solution: Networked Health Belt

Implantable doppler probe

Implantable Sensors

Pictures courtesy of CardioMems, Novosis, and Coneyl Jay Science Library

MEMS pressure sensor

Delivers medicine to red blood cells

Implantable Drug Delivery

CardioMEMS sensor

“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

Anti-swimmer technology:

Swimmer or diver is covert delivery method for explosives, sabotage or chem/bio agent

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

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)

Network Size

Del

iver

y Ra

tio

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%20UPS

ec.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