Other CITRIS Research Programs

Jim Demmel, Chief ScientistEECS and Math Depts.

www.citris.berkeley.edu

UC Santa CruzUC Santa Cruz

OutlineOutline Security (Wagner, Tygar)Security (Wagner, Tygar) Software Reliability (Aiken, Necula, Henzinger)Software Reliability (Aiken, Necula, Henzinger) Sensors Webs (Sastry,…)Sensors Webs (Sastry,…) Transportation Networks (Hedrick, Varaiya, …)Transportation Networks (Hedrick, Varaiya, …) Visualization (Hamann, Joy, Max, Staadt)Visualization (Hamann, Joy, Max, Staadt) CAD for MEMS (Demmel, Govindjee, Agogino, Pister, CAD for MEMS (Demmel, Govindjee, Agogino, Pister,

Bai)Bai)

SecuritySecurity

Software Security – D. WagnerSoftware Security – D. Wagner

Security programming is pitfall-ladenSecurity programming is pitfall-laden It’s too easy to unintentionally violate implicit usage It’s too easy to unintentionally violate implicit usage

rules of OS API’srules of OS API’s

Our approach: enforce defensive codingOur approach: enforce defensive coding Enumerate rules of prudent security codingEnumerate rules of prudent security coding Use tools to automatically verify that SW follows rulesUse tools to automatically verify that SW follows rules

Prudent Coding RulesPrudent Coding Rules

system() or exec()

seteuid(0)

seteuid(0)

Example of a rule:Example of a rule: Avoid calling system() or Avoid calling system() or

exec() with root privilegeexec() with root privilege

In our tool, MOPS:In our tool, MOPS: Rules are finite-state machinesRules are finite-state machines

Good for ordering propertiesGood for ordering properties Intuitive for programmersIntuitive for programmers

Programs are PDAsPrograms are PDAs Use model checking to verify Use model checking to verify

absence of security holesabsence of security holes Numerous bugs uncoveredNumerous bugs uncovered www.cs.berkeley.edu/

~daw/mops

On going work – Security in Sensor NetsOn going work – Security in Sensor Nets

with D. Culler, D. Tygarwith D. Culler, D. Tygar Motivation: resist attack on sensor netsMotivation: resist attack on sensor nets Secure routingSecure routing Secure location findingSecure location finding Challenge: low resource environmentChallenge: low resource environment

Security with Privacy – D. TygarSecurity with Privacy – D. Tygar

DARPA ISAT study, co-lead by E. FeltonDARPA ISAT study, co-lead by E. Felton Security: protection of people and property by Security: protection of people and property by

intelligence and law enforcementintelligence and law enforcement Privacy: while respecting legal, political, ethical rules Privacy: while respecting legal, political, ethical rules

on use of personal dataon use of personal data Data Sources: US Govt, other govts, commercial, Data Sources: US Govt, other govts, commercial,

private private

ApproachApproach

Focus on two application areasFocus on two application areas Profiling: On whom should security personnel focus?Profiling: On whom should security personnel focus? Data mining: What can we learn by automated analysis of Data mining: What can we learn by automated analysis of

available data?available data? Understand how to do these things betterUnderstand how to do these things better

Constrained by privacy concerns (legal and policy)Constrained by privacy concerns (legal and policy) Constrained by real-world organizational issuesConstrained by real-world organizational issues

Look for technological leverage pointsLook for technological leverage points

Conclusions [to date]Conclusions [to date]

Biggest technical challenge is data fragmentationBiggest technical challenge is data fragmentation Selective revelation (shorter term), Selective revelation (shorter term), General function shipping (longer term)General function shipping (longer term)

Privacy metrics valuable, if feasiblePrivacy metrics valuable, if feasible Entropy based?Entropy based?

Law has been slow to track changes in technology; would help to Law has been slow to track changes in technology; would help to redraw some legal lines to maintain original spirit of laws.redraw some legal lines to maintain original spirit of laws.

Final Report due: August 2002Final Report due: August 2002 Impact on design of Societal Scale Information SystemsImpact on design of Societal Scale Information Systems

Software ReliabilitySoftware Reliability

OSQ: Open Source QualityOSQ: Open Source Quality

Goals: Automatic analysis of software forGoals: Automatic analysis of software for Finding bugsFinding bugs Checking specificationsChecking specifications

Of a at least simple properties

Help with writing specificationsHelp with writing specifications

FocusFocus Large, ubiquitous systems programsLarge, ubiquitous systems programs Linux kernel, sendmail, apache, etc.Linux kernel, sendmail, apache, etc.

ToolsTools

CCuredCCured Automatically enforce memory safety for CAutomatically enforce memory safety for C

Array index out of bounds, wild pointer dereferences CQualCQual

Specification and checking of system-specific propertiesSpecification and checking of system-specific properties Locking, file handling, ordering of method calls, …

BLASTBLAST Software model checkerSoftware model checker

E.g., for checking complex control-flow in device drivers

http://www.cs.berkeley.edu/~weimer/osq

Sensor WebsSensor Webs

Activities of the SensorWebs Group (Sastry)Activities of the SensorWebs Group (Sastry) Studied theory and algorithms for Studied theory and algorithms for

networks of wireless sensors networks of wireless sensors (SensorWebs)(SensorWebs)

Basic idea: A large number of Basic idea: A large number of SmartDust motes distributed in an SmartDust motes distributed in an environment; they sense it, environment; they sense it, compute, and communicatecompute, and communicate

Main problems:Main problems: LocalizationLocalization Environmental monitoringEnvironmental monitoring TrackingTracking Map building Map building

LocalizationLocalization:: some nodes have known, some nodes have known, some unknown locations – compute the some unknown locations – compute the unknown onesunknown ones

Environmental monitoringEnvironmental monitoring:: given a scalar given a scalar environmental variable (temperature, air environmental variable (temperature, air pressure, intensity of light, etc.), monitor pressure, intensity of light, etc.), monitor it using a (possibly random) sensor it using a (possibly random) sensor network and visualize its gradientnetwork and visualize its gradient

Tracking of moving objectsTracking of moving objects:: track one or track one or more moving objects through a sensor more moving objects through a sensor networknetwork

Map buildingMap building:: use a mobile sensor use a mobile sensor network (e.g., robots carrying sensors) to network (e.g., robots carrying sensors) to create a map of an unknown create a map of an unknown environment)environment)

Main Results and ApplicationsMain Results and Applications

Designed Designed distributed, distributed, computationally efficient algorithmscomputationally efficient algorithms for localization, environmental for localization, environmental monitoring (static and dynamic), monitoring (static and dynamic), tracking, and map buildingtracking, and map building

Obtained Obtained analytical estimatesanalytical estimates on on the required density of sensor the required density of sensor nodes to achieve desired nodes to achieve desired average accuracy average accuracy

Preparing to Preparing to implementimplement and test and test the algorithms on a test-bed with the algorithms on a test-bed with several hundred nodes in several hundred nodes in collaboration with the collaboration with the NEST NEST ProjectProject (D. Culler) (D. Culler)

ApplicationsApplications Environmental monitoringEnvironmental monitoring:: of the of the

gradient of environmental variables, gradient of environmental variables, to close control loop for cutting to close control loop for cutting power use, energy conservation, power use, energy conservation, increasing comfort in smart increasing comfort in smart buildings; also, tracking hazardous buildings; also, tracking hazardous plumes.plumes.

Map buildingMap building:: investigation of investigation of dangerous areas (e.g., following a dangerous areas (e.g., following a major natural disaster) using mobile major natural disaster) using mobile robotsrobots

TrackingTracking:: possible applications in possible applications in preventing terrorist activity.preventing terrorist activity.

Experimental Results: Pursuit-Evasion Experimental Results: Pursuit-Evasion Games with 4UGVs and 1 UAVGames with 4UGVs and 1 UAV

Where does the Sensor Network fit in?Where does the Sensor Network fit in?

Ground Monitoring System

Ground Mobile Robots

UAVs

Sensor Webs

Gateways

Courtesy of Jin Kim

Lucent

Orinoco (WaveLAN)

(Ad Hoc Mode)

TransportationTransportation

Karl HedrickDirector, California PATH

Research Center on Intelligent Transportation Systems

PATH Activities

Advanced Vehicle Control and Safety Systems (AVCSS) Steven Shladover, Senior Deputy Director

Advanced Transportation Management and Information Systems (ATMIS)

Hamed Benouar, Acting Deputy Director Center for Commercialization of ITS

Technologies (CCIT)

Hamed Benouar, Executive Director

Partners for Advanced Transit and Highways Partners for Advanced Transit and Highways (PATH Program)(PATH Program)

Applying information technology to improve surface Applying information technology to improve surface transportation operationstransportation operations

Partnership between California Department of Partnership between California Department of Transportation and UCB Institute of Transportation Transportation and UCB Institute of Transportation Studies since 1986Studies since 1986

Started national interest in Intelligent Transportation Started national interest in Intelligent Transportation Systems (ITS)Systems (ITS)

Annual statewide RFP for new research projectsAnnual statewide RFP for new research projects Combination of faculty/graduate student and full-time Combination of faculty/graduate student and full-time

research staff projectsresearch staff projects- 100 person level of effort- 100 person level of effort

PATH-Identified Research NeedsPATH-Identified Research Needs Enabling technologies for intelligent transportation Enabling technologies for intelligent transportation

systems:systems: Remote sensing of macroscopic traffic conditionsRemote sensing of macroscopic traffic conditions Remote sensing of microscopic vehicle positions and Remote sensing of microscopic vehicle positions and

surroundingssurroundings Wireless communications (vehicle-vehicle and vehicle-Wireless communications (vehicle-vehicle and vehicle-

roadside)roadside) Safety-critical software systemsSafety-critical software systems

More information at poster sessionMore information at poster session

Center for Commercialization of ITS Center for Commercialization of ITS Technologies (CCIT): FocusTechnologies (CCIT): Focus

Bring the best minds together to conduct R&D, testing, and Bring the best minds together to conduct R&D, testing, and evaluation of ITSevaluation of ITS

Collaboration among researchers, industry professional, and Collaboration among researchers, industry professional, and practitioners practitioners

Accelerate commercial deployment of transportation products and Accelerate commercial deployment of transportation products and servicesservices

Solve transportation problems using new products and servicesSolve transportation problems using new products and services Facilitate traffic data disseminationFacilitate traffic data dissemination Focus researcher and industry efforts on Information Technology Focus researcher and industry efforts on Information Technology

(IT) solutions for transportation (IT) solutions for transportation

CCIT ProgramsCCIT Programs

Traveler InformationTraveler Information Traffic Data Collection and dissemination

Vehicle Information and ControlVehicle Information and Control In-vehicle information systems

Transportation Management SystemsTransportation Management Systems Performance management

Innovative Mobility System Concepts Innovative Mobility System Concepts Electronic and wireless technologies to support transit and

carsharing, smart parking management, and smart growth

Advanced Traffic Management and Advanced Traffic Management and Information Systems(ATMIS)/CCIT ProjectsInformation Systems(ATMIS)/CCIT Projects

IT to Improve Transportation, Safety, Efficiency, Security, and the IT to Improve Transportation, Safety, Efficiency, Security, and the EnvironmentEnvironment

Caltrans Performance Measurement System (PeMS) Caltrans Performance Measurement System (PeMS) Integrated Transportation Performance ManagementIntegrated Transportation Performance Management

Traffic Data collection/disseminationTraffic Data collection/dissemination Partnership with Information Service ProvidersPartnership with Information Service Providers

Smart Detector Technology (Vehicle Signature)Smart Detector Technology (Vehicle Signature) Border Crossing ITS Technologies (US-Mexico)Border Crossing ITS Technologies (US-Mexico) Technologies for Carsharing and Smart Parking Management Technologies for Carsharing and Smart Parking Management Cellular Technology for traffic data collection/traveler informationCellular Technology for traffic data collection/traveler information

VisualizationVisualization

Interactive and Collaborative Interactive and Collaborative Visualization and Exploration of Massive Visualization and Exploration of Massive

Data SetsData Sets --------

UC Davis Visualization Investigators:UC Davis Visualization Investigators:Bernd Hamann, Bernd Hamann,

Ken Joy, Kwan-Liu Ma,Ken Joy, Kwan-Liu Ma, Nelson Max and Oliver StaadtNelson Max and Oliver Staadt

http://graphics.cs.ucdavis.eduhttp://graphics.cs.ucdavis.edu

Massive Data Visualization -Massive Data Visualization -The ChallengeThe Challenge

Massive amountsMassive amounts of data acquired by millions of multi-modal sensors – of data acquired by millions of multi-modal sensors – embedded in civil infrastructureembedded in civil infrastructure

Exploration for Exploration for multiple purposesmultiple purposes Traffic flow monitoringTraffic flow monitoring Behavior of structures during earthquakesBehavior of structures during earthquakes Environmental monitoring (water, air, land)Environmental monitoring (water, air, land) Crisis managementCrisis management ……

Automatic Automatic “filtering” and compression“filtering” and compression of data of data Real-time visualizationReal-time visualization for different groups for different groups

Decision and policy makersDecision and policy makers Emergency response teamsEmergency response teams Civil engineersCivil engineers ……

Major technological challenges!Major technological challenges!

Collaborative VisualizationCollaborative Visualization

ConnectionConnection of multiple data of multiple data exploration and exploration and visualization centersvisualization centers

CollaborativeCollaborative data data explorationexploration by by interdisciplinary expert interdisciplinary expert teamsteams

Contribution to CITRISContribution to CITRIS

Compression of massive data streams supporting Compression of massive data streams supporting analysis at multiple levels of abstraction analysis at multiple levels of abstraction – quantitative / – quantitative / qualitativequalitative

Efficient and automatic Efficient and automatic feature extractionfeature extraction Visualization in Visualization in immersiveimmersive three-dimensional three-dimensional

environmentsenvironments Interactive visualization Interactive visualization – real-time– real-time Techniques for large, room-size Techniques for large, room-size “display walls”“display walls” Parallel and distributed computingParallel and distributed computing in support of scalable, in support of scalable,

multiresolution-based data exploration techniquesmultiresolution-based data exploration techniques Hybrid display environmentsHybrid display environments - virtual environments, - virtual environments,

augmented virtuality, augmented reality, voice, augmented virtuality, augmented reality, voice, gesture, force, …gesture, force, …

Computer Aided Design ofComputer Aided Design ofMEMSMEMS

SUGARSUGAR

Pister, Demmel, Govindjee, Agogino, BaiPister, Demmel, Govindjee, Agogino, Bai Tool for system-level MEMS simulationTool for system-level MEMS simulation Goal: Be SPICE to the MEMS worldGoal: Be SPICE to the MEMS world Analyzes static, dynamic, and linearized steady-state Analyzes static, dynamic, and linearized steady-state

behaviorbehavior Challenges:Challenges:

Be fast enough for design and optimization (not just verification)Be fast enough for design and optimization (not just verification) Handle coupled physical effects Handle coupled physical effects

electrical, mechanical, thermal, optical, …

SUGAR: Current workSUGAR: Current work

Broad set of component modelsBroad set of component models Validation against optical measurementsValidation against optical measurements Deployment of Millennium-based web service (used Deployment of Millennium-based web service (used

in EE245 in Fall 2001)in EE245 in Fall 2001) Analyze dependence on parameters (sensitivity Analyze dependence on parameters (sensitivity

analysis, bifurcation analysis)analysis, bifurcation analysis) Design synthesis and optimizationDesign synthesis and optimization Integration of state-of-the-art solversIntegration of state-of-the-art solvers

Architecture

System Assembly

Device Models

Solvers

Netlist

uses mumps.net

param ox=0, oy=0, oz=0

gap3dV2 p1 [b c D E]

[l=100u w1=5u w2=5u

gap=2u t1=0u t2=500u

V1=5 V2=12

ox=ox oy=oy oz=oz]

Analysis Results

transient analysis

steady-state analysis

static analysis

sensitivity analysis

User Interfaces

MATLAB™ Web Library

Torsional micromirror. MEMS Design by: M. Last, K.S.J. Pister

• Complex system with ~1000 comb fingers and torsional springs

• Finite Element Analysis might use O(106) continuum elements

• Sugar: system reduces to 2,621 elements and 11,706 unknowns

• Device described using parameterized substructures

Cosine-shaped beams

Perforated beams

Mirror

Torsional hinge

Perforated comb drive array

Actuation direction

Moment arm

Recessedinner plate

M&MEMS: SUGAR on the Web• Hosted on Berkeley Millennium cluster

• Requires only a web browser (with Java for graphics)

• Used in Berkeley’s Fall 2001 introductory MEMS course