Quasar Group

Quality Aware Sensor Infrastructure (QUASAR) Project**

Team

Faculty: Sharad Mehrotra, Nalini Venkatasubramanian

Postdoc: Dimitr Kalashnikov

Students Qi Han, Iosif Lazaridis, Xingbo Yu

**Supported in part by a collaborative NSF ITR grant entitled “real-time data capture, analysis, and querying of dynamic spatio-temporal events” in collaboration with UCLA, U. Maryland, U. Chicago

Quasar Group

Ubiquitous Sensor Environments

Sensor Network

s

Battlefield MonitoringHabitat Monitoring

Earthquake Monitoring

Oceanographic current monitoring

Medical Condition Monitoring

Traffic Congestion Detection

Target Tracking & Detection Intrusion Detection

Video Surveillance

• Generational advances to computing infrastructure– sensors will be everywhere

• Continuous monitoring and recording of physical world and its phenomena– Limitless possibilities

• New challenges – limited bandwidth & energy – highly dynamic systems

• System architectures are due for an overhaul– at all levels of the system networks,

OS, middleware, databases, applications

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Taxonomy of Applications (1)

• Data Access needs of applications– Historical data

• Analysis to better understand the physical world

– Current data• Monitoring and control to optimize the processes that

drive the physical world

– Future data• Forecasting trend in data for decision making

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Taxonomy of Applications (2)

• Predictability of Data access– Fixed

• data access needs of applications known a-priori

– Unpredictable (ad-hoc)• Data access needs of applications not known at any

instance of time

– Predictable (continuous)• Data access needs of applications can be predicted for

some time in the future with high probability

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Application Landscape

no knowledge some knowledge full knowledge

Temporal property of data accessed

Predictability of data access

the present

the future

Each evening at 8pm predict the temperature for the next 5 days

Notify me immediately when there is a forest fire

Every month, calculate the average humidity in California for the last 30 days

Did the temperature rise above 40oC in the last year?

Is Mr. Doe’s newly proposed weather model accurate for 1996-2000?

How much snow is there in Aspen?

I’m going surfing on Sep. 30! Will it be windy?

Visualize current humidity with Mrs. Doe’s new interpolation scheme.

Predict noise levels around the airport if runway 2 becomes operational

the

past

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Sensor Data Management Infrastructure

• A data collection and management middleware infrastructure that– provides seamless access to data dispersed across a hierarchy of

sensors, servers, and archives

– supports multiple concurrent applications of diverse types

– adapts to changing application needs

• Fundamental Issues:– Where to store data?

• do not store, at the producers, at the servers

– Where to compute?• At the client, server, data producers

Quasar Group

Existing DBMS technologies…

• Traditional data management– client-server architecture– efficient approaches to data storage & querying – query shipping versus data shipping– data changes with explicit update

• Limitations– Sensors generate continuously changing data

• Producers must be considered as “first class” entities

– Does not exploit the storage, processing, and communicating capabilities of sensors

data/query request

data/query result clientserverdata producers

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Stream Data Management

• Data streams through the server but is not stored• Continuous queries evaluated against streaming data• Deals with problems due to dynamic data on the server side• But

– Does not converse sensor resources (e.g., power)– Does not exploit the storage and processing capabilities of sensors– Geared towards continuous monitoring and not archival applications

stream processingengine

(Approximate) Answer

synopsis in memory

data streams continuous queries

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Quasar Architecture

• Hierarchical architecture– data flows from producers to server to

clients periodically– queries flow the other way:

• If client cache does not suffices, then• query routed to appropriate server• If server cache does not suffice, then

access current data at producer

– This is a logical architecture• producers could also be clients• A server may be a base station or a

(more) powerful sensor node• Servers might themselves be

hierarchically organized• The hierarchy might evolve over time

server

clientclient cache

server cache and archive

Producer & its cacheQ

UE

RY

FL

OW

DA

TA

FL

OW

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Quasar: Observations & Approach

• Applications can tolerate errors in sensor data– applications may not require exact answers:

• small errors in location during tracking or error in answer to query result may be OK

– data cannot be precise due to measurement errors, transmission delays, etc.

• Communication is the dominant cost – limited wireless bandwidth, source of major energy drain

• Quasar Approach– exploit application error tolerance to reduce communication

between producer and server and/or to conserve energy– Two approaches

• Minimize resource usage given quality constraints • Maximize quality given resource constraints

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Quasar Issues …

• Mapping application quality requirement to data quality requirements– Examples:

• Target tracking: quality of track --> accuracy of data• Aggregation Queries: accuracy of results --> accuracy of data

– Strategy should adapt to expected application load

• Quality-based data collection – Minimize sensor resource consumption while guaranteeing required

data quality

• Quality-cognizant query processing– imprecise data representation– Optimal execution of queries over imprecise data

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Quasar Progress …

• Mapping application quality requirement to data quality requirements– Target Tracking using acoustic sensors [MW ‘03]– Spatial range queries [DEXA ‘03]

• Quality-based data collection – General framework [DS Online ‘03]– To support monitoring queries over current data [Qi+03]– For sensor data archival [ICDE ‘03]– With real-time constraints [RTSS ‘03]– With support for in-network aggregation [Yu+03]

• Quality-cognizant query processing– Aggregation queries [Sigmod ‘01]– Selection Queries [ICDE ‘04]

Quasar Group

Quality Aware Data Collection Problem

• Let P = < p[1], p[2], …, p[n] > be a sequence of environmental

measurements (time series) generated by the producer, where n = now

• Let S = <s[1], s[2], …, s[n]> be the server side representation of the

sequence

• A within- quality data collection protocol guarantees that

for all i error(p[i], s[i]) <

is derived from application quality tolerance

Sensor time series…p[n], p[n-1], …, p[1]

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Simple Data Collection Protocol

• sensor Logic (at time step n)

Let p’ = last value sent to server

if error(p[n], p’) > or on timeout

send p[n] to server --- sensor if switch radio on, if need be

• server logic (at time step n)

If new update p[n] received at step n

s[n] = p[n]

Else

s[n] = last update sent by sensor

– guarantees maximum error at server less than equal to

Sensor time series…p[n], p[n-1], …, p[1]

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Exploiting Prediction Models

• Producer and server agree upon a prediction model (M, )

• Let spred[i] be the predicted value at time i based on (M, )

• sensor Logic (at time step n)

if error(p[n], spred[n] ) >

send p[n] to server

• server logic (at time step n)

• If new update p[n] received at step n

s[n] = p[n]

Else

s[n] = spred[n] based on model (M, )

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Challenges in Prediction

• Simple versus complex models?• Complex and more accurate models require more parameters (that will need to

be transmitted).

• Goal is to minimize cost not necessarily best prediction

• How is a model M generated?• static -- one out of a fixed set of models

• dynamic -- dynamically learn a model from data

• When should a model M or parameters be changed?

• immediately on model violation:

– too aggressive: violation may be a temporary phenomena

• never changed:

– too conservative: data rarely follows a single model

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Challenges in Prediction (cont.)

• who updates the model?

• Server

– long-haul prediction models possible, since server maintains history

– might not predict recent behavior well since server does not know exact S

sequence; server has only samples

– extra communication to inform the producer

• Producer

– better knowledge of recent history

– long haul models not feasible since producer does not have history

– producers share computation load

• Both

– server looks for new models, sensor performs parameter fitting given

existing models.

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Answering Queries

• If query quality tolerance satisfied at server (more than )

– Answer query at the server

• Else

– Probe the sensor

– Sensor guaranteed to respond within a bounded time

• Approach guarantees quality tolerance of queries

Probe result

… sensor-initiated update(sensor time series: …p[n], p[n-1], …, p[1])

probe

query Q1

(A1)query Qm

(Am)

i=[li,ui]sensor si

Imprecise data representation

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The Challenge …

• How should sensor state be managed to minimize energy consumption in maintaining data at required quality– Sensor State: error precision, power states

• Power consumption of sensors

0.016offsleeping

12.36idlelistening

12.50Rxlistening

14.88Txactive

Power consumption (mW)Radio modeSensor state

Quasar Group

Sensor State Models

sleeping

listening

active

Upon first sensor-initiated updateOr after Ts

After Tl without traffic

Upon first sensor initiated update or probe

Ta after processing last sensor-initiated update or probe

Active-Listening-Sleeping Model (ALS):

Other Models: Always-Active (AA) [Ta is infinite]Active-Listening (AL) [Tl is infinite]Active-Sleeping (AS) [Tl is 0]

Quasar Group

Issues in Energy Efficient Data Collection

• Issues– How to maintain the precision range for each sensor

• Larger increases possibility of expensive probes• Small wastes communication due to sensor-initiated updates

– When to transition between sensor states (I.e, set Ta, Tl, Ts)

• Powering down might not be optimal if we have to power up immediately

• Powering down may increases query response time

• Objective – set values for Ta, Tl, Ts, and that minimizes energy cost

normalized energy cost= energy consumed at each state + state transition energy

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Our Approaches to Energy Efficient Sensor Data Collection

• We solve the energy optimization problem by solving two sub-problems– Optimize energy consumption by adjusting range

size under the assumption that the state transition is fixed

• I.e., Ta, Tl, and Ts have been optimally set

– Optimize energy consumption by adapting sensor states while assuming that the precision range for sensor is fixed

Quasar Group

Range size Adjustment for the AA/AL Model

• Optimal precision range that minimizes E occurs when

– Optimal range can be realized by maintaining this probability ratio – Can be done at the sensor

• Assuming that is the ratio of sensor-initiated update probability to probe probability:

for sensor-initiated update:

with probability min{,1}, set ’= (1+);

for probe:

with probability min{1/ ,1}, set ’=/(1+ );

Quasar Group

Range Size Adjustment for the AS/ALS Model

• Sensor side– Keep track of the number of state transitions of the last k updates

– Piggyback the probability of state transitions with the Kth update

• Server side– Keep track of the number of sensor-initiated updates and probes of

the last k updates

– Upon receiving the Kth update from the sensor• Compute the optimal precision range • Inform the sensor about the new

Quasar Group

Server-side Algorithm:

while (1) {

if (received an update) {

i ++;

if (source-initiated update) Nsu ++;

if (consumer-initiated update) Ncu ++;

if (i == k) {

Psu = Nsu/T; Pcu = Ncu/T;

compute K1 and K2 for current

sliding window;

compute r;

send to sensor: r;

}

}

}

Sensor-side Algorithm:

while (1) {

if (transition from active to sleeping) Nas ++;

if (transition from sleeping to active) Nsa ++;

if (received an update) {

i++;

l’ = e’ – r/2; u’= e’ + r/2;

send to server: (l’,u’);

if (i == k) {

Pas = Nas /T; Psa = Nsa /T;

compute and for current window;

send to server: ( , ); i=0;

}

}

}

Adaptive Range Setting for the AS/ALS Model

Quasar Group

Adaptive State Management

• Consider the AS model for derivation of optimal Ta to minimize energy consumption– Assuming (t) is the probability of receiving a request at time

instant t, the expected energy consumption for a single silent period is

– E is minimized when Ta=0 if requests are uniformly distributed in interval [0, Ta+Ts].

• In practice, learn (t) at runtime and select Ta adaptively– Choose a window size w in advance– Keep track of the last w silent period lengths and summarizes

this information in a histogram– Periodically use the histogram to generate a new Ta

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Adaptive State Management (Cont)

• ci : the number of silent periods for bin i among the last w silent periods

• estimate by the distribution which generates a silent period of length ti with probability ci/w

• Ta is chosen to be the value tm that minimizes the energy consumption as follows:

bin 0bin 1

bin 2bin n-1

t0 t1 t2 t3…… tn-1 tn=Ta+Ts

c0

c1

c2

cn-1

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Performance Study

• Simulation Environments• Modeling sensor

– Power consumption parameters: Berkeley motes – Sensor values:

• uniformly from the range [-150, 150]; • perform a random walk in one dimension: every second, the

values either increases or decreases by an amount sampled uniformly from [0.5,1.5].

• Modeling queries– query arrival times at the server are Poisson distributed

• mean inter-arrival time = 2 seconds.

– each query is accompanied by an accuracy constraint A• A=uniform( Aavg(1- Avar ), Aavg(1+ Avar ))• Aavg =20 (average accuracy constraint) • Avar=1 (accuracy constraint variation)

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System Performance Comparison

Query Response Time Comparison

0

100

200

300

400

500

600

700

800

AA AL AS ALSaverage query respone time (us)

Sensor Energy Consumption Comparison

0

2

4

6

8

10

12

14

16

AA AL AS ALS

normalized sensor energy

consumption(uJ)

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Impact of Ta adaptation on System Performance

Impact of Ta Selection on Query Response Time

700

720

740

760

780

800

820

840

static Ta(0) adaptive Taaverage query response time(us)

Impact of Ta Selection on Sensor Energy Consumption

0

1

2

3

4

5

6

7

8

9

static Ta(0) adaptive Ta

normalized sensor energy

consumption(uJ)

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Impact of Range Size Adaptation on System Performance

Impact of Range Size Adjustment on Query Response Time

0

500

1000

1500

2000

2500

fixed(0) average accuracyconstraint

adaptiveadjustment

fixed(large)

average query response time (ms)

Impact of Range Size Adjustment on Sensor Energy Consumption

0

0.01

0.02

0.03

0.04

0.05

fixed(0) average accuracyconstraint

adaptiveadjustment

fixed(large)

normalized sensor

energy consumption(uJ)

Quasar Group

Fusing Energy Efficient Data Collection and In-network Aggregation

• Issues– Hierarchical precision range adjustment– Cluster forming and dynamic maintenance

access point access point……

……

Quasar Group

Quasar Progress …

• Mapping application quality requirement to data quality requirements– Target Tracking using acoustic sensors [MW ‘03]– Spatial range queries [DEXA ‘03]

• Quality-based data collection – General framework [DS Online ‘03]– To support monitoring queries over current data [Qi+03]– For sensor data archival [ICDE ‘03]– With real-time constraints [RTSS ‘03]– With support for in-network aggregation [Yu+03]

• Quality-cognizant query processing– Aggregation queries [Sigmod ‘01]– Selection Queries [ICDE ‘04]

Quasar Group

Problem Definition

• There is a collection T of imprecise objects– E.g., { [1,3], [2,5], [4,9] } represents {2, 3, 5}

• The query is: “Retrieve objects from T which satisfy predicate ”

– The query specifies quality requirements

– The system must return some approximate result that meets the quality requirements and with minimum overall cost.

Quasar Group

Impact of Data Imprecision

• Objects are classified as:– a is a NO object– b, f are MAYBE objects– c, d, e are YES objects

• The exact set is E = { b,

c, d,

e}

Imprecise Object o

Precise Object o can

be retrieved with a probe

Selection

a b c d e f

Quasar Group

Defining Quality

• Measures the accuracy of an Approximate answer A• Set-based Quality

– Precision: p = |A E | / | A |– Recall: r = | A E | / | E |

• Value-based Quality– Laxity of an object is l (o ). E.g., l ([2,3]) = 3-2=1

– Laxity of A is l max = max xA l (x)

• Query specifies upper bounds pq, rq, lmaxq

Selection

a b c d e f

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Evaluating QaQ Selection Operator

Read Object through either a linear scan (currently assumed) or an index scan [SIGMOD ‘01]

YESNO

MAYBE

• Probe

• Forward

• Ignore• Probe

• Forward

• Ignore

•Another possibility is to store the object and deal with it later

•Might be good under certain situations based on available memory at the server

Quasar Group

Total

Yes No Maybe M = Ms Mns

Seen Not Seen

MnsMsNY

TIn the beginning:

At some point of operator evaluation:

Answer set A contains some seen YES and MAYBE:

Objects are classified as:

Y A Ms A

State of QaQ Selection in the middle of execution

= A

Quasar Group

Answer Quality Bounds

• guaranteed precision, guaranteed recall, and guaranteed laxity at any stage of the execution

– Precision: p p G =|Y A | / |A|

– Recall: r r G = | Y A| / (|Y |+|Mns|+|Ms-A|)

– Laxity: lmax = max xA l (x)

Y APrecision

Y A Ms A

RecallY A

MnsMs-AY A

+

+

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The Decision Problem

• How should the QaQ selection operator decide – When to probe– When to forward– When to ignore

• Objective:– Meet query quality requirement – Minimize cost

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Cost Model: Combined Data Access & Probe Cost

CostRead Probe Write

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Impact of Probe, Forward, Ignore actions to quality

• + increase, - decrease, = remains the same

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Constraints on the Decision

• Some decisions are fixed -- we have no choice!

• No objects with l(o) greater than the query tolerance lqmax must be forwarded

• The precision guarantee pG must never be less than the query tolerance pq

– If no new YES objects are seen might lead to pq violation

• If |A Y | / (|Y |+|Ms-A|) is less than the query tolerance rq you can’t ignore an object– This might lead to an rq violation if no new YES objects are seen

Quasar Group

The “decision” Plane

s(o): probability of a MAYBE object satisfying the selection

Laxit

y l(o

)

s(o)=0 0<s(o)<1 s(o)=1

1

lqmax

2 3

4 5

6

7s3

s5

Forward with probability pfm

or ignore

Ignore Probe

Probe

Probe with probability ppy

or ignore

Forward

No Maybe Yes

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The Optimization Problem

• Free parameters ppy, s3, s5 , pfm

• Estimate:– Number of YES/MAYBE/NO objects– Number of YES/MAYBE objects exceeding the

lqmax threshold

– Distribution of s (o )

• Minimize cost W in parameter space (ppy, s3 ,

s5 , pfm) subject to Precision, Recall, Laxity guarantees

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How it works

• Get selectivity estimates of the input set T • Solve the 4-parameter optimization problem

and obtain optimal values for ppy, s3 , s5 , pfm

• Read one object at a time and handle it according to the “decision plane”, instantiated with ppy, s3 , s5 , pfm

• Finish when quality requirements are met

Quasar Group

Performance Study

• Size of input |T | = 10,000• Laxity ranges in [0,100]• Probe cost = 100x read/write unit cost.• We vary:

– Precision, Recall, Laxity Requirement– Query selectivity– Input Uncertainty (ratio of YES/MAYBE objects)

• Costs are normalized by dividing with |T |

Quasar Group

Competing Algorithms

• We devised two simple heuristics:– STINGY avoids probes: it ignores MAYBE objects and

objects exceeding the lqmax threshold.

• STINGY is conservative, but sometimes it is forced to probe to meet the quality guarantees.

– GREEDY forwards all MAYBE objects and probes all objects that exceed the lqmax threshold.

• GREEDY tries to produce the result quickly by not ignoring objects, but sometimes it uses too many probes and forwards too many objects

Quasar Group

Varying Laxity

• Input has 20% YES, 20% MAYBE objects

• 90% Precision and 50% Recall is requested

• As the laxity requirement becomes looser, the cost is reduced since imprecise objects can be forwarded without a probe

Quasar Group

Varying Precision

• Input has 20% YES, 20% MAYBE objects

• 50% Recall and laxity=50 is requested

• Cost increases as Precision requirement increases, as objects can’t be forwarded unprobed

Quasar Group

Varying Recall

• Input has 20% YES, 20% MAYBE objects

• 90% Precision and laxity=50 is requested

• Cost increases as Recall requirement increases

• When Recall requirement is low, only part of the input needs to be read

• As Recall requirement tends to 100%, all the input must be read and no objects can be ignored

Quasar Group

Varying Selectivity

• Input has 20% YES, 20% MAYBE objects

• 90% Precision, 50% Recall, and laxity=50 is requested

• Cost increases as selectivity increases, since more objects need to be output

Quasar Group

Varying Input Uncertainty

• Input has 20% YES, 20% MAYBE objects

• 90% Precision, 50% Recall, and laxity=50 is requested

• When MAYBE objects are few, no probe cost needs to be paid: the few MAYBE objects can be ignored

• When MAYBE objects are many, they cannot be ignored (Recall might be violated), or forwarded (Precision violated). Hence, they are probed, increasing the cost

Quasar Group

Quasar Future Work

• Mapping application quality to data quality– Other notions of quality (probabilistic, spatial and temporal

resolution)

• Quality aware data collection– Incorporating new notions of quality– Fault tolerance– Co-optimizing data collection and network routing

• Quality aware query processing – More general class of SQL queries

Quasar Group

Plug-in (for UCI-ICS)….

• Newly established school with following departments– Computer Science, Informatics, Statistics

• 50 plus faculty currently, many open positions• Many new developments

– Lot of new funding– Cal-IT2 building well under way – New ICS building just getting started

• Endowed chair search – Approx. 2 million from anonymous donor

Quasar Group

Database Research @ UCI

• Folks: C. Li, S. Mehrotra, P. Smyth, G. Tsudik, N. Venkatasubramanian• Core Technologies

– Indexing, query processing, transactions, distributed systems, grids

• Service Model– Exploring the privacy, performance, and algorithmic challenges in providing databases as an internet service

• Customizable search and data analysis– Customizing/personalizing search, flexible similarity retrieval over structured, semi-structured and

unstructured data

• Event-entity data management– Extracting, representing, querying and analyzing a web of entities and events from multimodal data

• Data Cleansing– Entity resolution, event resolution

• Information Integration– Middleware for querying multiple heterogeneous databases

• Peer to Peer Systems– Search, resource discovery, data integration

• Data Mining– Discovering pattern/user models from digital traces, customizing systems based on models

• Sensor Databases– Management of data in highly dynamic, resource constrained environments

Quasar Group

Urban Crisis Response Center@ Cal-IT2

• NSF Large Information Technology Research Grant– $12.5 Million Over Five Years

• Research collaboration led by UCI and UCSD– UCI: Sharad Mehrotra (PI) Co-PIs: C. Butts, N. Venkatasubramanian, R. Eguchi

(ImageCat), M. Winslettt (Univ. of Illinois) – UCSD: Ramesh Rao (PI), Co-PIs: B. Rao, M. Trivedi

• Research Team

• Government Partners: – City of LA, County of LA, City of Irvine, City of San Diego, State of California

www.ucrec.net -- coming soon!

Decision support, remote sensing, transportation, damage simulation Eguchi, Kehrlein, Huyck, Cho, AdamsImageCat, Inc.

Use of sensors to detect damage to critical infrastructureChangUniv. of Maryland

Trust managementKentBrigham Young Univ.

Social and organizational behavior in disastersTierneyUniv. of Colorado

Trust managementWinslettUniv. of Illinois

Mobile computing, wireless technologiesB. Rao, R. RaoUCSD, Center for Wireless Comm.

Speech Recognition, Video Processing, NetworkingM. Trivedi, R. Rao, B. RaoUCSD, Elec. & Comp. Eng.

Notification services, Wireless System development and deploymentChokalingam, JaffarianUCSD, Cal-(IT)2

Social networking and organizational behaviorButtsUCI, Sociology

Simulation, transportation, traffic managementReckerUCI, Inst. For Trans. Studies

Damage assessment, optimization, sensorsShinozuka, FengUCI, Civil & Env. Eng.

Data management, mining, machine learning, distributed systems, networking, security

Li, Mark, Mehrotra, Smyth, Tsudik, Venkatasubramanian

UCI, Info. & Comp. Sci.

Research StrengthsResearchersOrganization

Now hiring postdocs, researchers, programmers, students

Quasar Group

Multidisciplinary Research Agenda of UCREC

• Information Technology– right information– right person– right time

• Social and Organizational Science– The right context

• the distinctive nature of dynamic virtual organizations

• their information needs• the social and cultural aspects

of information sharing across organizations and individuals