Automation & Robotics Research Institute (ARRI)The University of Texas at Arlington

F.L. LewisMoncrief-O’Donnell Endowed Chair

Head, Controls & Sensors Group

Talk available online athttp://ARRI.uta.edu/acs

Wireless Sensor Networks: Issues, Advances, and Tools

F.L. LewisMoncrief-O’Donnell Endowed Chair

Head, Controls & Sensors GroupAutomation & Robotics Research Institute (ARRI)

The University of Texas at Arlington

Wireless Sensor Networks:Issues, Advances, and Tools

Organized and invited by Lihua XieXiao Wendong

Sponsored byIEEE Singapore Control Chapter

PDA

BSC(Base Station

Controller, Preprocessing)BST

WirelessSensor

Machine Monitoring

Medical Monitoring

Wireless SensorWireless

Data Collection Networks

Wireless(Wi-Fi 802.11 2.4GHz

BlueToothCellular Network, -

CDMA, GSM)

Printer

Wireland(Ethernet WLAN,

Optical)

Animal Monitoring

Vehicle Monitoring

Onlinemonitoring Server

transmitter

Any where, any time to access

Notebook Cellular Phone PC

Ship Monitoring

Wireless Sensor Networks

RovingHumanmonitor

Data Distribution Network

Management Center(Database large storage,

analysis)Data Acquisition

Network

Applications

Wide area monitoring for personnel / vehiclesSecure area intrusion monitoring and denialEnvironmental monitoring

animal habitatsmigrationforest firesnatural disasters

Subsea monitoringEnvironmental toxin detectionBuilding monitoringUrban area environmental monitoring

sensors on buildingssensors in taxis or buses

Vehicle traffic monitoring & controlsensors on roadways and traffic lightssensors on vehicles

Remote site power substation monitoringRemote site patient medical monitoringSmart homeInventory management

Latency (delay)Energy efficiencyAccuracyFault-toleranceScalabilitySecurity

Metrics / QoS

LimitedRangePowerProcessing power / memoryCost

Large number of nodesProne to failuresEasy to be compromisedChanging topologyLack of global ID

Sensor Management Protocol (SMP)Attribute-based namingLocation-based addressingData-centric routingUser broadcast interest

DisseminateSensor dataInformationUser Interest

Long-term reliability

WSN Issues

User

Figure courtesyAkyildiniz, Su, et al. 2002

Self-OrganizeCommunicationLocalizationForm clusters Monitoring

continuousevent-basedquery

Random vs. Structured topology

Post-deploymentRedeployment of new nodesFault recovery

Deploy Operate Reconfigure

1. Program Missions2. Accomplish Missions

Mobilityuser / observersensorsphenomena / target

Changing Topologymobile nodesevent occurrencemobile target / phenomenachanging user queries/interests

node failuredeploy additional nodes

Network layer

Sensor Protocols for Information via Negotiation

RoutingData fusion

SPIN protocol

Directed diffusion

Interest disseminationResponsive actionEvent detection

publish / subscribe

advertise interest

Akyildiniz, Su, et al. 2002

RoutingMinimum energyMinimum hopMax. min power available

usersensor

User/

Election of cluster headsEvent-basedApplication-basedLEACH

Hierarchical Routing Allows Multicast – Efficient Routing

5 links

18links

source node destination

Standard peer-to-peer routing Multicast routing

1. Source to leader 2. Leader to destination

Taken from Chen et al. (2000)

group leader

15 linkstotal

7 links

23 linkstotal

8 links

Research TopicsDeploy

Self-organizationComms. wakeupLocalization

Sensing

Event detectionInterpret data

Responsive actionUser broadcast interestRespond to queries

CooperationDynamic Clustering

CommunicationsDynamically reconfigurableEvent-based routing

Data TransmissionEvent-basedData aggregationSensor Data fusionInformation fusionDecision fusion

Fault tolerancenode failurelink failure

Security

Programmable WSNProgram missions quickly

Task schedulingDynamic resource assignment

Scalability- NP complexityDistributed local algorithms vs. global

Meet QoS requirementsCommunicationsSensing High priority data

Decision-making & control

Use Mobility toLocalize nodesMaintain connectivityOptimize comms.Optimize sensor coverageReduce measurement uncertainty Lack of testbeds

Energy conservation

Cabling

L1 Physical Layer

L2 Link Layer

L3 Network Layer

L4 Transport Layer

L5 Session Layer

L6 Presentation Layer

L7 Applications Layer

Applications Programs

TelnetFTP

TCP, UDP

IP, ICMP

EthernetToken ringFDDIEtc.

OSI/RM

OSI- Open Systems InterconnectionProtocol Stack

L1 Physical Layer

L2 Link Layer

L3 Network Layer

L4 Transport Layer

L5 Session Layer

L6 Presentation Layer

L7 Applications Layer

Communicationsinfrastructure

L1 Physical Layer

L2 Link Layer

L3 Network Layer

L4 Transport Layer

L5 Session Layer

L6 Presentation Layer

L7 Applications Layer

Sensingapplication

Akyildiniz, Su, et al. 2002

Cross-layer design

ConfigureMaintainOptimize

e.g. Integrate navigation, communication, congestion control, and sensing

sensor signalconditioning

DSP

local userinterface

applicationalgorithms

data storage

communicationanalog-to-

digitalconversion

NETWORK

hardwareinterface

Network SpecificNetwork Independent

Virtual Sensor

IEEE 1451 Standard for Smart Sensor Networks

Concept of Smart Sensorcontains functions in addition to those needed foraccurate presentation of the measurand

XDCR ADC

XDCR DAC

XDCR Dig. I/O

XDCR ?

TransducerElectronic DataSheet (TEDS)

addresslogic

Smart Transducer Interface Module (STIM)

NETWORK

Network CapableApplication

Processor (NCAP)

1451.1 ObjectModel

TransducerIndependent

Interface (TII)

1451.2 Interface

Sensor Placement and Lifetime EstimationJain and Qilian Liang, 2005

Failure of two nodes causesloss of sensor coverage

Square grid

Hex grid

Reliability TheorySurvivor Function = prob. that a unit is still functioning at time t

s(t)= 1- cdf

Reliability block diagram of square grid

32

)1)(1(1

sss

ssss cbablock

−+=

−−−=

Nblocknet ss )(=

Reliability block diagram of hex grid

)1)(1(1 3sssblock −−−=2/)( N

blocknet ss =

∑−

=ii

thresholdinitnode Pw

EET

Node lifetime

power consumed in mode i

fraction of time spent in mode i

Assume only 2 modes, then binomial pdf xTxx ppcxwP −−== )1(}{ 1

Pr node is idle

Pr node is active (defined by net protocol)

Finding node lifetime pdf

Results T= nr. of time units

Energy Conserving Sensor Coverage

Sample time #1 Sample time #2

Selected Sensors

Extra nodes selectedfor connectivity

Selected Sensors

Extra nodes selectedfor connectivity

Grey= area not covered

Entire area covered in 2 sample timeslatency (delay) = 2

Formal algorithms for specifying QoS% coverage of sensorsmax latency

Choi and S. Das, Mar 2005

Math Basis

Select min. nr. K of sensors s.t.U

k

iiSRQDSC

1=

∩⊂

Circular sensing region SRi of radius r

and entire region is covered within desired latency T

TtN

ii ≤∑

=1Assume:

sensors are uniformly distributedlocation info not available

Find probability that a point (x,y) is not covered by randomly selected sensor ),( yxqPq

Then, min. number of sensors needed to cover DSC is

⎟⎟⎠

⎞⎜⎜⎝

⎛

+++

−=

22

2

44log

)1log(

raraara

DSCk

π

a

DSC= probability of coverage of point (x,y)1. Find Required Number of Sensors for DSC

2. Add Extra Routing Nodes for Comm. Connectivity

Test probable connectivity of k sensors in k-1 steps, adding nodes when needed

3. Construct Data Gathering Tree (DGT)For routing and sensor scheduling

Data sink sends flood messageEach sensor keeps a forwarding record

with best upstream candidateSensors broadcast join request setup msgs.

Localized sensor scheduling algorithm

Λ= /inodeofrangeradioPis

iterationnumber

Connectedset

Other nodes

Distributed Greedy Algorithm for Connected Sensor Cover

Find minimum connected sensor cover (MCSC)

Def. MCSC1. Monitored area contained in Union of node sensor regions2. Induced communication graph is connected via multihop

Energy conserving sensor coverage

Problem of finding MCSC is NP-hard [Garey and Johnson 1991]

Communication radius Rc

Induced comm. graph Gc=(V,ERc)edge i,j exists if d(si,sj)< Rc

Induced sensing graph Gs=(V,ERs)edge i,j exists if d(si,sj)< 2Rs

Graph = (nodes, edges)

Sensing radius RsAssume sc RR 2≥

Def. Independent SetA subset of vertices such that no two vertices has an edge in G.

Def. MISAn IS that is not contained in any other IS

Finding MIS for a general graph is NP-hard

Ghosh and S. Das, June 2005

Phase 1 – Find Maximal Independent Set (MIS)

Use greedy approach looking only at 1-hop nearest neighbors

Def. Eligible next node given node si1. sj not yet included in the connected MIS2. sj a one-hop neighbor of si3. sensing circle of sj does not overlap any selected sensing circles

Suboptimal MCSC using greedy approach

Phase 2- Select extra nodes to get full sensor coverage

Construct Voronoi Diagram for nodes selected in Phase 1

Voronoi Diagram divides the plane into convex polygons whose edges are equidistant from two nodes

Algorithm 2- Choose best 1-hop neighbor that maximally covers holes in its polygon

Voronoi structure allows efficient formal algorithm for doing this

Result of Phase I MIS

has holes

Time complexity of first algorithm is

Time complexity of second algorithm is

Math Analysis

Let N nodes be uniformly randomly distributed over area A. Density is

Then number of nodes in Phase I MIS is bounded by 225 ss R

NR

Nρπ

ζρπ

≤≤

AN /=ρ

)( NO ζ

)log( ζζO

Network SecurityGroup Key Distribution via Local Collaboration

Chadha, Y. Liu, and S. Das, Sept. 2005

1. (n,t) Threshold cryptography via polynomials

Random secret polynomial 1110)( −−++= t

t xaxaaxf where secret key is D= f(0)

f(x) Can be reconstructed from t points from the set {f(1), f(2), …, f(n)}, with n= number of nodes

Select masking polynomial h(x) and securely predeploy personal secrets h(i) on each node i.

The Sink broadcasts w(x)= f(x)g(x)+h(x)

Where revocation polynomial is ))...()(()( 21 wrxrxrxxg −−−=

with the set of compromised nodes },...,{ 1 wrr which has been broadcast to all nodes

Then each node i can evaluate its personal key)(

)()()(ig

ihiwif −=

Compromised nodes have g(i)=0 and cannot find personal key

Now, t nodes can collaborate to exchange personal keys f(i) and so compute f(x), and hence find the secret group key D= f(0)

Since h(i) is securely predeployed and f(x) is random, the scheme can be shown to be unconditionally secure

2. Enhancement to avoid disclosing personal key f(i)

Select a random concealing polynomial L(x) of degree t-1 and securely predeploy concealing secret L(i) on each node i

Instead of exchanging personal keys f(i), the t collaborating nodes exchange the concealed personal key s(i)= f(i) + L(i)

Since s(x) has degree t-1, it can be reconstructed using t concealed personal keys from t nodes

Then, the group secret is D= s(0)

The sink selects a random t-1 degree polynomial f(x) such that the secret isD= f(0) + L(0)

The concealing key allows improved defense against compromised nodes, who now do not know the personal keys f(i)

This also allows self-healing strategies, i.e. in the presence of lost broadcast messages from the Sink

Theorem 1. Assume that the local exchange of concealed secrets is secure.Then the scheme is unconditionally secure, and has t-revocation capability.

Multi-Layer Approach to Defense Against Compromised Nodes

Y. Liu and S. DasCross-Layer Design

Distributed Energy-Efficient Self-Organization Zhao, Hong, Qilian Liang, Nov. 2004

LEACH selects cluster heads based on energy availableSelects randomly and does not give evenly spaced headsRequires global info – total number of nodes, and total energy available in all nodes

Expellant Self-Organization (ESO)

Based on cluster radius Rc ,energy available, and number of neighbors

)()1()(max 11 iEmeaniEENiNi

th∈∈

−+= γγ

)()1()(max 11 inmeaninnNiNi

th∈∈

−+= γγ

Energy threshold

E(i)= energy of node IN= set of neighbors

Number of neighbors threshold

n(i)= number of neighbors of node i

Fault-Tolerant & Energy Efficient Cross-Layer Routing Qilian Liang, Oct. 2005

Use fuzzy logic to select node for next hop transmission using: 1. Distance of next node (NN) to destination- should be small 2. Remaining battery capacity of next node- should be large3. Mobility of next node- should be small

The required information is periodically locally broadcast via beacons

Rules: If (NN is near dest.) and (NN has large remaining energy) and (NN is stationary)THEN (NN is a strong candidate)

When a node wishes to transmit, it sends a ROUTE NOTIFICATION, and the receiving nodes send a REPLY packet

If a node fails, the previous node broadcasts a ROUTE DELETION packet

Cross-Layer Routing in Sensor Networks

Luo, Yonghe Liu, Sajal Das, 2006

Transmission cost t(e)= w(e) c(e), e= an edge

Graph (nodes, edges) G= (V,E) Find Data Gathering Tree

w(e)= amount of datac(e)= transmission cost – congestion, distance, latency, energy used

Fusion cost f(e)

Minimize the total cost ∑∈

+routee

etef )]()([

Fusion Cost for L bits = 2L x 5 nano Joulesthe resulting data has L(1+η) bits

re αη −−=1r = node separation, good for field measurements

Data correlation model

Minimize the cost with link cost factors given as

remaining energy at next node

(delay to reach next node) X (dist. from next node to destination )

Fonda, Zawodniok, S. Jagannathan, ISIC Munich 06

Dijkstra’s Algorithm can be used

Transmission Costs

Transmission cost per hop 42, ≤≤∝ γγd

Total energy per packet using N hops

∑+=hops

idNE γβα

Trans. costSetup cost

Sensor / Data Fusion Luo, Lin, Scherp 1988

)}}({{),( 121 LCEfPPd =x1 x2 x

P1= P1(x/x1) P2= P2(x/x2)

)/()/()(

22

11

xxPxxPxL =

General distance measure

J-divergence

}log)1{(),( 121 LLEPPJ −=

Likelihood ratio

2/12121 })1{({),( −= LEPPd

Matsusita distance

For data representing the same property:

)1log(),( 221 dPPB −−=

Bhattacharyya’s distance

f(.) an increasing fn.C(.) a convex fn.

AdxxPxxPdj

i

x

xiiiiij 2)()/(2 == ∫

BdxxPxxPdi

j

x

xjjjjji 2)()/(2 == ∫

Distance is not symmetric

For multiple sensors, use matrix

Confidence distance measures

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

=

mmm

m

dd

ddddd

D

1

2221

11211

OM

L

Draw a digraph having edge (i,j) if

thresholddij ≤

1 2

34

Sensor 2 supportsSensor 3

Outlier-Correct or discard

Luo, Lin, Scherp 1988

Information Fusion & Sensor Selection in WSN “MIDFUSION,” Alex, Mohan Kumar, Behrooz Shirazi

Select the best set of sensors that meet the goals of the applications with guaranteed QoS

Middleware for customized services and resource assignment

Use Bayesian Networks

Expected utility given evidence

∑=i

ninnin SGUSSGPSUE }))({(})/{})({(})({

})({ ni SG is the goal state reached as a result of the selected sensor set }{ nS

Utility of sensors can be written }{})({( nni uSGU =

Where utility factor for sensor sn is)(cost

1)1(n

nn sDu αα −+=

and Dn is a measure of the definitiveness or accuracy of sensor sn

given by user constructed by middleware

Security Threat Example

BN showing conditional probabilities

Utility is maximized by using sensor set {RFID2, Video3}This gives 70% threshold

Adaptive Sampling with Mobile Sensor Nodes Dan Popa, Sreenath, Mysorewala, F.L. LewisICCA Budapest 2005

kkkkk wuxhxx ++=+ ),(1

kkk xfy ξ+= )(

kkkk axgz ν+= ),(

kTkk QwwE =][

kTkk RE 1][ =ξξ

).(...)(11 kmmkok xgaxgaaz +++=

,1

kkkk

kk

AGzAA

ν+==+

))()...(...1( kmkik XgXgG = RE Tkk =][ νν

).(

,

),,(~

^

111

11

^

1

^1

11

111

_

0

kkkTkkkk

kTkkk

AoAoo

AGZRGPAA

GRGPP

PPPAAo

++−

+++

+−

+−−

+

−+=

+=

=

Mobile node dynamics

Mobile node position measurement

Distributed field measurement model

Sum of Gaussian model (RBF neural network)

A. Estimation of Field Without Localization Uncertainty

B. Estimation of Field With Localization Uncertainty

( ) ( ) ,10

01

,00

3

3

1

1

kk

kTkk

k

kTk

k

k

k

kkk

kkk

k

k

k

k

AX

XI

AXX

ZY

BUAXw

UI

AX

AX

λνξ

ϑ

+⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎟⎠

⎞⎜⎜⎝

⎛=⎟⎟

⎠

⎞⎜⎜⎝

⎛+⎟⎟

⎠

⎞⎜⎜⎝

⎛=⎟⎟

⎠

⎞⎜⎜⎝

⎛

++⎟⎟⎠

⎞⎜⎜⎝

⎛=⎟⎟

⎠

⎞⎜⎜⎝

⎛+⎟⎟

⎠

⎞⎜⎜⎝

⎛+⎟⎟

⎠

⎞⎜⎜⎝

⎛=⎟⎟

⎠

⎞⎜⎜⎝

⎛

+

+

( )

).(

,10

0

))((

,

1

11

1

1111

1

1^

1

^

1

1

13

11

11

111

^

^

1

11

⎟⎟⎠

⎞⎜⎜⎝

⎛−⎟⎟

⎠

⎞⎜⎜⎝

⎛+⎟⎟

⎠

⎞⎜⎜⎝

⎛=⎟

⎟

⎠

⎞

⎜⎜

⎝

⎛

+⎟⎟⎠

⎞⎜⎜⎝

⎛=⎟⎟

⎠

⎞⎜⎜⎝

⎛⎟⎟⎠

⎞⎜⎜⎝

⎛=

+=

+⎟⎟

⎠

⎞

⎜⎜

⎝

⎛=⎟⎟

⎠

⎞⎜⎜⎝

⎛+=

−+

−+

+−+

+−++−

+

−+

+

+

−+

−+

−+

−+

−−++

−+

−+−

+

k

kk

k

kTkk

k

k

k

k

kk

k

k

kTk

k

kTkkk

k

k

k

k

kkk

AX

GZY

RGPAX

A

X

BUAX

AX

XI

G

GRGPP

BUA

XAX

QPP

Cooperative Mapping and Localization

Distributed Network Architecture

Multi-sensor Fusion for Distributed Fields

Select next sample point to minimize covariance

Implementation at ARRI’s Distributed Intelligence & Autonomy Lab (DIAL)

Measured Field is a color map. Mobile robots have color sensors. ybxbbBygxggGyrxrrR 210210210 ,, ++=++=++=

0.001,0.00078,0.10.0018,,0.0002,00.00048,,0.0012,0.2307

2102

10210

−=−=====−===

bbbgggrrr

Mobile sensorsBuilt at DIAL LabBy Dan Popa

Raster Scan Adaptive Sampling

Dan Popa

Greedy Adaptive Sampling Algorithm

876

54

321

876

54

321

Current Sampled Location

Select next sample point to minimize covarianceonly among neighboring cells

Cross-Layer Navigation Using Potential Fields Dan Popa

)(rU r−∇=F attractive forces to the goals, repulsive forces among the robots and obstacles

)(),( ijijrestore uji rrF −= Restoring force to avoid getting out of communication range

iiiii vm Frr =+ &&& Mobile node eqs. of motion

⎟⎟⎠

⎞⎜⎜⎝

⎛+= αd

PWN

KWC t

o

1log2Link communication capacity with internode distance d

rrPk

∂∂

−=||))((||

infF )(rPk is the adaptive sampling error covariance calculated via the EKF

∫=+=t

o

iiiiioi dtEtEkt τττνν ν )()()()),(1()( rF & Conserve energy by making damping increase withmotion energy expended

Information potential

)ˆ()ˆ(ˆ111 kk

Tkkk XXWXXM −−= −

+−++ Work to go to next predicted state for adaptive sampling

CFc −∇=

Energy cons.

Initial configurationNode 20 at (0,0) is a sink

Final configuration after(7,8) is selected as a target point

Nodes 3, 12, 14 go to (7,8) to sense informationOther nodes move to maintain comm. links

Dynamic Localization of Mobile WSN Dang, F. Lewis, D. Popa

[ ]Tiii yxX =

⎥⎥⎦

⎤

⎢⎢⎣

⎡⎥⎦

⎤⎢⎣

⎡+⎥

⎦

⎤⎢⎣

⎡⎥⎦

⎤⎢⎣

⎡=⎥

⎦

⎤⎢⎣

⎡y

i

xi

i

i

i

i

ff

IO

XX

OOIO

XX

2

2

22

22&&&

&

∑∑=

≠=

−=N

i

N

jij

ijijijugs rrKV1 1

2)(21

iv

N

j ji

jiijijiji XK

XX

XXrrKf &

r−

−

−−−= ∑

=1

)()(

∑=

+=N

ii

Tiugs XXVL

1 21 &&

mpX aip

,...,2,1; =

∑∑∑== =

+−=m

p

ai

ai

m

p

N

j

aji

aji

ajiuav ppppp

eKrrKV1

2

1 1

2

21)(

21 [ ] 2

122 )()( a

ia

ia

ia

ia

i pppppyxxxe −+−=

∑ ∑

∑∑∑

+= =

= ==

−+

−+=

N

mp

N

jjijiji

m

p

N

j

aji

aji

aji

m

p

ai

aip

ppp

ppppp

rrK

rrKeKV

1 1

2

1 1

2

1

2

)(21

)(21

21

p

p

p

pppp iv

N

j ji

jijijijii XK

XX

XXrrKf &−

−

−−=∑

=1

)()(

ai

av

N

j ja

i

ja

iaji

aji

aji

ai

ai

ai

ai p

p

p

pppppppXK

XX

XXrrKXXKf &−

−

−−−−−= ∑

=1

)()()(

Node position

Estimator for position

Potential fn.

Theorem. Let virtual force be given by

Then the position estimates reach steady-state values that provide optimal estimates of the actual relative localization of the nodes in the sense that is minimized

Proof:

1. Relative Localization

2. Absolute Localizationm nodes with GPS

abs. loc. pot. fn. with

Theorem. Let virtual force be given by

nodes with no GPS

nodes with GPS

Proof:

Range-Free Localization of Mobile WSN Sreenath and F.L. Lewis, Dan PopaCIS/RAM Bangkok 2006

ik

ik

ik

ik

ik

ik

ik wGuBxAx ++=+1

ik

ik

ik

ik vxHz +=

⎥⎦

⎤⎢⎣

⎡=⎥

⎦

⎤⎢⎣

⎡=⎥

⎦

⎤⎢⎣

⎡=⎥

⎦

⎤⎢⎣

⎡=

1001

,1001

,0000

,1001 i

kik

ik

ik HGBA

,0

0,

⎥⎥⎦

⎤

⎢⎢⎣

⎡== Bot

y

BotxBotBot

kRσ

σσσ

const

BotyBot

yconst

BotxBot

xRangeRangeσ

σσ

σ == ,

Algorithm 1 : Static sensor node localization algorithm1At each discrete time instant,2if robot broadcast received by sensor3then4 Update sensor state and uncertainty estimates using KF5else6 Propagate estimates using time updates

7end if

1. Localization of Stationary Nodes

uncertainty in comm. range

The first reading localizes the node to a projection on the robot’s path

( ) ( )wtGtuXaX += ,,&

[ ] gpskk

gpsgpsk vktXhZ += ),(

( ) ( )⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

=

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

=

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

=

0000000000100001

,sin

sincoscoscos

, tGL

vvv

yx

tXat

t

t

αωα

φαφα

αφ&

&&

&

[ ] [ ] ⎥⎦

⎤⎢⎣

⎡=⎥

⎦

⎤⎢⎣

⎡=

yx

ktXhyx

ktXh kugs

kgps ),(,),(

const

iyi

yconst

ixi

x

iy

ixiiiugs

k

RangeRange

PR

σσ

σσ

σσ

σσ

==

⎥⎥⎦

⎤

⎢⎢⎣

⎡=⎥⎦

⎤⎢⎣⎡ +=

,

00

,22

Includes uncertainty in position and in comm. range

Algorithm 2: Mobile robot localization algorithm.1Navigate robot along desired path.2Broadcast location information at discrete intervals.3if broadcast from GPS received4 Update robot state and uncertainty estimates using measurement Eq. (20).5end if6if broadcast from sensor received7 Update robot state and uncertainty estimates using measurement Eq. (21).

8end if

2. Simultaneous Localization of Mobile Robot & Stationary Nodes

GPS update when available

Update from UGS position when available

[ ] ugskk

ugsugsk vktXhZ += ),(

Mobile robot localization turned off With Mobile robot localization

3. Adaptive Localization Mobile robot moves to localize the un-localized sensors

Network communication connectivity is exploitedInitiation of the navigation request "NAV-REQ" packet from the robot

Badly localized sensors reply back with a localization request "LOC-REQ" packet. Already localized adjacent receiving nodes add their location and forward the request.

Algorithm 3 : Adaptive localization algorithm.1Broadcast Navigation request, NAV-REQ, packet.2Wait to receive Localization request, LOC-REQ, packets.3for all LOC-REQ with the same friendly neighbor4 Combine uncertainty scalars of the requesting sensors.5end for6Pick friendly neighbor with maximum combined uncertainty scalar of the requesting sensors.7if multiple maximas arise8 Among the maxima, pick the most localized friendly neighbor.9end if10Navigate around the picked friendly neighbor executing the simultaneous localization algorithm, on the sensors and on the mobile robot.

11Repeat Steps 1-10 as required.

Problem- how does it know where to go to localize nodes with unknown positions?

⎣ ⎦∑ −−−=j

Njcjf sdTcjTtwts )()( /δ

where w(t) is the basic pulse of duration approx. 1ns, often a wavelet or a Gaussian monocycle, and Tf is the frame or pulse repetition time. In a multi-node environment, catastrophic collisions are avoided by using a pseudorandom sequence cj to shift pulses within the frame to different compartments, and the compartment size is Tcsec. Data is transmitted using digital pulse position modulation (PPM), where if the data bit is 0 the pulse is not shifted, and if the data bit is 1 the pulse is shifted by d. The same data bit is transmitted Ns times, allowing for very reliable communications with low probability of error.

Ultra Wideband Sensor WebUWB

Precise time of flight measurement is possible.Use UWB for all three:

CommunicationsNode Relative positioningTarget localization

1 2

3

T

d d2

d3

x

y y

1 2

3

T

d d2

d3

x

x’y’

θ213

a. Target, transmitter node, and 2 receiving nodes b. Ellipsoid solution for multi-static target localizing

1 2

3

T

d d2

d3

x

y

1 2

3

T

d d2

d3

x

y y

1 2

3

T

d d2

d3

x

x’y’

θ213

1 2

3

T

d d2

d3

x

x’y’

θ213

a. Target, transmitter node, and 2 receiving nodes b. Ellipsoid solution for multi-static target localizing

Multi-Static Radar Target Localization

22122 ,2/,2/)( sabdsdda −==+=

Intersection of two ellipses with semimajor and semiminor axes

Simultaneous solution of two quadratic equations, one for each ellipse

11

=

=

BXXAXX

T

T

Uses time of flight

gives position of target.

ARRI Distributed Intelligence & Autonomy LabDIAL

UnattendedGroundSensors

SmallmobileSensor-Dan Popa

Testbed containing MICA2 network (circle), Cricket network (triangle), Sentry robots, Garcia Robots & ARRI-bots

High Level Controller

Dispatching rules

To Generate uc

RS232 RS232 RS232

Wireless Network with Internet connection

- -Rule Based Real Time Controller

ucStart tasks/jobs

Mission result

Resource release

Sensor output u

Task completed v

Resource released r

Medium Level Tasks ControllersRobot 1

Task 1 Task 1

Robot 2

Task 1

Wireless sensors

Task 1

Robot 3

RS232

Pioneer arm

Cybermotion robot

Cybermotion robot

Xbow sensors

Environment

Task1

PC

urv uFrFvFx ⊗⊕⊗⊕⊗=xSv VS ⊗=

xSy y ⊗=xSr rS ⊗=

Finite state machine for each agent

UC-TDMA MAC protocol

Supervisor control level A

gent control levelN

etwork control level

Agents

Node Deployment & Failure- Modify Fr

RESOURCE RESET LOGIC:

MISSION COMPLETE LOGIC:

Wireless Sensor Net

Sensor reading events

Tasks performed

Resources available

PerformanceMeasures

Targets or Events In

Matrix DE Controller

u

v

y

vs

rs

vs

dudurv uFuFrFvFx +++=xSv vs =

xSr rs =

xSy y=

Program DEC For WSN Applications

Program Missions- Selection of matrices

Select Resources- Priority modification of Fr NEXT TASK LOGIC:

Missions completed

Sensor readings

Tasks completed

Resources Idle

Missions completed

Task Commands

Resource Reset Commands

WSN logical Status information

y

Node Deployment & Failure- Modify Fr

RESOURCE RESET LOGIC:

xSr rs =

rs

Deadlock avoidance policy

Discrete event Supervisory Controller -US Patent

Supervisory Control of Mobile Wireless Sensor Networks

LabVIEW User Interface

Fast programming of multiple missionsReal-time event responseDynamic assignment of shared resources

LabVIEW Real-time Signaling & Processing

CBM Database and real time Monitoring

PDA access Failure Data from anytime and

anywhere

User Interface, Monitoring, & Decision AssistanceWireless Access over the Internet

Xbow wireless sensor boards

• Temperature, ambient light, acoustic sensors, accelerometer,and magnetometer, (can get GPS)

• Each node is endowed with a microcontroller, programmable with a C-based operating system

• Cricket motes have ultrasound rangefinders

Environmental Monitoring & Secure Area Denial

Microstrain V-Link

Transceiver

MicrostrainTransceiver

Connect to PC

MicrostrainG-Sensor

Microstrain, Inc., Wireless Sensors

RFID node

http://www.microstrain.com/index.cfm

MicrostrainG-Sensor

MicrostrainTransceiver

Connect to PC

Microstrain V-Link

Transceiver

WSN for Machinery Monitoring- Diagnostics & Prognostics

The Battery Consumption Equation

( )[ ] ( )[ ]( )[ ] ( )[ ]( )[ ] ( )[ ] onturnrx/txsstxsstxssrxssrx

rxrxtxrxrxtxrxrxsrxrxs

txtxmtxtxrxtxtxrxtxtxmtxtxstxtxs

TITTINTTINTTINTTIN

TITTINTITTINAmpHrs/Hr

−−−−−

−−−−

−−−−

+++++++++

+++++=

Number of times per hour, radio switches totransmit mode from sleep/receive mode

Time taken by radio to switch to Transmit mode from sleep/receive mode

Actual time for which radio transmit,each time it is in transmit mode

Actual time for which radio receives, each time it is in receive mode

Time taken by radio to switch to receive mode from sleep/transmit mode

Number of times per hour, radio switches toreceive mode from sleep/transmit mode

Itx> Irx > Is

Sleep Schedule Calculationsfor Energy Conservation

[ ]( )[ ]( )[ ] )3(3600

)2(

givenRateupdatingiffTNdiagRS

givennotRateupdatingiffTNdiagNTS

SdiagUT

Tp

Ts

Tud

Tp

Ts

Tspd

rp

×−×=

×−×=

÷=

Given, sweep rates for all node, number of data points from each node, frequency at which each node transmits (every r hours), the sleep durations for all the nodes in network is given by :

Sweep Rate Matrix (1Xn)Time Period Matrix

No. Of Data Points Matrix (1Xn)

Sleep Duration Matrix(in Sec)

Total time taken by all nodes for transmitting their data

Time taken by each node in transmitting its data

Sleep duration for any given node is Total time – its own transmission time

Updating rate is actually – approximate sleep Duration for that particular node

If Updating rate is 1 hr for some node which transmits for 2 sec in each slot => the node will tx 2Sec, then sleep for 3600-2sec, and then again repeats..

Updating Rate Matrix

Sleep State

Receive State

Setup State Transmit State

Time out

Set cmd

Emergency

Data out

Transmit cmd

Sleep cmd

Done

Start

Status Quo

Set J=0

Is Node missing?

Calculate sleep schedule for each node

Is J > 0

Configure nodes with defined functions & sleep schedule

Set i=1, J=1, S=n+1

Is J > =10

Is S > n

Retrieve data from node i

Read node type, data rate no. of data points & sequence no.

Insert node in existing slot sequence assigned

Remove failed node from TDMA slot sequence

B.S. pings for new node.

Report user about missing node & node type

Set J=1

Add new node?

Set S=S+1

Append sleep schedule command data to node i.

Set S=1

Any Data?

Node i sleep

Is i=n?

Set i=i+1

Stop?

Stop

Set i=1, J=J+1

No

No

YesNo

Yes

No

Yes

Yes

User Interface

Functionality definition for each node

B.S. Checks availability of all defined nodes in N/W

Yes

Neural N

et

Artificial Intelligent

Fuzzy Logic

Neural N

et

Artificial Intelligent

Fuzzy Logic

Path to Decision & Display

Signal DataTransition

Information

Knowledge

Wisdom

Display

Display

CBM Network Developed and Implemented On ARRI Air Conditioning Machinery Room

Ankit Tiwari

UC-TDMA Protocol Running at Base Station

FSM Running at Each Sensor Node

OSI Layers Addressed

Data Link

Physical

Presentation

Session

Transport

Network

Application

Provided

UC-TDMA MAC Protocol

Application GUIs in LabVIEW

Provides all the services required by Application layer

OSI Layers

Network Configuration Wizard

Useful for making minor changes to node parameters

Loads with Default Values for Parameters

On Clicking, Current/default settings for that node appears in the next screen

Try to Eliminate Node Naming Issue

Install and Configure the Network in 1 hour

DSP- Data to Information

Discrete Event - triggersAdvise, Decision Assistance, Alarm

LabVIEW GUIs Developed

Multiple Time Signal Display

Analysis and FFT

Decision-MakingDiagnosis & Prognosis Alarm Functions

Wireless Sensor Nets for BCW Monitoring

• MEMS sensors for biochemical species including anthrax, nerve gases, NOx, organophosphorus

• Wireless Sensor Networks for remote site biochemical monitoring

Structured chemically-activenanosphere thin film- Rajeshwar

3x3 IGEFET sensor micro-array- Kolesar

DSP and C&C User Interface for wireless networks- Lewis

Molecular Recognition- RudkevichEnzyme-Based Detection- Bob Gracy

Interdigitated finger FET- Kolesar

References

\item W. Choi and S. K. Das,``A Novel Framework for Energy-Conserving Data Gathering inWireless Sensor Networks,"{\em Proceedings of IEEE INFOCOM}, Miami, Florida, Mar 2005.

\item A. Ghosh and S. K. Das,``A Distributed Greedy Algorithm for Connected Sensor Coverin Dense Sensor Networks,"{\em Proceeeings of IEEE International Conference on DistributedComputing in Sensor Systems} (DCOSS), Marina del Ray, CA,pp. 340-353, June 2005.

\item A. Chadha, Y. Liu and S. K. Das,``Group Key Distribution viaLocal Collaboration in Wireless Sensor Networks,"{\em Second IEEE International Conference on Sensorand Ad Hoc Communications and Networks} (SECON),Santa Clara, Sept 2005.

\itemW. Zhang, S. K. Das and Y. Liu, ``Security in Sensor Networks,"{\em Security in Wireless Sensor Networks: A Survey} (Ed. Y. Xiao), CRC Press, 2006.

\item H. Luo, J. Luo, Y. Liu and S. K. Das,``Routing Correlated Data with Fusion Cost in Wireless Sensor Networks,"{\em IEEE Transactions on Mobile Computing}, to appear, 2006.

Ekta Jain, Qilian Liang, “Sensor placement and lifetime of wireless sensor networks: theory and performance analysis,”Sensor Network Operations, edited by S. Phoha, T. F. La Porta, and C. Griffin, IEEE Press, 2005.

Qilian Liang, “Fault-Tolerant and Energy Efficient Wireless Sensor Networks: A Cross-Layer Approach,” accepted by IEEE Military Communication Conference, Atlantic City, NJ, Oct 2005.

Liang Zhao, Xiang Hong, Qilian Liang, “Energy-Efficient Self-Organization for Wireless Sensor Networks: A Fully Distributed Approach,” IEEE Globecom, Nov 2004, Dallas, TX.

I.F. Akyildiniz, W. Su, Y. Sankarasubramanian, and R. Cayirci, “A survey on sensor networks,” pp. 102-114, IEEE Comm. Mag., Aug. 2002.

R.C. Luo, M.-H. Lin, R.S. Scherp, “Dynamic multi-sensor data fusion system for intelligent robots,” IEEE J. Robotics & Automation, vol. 4, pp. 386-396, Aug. 1988

1.G. Vachtsevanos, F.L. Lewis, M. Roemer, A. Hess, B. Wu, Intelligent Fault Diagnosis and Prognosis for Engineering Systems, John Wiley, New York, 2006, to appear.

2.J. Mireles, F.L. Lewis, A. Gurel, and S. Bogdan, “Deadlock Avoidance Algorithms and Implementation, a Matrix-Based Approach,” in Deadlock Resolution in Computer-Integrated Systems, chapter 7, ed. Mengchu Zhou, Marcel Dekker, New York, 2004.

3.F.L. Lewis, “Wireless Sensor Networks,” in Smart Environments: Technologies, Protocols, Applications, ed. D.J. Cook and S.K. Das, Wiley, New York, 2004.

4.V. Giordano, F.L. Lewis, P. Ballal, and B. Turchiano, “Supervisory control for task assignment and resource dispatching in mobile wireless sensor networks,” in Cutting Edge Robotics, ed. V. Kordic, p. 133-152, 2005.

5.D.O. Popa and F.L. Lewis, “Algorithms for robotic deployment of WSN in adaptive sampling applications,” in Wireless Sensor Networks and Applications, ed. Y. Li, M. Thai, and W. Wu, Springer-Verlag, Berlin, 2005.

6.N. Swamy, O. Kuljaca, and F.L. Lewis, “Internet-based educational control systems lab using NetMeeting,” IEEE Trans. Education, vol. 45, no. 2, pp. 145-151, May 2002.

7.V. Giordano, P.Ballal, F.L. Lewis, B. Turchiano, J.B. Zhang, “Supervisory control of mobile sensor networks: Matrix formulation, simulation and implementation,” IEEE Trans. Systems, Man, Cybernetics, Part B, to appear, 2006.

8.B. Harris, D. Cook, and F.L. Lewis, "Automatically generating plans for manufacturing," J. Intelligent Systems, vol. 10, no. 3, pp. 279-319, 2000.

9.N. Swamy and F.L. Lewis, “Routing algorithms in a novel hierarchical mesh network,” Proc. IEEE 12th Symp. Mobile Computing, Bangalore, Nov. 2003.

10.A. Tiwari, F.L. Lewis, and S.S. Ge, “Wireless Sensor Networks for Machine Condition Based Monitoring,” Proc. Int. Conf. Control, Automation, Robotics, and Vision, pp. 461-467, invited paper, Kunming, China, Dec 2004.

11.V. Giordano, F.L. Lewis, J. Mireles, B. Turchiano, “Coordination control policy for mobile sensor networks with shared heterogeneous resources,” Proc. Int. Conf. Control & Automation, pp. 191-196, Budapest, June, 2005.

12.V. Giordano, F.L. Lewis, B. Turchaino, P. Ballal, V. Yeshala, “Matrix computational framework for discrete event control of wireless sensor networks with some mobile agents,” Proc. Mediterranean Conf. Control & Automation, Limassol, Cyprus, June 2005. This paper won an award at MED 05.

13.O. Kuljaca, N. Swamy, J. Gadewadikar, F.L. Lewis, “Transfer Function Illustration With Simple Electronic Circuits”, Proc. XXVII Int. Meeting MIPRO 2005, CE, Conference on Computers in Education, 2005.

14.D.O. Popa, K. Sreenath, and F.L. Lewis, “Robotic deployment for environmental sampling applications,” Proc. Int. Conf. Control and Applics., pp. 197-202, Budapest, June 2005.