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
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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
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⎞⎜⎜⎝
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−−++
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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 =
⎥⎥⎦
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⎢⎢⎣
⎡⎥⎦
⎤⎢⎣
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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 +=
⎥⎦
⎤⎢⎣
⎡=⎥
⎦
⎤⎢⎣
⎡=⎥
⎦
⎤⎢⎣
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kik
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⎥⎥⎦
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⎢⎢⎣
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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 += ),(
( ) ( )⎥⎥⎥⎥
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⎢⎢⎢⎢
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,
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.