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WIRELESS SENSOR NETWORKSPart of slides from Mobicom 2002 tutorials
OutlineOutline
IntroductionMotivating applicationsEnabling technologiesUnique constraintsqApplication and architecture taxonomy
Embedded Networked Sensing PotentialEmbedded Networked Sensing Potential
Micro-sensors, on-board processing, and wireless interfaces all feasible at very small scale
can monitor phenomena “up close”
Seismic Structure ContaminantWill enable spatially and temporally denseenvironmental monitoring
Seismic Structure response
Contaminant Transport
environmental monitoringEmbedded Networked Sensing will reveal previously unobservable
Marine Microorganisms
Ecosystems, Biocomplexity
previously unobservable phenomena
App: Ecosystem MonitoringApp: Ecosystem Monitoring
S iScienceUnderstand response of wild populations (plants and animals) to habitats over time.Develop in situ observation of species and ecosystem dynamics. Develop in situ observation of species and ecosystem dynamics.
TechniquesData acquisition of physical and chemical properties, at various spatial and
l l i h i d h bitemporal scales, appropriate to the ecosystem, species and habitat.
Automatic identification of organisms(current techniques involve close-range (cu e ec ques vo ve c ose a ge human observation).
Measurements over long period of time,taken in-situ.
Harsh environments with extremes in t t i t b t ti temperature, moisture, obstructions, ...
Field Experiments Field Experiments
Monitoring ecosystem processesImaging, ecophysiology, and environmental sensorsStudy vegetation response to climatic trends and diseases.y g p
Species MonitoringVisual identification, tracking, and population measurement of birds and other vertebratesmeasurement of birds and other vertebratesAcoustical sensing for identification, spatial position, population estimation.
Education outreach V h dEducation outreachBird studies by High School Science classes (New Roads and Buckley Schools).
Vegetation change detection
Avian monitoring Virtual field observations
ENS Requirements for Habitat/EcophysiologyApplications
Diverse sensor sizes (1-10 cm), spatial sampling intervals (1 cm - 100 m), and temporal sampling intervals (1 μs - days), depending on habitats and
iorganisms.
Naive approach → Too many sensors →Too many data.
I t k di t ib t d i l iIn-network, distributed signal processing.
Wireless communication due to climate, terrain, thick vegetation.
Ad ti S lf O i ti t hi li bl l li d ti i Adaptive Self-Organization to achieve reliable, long-lived, operation in dynamic, resource-limited, harsh environment.
Mobility for deploying scarce resources (e.g., high resolution sensors).Mobility for deploying scarce resources (e.g., high resolution sensors).
Transportation and Urban MonitoringTransportation and Urban Monitoring
Disaster Responsep
Intelligent Transportation Project (Muntz et al.)
Smart Kindergarten Project: Sensor based Wireless Networks of ToysSensor-based Wireless Networks of Toysfor Smart Developmental Problem-solving Environments
Middleware Framework
WLAN AccessPoint
SensorManagement
SensorFusion
SpeechRecognizer
Database& Data Miner
Wired Network
NetworkManagement
High-speed Wireless LAN (WLAN)WLAN-Piconet
BridgeWLAN-Piconet
Bridge
SensorsModules
Piconet Piconet
Sensor Badge
Networked ToysNetworked Toys
Enabling TechnologiesEnabling Technologies
Embed numerous distributed devices to monitor and interact with physical world
Network devices to coordinate and perform higher-level tasks
Embedded NetworkedExploit
physical world higher-level tasks
Control system w/Small form factorUntethered nodes
ExploitcollaborativeSensing, action
Sensing
Tightly coupled to physical worldTightly coupled to physical world
Exploit spatially and temporally dense, in situ, sensing and actuation
SensorsSensors
Passive elements: seismic, acoustic, infrared, strain, salinity, humidity, temperature, etc.
Passive Arrays: imagers (visible, IR), biochemical
Active sensors: radar sonarActive sensors: radar, sonar
High energy, in contrast to passive elements
T h l t d f IC t h l f i d b t Technology trend: use of IC technology for increased robustness, lower cost, smaller size
COTS d t i f th d i k i t b d COTS adequate in many of these domains; work remains to be done in biochemical
Some Networked Sensor Node DevelopmentsSome Networked Sensor Node Developments
LWIM III
UCLA, 1996
G h RFM
AWAIRS I
UCLA/RSC 1998
G h DS/SSGeophone, RFM
radio, PIC, star
network
Geophone, DS/SS
Radio, strongARM,
Multi-hop networksnetwork Multi hop networks
WINS NG 2 0WINS NG 2.0Sensoria, 2001Node development
UCB Mote, 20004 Mhz, 4K Ram512K EEProm platform; multi-
sensor, dual radio,Linux on SH4,
512K EEProm,128K code, CSMA
Processor
,Preprocessor, GPShalf-duplex RFM radio
New Design ThemesNew Design ThemesLong-lived systems that can be untethered and unattended
Low-duty cycle operation with bounded latencyExploit redundancy and heterogeneous tiered systems
Leverage data processing inside the networkThousands or millions of operations per second can be done using energy of sending a bit over 10 or 100 meters (Pottie00)Exploit computation near data to reduce communication
Self configuring systems that can be deployed ad hocUn-modeled physical world dynamics makes systems appear ad hocMeasure and adapt to unpredictable environmentExploit spatial diversity and density of sensor/actuator nodes
Achieve desired global behavior with adaptive localized algorithmsCant afford to extract dynamic state information needed for centralized control
Sample Layered Architecture p y
User Queries, External Database
Resource constraints call for more tightly In-network: Application processingintegrated layers
O Q i
In network: Application processing, Data aggregation, Query processing
D t di i ti t hiOpen Question:
Can we define anI t t lik
Data dissemination, storage, caching
Internet-like architecture for such application-specific systems??
Adaptive topology, Geo-Routing
systems??MAC, Time, Location
Phy: comm, sensing, actuation, SP
Systems Load/Event Metrics
• Spatial and Temporal Scale– Extent
Taxonomy• Frequency
– spatial and temporal
ModelsMetrics
• Efficiency– System
– Spatial Density (of sensors relative to stimulus)D t t f ti lii
spatial and temporal density of events
• Locality – spatial, temporal
System lifetime/System resources
• Resolution/Fidelity– Data rate of stimulii
• Variability– Ad hoc vs. engineered
system structure
spatial, temporal correlation
• Mobility– Rate and pattern
/– Detection,
Identification• Latencysystem structure
– System task variability– Mobility (variability in
space)
p– Response time
• Robustness– Vulnerability to
• Autonomy– Multiple sensor
modalitiesComputational model
node failure and environmental dynamics
S l bili– Computational model complexity
• Resource constraints– Energy, BW
• Scalability– Over space and
timegy,
– Storage, Computation
OutlineOutline
Survey of sensor node platformsEnergy issues for wireless sensor networksgy
Sources of energy consumptionEnergy management techniquesEnergy management techniques
Sensor Node H/W-S/W PlatformsSensor Node H/W S/W PlatformsIn-node
Wireless communication
In node processing
Event detection with neighboring nodes
sensors CPU radioAcoustic, seismic, image, magnetic etc interface
Electro-magnetic interfacemagnetic, etc. interface
batteryLimited battery supply
Energy efficiency is the crucial h/w and s/w design criterion
Variety of Real-life Sensor Node PlatformsVariety of Real life Sensor Node Platforms
RSC WINS & Hidra
Sensoria WINS
UCLA’s iBadge
UCLA’s Medusa MK-II
Berkeley’s Motes
Berkeley Piconodes
MIT’ AMPMIT’s μAMPs
And many more…
Different points in (cost, power, functionality, form factor) space
Berkeley MotesBerkeley Motes
Devices that incorporate communications, processing, sensors, and batteries into a small package
Atmel microcontroller with sensors and a communication unit
RF i l d l RF transceiver, laser module, or a corner cube reflector
temperature, light, humidity, p , g , y,pressure, 3 axis magnetometers, 3 axis accelerometers
Ti OSTinyOS
light, temperature,
II-19
g , p ,10 kbps @ 20m
The Mote FamilyThe Mote Family
Ref: from Levis & Culler, ASPLOS 2002
UCLA iBadgeUCLA iBadge
Wearable Sensor Badgeacoustic in/out + DSPtemperature, pressure, humidity, magnetometer, accelerometeracce e o e eultrasound localizationorientation via magnetometer and accelerometerbluetooth radiobluetooth radio
Sylph Middleware
BWRC’s PicoNode TripWire Sensor NodeBWRC s PicoNode TripWire Sensor Node
Chip encapsulation
Solar Cell(0.5 mm) Battery (3.6
mm)
PCB (1 mm)
(1.5 mm)
Components and batteryVersion 1: Light Powered Components and batterymounted on back
Size determined by powerdissipation (1 mW avg)
Version 2: Vibration Powered dissipation (1 mW avg)
Ref: from Jan Rabaey, PAC/C Slides
Sensor Node Energy RoadmapSensor Node Energy Roadmap
10,00010,000
• Deployed (5W) Rehosting to Low
1,0001,000
r (m
W)
ep oyed (5 )
• PAC/C Baseline (.5W)
Power COTS(10x)
100100
1010ge P
ower
• (50 mW) -System-On-ChipAdv Power 1010
11Ave
rag -Adv Power
ManagementAlgorithms (50x)
.1.1
(1mW)
20002000 20022002 20042004
Source: ISI & DARPA PAC/C Program
Comparison of Energy SourcesComparison of Energy Sources
Comparison of Potential Energy Scavenging Techniques
Power (Energy) Density Source of Estimates
B tt i (Zi Ai ) 1050 1560 Wh/ 3 (1 4 V) P bli h d d t f f tBatteries (Zinc-Air) 1050 -1560 mWh/cm3 (1.4 V) Published data from manufacturersBatteries(Lithium ion) 300 mWh/cm3 (3 - 4 V) Published data from manufacturers
Solar (Outdoors)15 mW/cm2 - direct sun
0.15mW/cm2 - cloudy day. Published data and testing.
Solar (Indoor).006 mW/cm2 - my desk
0.57 mW/cm2 - 12 in. under a 60W bulb TestingVibrations 0.001 - 0.1 mW/cm3 Simulations and Testing
3E-6 mW/cm2 at 75 Db sound levelAcoustic Noise
3E-6 mW/cm at 75 Db sound level 9.6E-4 mW/cm2 at 100 Db sound level Direct Calculations from Acoustic Theory
Passive Human Powered 1.8 mW (Shoe inserts >> 1 cm2) Published Study.
0 0018 W 10 d C di t P bli h d St dThermal Conversion 0.0018 mW - 10 deg. C gradient Published Study.
Nuclear Reaction80 mW/cm3
1E6 mWh/cm3 Published Data.
With aggressive energy management, ENS might live off the environment.
Source: UC Berkeley
Communication/Computation T h l P j iTechnology Projection
1999 (Bluetooth 2004
Technology)(150nJ/bit) (5nJ/bit)1 5 W* 50 W
Communication1.5mW* 50uW
~ 190 MOPS(5 J/OP)
Computation
Communication
Assume: 10kbit/sec. Radio, 10 m range.
(5pJ/OP)p
Large cost of communications relative to computation continues
Source: ISI & DARPA PAC/C Program
Where Does the Energy Go?Where Does the Energy Go?
Processing
excluding low-level processing for radio, sensors, actuators
Radio
Sensors
A t t
II-26
Actuators
Power supply
ProcessingProcessing
Common sensor node processorsCommon sensor node processors:Atmel AVR, Intel 8051, StrongARM, XScale, ARM Thumb, SH Risc
Power consumption all over the map, e.g.16 5 mW for ATMega128L @ 4MHz16.5 mW for ATMega128L @ 4MHz75 mW for ARM Thumb @ 40 MHz
But, don’t confuse low-power and energy-efficiency!ExampleExample
242 MIPS/W for ATMega128L @ 4MHz (4nJ/Instruction)480 MIPS/W for ARM Thumb @ 40 MHz (2.1 nJ/Instruction)
Other examples:0.2 nJ/Instruction for Cygnal C8051F300 @ 32KHz, 3.3V0.35 nJ/Instruction for IBM 405LP @ 152 MHz, 1.0V0.5 nJ/Instruction for Cygnal C8051F300 @ 25MHz, 3.3V0.8 nJ/Instruction for TMS320VC5510 @ 200 MHz, 1.5V0.8 nJ/Instruction for TMS320VC5510 @ 200 MHz, 1.5V1.1 nJ/Instruction for Xscale PXA250 @ 400 MHz, 1.3V1.3 nJ/Instruction for IBM 405LP @ 380 MHz, 1.8V1.9 nJ/Instruction for Xscale PXA250 @ 130 MHz, .85V (leakage!)
A f ffAnd, the above don’t even factor in operand size differences!However, need power management to actually exploit energy efficiency
Idle and sleep modes, variable voltage and frequency
RadioRadio
Energy per bit in radios is a strong function of desired communication performance and choice of modulation
R d BER f i h l diti ( i lti th d D l f di )Range and BER for given channel condition (noise, multipath and Doppler fading)
Watch out: different people count energy differentlyE.g.
Mote’s RFM radio is only a transceiver, and a lot of low-level processing takes place in the main CPU
While, typical 802.11b radios do everything up to MAC and link level encryption in the “radio”
Transmit, receive, idle, and sleep modes
Variable modulation, coding
C tl d 150 J/bit f h t Currently around 150 nJ/bit for short ranges
More later…
Computation & CommunicationComputation & Communication
Encode Decode TransmitDecode
Energy breakdown for voice Energy breakdown for MPEG
Encode Decode TransmitEncode
Decode
TransmitReceive Receive
Processor: StrongARM SA-1100 at 150 MIPSRadio: Lucent WaveLAN at 2 Mbps
Radios benefit less from technology improvements than processorsTh l ti i t f th i ti b t th t The relative impact of the communication subsystem on the system energy consumption will grow
SensingSensing
Several energy consumption sourcestransducerfront-end processing and signal conditioning
analog, digitalADC conversionADC conversion
Diversity of sensors: no general conclusions can be drawnLow-power modalitiesp
Temperature, light, accelerometerMedium-power modalities
Acoustic, magneticHigh-power modalities
Image, video, beamformingImage, video, beamforming
ActuationActuation
Emerging sensor platformsMounted on mobile robotsAntennas or sensors that can be actuatedAntennas or sensors that can be actuated
Energy trade-offs not yet studiedSome thoughts:
Actuation often done with fuel, which has much higher energy density than batteries
E.g. anecdotal evidence that in some UAVs the flight time is longer than E.g. anecdotal evidence that in some UAVs the flight time is longer than the up time of the wireless camera mounted on it
Actuation done during boot-up or once in a while may have significant payoffssignificant payoffs
E.g. mechanically repositioning the antenna once may be better than paying higher communication energy cost for all subsequent packetsE g moving a few nodes may result in a more uniform distribution of E.g. moving a few nodes may result in a more uniform distribution of node, and thus longer system lifetime
Power Analysis of RSC’s WINS NodesPower Analysis of RSC s WINS Nodes
• Summary• Processor
– Active = 360 mWActive 360 mW• doing repeated
transmit/receive
– Sleep = 41 mWSleep 41 mW– Off = 0.9 mW
• Sensor = 23 mW• Processor : Tx = 1 : 2• Processor : Rx = 1 : 1• Total Tx : Rx = 4 : 3 at maximum
range– comparable at lower Tx
Power Analysis of Mote-Like NodePower Analysis of Mote Like Node
Some ObservationsSome Observations
Using low-power components and trading-off unnecessary performance for power savings can have orders of magnitude impactNode power consumption is strongly dependent on the operating mode
E.g. WINS consumes only 1/6-th the power when MCU is asleep as opposed to active
At short ranges, the Rx power consumption > T power consumptionmultihop relaying not necessarily desirablep y g y
Idle radio consumes almost as much power as radio in Rx modeRadio needs to be completely shut off to save power as in sensor networks idle time dominates
MAC protocols that do not “listen” a lotMAC protocols that do not listen a lot
Processor power fairly significant (30-50%) share of overall powerIn WINS node, radio consumes 33 mW in “sleep” vs. “removed”
Argues for module level power shutdown
Sensor transducer power negligibleUse sensors to provide wakeup signal for processor and radioNot true for active sensors though…
Energy Management ProblemEnergy Management Problem
Actuation energy is the highestActuation energy is the highestStrategy: ultra-low-power “sentinel” nodes
Wake-up or command movement of mobile nodes
Communication energy is the next important issueStrategy: energy-aware data communication
Ad h i f h i i d i Adapt the instantaneous performance to meet the timing and error rate constraints, while minimizing energy/bit
Processor and sensor energy usually less important
Transmit 720 nJ/bit Processor 4 nJ/op
R i 110 J/bit 200 /bit
MICA moteBerkeley
Receive 110 nJ/bit ~ 200 ops/bit
WINS node Transmit 6600 nJ/bit Processor 1.6 nJ/opW NS odeRSC
p
Receive 3300 nJ/bit ~ 6000 ops/bit
Processor Energy ManagementProcessor Energy Management
K bKnobsShutdownDynamic scaling of frequency and supply voltagegMore recent: dynamic scaling of frequency, supply voltage, and threshold voltage
All of the above knobs incorporated into d OS h d l
Application
PA-APIsensor node OS schedulers
e.g. PA-eCos by UCLA & UCI has Rate-monotonic Scheduler with shutdown and DVS
Gains of 2x-4x typically in CPU power with
PA-Middleware
POSIX PA-OSLGains of 2x 4x typically, in CPU power with typical workloadsPredictive approaches
Predict computtion load and set
OperatingSystem
OperatingSystem
Modified OS Services
PA HALpvoltage/frequency accordinglyExploit the resiliency of sensor nets to packet and event lossesNow losses due to computation noise
Hardware Abstraction Layer
PA-HAL
HardwareNow, losses due to computation noise
Radio Energy ManagementRadio Energy Management
Tx Rx
?
?
time
During operation, the required performance is often less than the peak performance the radio is designed for
How do we take advantage of this observation, in both the sender and the receiver?
Energy in Radio: the Deeper Story….Energy in Radio: the Deeper Story….
Tx: Sender Rx: Receiver
ChannelIncomingi f i
Outgoinginformation information
TxE RxEEelecE elecERFETransmit
electronicsReceive
electronicsPower
amplifier
Wireless communication subsystem consists of three components with b t ti ll diff t h t i tiwith substantially different characteristicsTheir relative importance depends on the transmission range of the radiothe radio
ExamplesExamples
N k C021 M d S N d
nJ/bit nJ/bit nJ/bit
GSM Nokia C021 Wireless LAN
Medusa Sensor Node (UCLA)
4000
6000
8000
200
300
400
600
0
2000
4000
0
100
0
200
TxelecE Rx
elecERFE TxelecE Rx
elecERFE TxelecE Rx
elecERFE~ 1 km ~ 50 m ~ 10 m
• The RF energy increases with transmission range• The electronics energy for transmit and receive are typically comparablegy yp y p
Energy Consumption of the SenderEnergy Consumption of the Sender
Parameter of interest:
energy consumption per bitTx: Sender
I i PIncominginformation
bitbit T
PE =
TxelecP
RFP TotalP
ergy rgy
ergy
RFDominates
Electronics Dominates
Ene
Ener Ene
Transmission timeTransmission time Transmission time
Effect of Transmission RangeEffect of Transmission Range
Short rangeL Short-rangeLong-range
Ener
gy
Medium-range
E
Transmission timeTransmission time
Power Breakdowns and TrendsPower Breakdowns and Trends
Radiated powerRadiated power63 mW (18 dBm)
Intersil PRISM II
Power amplifier 600
e s SM (Nokia C021 wireless LAN)
Analog electronics240 mW
Digital electronics170 mW
Power amplifier 600 mW
(~11% efficiency)Trends:
Move functionality from the analog to the digital electronicsDigital electronics benefit most from technology improvements
Borderline between ‘long’ and ‘short’-range moves towards shorter transmit distances
Radio Energy Management #1: Shutdown
PrincipleOperate at a fixed speed and power level
Poweravailable time
Shut down the radio after the transmissionNo superfluous energy consumption timei i iNo supe uous e e gy co su p o
GotchaWhen and how to wake up?M l t
timetransmission time
More later …
Energy
transmission time
shutdown
no shutdown
allowed time
Radio Energy Management #2: Scaling along the Performance-Energy Curve
II-44 Poweravailable time
Principle
– Vary radio ‘control knobs’ such as modulation and
timetransmission time
such as modulation and error coding
– Trade off energy versus t i i ti
Modulation scalingfewer bits per symbol
transmission time
RFEfewer bits per symbol
Code scalingmore heavily coded
TxelecE Rx
elecEmore heavily coded
Ener
gy
Ener
gy
transmission time transmission time
When to Scale?When to Scale?
RF dominates Electronics dominates
ergy
Ene
E
transmission time
Emin
t*
• Scaling results in a convex curve with an energy minimum EminI l k l d i i i t* di • It only makes sense to slow down to transmission time t* corresponding to this energy minimum
Scaling vs. ShutdownScaling vs. Shutdown
yRegion of Region of
• Use scaling while it reduces the energy
• If ti i En
ergy scaling shutdown • If more time is
allowed, scale down to the minimum
Emin
energy point and subsequently use shutdown
t* time
Power
allowed time
Power
allowed time
Powerallowed timetransmission time
timetransmission time = t*
timetransmission time = t*
time
transmission time
Long-range SystemLong range System
II-47
The shape of the curve depends on the relative i f RF d
li bl i
importance of RF and electronics
This is a function of the
Ener
gy
realizable region transmission range
Region of scaling
Long-range systems have an operational region where they benefit from scaling
transmission time t*
y g
transmission time
Short-range SystemsShort range Systems
II-48 • Short-range systems have an operational have an operational region where scaling isnot beneficial
nerg
y realizable region
Region of
• Best strategy is to transmit as fast as possible and shut down
E Region of shutdown
i i it* transmission timet*
Sensor Node Radio Power Management Summary
Sh t li k Short-range links
• Shutdown based
rgy
• Turn off sender and receiver• Topology management schemes exploit this
e.g. Schurgers et. al. @ ACM MobiHoc ‘02
Ener
g S g @ CM M 0transmission time
Long-range links
Scaling based
y
Slow down transmissions
Energy-aware packet schedulers exploit this R h h l @ ACM ISLPED ‘02
Ener
gy
e.g. Raghunathan et. al. @ ACM ISLPED ‘02transmission time
Another Issue: Start-up TimeAnother Issue: Start up Time
Ref: Shih et. al., Mobicom 2001
Wasted EnergyWasted Energy
Fixed cost of communication: startup timeHigh energy per bit for small packets
Ref: Shih et. al., Mobicom 2001
Sensor Node with Energy-efficient Packet Relaying [Tsiatsis01]
• Problem: sensor noes often simply relays packets– e.g. > 2/3-rd pkts. in some sample tracking simulations
• Traditional : main CPU woken up, packets sent across bus– power and latency penalty
• One fix: radio with a packet processor handles the common case of relaying– packets redirected as low in the protocol stack as possible
Ch ll h d h l ll d• Challenge: how to do it so that every new routing protocol will not require a new radio firmware or chip redesign?
– packet processor classifies and modifies packets according to application-defined rules l d h bi i f k i h d d i f i
…zZZ– can also do ops such as combining of packets with redundant information
MultihopPacket
MultihopCommunication
Subsystem
Radio
GPS
Micro
Rest of the Node
CPU Sensor
Packet CommunicationSubsystem
RadioM d
GPS
Micro
Rest of the Node
CPU Sensor
Packet
Modem ControllerCPU Sensor Modem Controller
CPU Sensor
Traditional Approach Energy-efficient Approach
Putting it All Together: Power-aware Sensor Node
Sensors RadioCPU
Dynamic Voltage & Freq.
Scaling
Scalable Sensor
Processing
Freq., Power, Modulation, & Code ScalingScaling Processing Code Scaling
Coordinated Power Management
PA-APIs for Communication, Computation, & Sensing
Energy-aware RTOS, Protocols, & Middleware
PASTA Sensor Node Hardware Stack
OutlineOutline
Exploiting Node Mobility in Wireless Sensor NetworksL li i i h N d M biliLocalization with Node MobilityMobile Assist Event Collection
54
Mobile Sensor NodeMobile Sensor Node
55
Mobile Sensor NetworksMobile Sensor Networks
Traditional Static Sensor NetworksSensing, monitoring, and data collection applicationsFixed deployment
Mobile Sensor NetworksAnalyze the effect of node mobility on system capability, design and performance
Research DirectionsUncontrollable Mobility (U-Mobility)
E bl li ti ith t ll bl d t i t d Enable sensor applications with uncontrollable and uncertainty node movement
Controllable Mobility (C-Mobility)y ( y)Leverage controllable mobile node to improve the performance
56
Challenges & IssuesChallenges & Issues
In static sensor network localization is considered as a basic In static sensor network, localization is considered as a basic capability of sensor node.
It is essential to interpret the collected data
Upper layer protocols (e.g., Geographic Routing) are running with node’s location
Th f d t l i t f bil t kThe fundamental impact of mobile sensor networksThe location of node keeps changing
Reconsidering localization with U-Mobility is a critical challenge
Quick re-localizable
Distributed
Accurate and GPS-free (low cost)57
Challenges & Issues (cont )Challenges & Issues (cont.)
L li tiLocalizationwith node mobility
Coverage Application
1) Probabilistic coverage2) Maintain the coverage with C-Mobility against with C-Mobility against U-Mobility
• Probabilistic Coverage Map▫ A place is not just “yes” or “no”, but “probably be” covered.
• Maintain the coverage
58
g▫ Break by U-Mobility; Recover by C-Mobility.
Challenges & Issues (cont )
L li ti
Challenges & Issues (cont.)
Localizationwith node mobility
Coverage ApplicationEvent CollectionApplication1) Probabilistic coverage2) Maintain the coverage with C-Mobility against
1) Mobile assist in energy balancing
2) Design feasible path
pp
with C-Mobility against U-Mobility2) Design feasible path
of C-Mobility
• Mobile Assist in Energy Balancing▫ Balance the query cost among sensing nodes to extend the network lifetime.
• Feasible Path Design of C-Mobility▫ Feasible path: velocity requirement, total travelling distance
Challenges & Issues (cont )
L li ti
Challenges & Issues (cont.)
Localizationwith node mobility
1) Probabilistic coverage
Coverage ApplicationEvent CollectionApplicationOther Applications
) g2) Maintain the coverage with C-Mobility against U-Mobility
1) Mobile assist in energy balancing
2) Design feasible path
pp
1) Data Aggregation2) Mobile Routing3) V hi l t k2) Design feasible path
of C-Mobility3) Vehicular networks4) …
• We focus on exploiting node mobility of• We focus on exploiting node mobility of▫ Localization▫ Coverageg▫ Event Collection
OutlineOutline
I t d tiIntroductionLocalization with Node Mobility
Ji Luo and Qian Zhang, “Relative Distance Based Localization for Mobile Sensor Networks”, GlobeCom 2007, Best Paper Award.
Mobile Assist Event CollectionMobile Assist Event CollectionFuture WorkC l iConclusion
Challenges & IssuesChallenges & Issues
In static sensor network, localization is considered as a basic capability of sensor node.
It is essential to interpret the collected dataIt is essential to interpret the collected dataUpper layer protocols (e.g., Geographic Routing) are running with node’s location
Th f d l i f bil kThe fundamental impact of mobile sensor networksThe location of node keeps changing
Reconsidering localization with U-Mobility is a critical challenge
Q k l l blQuick re-localizableDistributedAccurate and GPS-free (low cost)( )
62
L li tiLocalizationExisting Localization Algorithm Existing Localization Algorithm
Static Sensor NetworksDV-HOP (Niculescu & Nath, 2003)( , )APIT (Tian He, et. Al, 2003)... ...Not suitable for mobile sensor networks
Mobile Sensor NetworksMCL (Hu & Evan, 2004)MCL (Hu & Evan, 2004)Simple Monte Carlo approachNot accurate enough
We face sensor nodes moving in a region uncontrollablyOnly some of them known their locations (e.g., seeds), others are GPS-freeH l bili i l li iHow to leverage mobility in localization
L li iR l i Di C i
LocalizationRelative Distance Constraints
Constraint from static location information1 h t i t1-hop constraint
Seed B in the list of node A’s 1-hop neighbors
Sensor A
Inequality:
2 hop constraint
222 )()( Ryyxx BABA ≤−+− R
Sensor B
2-hop constraintSeed B in the list of node A’s 2-hop neighbors but not in the list of A’s Sensor A1-hop neighborsInequality: 2222 4)()( RyyxxR BABA ≤−+−≤
R Sensor B
Localization
R l i Di C i
Localization
Relative Distance ConstraintsConstraint from static location informationC i l i bili i f i 2VConstraint leveraging mobility information
Motion on boundary constraintSeed B in the list of A’s 1-hop neighbors Sensor A
2Vmax
Seed B in the list of As 1 hop neighborsbut not in the list of A’s 1-hop neighborsat previous time slotI li
Sensor A
R
Inequality:
Inherited Constraint
2222max )()()2( RyyxxVR BABA ≤−+−≤−
Sensor B
Inherited ConstraintIf we have inequality at previous time slot:
dcybxa AA ≤−+−≤ 22 )()(
We can obtain new inequality for current time slot:
( )2max222
max )()(}),0{(max VdcybxVaof BA +≤−+−≤−
LocalizationLocalization
Relative Distance ConstraintsConstraint from static location information
C i l i bili i f iConstraint leveraging mobility information
Our solution: MIL (Mobile Inequality Localization)Basic IdeaBasic Idea
Leverage relative distance constraints (knowledge from neighbor and history motion) to restrict current node’s position in a small region
Technical problemSolve set of ring inequalities
( ) ( )⎧ 22( ) ( )( ) ( )
⎪
⎪⎨
⎧
≤−+−≤≤−+−≤
22
22
22
12
12
11
dcybxadcybxa
⎪⎩ KK
Localization
MIL i d i h d
Localization
MIL, estimated weighted centerNumerical method
maxYWeighted Center
(0,0)
T i li i
Outer BoundaryminX maxX
minY
222 5)0()0( ≤−+− AA yx2222 5)0()5(2 ≤+≤ yx
Two inequality constraints
55,55 ≤≤−≤≤− AA yx=>
55100 ≤≤−≤≤ yx=> 5550
≤≤−≤≤
A
A
yx
5)0()5(2 ≤−+−≤ AA yx 55,100 ≤≤≤≤ AA yx=> Ay
LocalizationLocalization
EvaluationEstimated error of MIL in theory
( )( ) ( ) 1,1111)(222
≤⎟⎠⎞
⎜⎝⎛ −−=−−≥≤ εεεεδ π
SdmP
Simulation
Accuracy Impact of velocityy p y
OutlineOutline
I t d tiIntroductionLocalization with Node MobilityMobile Assist Event Collection
Ji Luo, Xing Xu, and Qian Zhang, “Delay Tolerant Event Collection for Undergro nd Coal Mine sing Mobile Sinks” IWQoS 2009 (best paper Underground Coal Mine using Mobile Sinks”, IWQoS 2009. (best paper award candidate)
Xing Xu, Ji Luo, and Qian Zhang, “Delay Tolerant Event Collection in Sensor Networks with Mobile Sink”, InfoCom 2010.
Future WorkConclusion
Mobile Assist Event CollectionMobile Assist Event Collection
Basic ScenarioBasic ScenarioStatic sensors deployed in underground tunnel to monitor events
Tramcars move in a fixed path with fixed velocity and fixed time intervalTramcars move in a fixed path with fixed velocity and fixed time interval
Path of tramcarLocation
Path of tramcar
event
Sensors
event
TimeTime interval
Mobile Assist E ent CollectionMobile Assist Event Collection
Ob iObservationTramcars could be exploited as mobile sink to relay delay tolerant events
Tough communication environment (static sensor could not communicate with each other)Tough communication environment (static sensor could not communicate with each other)
Selectively query portion of static sensors but not allSpatial-Temporal Correlation: one event may last for a period of time and might be sensed by multiple static sensorsmultiple static sensors
More about CorrelationMore about Correlation
Collect sensor readings from all sensor nodes
Redundancy due to Correlation of Sensor readings:
Spatial correlation: nearby sensors have similar sensor readings
Temporal correlation: sensor readings would not change significantly during a short period of timeshort period of time
Leveraging Correlation: Reading EventLeveraging Correlation: Reading Event
Spatial-Temporal Correlation of sensor readingsSpatial-Temporal Correlation of event:Spatial Temporal Correlation of event:
Temp. in LabT > 36 °C
Mobile Assist E ent CollectionMobile Assist Event Collection
Wi h hi b iWith this observationSelectively query portion of static sensors but not all
Spatial Temporal Correlation one event may last for a period of Spatial-Temporal Correlation: one event may last for a period of time and might be sensed by multiple static sensors
Event Collection Problem: set of events to be collected
Design a set of event collection actionsΩ
ΨTarget
Collect all events:
Maximize network lifetime
Mobile Assist Event CollectionMobile Assist Event Collection
Samples of Learning Sample SetTime 1
Collected events −Ω 0t
Sensor Readings
Predict Recent Sensor
ProcessProcess
Sample Set
P
Time 2 Generate the inputby prediction
+Ω 0t
Online
Use existing samples to predict mean and variance of recent sensor readings1) Local optimization is NP-hard
Readings Spatial-Temporal
Correlation Model
Process
Sample SetTime 3
e Process
1) Local optimization is NP hard2) Heuristic Solution
add event collection action withsmallest
of Event RegionGenerate Possible Event Regions
Sample SetTime k
Local Optimization
singto maximize the minimal residual energy
Make the Decision of Event Collection Actions
Knowledge Obtained by
Process
Sample SetUpdate Samples
Knowledge Obtained by Current Event Collection Actions
Mobile Assist E ent CollectionMobile Assist Event CollectionEvaluationEvaluation
Three metrics:• Network Lifetime
Double lifetime• Energy balance• Event collection rate
Low energy deviation
Stable event collection rate after initial stagerate after initial stage
Mobile Assist Event CollectionMobile Assist Event Collection
P i iPrevious scenarioU-Mobility: tramcar is uncontrollable but the path is known in advance
Another scenarioC M bili h h d if ld l h bil i k C-Mobility: what happened if we could control the mobile sink to move and collect the event
Selective querying still work for spatial-temporal correlationq y g p pFeasible path design for C-Mobility is required
Velocity requirementTotal travelling distance
Event Collection in 2D RegionEvent Collection in 2D Region
Static sensors are deployed to sense the environmentSpecial sensor readings indicate some event
C ll t d b iCollect event data to a base station
Lifetime is main concern
Potential Collection SchemePotential Collection Scheme
Forwarding packets Great energy consumption
Asymmetry location Imbalanced consumption
Full connectivity Costly & unrealistic
Introduce a Mobile SinkIntroduce a Mobile Sink
No forwarding tasksForwarding packets Great energy consumption
g
No imbalanced consumption due to forwarding
No need to maintain full connectivity
Asymmetry location Imbalanced consumption
Full connectivity Costly & unrealistic
Potential Reporting SchemePotential Reporting Scheme
Collect sensor readings from all sensor nodesRedundancy due to Correlation of Sensor readings:
Spatial correlation: nearby sensors have similar sensor readingsTemporal correlation: sensor readings would not change significantly Temporal correlation: sensor readings would not change significantly during a short period of time
Again Leveraging CorrelationAgain: Leveraging Correlation
Spatial-Temporal Correlation of sensor readingsSpatial-Temporal Correlation of event:Spatial Temporal Correlation of event:
Temp. in LabT > 36 °C
Leveraging Correlation (2)Leveraging Correlation (2)
Event Region:Boundary of connected events, indicating the “same” event
Event region contains an Upright CylinderS ti l T l C l ti f tSpatial-Temporal Correlation of event
Collect Event Region, instead of all events
Energy Efficient Event Collection SchemeEnergy Efficient Event Collection Scheme
1. Full sensor readingsAll pointsAll points
2. Data indicating events Highlighted red pointsHighlighted red points
3. Representative data of event regionevent region
One S-T point for each cylinder
Less collections Energy saving
Problem FormulationProblem Formulation
Event Collection Problem: set of event regions to collect
: find a set of query actions (collections)Objectives:j
(1) Provide certain collection rate of ;(2) Maximize the network lifetime .
Maximize the minimum residual energy of static sensorsMaximize the minimum residual energy of static sensors
S l ti ith P f t K l dSolution with Perfect Knowledge
AssumptionSet of event regions is given in advance
Sensor Selection ProblemSelect a set of sensors to query
Selected sensor will consume energyAfter the selection, the lifetime can be maximized
Network Flow Graph for Lifetime MMaximization
Framework with Perfect KnowledgeFramework with Perfect Knowledge
Perfect Knowledge• Given Event Regions
Unrealistic Assumption• Given Event Regions p
Sensor Selection Problem• Selected Sensor SetSelected Sensor Set
Design Route• Perform CollectionsPerform Collections
P ti l S hPractical Scheme
Predicted Event Regions• Based on collected readings
Predict
• Based on collected readings
Sensor Selection Problem• Selected Sensor Set
UpdateSelected Sensor Set
Process
Design Route• Perform CollectionsPerform Collections
E l tiEvaluation
Simulation Setting100 static sensorsSensor readings
Intel Berkeley Research Lab Trace Data
Competitors
Our Scheme Stochastic CoverageOur Scheme Stochastic Coverage
S-T Correlation √ √ √
Prediction √ √ ╳╳
LifetimeMaximization
√ Randomly N.A.
Lifetime Performance (1)Lifetime Performance (1)
Similar acceptable event collection rate: 85%2.5X lifetime comparing to Stochastic Schemep g5X lifetime comparing to Coverage Scheme
Lifetime Performance (2)Lifetime Performance (2)
Standard-deviation of residual energy of all static sensorsConverged standard-deviation, Others keep increasing
Balanced energy consumption
E ent Collection Concl sionEvent Collection - Conclusion
Energy-Efficient Event Collection SchemeLeverage
Mobility of mobile sinkSpatial-temporal correlation of event
Concept of event region for more efficient collection scheme
Practical event collection schemeB ild di i d lBuild prediction modelProved guaranteed event collection rate in theoryVerified good performance on real trace dataVerified good performance on real trace data
ConclusionConclusion
Categorize node mobilityU-Mobility and C-Mobility
E bl l li i i M bil S N kEnable localization in Mobile Sensor NetworksAddress several issues
CoverageCoverageProbabilistic coverage mapDouble mobility to maintain coverage
Event CollectionSensor selection with Spatial-Temporal CorrelationFeasible Movement Path DesignFeasible Movement Path Design
Identify several future research topics based on the current progressp g