wireless sensor & actuator networkstwiki.di.uniroma1.it/.../aa0910/webhome/corsora1.pdf ·...
TRANSCRIPT
I-1
Short Course
Wireless Sensor & Actuator Networks
Mani [email protected] & Embedded Systems Lab(http://nesl.ee.ucla.edu)& Center for Embedded Networked Sensing(http://www.cens.ucla.edu)
Acknowledgment: many slides are from: (I) Mobicom 2002 tutorial with Deborah Estrin & Akbar Sayeed(II) Various presentations & courses at UCLA CENS
Copyright © 2003
I-2
Embedded Wireless Networked Sensing & Actuation
• “Communication” between people and their physical environment– Allow users to query, sense, and manipulate the state of the physical
world• Technology enablers
– Cheap, ubiquitous, high-performance, low-power embedded processing
• e.g. low-power processor cores– Cheap, ubiquitous (wireless) networking
• e.g. single-chip CMOS radios – Cheap, ubiquitous, high-performance sensors and actuators
• e.g. MEMS devices
Soon, all on a single system-on-chip!Networked physical objects with embedded processing,
wireless communication, and sensing/actuation
I-3
“The Network is the Sensor”
• Distributed and large-scale, connected to other networks such as like the Internet
• But different from previous networks,– physical instead of virtual– resource constrained– real-time control loops instead of interactive human loops
Gateway
I-4
Enabled by Wirelessly NetworkedSensor Nodes
LWIM III
UCLA, 1996
Geophone, RFM
radio, PIC, star
network
AWAIRS I
UCLA/RSC 1998
Geophone, DS/SS
Radio, strongARM,
Multi-hop networks
Sensor Mote
UCB, 2000
RFM radio,
PIC
Medusa, MK-2
UCLA NESL
2002
Predecessors in• DARPA Packet Radio program• USC-ISI Distributed Sensor Network Project (DSN)
I-5
Environmental Potential of ENS Technology (Applications being pursued at CENS)
• Micro-sensors, on-board processing, wireless interfaces feasible at very small scale--can monitor phenomena “up close”
• Enables spatially and temporally dense environmental monitoring
Embedded Networked Sensing will reveal previously unobservable phenomena
Contaminant TransportEcosystems, Biocomplexity
Marine Microorganisms Seismic Structure Response
I-6
Example Application: Seismic
• Interaction between ground motions and structure/foundation response not well understood.
– Current seismic networks not spatially dense enough to monitor structure deformation in response to ground motion, to sample wavefield without spatial aliasing.
• Science– Understand response of buildings and
underlying soil to ground shaking – Develop models to predict structure response
for earthquake scenarios.• Technology/Applications
– Identification of seismic events that cause significant structure shaking.
– Local, at-node processing of waveforms.– Dense structure monitoring systems.
ENS will provide field data at sufficient densities to develop predictive models of structure, foundation, soil response.
I-7
Field Experiment
?? ? ? ? ? ? ? ??1 km ? ? ? ? ? ? ?
• 38 strong-motion seismometers in 17-story steel-frame Factor Building.• 100 free-field seismometers in UCLA campus ground at 100-m spacing
I-8
Example Application: Contaminant Transport
• Science
– Understand intermedia contaminant transport and fate in real systems.
– Identify risky situations before they become exposures. Subterranean deployment.
• Multiple modalities (e.g., pH, redox conditions, etc.)
• Micro sizes for some applications (e.g., pesticide transport in plant roots).
• Tracking contaminant “fronts”.• At-node interpretation of potential
for risk (in field deployment).
Soil Zone
Groundwater
Volatization
SpillPath
Air Emissions
Dissolution
Water Well
I-9
• Ecological / Health: – Contaminant monitoring / mapping
• Agricultural– Precision farming
Application Scenario
I-10
Contaminantplume
ENS Research Implications
• Environmental Micro-Sensors– Sensors capable of recognizing
phases in air/water/soil mixtures.
– Sensors that withstand physically and chemically harsh conditions.
– Microsensors.• Signal Processing
– Nodes capable of real-time analysis of signals.
– Collaborative signal processing to expend energy only where there is risk.
Ion Selective MembraneGround Water
Ionic Currents
NO3-NO3
-
Pt (c.e.)Ag (w.e.)SupportingElectrolyte
I-11
Example Application: Ecosystem Monitoring
Science• Understand response of wild populations (plants and animals) to habitats
over time.• Develop in situ observation of species and ecosystem dynamics.
Techniques• Data acquisition of physical and chemical properties, at various
spatial and temporal scales, appropriate to the ecosystem, species and habitat.
• Automatic identification of organisms(current techniques involve close-range human observation).
• Measurements over long period of time,taken in-situ.
• Harsh environments with extremes in temperature, moisture, obstructions, ...
I-12
Field Experiments
• Monitoring ecosystem processes– Imaging, ecophysiology, and
environmental sensors– Study vegetation response to
climatic trends and diseases.• Species Monitoring
– Visual identification, tracking, and population measurement of birds and other vertebrates
– Acoustical sensing for identification, spatial position, population estimation.
• Education outreach– Bird studies by High School
Science classes (New Roads and Buckley Schools).
QuickTime™ and aPhoto - JPEG decompressor
are needed to see this picture.
Vegetation change detection
Avian monitoring Virtual field observations
I-13
CENS Habitat Monitoring Network@ James Reserve
• Microclimate and ecophysiological studies• Continuous Monitoring System - deployed
– ~ 20-30 nodes• Extensible Sensing System - in development
– > ~ 100 nodes• Hierarchical architecture
– Weather boards + MICA– iPaqs/802.11 as cluster heads
• Mote and iPaq software stack– Directed diffusion routing (Tiny-diffusion)– Sampling management– Neighbor discovery, link quality
management, etc.– Sensor device drivers
• Backend server software– Transport an recording of sensor data
from remote sensor nets– Storage schemas– Internet-based publish-subscribe bus
I-14
Micro-Climate Monitoring
• Weather-motes (Berkeley Intel Lab and UCLA)
– Miniature wired probes to off-board sensors
• Leaf wetness• Light: PAR, UV, Solar radiation, Visible
light• Rain fall• Wind speed and direction• Soil moisture• Temperature probes
– Onboard• Temp • Humidity• Pressure• Thermopile• Light
I-15
System Hierarchy
Back BoneNodes
MotesMotes with wired probe sensor
Camera and High Power Sensors
IP Connection To Internet
Wireless802.11b
Low PowerCommunication
Low Power
Local Data BaseAt the reserve Lodge
I-16
ENS Requirements for Habitat/Ecophysiology Applications
• Diverse sensor sizes (1-10 cm), spatial sampling intervals (1 cm - 100 m), and temporal sampling intervals (1 ?s - days), depending on habitats and organisms.
• Naive approach ? Too many sensors ? Too many data.
– In-network, distributed signal processing.• Wireless communication due to climate, terrain, thick vegetation.
• 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).
I-17
Transportation and Urban Monitoring
Disaster Response
I-18
The Smart Kindergarten Project: Fusing the Physical and the Cognitive
• Wireless networked sensors densely embedded in a kindergarten room– create a problem solving environment that can is continually
sensed in detail– kids, toys, blocks, playthings, classroom “woodwork”
• Background computing & data management infrastructure for on-line and off-line sensor data processing and mining
• Sensor information used for– assessment of student learning and group dynamics– problem solving tasks that are adaptive and reactive– services beneficial to teacher and students
Smart Kindergarten Project: Sensor-based Wireless Networks of Toys
for Smart Developmental Problem-solving Environments
SensorsModules
High-speed Wireless LAN (WLAN)WLAN-Piconet
Bridge
Piconet
WLAN-PiconetBridge
WLAN AccessPoint
Piconet
SensorManagement
SensorFusion
SpeechRecognizer
Database& Data Miner
Middleware Framework
Wired Network
NetworkManagement
Networked Toys
Sensor Badge
I-20
The Smart Kindergarten Ecology
Medusa MK-2 == Motes +StrongThumb + Ultrasound
iBadge: Wearable Sensor Node
row
sel
ecto
r
column selectorsensorscanner
table surface
sensor grid
objects
1 2
4
3
5
Smart Table: Sensor-instrumented Surfacefor Object Id and Localization
CompaqiPaq
802.11b
RS-485
Host ComputerBasestation
Table Surface
25 Sensing PCB's
Serial Bus
WirelessTransmission
1000
mm
1000mmSmart Table
I-21
ENS in the Battlefield
• Mobile ‘users’ query and track mobile targets in a battle space instrumented with a number of ‘sensor networks’ composed of a large number of energy limited air-borne and ground-based ‘sensor nodes’ (e.g. cameras)– Users: rovers, UAVs, soldiers– Sensors: rovers & UAVs carrying sensors, static sensor nodes– Targets: vehicles, soldiers
• UCLA Minuteman Project
I-22
Existing Systems Inadequate in Understanding ENS
• Large-scale (O(10.000)), unaccessible environments• Distributed is a MUST• Real-time (control loops and events)• Physically-coupled• Resource-constrained• Wireless• Computation, and not just communication• Data fusion ?highly redundant data• Communication from nodes directed toward sink(s)
Need to redesign the protocol stack!!!
Attribute based communicationrather than address based?time, location
I-23
Long-lived Self-configuring Systems
Long network lifetime
? Irregular deployment and environment, often unaccessible
? Dynamic network topology (awake/asleep nodes, nodes deplete
their energy)? Hand configuration will fail• Scale, variability, maintenance
Event Detection
Localization &Time Synchronization
Programming Model
Information Aggregation and Storage
I-24
Next week
• EYES prototype: how to program it• EYES: first european project on sensor networks• Aim: building a complete architecture for sensor
networks• Partners: Infineon, Nedap, Rome Univ., Ferrara
Univ., University of Twente, Technical University of Berlin
• Target applications: Diary cattles, Smart buildings (Nedap’s business)
I-25
• Spatial and Temporal Scale
– Extent– Spatial Density (of
sensors relative to stimulus)
– Data rate of stimulii• Variability
– Ad hoc vs. engineered system structure
– System task variability– Mobility (variability in
space)• Autonomy
– Multiple sensor modalities
– Computational model complexity
• Resource constraints– Energy, BW– Storage, Computation
Systems Taxonomy
• Frequency – spatial and
temporal density of events
• Locality – spatial, temporal
correlation• Mobility
– Rate and pattern
Load/Event Models
Metrics
• Efficiency– System
lifetime/System resources
• Resolution/Fidelity– Detection,
Identification• Latency
– Response time• Robustness
– Vulnerability to node failure and environmental dynamics
• Scalability– Over space and
time
I-26
Sensor network features
• high volumes of– Energy and resource constrained, small, cheap
devices
• sensor-sink communication– attribute based– low data rate– redundant data