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

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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

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“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

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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

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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.

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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

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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

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• Ecological / Health: – Contaminant monitoring / mapping

• Agricultural– Precision farming

Application Scenario

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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

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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, ...

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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

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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

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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

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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

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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).

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Transportation and Urban Monitoring

Disaster Response

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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

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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

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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

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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

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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

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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)

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• 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

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Sensor network features

• high volumes of– Energy and resource constrained, small, cheap

devices

• sensor-sink communication– attribute based– low data rate– redundant data