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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
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.
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