Embedded Networked Sensing for Environmental Monitoring: Applications and Challenges

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Embedded Networked Sensing for Environmental
Monitoring Applications and Challenges

• Deborah Estrin
• Center for Embedded Networked Sensing (CENS),
  Director
• UCLA Computer Science Department, Professor
• Work summarized here is largely that of students
  and staff at CENS

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Embedded Networked Sensing Potential

• 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 Transport
Ecosystems, Biocomplexity
Marine Microorganisms
Seismic Structure Response
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ENS enabled by Networked Sensor Node Developments
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, Atmel
Medusa, MK-2 UCLA NESL 2002

• Predecessors in
• DARPA Packet Radio program
• USC-ISI Distributed Sensor Network Project (DSN)

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ENS Technology Design Themes

• Long-lived systems that can be untethered
  (wireless) and unattended
• Communication will be the persistent primary
  consumer of scarce energy resources (Mote
  720nJ/bit xmit, 4nJ/op)
• Autonomy requires robust, adaptive,
  self-configuring systems
• Leverage data processing inside the network
• Exploit computation near data to reduce
  communication, achieve scalability
• Collaborative signal processing
• Achieve desired global behavior with localized
  algorithms (distributed control)
• The network is the sensor (MangesSmith,
  Oakridge Natl Labs, 10/98)
• Requires robust distributed systems of hundreds
  of physically-embedded, unattended, and often
  untethered, devices.

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ENS Architecture Drivers
DRIVERS
TECHNICAL CAPABILITIES
Adaptive Self-Configuring Wireless Systems
Varied and variableenvironments
Energy and scalability
Distributed Signal and Information Processing
Heterogeneity of devices
Networked Info-Mechanical Systems
Smaller component size and cost
Embeddable Microsensors
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CENS Systems under design/construction

• Biology/Biocomplexity
• Microclimate monitoring
• Triggered image capture
• Canopy-net (Wind River Canopy Crane Site)
• Contaminant Transport
• County of Los Angeles Sanitation Districts
  (CLASD) wastewater recycling project, Palmdale,
  CA
• Seismic monitoring
• 50 node ad hoc, wireless, multi-hop seismic
  network
• Structure response in USGS-instrumented Factor
  Building w/ augmented wireless sensors

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Ecosystem Monitoring

• Sensor system logical components
• Tasking, configuration (sample rates, event
  definition, triggering)
• Data Transport
• Device management, sample manipulation and
  caching with timing
• Duty cycling
• Other important examples of habitat monitoring
  systems
• Berkeley/Intel GDI and Botanical gardens

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Extensible Sensing System (ESS) Software

• Tiered architecture components
• Mica2 )motes (8 bit microcontrollers w/TOS with
  Sensor Interface Board hosting in situ sensors
• Microservers are solar powered, run linux, 32-bit
  processors
• Pub/sub bus over 802.11 to Databases,
  visualization and analysis tools, GUI/Web
  interfaces
• Data multicast over Internet on
  publish-and-subscribe bus system (called
  Subject Servers) to databases, GUIs, other
  data analysis tools, clients.
• Osterweil, Rahimi, Mysore, Wimbrow

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Long-lived, Self-configuring Systems

• Irregular deployment and environment
• Dynamic network topology
• Hand configuration will fail
• Scale, variability, maintenance

Localization Time Synchronization
Calibration
Information Transport, Aggregation and Storage
Common theme local adaptation and redundancy
Programming Model
Event Detection
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Network Architecture Can we adapt Internet
protocols and end to end architecture?

• Internet routes data using IP Addresses in
  Packets and Lookup tables in routers
• Humans get data by naming data to a search
  engine
• Many levels of indirection between name and IP
  address
• Works well for the Internet, and for support of
  Person-to-Person communication
• Embedded, energy-constrained (un-tethered,
  small-form-factor), unattended systems cant
  tolerate communication overhead of indirection

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Directed Diffusion--Data Centric Routing

• Data centric approach has the right scaling
  properties
• name data (not nodes) with externally relevant
  attributes (data type, time, location of node,
  SNR, etc)
• diffuse requests and responses across network
  using application driven routing (e.g., geo
  sensitive)
• support in-network aggregation and processing
• Not end to end data delivery
• Not just a database query

Heidemann et.al. SOSP 01, Krishnamachari et
al. 02
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Diffusion One Phase Pull
Sources
Sink

• Optimized version of general diffusion (Heidemann
  et al.)
• Pulls data out to only one sink at a time (saves
  energy)
• Used in Ecosystem application over Mica 2
  motesTinyDiffusion (Osterweil et al)

Interest
Routed Data
Gradient
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Voronoi Scoping Restricted Floods from Multiple
Sinks

• Benefits of multiple sinks
• Reduce average path length
• Equalize load over multiple trees
• Tiered architecture, redundancy
• BUT Linear increase in interests flooded!
• Voronoi clusters partition topology, each subset
  contains nodes closest to associated sink.
• Only fwd interests from closest sink
• No overlap between floods
• Motes receive interest from their closest sink
• Scalable both tiers grow, load per mote remains
  constant.

• Live network (emstar/emview)
• 3 sinks, 55 motes
• color-coded clusters

With Henri Dubois-Ferrière, EPFL
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Multi-hop data extraction characteristics using
Tiny Diffusion

• Collected basic network characteristics to
  verify readiness for sensor deployment
• Average system loss rates analyzed over fixed
  intervals and related to nodes of with various
  average, minimum, and maximum hop counts (under
  3 end to end)
• Additional nodes deployed to augment persistent
  ESS topology to study effects such as loss
  experienced by nodes introduced with less ground
  clearance.
• UCB/Intel GDI deployment has good results from
  their fielded borrow monitoring system using same
  Mote platform

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Characterizing wireless channels

• Great variability over distance (50-80 of
  communication range, vertical lines).
• Reception rate not normally distributed around
  mean and standard deviation.
• Real communication channel is not circular.
• 5 to 30 asymmetric links.
• Not correlated with distance or transmission
  power.
• Primary cause differences in hardware
  calibration (rx sensitivity, energy levels,
  etc.).
• Time variations correlated to mean reception
  rate, not distance from transmitter.

Cerpa, Busek et. al
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Research Challenge Networked Info Mechanical
Systems (NIMS)

• NIMS Architecture Robotic, aerial access to full
  3-D environment
• Enable sample acquisition
• Coordinated Mobility
• Enables self-awareness of Sensing Uncertainty
• Sensor Diversity
• Diversity in sensing resources, locations,
  perspectives, topologies
• Enable reconfiguration to reduce uncertainty and
  calibrate
• NIMS Infrastructure
• Enables speed, efficiency
• Low-uncertainty mobility
• Provides resource transport for sustainable
  presence
• (Kaiser, Pottie, Estrin, Srivastava, Sukhatme,
  Villasenor)

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Broadband ad hoc seismic array
P. Davis

• Core requirement is multi-hop time
  synchronization to eliminate dependence on GPS
  access at every node

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GPS is the usual way to time-sync data collection
-- but satellites are blocked in some
interesting places
Under Foliage
Underwater
Indoors
Sensor networks can propagate time from nodes
that have a sky view to those that dont.
Canyons
Elson et al. OSDI 12/02
Enabling technology RBS -- a new form
ofsynchronization that exploits the nature of
awireless channel to achieve exceptional
precision
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Time Synchronization in Sensor Networks

• Also crucial in many other contexts
• Ranging, tracking, beamforming, security, MAC,
  aggregation etc.
• Global time not always needed
• NTP often not accurate or flexible enough
  diverse requirements!
• New ideas
• Local timescales
• Receiver-receiver sync
• Multihop time translation
• Post-facto sync
• Mote implementation
• 10 ?s single hop
• Error grows slowly over hops

Elson et al. OSDI 12/02
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Contaminant Transport Monitoring Palmdale Pivot
Study

• Regulators require proof that the nitrate-laden
  treated water will not impact groundwater if used
  for irrigation.
• monitoring wells cost of 75K each
• Vertical array of sensors will measure rate of
  diffusion of water and nitrate levels
• Observed nitrate levels, local model will
  trigger contribute to field-wide estimate of
  hazardous Nitrate levels
• Field wide estimate re. concentrations and trends
  fed back to sprinkler quantity

T. Harmon
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Research Challenge Distributed Representation,
Storage, Processing

• In network interpretation of spatially
  distributed data
• Statistical or model based filtering
• In network event detection and reporting
• Direct queries towards nodes with relevant data
• Trigger autonomous behavior based on events
• Expensive operations high end sensors or
  sampling
• Robotic sensing, sampling
• Support for Pattern-Triggered Data Collection
• Multi-resolution data storage and retrieval
• Index data for easy temporal and spatial
  searching
• Spatial and temporal pattern matching
• Trigger in terms of global statistics (e.g.,
  distribution)
• Exploit tiered architectures

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Tiered Data Processing

• Processing uses a two tiered network.
• Task divided into local computation and cluster
  head computation.
• Scope of local computation depends on relative
  cost of local (blue-blue) and cluster-head
  (blue-red) communication
• Example identify regions over which large
  gradient occurring
• Locally, large gradients detected and traversed
  (up to some scope)
• Gradients paths over length threshold identified
  and reported
• Each cluster head combines identification results
  and classifies

T. Schoellhammer, et al
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Research ChallengeCalibration, or lack thereof
Un-calibrated Sensors

• Storage, forwarding, aggregation, triggering
  useless unless data values calibrated
• Calibration correcting systematic errors
• Sources of error noise, systematic
• Causes manufacturing, environment, age, crud
• Traditional in-factory calibration not sufficient
• must account for coupling of sensors to
  environment
• Nearer term identify faulty sensors and flag
  data, discard for in-network processing
• Significant concern that faulty sensors can wreak
  havoc on in network processing

72º
Factory Calibrated Sensors T0
72º
72º
72º
72º
72º
72º
Factory Calibrated Sensors Later
62º
70º
72º
71º
72º
72º
Dust
Bychkovskiy , Megerian, Potkonjak
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Research ChallengeMacroprogramming

• How to specify what, where and when?
• data modality and representation,
  spatial/temporal resolution, frequency, and
  extent
• How to describe desired processing?
• Aggregation, Interpolation, Model parameters
• Triggering across modalities and nodes
• Adaptive sampling
• Primitives
• Annotated topology/resource discovery
• Region identification and characterization
• Intra-region coordination/synch
• System health data, alerts
• Topology, Resources (energy, link, storage)
• Sensor data management (buffering, timing)

Greenstein, Culler, Kohler
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Lessons

• Channel models
• Simplistic circular channel models can be very
  deceiving so experimentation and emulation are
  critical
• Named data
• Is the right model but its only a small step
  toward the bigger problem of Macroprogramming
• Duty cycling
• Critical from the outsetand tricky to get
  right--granularity, level (application or
  communication)
• Tiered Architectures
• One size doesnt fit all and maybe it doesnt fit
  any--distribution of resources (energy, storage,
  comm, cpu) across the distributed system is an
  interesting problem
• Its all a lot harder, and even more interesting
  than it looked 5 years ago

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Follow up regarding IT aspects

• Embedded Everywhere A Research Agenda for
  Networked Systems of Embedded Computers, Computer
  Science and Telecommunications Board, National
  Research Council - Washington, D.C.,
  http//www.cstb.org/
• Conferences ACM Sensys (Nov 03), WSNA (today),
  IPSN, SNPA (ICC), Mobihoc, Mobicom, Mobisys,
  Sigcomm, Infocom, SOSP, OSDI, ASPLOS, ICASSP,
• Whose involved
• Active research programs in many CS (networking,
  databases, systems, theory, languages) and EE
  (low power, signal processing, comm, information
  theory) departments
• Industrial research activities at Intel, PARC,
  Sun, HP, Agilent, Motorola
• Startup activity at Crossbow, Sensicast, Dust
  Inc, Ember,
• Related Funding Programs
• DARPA SenseIT, NEST
• NSF ITR, Sensors and sensor networks