Transcript This Century Challenges: Sensor Networks for Environmental

Sensor Networks for Environmental Monitoring:
Lessons for DERNs?
Deborah Estrin
Director, NSF Science and Technology
Center for Embedded Networked Sensing (CENS)
Professor, UCLA Computer Science Department
[email protected]
http://lecs.cs.ucla.edu/estrin
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Embedded Networked Sensing Potential
Seismic Structure
response
Marine
Microorganisms
• Micro-sensors, onboard processing, and
wireless interfaces all
feasible at very small
scale
– can monitor
phenomena “up
close”
• Will enable spatially
and temporally dense
environmental
monitoring
• Embedded Networked
Sensing will reveal
previously
unobservable
phenomena
Contaminant
Transport
Ecosystems,
Biocomplexity
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“The network is the sensor”
(Oakridge National Labs)
Requires robust distributed systems of thousands of
physically-embedded, unattended, and often untethered,
devices.
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New Design Themes
• Long-lived systems that can be untethered and unattended
– Low-duty cycle operation with bounded latency
– Exploit redundancy and heterogeneous tiered systems
• Leverage data processing inside the network
– Thousands 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 hoc
– Un-modeled physical world dynamics makes systems appear ad hoc
– Measure and adapt to unpredictable environment
– Exploit spatial diversity and density of sensor/actuator nodes
• Achieve desired global behavior with adaptive localized algorithms
– Cant afford to extract dynamic state information needed for centralized
control
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From Embedded Sensing to Embedded Control
•
Embedded in unattended “control systems”
– Different from traditional Internet, PDA, Mobility applications
– More than control of the sensor network itself
•
Critical applications extend beyond sensing to control and actuation
– Transportation, Precision Agriculture, Medical monitoring and drug
delivery, Battlefied applications
– Concerns extend beyond traditional networked systems
• Usability, Reliability, Safety
•
Need systems architecture to manage interactions
– Current system development: one-off, incrementally tuned, stovepiped
– Serious repercussions for piecemeal uncoordinated design:
insufficient longevity, interoperability, safety, robustness,
scalability...
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Sample Layered Architecture
User Queries, External Database
Resource
constraints call
for more tightly
integrated layers
In-network: Application processing,
Data aggregation, Query processing
Data dissemination, storage, caching
Open Question:
Can we define an
Internet-like
architecture for
such applicationspecific
systems??
Adaptive topology, Geo-Routing
MAC, Time, Location
Phy: comm, sensing, actuation, SP
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ENS Research
• Some building blocks for experimental systems
– Fine grained time and location
– Adaptive MAC
– Adaptive topology
New designs motivated by
new combination of
constraints and requirements
– Data centric routing
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Fine Grained Time and Location
(Elson, Girod, et al.)
• Unlike Internet, the location of nodes in time and space is
essential for local and collaborative detection
• Fine-grained localization and time synchronization needed to
detect events in three space and compare detections across
nodes
• GPS provides solution where available (with differential GPS
providing finer granularity)
• Acoustic or Ultrasound ranging and multi-lateration algorithms
promising for non-GPS contexts (indoors, under foliage…)
• Fine grained time synchronization needed to support ranging
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Tiered System Design: IPAQs and UCB Motes
•
Localization
– Mote periodically emits coded acoustic
“chirps” (511 bits)
– IPAQs listen for chirps (buffer time series mote can’t do this)
– run matched filter and record time diff btwn
emit- and receive-time of coded sequence
– Share ranges with each other via 802.11;
trilaterate
– IPAQs currently configured with their
position; future: range to each other; selfconfigure
•
Time sync
– Allows computation of acoustic time-of-flight
– One IPAQ has a “MoteNIC” to sync mote
and IPAQ domains
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Energy Efficient MAC design
(Wei et al.)
0.14
0.12
0.1
Diffusion
Flooding
Omniscient Multicast
0.08
0.06
0.04
0.02
00
50
100
150
200
Network Size
•
•
250
300
Average Dissipated Energy
(Joules/Node/Received Event)
Major sources of energy waste
• Idle listening when no sensing events, Collisions, Control overhead,
Overhearing
(Joules/Node/Received Event)
Average Dissipated Energy
•
0.018
0.016
0.014
0.012
0.01
0.008
0.006
0.004
0.002
00
Flooding
Omniscient Multicast
Diffusion
50
100
150
200
250
300
Network Size
Over energy-aware MAC
Over 802.11-like MAC
Major components in S-MAC
• Massage passing
• Periodic listen and sleep
Combine benefits of TDMA + contention protocols
• Tradeoff latency and fairness for efficiency
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Adaptive Topology:
An example of Self-Organization with Localized Algorithms
• Self-configuration and reconfiguration essential to lifetime of unattended
systems in dynamic, constrained energy, environment
– Too many devices for manual configuration
– Environmental conditions are unpredictable
• Example applications:
– Efficient, multi-hop topology formation: node measures
neighborhood to determine participation, duty cycle, and/or power
level
– Beacon placement: candidate beacon measures potential reduction
in localization error
• Requires large solution space; not seeking unique optimal
• Investigating applicability, convergence, role of selective global
information
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Context for creating a topology:
connectivity measurement study (Ganesan et al)
Packet reception over distance has a heavy tail. There is a nonzero probability of receiving packets at distances much greater
than the average cell range
Can’t just
determine
Connectivity
clusters thru
geographic
Coordinates…
For the same
reason you cant
determine
coordinates
w/connectivity
169 motes, 13x13 grid, 2 ft spacing, open area, RFM radio, simple
CSMA
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Example Performance Results (ASENT)
(Cerpa et al., Simulations and Implementation)
Energy Savings (normalized to the Active case, all nodes turn on) as a function of density.
ASCENT provides significant amount of energy savings, up to a factor of 5.5 for high
density scenarios.
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Data Centric vs. Address Centric approach
• Address Centric
• Distinct paths from each source to sink.
• Traditional IP model
• Works well when energy (and thus communication) is not at a
premium
• Data Centric
• Name data (not nodes) with externally relevant attributes
•Data type, time, location of node, SNR, etc
• Publish/Subscribe
• Support in-network aggregation and processing where paths/trees
overlap
• Essential difference from traditional IP networking
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Comparison of energy costs
(Krishnamachari et.al.)
Data centric has many fewer transmissions than
does Address Centric; independent of the tree
building algorithm.
Address Centric
Shortest path data centric
Greedy tree data centric
Nearest source data centric
Lower Bound
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ENS Research in progress
• Work in progress--in network processing mechanisms and models
– Fine grained data collection, methods, tools, analysis, models (D.
Muntz (UCLA), G. Pottie (UCLA), J. Reich (PARC))
– Collaborative, multi-modal, processing among clusters of nodes (e.g.,
F. Zhao (PARC), K. Yao (UCLA)
– Enable lossy to lossless multi-resolution data extraction (Ganesan
(UCLA), (Ratnasamy (ICSI))
– Primitives for programming the “sensor network” (Estrin (UCLA),
Database perspective: S. Madden (UCB))
– Modeling capacity and capability (M. Francischetti (Caltech), PR
Kumar (Ill), M. Potkonjak (UCLA), S. Servetto (Cornell))
• Future areas--constructing models
– Architecture design principles
– Global properties: responsiveness, predictability, safety
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Follow up
•
•
•
•
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/
Related projects at UCLA and USC-ISI
• http://cens.ucla.edu
• http://lecs.cs.ucla.edu
• http://rfab.cs.ucla.edu
• http://www.isi.edu/scadds
Many other emerging, active research programs, e.g.,
• UCB: Culler, Hellerstein, BWRC, Sensorwebs, CITRIS
• MIT: Balakrishnan, Chandrakasan, Morris
• Cornell: Gehrke, Wicker
• Univ Washington: Boriello
• Wisconsin: Ramanathan, Sayeed
• UCSD: Cal-IT2
DARPA Programs
• http://dtsn.darpa.mil/ixo/sensit.asp
• http://www.darpa.mil/ito/research/nest/
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