Transcript This Century Challenges: Sensor Networks for Environmental

Wireless Sensor Networks:
In Search of Principles
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
Contributors: Vlad Bychkovskiy, Alberto Cerpa, Jeremy Elson,
Deepak Ganesan, Lew Girod, Ramesh Govindan, John Heidemann,
Bhaskar Krishnamachari, Fabio Silva, Wei Ye and
members of CENS, LECS, and IPAM programs
Sponsors: DARPA, NSF, Intel, Sun, HS-SEAS
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Roadmap
• Motivation
– Driving applications
– Need for new systems and algorithms research
– Segue…Some structure: stack, taxonomy, load, metrics
• Recently-developed building blocks
– Time synchronization
– MAC
– Adaptive Topology
– Data centric routing and In-network processing
• Some emerging principles
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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
3
Enabling Technologies
Embed numerous distributed
devices to monitor and interact
with physical world
Embedded
Network devices
to coordinate and perform
higher-level tasks
Networked
Exploit
collaborative
Sensing, action
Control system w/
Small form factor
Untethered nodes
Sensing
Tightly coupled to physical world
Exploit spatially and temporally dense, in situ, sensing and actuation
4
“The network is the sensor”
(Oakridge National Labs)
Requires robust distributed systems of thousands of
physically-embedded, unattended, and often untethered,
devices.
5
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 and an understanding for underlying
algorithms to manage interactions
– Current system development: one-off, incrementally tuned, stovepiped
– Serious repercussions for piecemeal uncoordinated design:
insufficient longevity, interoperability, safety, robustness,
scalability...
6
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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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
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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Load/Event
Models
Systems Taxonomy
•
•
•
•
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
•
•
•
Frequency
– spatial and
temporal density of
events
Locality
– spatial, temporal
correlation
Mobility
– Rate and pattern
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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ENS Research
• Building blocks for experimental systems
– Fine grained time and location
– Adaptive MAC
– Adaptive topology
These examples illustrate
new combination of
constraints and requirements
– Data centric routing
• Emerging principles…
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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
and many other sensor network functions
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Tiered System Design: IPAQs and UCB Motes
•
Localization
– IPAQs range to each other, create a
coordinate system
– 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
•
Time sync
– Allows computation of acoustic time-of-flight
– One IPAQ has a “MoteNIC” to sync mote
and IPAQ domains
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Reference Broadcast Synchronization
•
Distributed system of sensor nodes
– Distinct nodes need “inter-node” synchronization
• Uses radio channel to relate local clocks of two nodes
• “Multihop” synchronization: composition of time conversions.
• Can be done post facto
•
•
Eliminates effect of transmission variation
Receiver latency is low-variance
010101001
CPU 1
Mote
Mote Sender
010101001
CPU 2
Mote
50kb/S (20uS per bit)
– Reception of broadcasts are closely correlated in real time
– First bit arrives at receivers with small variations (and easy to filter)
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Fine Grained Multi-hop Time Synch Results
Snapshot from Running System that achieves
msec time synchronization relative to NTP ms over lossy wireless
Motes
Lines
annotated
with offset
achieved
between
connected
clocks
9 ms
2 ms
IPAQ CPUs
And Codecs
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Energy Efficient MAC design
(Ye 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
• Message passing
• Periodic listen and sleep
Combine benefits of TDMA + contention protocols
• Tradeoff latency and fairness for efficiency
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Message Passing
• Problem: In-network processing requires entire message
• Solution: Don’t interleave different messages
– Long message is fragmented & sent in burst
– RTS/CTS reserve medium for entire message
– Fragment-level error recovery
— extend Tx time and re-transmit immediately
• Other nodes sleep for whole message time
• Tradeoff fairness for energy and single-message level latency
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Periodic Listen and Sleep
•
•
Problem: Idle listening consumes significant energy
Solution: Periodic listen and sleep policy and mechanism to coordinate
• Turn off radio when sleeping; tradeoff latency for energy
• Reduce duty cycle to ~ 10% (200ms on/2s off)
• Schedules created using SYNCH
Node 1
Node 2
listen
sleep
listen
listen
sleep
sleep
listen
sleep
• Prefer neighboring nodes have same schedule for easy broadcast &
low control overhead
Schedule 1
Schedule 2
Border nodes: two
schedules requires
two broadcasts
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S-MAC Experimental results
(implemented on UCB Mote over RFM radio)
• Topology and measured energy consumption on source nodes
Energy consumed
Average energy consumption in the source nodes
Source 2
Sink 1
Sink 2
• Each source node sends
10 messages
— Each message has 10
fragments x 40B
• Measure total energy
— Data + control + idle
802.11-like protocol
Overhearing avoidance
S-MAC
1600
Energy consumption (mJ)
Source 1
1800
1400
1200
1000
800
600
400
200
0
2
4
6
8
10
Message inter-arrival period (second)
Message
Inter-arrival period
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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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Adaptive Topology Schemes
•
Goal: exploit the redundancy in the system (high density) to save
energy while providing a topology that adapts to the application
needs
•
Mechanism: empirical adaptation. Each node assesses its
connectivity and adapts participation in multi-hop topology based on the
measured operating region.
•
Does not detect partitions, less efficient cases due to lack of global
knowledge
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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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Directed Diffusion: Data Centric Routing
•
Basic idea
– 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 or not)
– optimize path with gradient-based feedback
– support in-network aggregation and processing
•
Data sources publish data, Data clients subscribe to data
– However, all nodes may play both roles
• A node that aggregates/combines/processes incoming sensor
node data becomes a source of new data
• A sensor node that only publishes when a combination of
conditions arise, is a client for the triggering event data
– True peer to peer system
•
Implementation defines namespace and simple matching rules with
filters
– Linux (32 bit proc) and TinyOS (8 bit proc) implementations
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Diffusion as a construct for
in-network processing
• Nodes pull, push, and store named data (using tuple space) to create
efficient processing points in the network
– e.g. duplicate suppression, aggregation, correlation
• Nested queries reduce overhead relative to “edge processing”
• Complex queries support
collaborative signal
processing
– propagate function
describing desired
locations/nodes/data
(e.g. ellipse for tracking (Zhao et al))
– Interesting analogs to emerging
peer-to-peer architectures
• Build on a data-centric architecture
for queries and storage
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Nested Query Evaluation
(A real experiment w/sub-optimal hardware)
• Eevn simple nested queries
greatly improve event delivery
rate
• Specific results depend on
experiment
– placement
– limited quality MAC
• General result: app-level info
needed in sensor nets; diffusion
is a good platform
• Concept of Data Centric vs.
Address Centric more important
than specific implementation
events successfully received (%)
(Heidemann et al.)
nested
80
60
40
flat
20
1
2
3
4
number of light sensors
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A more general look at
Data Centric vs. Address Centric approach
(Krishnamachari et al.)
• Address Centric
• Distinct paths from each source to sink.
• Data Centric
• Support aggregation in the network where paths/trees overlap
• Essential difference from traditional IP networking
• Building efficient trees for Data centric model
• Aggregation tree: On a general graph if k nodes are sources and one is a
sink, the aggregation tree that minimizes the number of transmissions is the
minimum Steiner tree. NP-complete….Approximations:
• Center at Nearest Source (CNSDC): All sources send through source
nearest to the sink.
• Shortest Path Tree (SPTDC): Merge paths.
• Greedy Incremental Tree (GITDC): Start with path from sink to nearest
source. Successively add next nearest source to the existing tree.
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Source placement: event-radius model
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Comparison of energy costs
Data centric has many fewer transmissions than
does Address Centric; independent on the tree
building algorithm.
Address Centric
Shortest path data centric
Greedy tree data centric
Nearest source data centric
Lower Bound
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Opportunism always pays;
Greed pays only when things get very crowded
(Intanagowiwat et al. ns-2 more detailed simulations)
Programming Paradigm
•
Move beyond simple query with in-network aggregation model
•
How do we task a 1000+ node dynamic sensor network to conduct
complex, long-lived queries and tasks ??
– What constructs does the query language need to support?
•
What sorts of mechanisms need to be “running in the background” in
order to make tasking efficient?
– Small databases scattered throughout the network, actively
collecting data of nearby nodes, as well as accepting messages
from further away nodes?
– Active messages traveling the network to both train the network
and identify anomalous conditions?
•
Storage architecture
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(Still hypothetical) Examples
•
Map isotherms and other “contours”, gradients, regions
– Record images wherever acoustic signatures indicate significantly
above-average species activity, and return with data on soil and air
temperature and chemistry in vicinity of activity.
– Mobilize robotic sample collector to region where soil chemistry and
air chemistry have followed a particular temporal pattern and where
the region presents different data than neighboring regions.
•
Raises requirements for some global context, e.g. “average” levels
– Emerging role for distributed storage architecture
•
Pattern identification: how much can and should we do in a distributed
manner?
– Similar to some vision/image analysis problems but distributed
noisy inputs
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In search of Principles
…All we have thus far are heuristics/design themes
• Exploit density
• Use “localized” algorithms
• Procrastination Pays
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Exploiting redundancy, density
•
Design objectives
– Maximize system lifetime, coverage, accuracy, reliability
– … NOT to minimize nodes deployed
•
Spatial and modal diversity can contribute to all objectives, e.g.:
– Adaptive topology/load sharing to increase system lifetime
– Spatial diversity to achieve coverage around obstacles
– Modal diversity to detect outliers in acoustic ranging
– Correlated measurements to calibrate
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Localized algorithms
•
Localized algorithms and in-network processing are mandated by
energy constraints and scale
– Challenge is to characterize and constrain global behavior that
result, e.g., designing for predictability in highly uncertain
environments
•
Localized doesn’t mean flat fully-decentralized--Exploit self-configuring
“structure”
– Tiered and clustered systems
– Exploit some centralized resources and information
– Exploit built-in structures in Globally Ad hoc, Locally regular
systems (GALORE)
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Lazy/Procrastinating/Just-in-time
systems
•
Post facto coordination
– Time synchronization
– Sensor calibration
•
Don’t move a bit until needed: Leave data where it is detected until
needed
– Triggered systems
– Multi-resolution distributed storage architecture, Data centric
storage (DCS, (Ratnasamy (ICSI))
– Not really that simple: When and where is data needed to detect
patterns?
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Some work in progress
• In network processing mechanisms and data, a few examples.
– 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))
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Towards a Unified Framework for ENS
•
General theory of massively distributed systems that interface
with the physical world
– low power/untethered systems, scaling, heterogeneity,
unattended operation, adaptation to varying environments
•
Understanding and designing for the collective
– Local-global (global properties that result…local behaviors
that support)
– Programming model for instantiating local behavior and
adaptation
– Abstractions and interfaces that do not preclude efficiency
•
Cautionary questions
– Will we be able to generalize away from application-specific
stove-pipe solutions?
– How to address social concerns about passive monitoring?
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Pulling it all together
CENS Core Research
Collaborative
Signal
Processing and
Active
Databases
Sensor
Coordinated
Actuation
Adaptive
Self-Configuration
Environmental
Microsensors
Academic Disciplines
Networking
Communications
Signal Processing
Databases
Embedded Systems
Controls
Optimization
…
Biology
Geology
Biochemistry
Structural Engineering
Education
Environmental Engineering
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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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