Some Distributed Coordination Schemes
for Wireless Sensor Networks
Deborah Estrin
UCLA Computer Science Department
and
USC/ISI
http://lecs.cs.ucla.edu/estrin
destrin@cs.ucla.edu
Collaborative work with SCADDS researchers Heidemann,
Govindan, Bulusu, Cerpa, Elson, Ganesan, Girod,
Intanagowat, Yu, and Zhao (USC/ISI and UCLA);
and Shenker (ACIRI)
7/24/2016
1
I. Motivation
Embed numerous distributed
devices to monitor and interact
with physical world: work-spaces,
hospitals, homes, vehicles, and
“the environment”
Circulatory Net
Disaster Response
Network these devices so that
they can coordinate to perform
higher-level tasks.
7/24/2016
Requires robust distributed
systems of hundreds or
thousands of devices.
2
Motivating Applications
Laboratory
Environmental Monitoring
Bio-Tank
-scaled
Tethered
Robot
Algae
Sensors
Inner wall of storm drain
2 meters
Sensors
Model Development
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Complex Structures
3
Theme: New Constraints
• Tight coupling to the physical world
– Need better physical models
– More experimentation
• Designing for energy constraints
• Coping with “apparent” loss of layering
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4
Theme: New Design Goals
• Designing for long-lived (and often energyconstrained) systems
– Exploiting redundancy
– Low-duty cycle operation
– Tiered architectures
• Self configuring systems
– Measure and adapt to unpredictable environment
– Exploit spatial diversity of sensor/actuator nodes
– Localization and Time synchronization are key building
blocks
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Implications for Wireless Sensor
Network Design
• Achieve desired global behavior through localized
interactions, without global state
– Avoid communication over long distances [Pottie 2000]
– Energy propagation loss: E α R4 (10 m: 5000
ops/transmitted bit; 100 m: 50,000,000 ops/transmitted bit)
• Empirically adapt to observed environment
– Dynamic, messy, environments preclude pre-configured
behavior
• Leverage data processing/aggregation inside the
network
Roadmap
I. Motivation
II. Directed Diffusion
III. Other enabling schemes: time synch,
localization, self configuration
IV. Wrap up: tiered architecture, future work
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II. Example: Directed Diffusion
• In-network data processing (e.g., aggregation,
caching)
• Application-aware communication primitives
– expressed in terms of named data (not in terms of the
nodes generating or requesting data)
• Distributed algorithms using localized interactions
and measurement based adaptation
Basic Directed Diffusion
Setting up gradients
Source
Sink
Interest = Interrogation in terms of data
attributes
Gradient = direction and strength
Basic Directed Diffusion
Sending data and Reinforcing the “best” path
Source
Sink
Low rate event
Reinforcement = Increased interest
Directed Diffusion and Dynamics
Source
Sink
Recovering
from node failure
Low rate event
High rate event
Reinforcement
Directed Diffusion and Dynamics
Source
Sink
Stable path
Low rate event
High rate event
Local Behavior Choices
• For propagating interests
• For data transmission
– In our example, flood
– More sophisticated behaviors
possible: e.g. based on cached
information, GPS
•
For setting up gradients
•
•
data-rate gradients are set
up towards neighbors who
send an interest.
Others possible:
probabilistic gradients,
energy gradients, etc.
– Multi-path delivery with
selective quality along
different paths
– probabilistic forwarding
– single-path delivery, etc.
•
For reinforcement
•
•
reinforce paths, or parts
thereof, based on observed
delays, losses, variances
etc.
other variants: inhibit
certain paths because
resource levels are low
Initial simulation study of diffusion
• Key metric
– Average Dissipated Energy per event delivered
• indicates energy efficiency and network lifetime
• Compare diffusion to
– flooding
– centrally computed tree (omniscient multicast)
Diffusion Simulation Details
•
•
•
•
•
•
Simulator: ns-2
Network Size: 50-250 Nodes
Transmission Range: 40m
Constant Density: 1.95x10-3 nodes/m2 (9.8 nodes in radius)
MAC: Modified Contention-based MAC
Energy Model: Mimic a realistic sensor radio [Pottie 2000]
– 660 mW in transmission, 395 mW in reception, and 35 mw in idle
Diffusion Simulation
• Surveillance application
– 5 sources are randomly selected within a 70m x 70m
corner in the field
– 5 sinks are randomly selected across the field
– High data rate is 2 events/sec
– Low data rate is 0.02 events/sec
– Event size: 64 bytes
– Interest size: 36 bytes
– All sources send the same location estimate for base
experiments
Average Dissipated Energy
(Standard 802.11 energy model)
Average Dissipated Energy
(Joules/Node/Received Event)
0.14
Diffusion
0.12
Flooding
Omniscient Multicast
0.1
0.08
0.06
0.04
0.02
0
0
50
100
150
200
250
300
Network Size
Standard 802.11 is dominated by idle energy
Average Dissipated Energy
(Sensor radio energy model)
Average Dissipated Energy
(Joules/Node/Received Event)
0.018
0.016
Flooding
0.014
0.012
0.01
0.008
Omniscient Multicast
0.006
0.004
Diffusion
0.002
0
0
50
100
150
200
250
300
Network Size
Diffusion can outperform flooding and even omniscient multicast.
WHY ?
Average Dissipated Energy
(Joules/Node/Received Event)
Impact of In-network Processing
0.025
Diffusion Without
Suppression
0.02
0.015
0.01
Diffusion With
Suppression
0.005
0
0
50
100
150
200
250
300
Network Size
Application-level suppression allows diffusion to reduce traffic
and to surpass omniscient multicast.
Average Dissipated Energy
(Joules/Node/Received Event)
Impact of Negative Reinforcement
0.012
0.01
Diffusion Without
Negative Reinforcement
0.008
0.006
0.004
Diffusion With Negative
Reinforcement
0.002
0
0
50
100
150
200
250
300
Network Size
Reducing high-rate paths in steady state is critical
Summary of Diffusion Results
• Under the investigated scenarios, diffusion
outperformed omniscient multicast and flooding
• Application-level data dissemination has the
potential to improve energy efficiency significantly
– Duplicate suppression is only one simple example out of
many possible ways.
– Aggregation (in progress)
• All layers have to be carefully designed
– Not only network layer but also MAC and application
level
• Experimentation on our testbed in progress
Implied direction: hierarchical queries
• Create processing points in the network
– High level interests/queries for activity triggers
lower level local queries for particular data
modalities and signatures (e.g. acoustic and
vibration patterns that are mapped to the activity
of interest)
– As opposed to generating detailed queries at sink
points and relying on opportunistic aggregation
alone.
Source
Acoustic?
Large animal?
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Sink
22
Ongoing work in Diffusion
• Multipath: reinforcing multiple upstream
neighbors for load balancing and robustness
– Braided vs. Disjoint paths
•
•
•
•
Opportunistic aggregation of source data
Managing gradients/resources
Tiny diffusion for Motes
Diffusion under mobility: objects, nodes
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III. Enabling Sensor Networks:
works in progress
• Time synchronization
• Localization
• Self-configuration
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Time Synchronization
• Critical at many layers
– TDMA guard bands
– Data aggregation, collaborative processing
– Localization
• But time sync needs are non-uniform
–
–
–
–
Precision
Lifetime
Scope & Availability
Cost and form factor
• And time sync can be expensive in terms of
communications…energy
• No single method optimal on all axes
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Pulse Synchronization
• “External” node generates pulse. Synchronizing
nodes compare reception times.
• Create locality of synchronized nodes, quickly and
energy-efficiently
– NTP good at correcting frequency
– Local pulse good at correcting phase
– Use combination
• Initial experiment using wired stimulus sent to 10
nodes…evaluated precision of achievable timestamp
– 1 usec clock resolution achieved (vs 100 usec with NTP
alone)
– Combination is 10x better than either solution alone:
multimodal is good
– Do as well when NTP used in pre-training!
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Localization
• Needed for coordination of many 3-space related
tasks
– Coordination/scoping of network operation as well
• Multi-modal ranging and localization:
– RF RSSI: inadequate for most environments due to multipath, shadowing
– Acoustic ranging: measure time of flight of chirp, using RF
for synchronization
• Non Line of Site propagation effects distort measurements
• Hard to determine source of geometrical inconsistencies
– Investigating imaging to identify NLOS sources and
combine with acoustic
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Results
This graph shows the
results of a series of tests
in a noisy machine room.
Each point represents
about 10 trials. The tests
were conducted at 1 m
intervals. The data in
each point ranges about
1.5 cm. The variance is
about 0.01 cm
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Self-configuration
• Each node assesses its connectivity and signals or
actuates when it detects a depleted (BW/fidelity)
region.
• 'Healing' is collaborative self-organized deployment
of nodes
– Activate more/fewer nodes
– Mobilize more/fewer nodes
– Adjust duty cycle/power level of existing nodes…
• Assumptions:
– No centralized processing; all nodes act based on locally
available information.
– A very large solution space; not seeking unique optimal
solution.
– Some links have high packet loss..
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IV. Wrapping up…
Tiered Architecture
• We are implementing a sensor net hierarchy: PC104s, tags, motes, ephemeral one-shot sensors
• Save energy by
– Running the lower power and more numerous nodes at
higher duty cycles than larger ones
– Having low-power “pre-processors” activate higher power
nodes or components (Sensoria approach)
• Components within a node can be tiered too
– Our “tags” are a stack of loosely coupled boards
– Interrupts active high-energy assets only on demand
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Tiered Platform for experimentation
with SCADDS algorithms
ISI PC-104
•
Embedded PC:
–
COTS PC104 CPU module
•
–
–
–
–
•
UCLA Tag (Girod)
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UCB Mote
AMD ELANSC400, 16MB
RAM+16MB FlashDisk, 4 serial/1
parallel ports
Phasing out current radio: 418Mhz
RPC from Radiometrix
Moving to RFM
OS: Slimmed Redhat 6.1.
(2.2.x/Libc6)
Incoporating PC104+ for higher end
processing, image capture, etc
Tags and Motes:
–
–
–
(Pister)
–
–
8 bit proc (ATMEL/PIC)
RFM Radio
Mote nicely packaged
Tag for more experimentation
Culler’s TOS
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Technical challenges
• Ad hoc, self organizing, adaptive systems with
predictable behavior
• Collaborative processing, data fusion, multiple
sensory modalities
• Data analysis/mining to identify collaborative
sensing, triggering thresholds, etc
• Combining experimentation, simulation, and analysis
• Engaging theory community (Algorithms? Controls?)
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Enormous Potential Impact
Earth Science
Exploration
Medical monitoring
Disaster Recovery
and Urban Rescue
Networked Embedded
Systems
Smart spaces
Condition Based
Maintenance
Wearable computing
Transportation
Environmental
Monitoring
Biological
Monitoring
Active Structures
Bio-Tank
-scaled
Tethered
Robot
Strand
Stand
Algae
Sensors
34
7/24/2016
2 meters
More information
• UCLA Laboratory for Embedded Collaborative
Systems (LECS)
– http://lecs.cs.ucla.edu
• UCLA Distributed Embedded Systems Program
(DESP)
– http://desp.cs.ucla.edu (joint EE and CS)
• SCADDS project
– http://www.isi.edu/scadds
• ns-2: network simulator (with diffusion supports)
– http://www.isi.edu/nsnam/dist/ns-src-snapshot.tar.gz
• Our testbed and software
– http://www.isi.edu/scadds/testbeds.html
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Some Other Related Work
(NOT complete)
• Sensor networks
– www.isi.edu/scadds
– www.janet.ucla.edu/WINS
– wins.rsc.rockwell.com
– wind.lcs.mit.edu/~hari
– www.nesl.ee.ucla.edu/people/mbs
– tinyos.millennium.berkeley.edu
• Smart Matter
– www.parc.xerox.com/spl/projects/smart-matter
– www-swiss.ai.mit.edu/projects/amorphous
• Internet design inspiration
– irl.cs.ucla.edu/AWC/
– www-mash.cs.berkeley.edu/mash
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