Some Distributed Coordination Schemes for Wireless Sensor Networks

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Some Distributed Coordination Schemes for
Wireless Sensor Networks

• Deborah Estrin
• UCLA Computer Science Department
• and
• USC/ISI
• http//lecs.cs.ucla.edu/estrin
• destrin_at_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)

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I. Motivation
Embed numerous distributed devices to monitor and
interact with physical world work-spaces,
hospitals, homes, vehicles, and the environment
Network these devices so that they can coordinate
to perform higher-level tasks. Requires robust
distributed systems of hundreds or thousands of
devices.
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Motivating Applications
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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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Theme New Design Goals

• Designing for long-lived (and often
  energy-constrained) 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 a 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

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Roadmap

• Motivation
• Directed Diffusion
• Other enabling schemes time synch, localization,
  self configuration
• 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

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Basic Directed Diffusion
Setting up gradients
Source
Sink
Interest Interrogation in terms of data
attributes
Gradient direction and strength
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Basic Directed Diffusion
Sending data and Reinforcing the best path
Source
Sink
Low rate event
Reinforcement Increased interest
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Directed Diffusion and Dynamics
Source
Sink
Recovering from node failure
Low rate event
Reinforcement
High rate event
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Directed Diffusion and Dynamics
Source
Sink
Stable path
Low rate event
High rate event
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Local Behavior Choices

• For propagating interests
• In our example, flood
• More sophisticated behaviors possible e.g. based
  on cached information, GPS

• For data transmission
• Multi-path delivery with selective quality along
  different paths
• probabilistic forwarding
• single-path delivery, etc.

• For setting up gradients
• data-rate gradients are set up towards neighbors
  who send an interest.
• Others possible probabilistic gradients, energy
  gradients, 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

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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)

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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

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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

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Average Dissipated Energy (Standard 802.11 energy
model)
0.14
Diffusion
0.12
Omniscient Multicast
Flooding
0.1
0.08
Average Dissipated Energy
(Joules/Node/Received Event)
0.06
0.04
0.02
0
0
50
100
150
200
250
300
Network Size
Standard 802.11 is dominated by idle energy
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Average Dissipated Energy (Sensor radio energy
model)
0.018
0.016
Flooding
0.014
0.012
0.01
0.008
(Joules/Node/Received Event)
Omniscient Multicast
Average Dissipated Energy
0.006
Diffusion
0.004
0.002
0
0
50
100
150
200
250
300
Network Size
Diffusion can outperform flooding and even
omniscient multicast. WHY ?
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Impact of In-network Processing
0.025
Diffusion Without Suppression
0.02
0.015
(Joules/Node/Received Event)
Average Dissipated Energy
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.
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Impact of Negative Reinforcement
0.012
0.01
Diffusion Without Negative Reinforcement
0.008
Average Dissipated Energy
(Joules/Node/Received Event)
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
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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

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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.

Acoustic?
Source
Large animal?
Sink
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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 Networksworks 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
  communicationsenergy
• 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 nodesevaluated 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
  multi-path, 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 upTiered Architecture

• We are implementing a sensor net hierarchy
  PC-104s, 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

• Embedded PC
• COTS PC104 CPU module
• AMD ELANSC400, 16MB RAM16MB 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
• 8 bit proc (ATMEL/PIC)
• RFM Radio
• Mote nicely packaged
• Tag for more experimentation
• Cullers TOS

ISI PC-104
UCB Mote (Pister)
UCLA Tag (Girod)
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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
Disaster Recovery and Urban Rescue
Earth Science Exploration
Condition Based Maintenance
Wearable computing
Medical monitoring
Networked Embedded Systems
Smart spaces
Transportation
EnvironmentalMonitoring
Active Structures
Biological Monitoring
Bio-Tank
Strand Stand
?-scaled Tethered Robot
Algae
Sensors
2 meters
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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