Deborah Estrin, Ramesh Govindan, John Heidemann

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SCADDS Research UpdateOctober 2000

• Deborah Estrin, Ramesh Govindan, John Heidemann
• USC/ISI and UCLA
• SCADDS Staff and Students
• Jeremy Elson, Deepak Ganesan, Chalermek
  Intanagonwiwat, Fabio Silva, Jerry Zhao
• For more information http/www.isi.edu/scadds

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Research Update

• Directed diffusion studies
• Update
• Aggregation
• Multipath
• Systems contributions
• API and implementation for Diffusion and SenseIT
  routing
• Address free fragmentation
• Experimental platform and experience
• PC-104s
• Instrumentation/debug support!
• Plans and related projects
• Aggregation and multipath simulations and
  implementations
• Adaptive fidelity evaluations
• Related projects Localization, Time
  synchronization, Tags, Tiered architecture

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PART I Algorithm/Protocol/Diffusion Studies

• Diffusion recap
• Aggregation
• Multipath

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Diffusion-Recap

• Directed diffusion
• Can provide significantly longer network
  lifetimes than existing schemes
• Keys to achieving this
• In-network aggregation
• Empirical adaptation to path

0.03
0.025
Diffusion without suppression
0.02
0.015
Average Dissipated Energy
flooding
(Joules/Node/Received Event)
0.01
Omniscient multicast
0.005
Diffusion with suppression
0
0
50
100
150
200
250
300
Network Size (nodes)
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Latency in Data Diffusion

• Compare latency with
• flooding large amount of traffic causes delay
• omniscient multicast theoretical centralized
  optimum (unrealizable in practice)
• data diffusion without suppression
• data diffusion with suppression
• Diffusions empirical adaptation and in-network
  processing (suppression) achieves latency as low
  as optimum (o. multicast).

0.8
0.7
0.6
Diffusion without suppression
0.5
Delay (Seconds)
0.4
0.3
flooding
0.2
Diffusion w/suppression
o. multicast
0.1
0
0
50
100
150
200
250
300
Network Size (nodes)
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Diffusion Status

• Preliminary simulation results were presented in
  Mobicom 2000 (and April00 PI meeting)
• Diffusion version 1 integrated into current ns
  snapshot and released to research community
• A simple TDMA MAC is implemented in ns for better
  simulations of sensor radio
• Tracking other researchers group TDMA work for
  future incorporation (e.g., Srivastava et. al.)

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Diffusion Work in Progress

• Aggregation mechanisms for energy savings
• Multipath

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Aggregation

• Opportunistic and greedy aggregation
• Distributed aggregation points automatically and
  locally selected such that they are close to
  sources
• Opportunistic aggregation on existing tree
• Greedy use reinforcement to increase aggregation
  closer to sources..favoring energy reduction over
  latency

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Simplified Problem Statement

• Where should network aggregate ?
• B, C, D, E, or F?
• If aggregation reduces size only slightly
• F is acceptable, shortest path tree
• opportunistic aggregation minimizes latency to
  sink
• If aggregation reduces size significantly
• D is preferred (closer to A), greedy(ier) tree
• Conserved energy compared to F
• May increase A to F latency

Data Source 1
B
C
A
New Data Source 2
D
E
F
Sink
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Simplified Problem (Continued)
Data Source 1

• Naïve local-rules may not work
• If local rule always favors aggregated data
  paths, B may be selected as aggregation
  pointinefficient and higher latency

B
C
A
New Data Source 2
D
E
F
Sink
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Desired Aggregation Behavior

• A sample local reinforcement rule to provide
  greedy(ier) tree
• A, already getting source x1,y1 data at high
  rate from neighbor B
• A receives x2,y2 aggregatable data from
  neighbor C
• A decides whether to aggregate at A or let B
  (upstream neighbor) aggregate
• if (DelayViaB-DelayViaC lt d), A reinforces B,
  else reinforces C
• - d is an adjustable parameter

x1,y1,SNR1
B
x2,y2,SNR2
A
C
Sink
Gradient
Low rate data
Reinforcement
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Desired Aggregation Behavior

• A sample local reinforcement rule for new data
  x2, y2, SNR2
• if A sees ( delay(B)-delay(C) lt d) then A
  reinforces B, else reinforces C
• B is an upstream neighbor that has a high-rate
  gradient toward A for data that is aggregatable
  with new data x2, y2, SNR2
• - d is an adjustable parameter

B
A
C
Gradient
Low rate data
Reinforcement
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Challenges

• Some aggregation/processing problems are more
  challenging than others
• Future work
• Bounding box applications as initial target
• More general applications will require additional
  mechanism
• identify classes of problems for which
  opportunistic aggregation does not produce
  imprecise or incorrect results
• establish error bounds for class of problems for
  which opportunistic aggregation produces
  imprecise results

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Multipath for Low-Latency Robustness in Lossy
Networks

• In the same design space as FEC and spread
  spectrum approaches to minimize losses and
  latency due to disturbances in the network
• Use local rules for redundancy in lossy regions
  to achieve higher likelihood of delivery.
• Local metrics for Path selection
• Latency
• Loss
• Energy

Shaded regions correspond to regions of high
losses. Darker shades correspond to greater losses
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Braided Multipath

• Disjoint Paths
• Stringent restriction
• Allow end-to-end decisions only
• Unsuitable for broadcast model
• Braided paths
• enable distributed decision making
• Offers greater flexibility to route around losses
• May offer greater robustness for same energy
  constraints
• May be better suited for changing losses in the
  network.

Braided multi-path
Alternate path (higher latency)
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Exploring Multipath

• Exploring tradeoff between choosing higher
  latency path that avoids regions of high losses
  vs sending redundant packets through lossy
  regions
• Exploring Localized mechanisms for low-energy
  notifications
• Piggybacking on data packets
• Nodes use notifications to trigger multipath
  explorations
• Tradeoff-increased latency

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Adaptive Fidelity

• extend system lifetime while maintaining accuracy
• approach
• estimate node density needed for desired quality
• automatically adapt to variations in current
  density due to uneven deployment or node failure
• assumes dense initial deployment or additional
  node deployment

zzz
zzz
zzz
zzz
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Adaptive Fidelity Status

• applications
• maintain consistent latency or bandwidth in
  multihop communication
• maintain consistent sensor vigilance
• status
• probablistic neighborhood estimation for ad hoc
  routing
• 30-55 longer lifetime with 2-6sec higher initial
  delay
• currently underway location-aware neighborhood
  estimation

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Part IISystem Developments

• API for Diffusion/Network Routing
• Using Random Identifiers

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Integration Participation

• Coordinated integration effort
• BAE (Signal Processing)
• ISI-W (Diffusion Routing)
• Penn State (CSP)
• Included 4 SensIT nodes along the road
• Local detection of vehicles
• Messages exchanged via Diffusion

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Diffusion Routing Implementation

• Two implementations
• WinCE (WINS NG 1.0 Nodes)
• PC104s Radiometrix Radios or Wired
• Main development platform
• Easily portable to QNX
• Develop various in-house applications
• Evaluate implementation
• Gain experience with API

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Diffusion Routing API

• Objective Improve current Network Routing API to
  better match distributed applications needs
• Solution Allow more control over routing
  decisions and packet forwarding
• Support in-network processing and aggregation
  with flexible application interface

App 1
App 2
Diffusion
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Future Directions

• TDMA
• Release updated network routing API after
  gaining experience with in-house experiments

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Random Transaction Identifiers

• Maximize usefulness of every bit
• each bit transmitted reduces net lifetime
• cant amortize large headers or claim-collide
  overhead for low data rates high dynamics
• Still need to identify transmitter
• Reinforcements, Fragmentation
• Use small, random transaction identifiers
  (locally selectedlike multicast addresses)
• Treat identifier collisions as any other loss
• Address-free method wins in networks with
  locality
• simultaneous transactions at any one point is
  much less than in network as a whole

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Example A model of address-free fragmentation
(16 bit data)
AFF Allows us to optimize bits used for
identifiers Fewer bits fewer wasted bits per
data bit, but high collision rate vs.
More bits less waste due to ID
collisions but
many bits wasted on headers
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Testbed Validation of AFF Collision Model 5
Transmitters and 1 Receiver
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Part III Experimental Infrastructure
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Platform for experimentation with SCADDS
algorithms

• Complementary platform to Sensoria nodes
• Not for desert-field testing ! COTS, rather than
  custom low-power, real-time, integrated sensor
  platform
• Can provide larger scale networking studies and
  flexibility via COTS
• Model explore on this testbed and feedback
  lessons to integrated, Sensoria platform
• Will be much easier to move back and forth with
  any Unix variant (e.g., QNX)

• Specifications
• COTS PC104 CPU module
• AMD ELANSC400, 16MB RAM16MB FlashDisk, 4
  serial/1 parallel ports
• Radio 418Mhz RPC from Radiometrix
• Moving to RFM
• OS Slimmed Redhat 6.1. (2.2.x/Libc6)

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Using Testbed for SCADDS Experimentation

• Expanded the testbed size to explore SCADDS
  related algorithms
• Currently 30, Target 50-100
• Debugging/Management Utilities
• Special debug-stations with Ethernet and
  8-serial-port adapters, acting as a bridge for
  interactive debugging from host PCs.
• CVS-like Scripts to automatically update binaries
  when newer version is available.
• Iteratively improving SCADDS algorithms based on
  experimental feedback
• E.g., per-hop filters underway since v.1
• Validating and feeding back into simulation
  results

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Leveraging Tiered architecture

• Leveraging other funding to enrich SCADDS
  experiments
• Designing Tags under a complementary NSF grant
  (NSF SCOWR and ONR DURIP)
• Modular architecture, reusable components
• Module Bus 80pin connector I2C, INTQ/A and
  GPIOs
• Modules PIC based master module, sensor module,
  RFM based radio module.
• Experiments with low power architecture
• Software selectable clocking
• Also collaborate with UC Berkeley folks to
  incorporate their silver-dollar sized motes.
• Developing a beaconing application to complement
  SCADDS testbed as well as an objecting tracking
  application.

• Photo From http//www.cs.berkeley.edu/jhill/

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Planned Work

• Diffusion
• Aggregation simulation and implementation
• Multipath simulation and implementation
• Exploring power-aware and geographic routing
  assist
• Adaptive fidelity
• Testbed experimentation
• Beyond SCADDS
• Timing and coordinate synchronization
• Localization (ranging and self-configuring beacon
  placement)
• Sensor network health monitoring and debugging
• Other collaborators
• Nirupama Bulusu, Alberto Cerpa, Lewis Girod,
  Satish Kumar, Yan Yu