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									       SCADDS: Research Update
            October 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
            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
           PART I:
 Algorithm/Protocol/Diffusion
           Studies

• Diffusion recap
• Aggregation
• Multipath




              SENSIT PI-MTG October 00   3
                                                                                         Diffusion-Recap

                                                            0.03
                                                                                                                         • Directed diffusion
                                                                                                                                 – Can provide
Average Dissipated Energy




                                                           0.025
                            (Joules/Node/Received Event)




                                                                                Diffusion without suppression

                                                            0.02
                                                                                                                                   significantly longer
                                                                                                                                   network lifetimes than
                                                           0.015
                                                                                         flooding
                                                                                                                                   existing schemes
                                                            0.01                                                                 – Keys to achieving this:
                                                                                           Omniscient multicast
                                                                                                                                    • In-network
                                                                                                                                      aggregation
                                                           0.005
                                                                                    Diffusion with suppression
                                                                   0
                                                                       0   50      100     150      200   250     300
                                                                                                                                    • Empirical adaptation to
                                                                                                                                      path
                                                                                Network Size (nodes)




                                                                                                      SENSIT PI-MTG October 00                          4
                               Latency in Data Diffusion
                                                                         Compare latency with:
                                                                         • flooding: large amount of traffic
                                                                         causes delay
                  0.8
                                                                         • omniscient multicast: theoretical
                                                                         centralized optimum (unrealizable in
                  0.7
Delay (Seconds)




                                                                         practice)
                  0.6       Diffusion without
                            suppression
                                                                         • data diffusion without
                  0.5

                                                                         suppression
                  0.4


                                                                         • data diffusion with suppression
                  0.3
                  0.2                                   flooding

                                                                         Diffusion’s empirical adaptation and
                  0.1       Diffusion w/suppression   o. multicast
                    0
                        0     50     100    150   200     250      300
                                                                         in-network processing (suppression)
                                   Network Size (nodes)                  achieves latency as low as optimum
                                                                         (o. multicast).


                                                                                                       5
           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.)

                   SENSIT PI-MTG October 00           6
  Diffusion Work in Progress

• Aggregation mechanisms for energy
  savings
• Multipath




             SENSIT PI-MTG October 00   7
                                              Aggregation
                    0.03
                                                                • Application-level data
Dissipated Energy
                    0.025
                                                                processing can improve
                                    Diffusion- No suppression

                    0.02
                    0.015                 Flooding              energy efficiency
                                      Omnicient Multicast
                    0.01
                    0.005                Diffusion
                       0
                            0   50 100 150 200 250 300


         • 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

                                                                                           8
    Simplified Problem Statement
                      • Where should network
        Data Source 1   aggregate ?
                           – B, C, D, E, or F?
                    B
                        • If aggregation reduces size
                    C     only slightly
         A                 – F is acceptable, ―shortest path
New Data            D        tree‖
                           – ―opportunistic aggregation‖
Source 2                     minimizes latency to sink
                    E
                        • If aggregation reduces size
                    F     significantly
             Sink          – D is preferred (closer to A),
                             ―greedy(ier) tree‖
                           – Conserved energy compared to F9
                           – May increase A to F latency
    Simplified Problem (Continued)
        Data Source 1 • Naïve local-rules may not work
                 B      – If local rule always favors
                                 aggregated data paths, B may be
                    C            selected as aggregation point—
         A                       inefficient and higher latency
New Data            D
Source 2
                    E
                    F
             Sink

                        SENSIT PI-MTG October 00               10
       Desired Aggregation Behavior
                                • A sample local reinforcement rule
      [x1,y1,SNR1]                to provide ―greedy(ier)‖ tree
                            B      – A, already getting source
[x2,y2,SNR2]
                                     [x1,y1] data at high rate from
                                     neighbor B
                                   – A receives [x2,y2]
       C                    A        aggregatable data from
                                     neighbor C
                                   – A decides whether to
                     Sink            aggregate at A or let B
                                     (upstream neighbor) aggregate
           Gradient                – if (DelayViaB-DelayViaC < d), A
           Low rate data             reinforces B, else reinforces C
           Reinforcement
                                   - d is an adjustable parameter11
       Desired Aggregation Behavior
      [x1,y1,SNR1]
                                • A sample local reinforcement
                                  rule for new data [x2, y2,
                            B     SNR2]
                                   – if A sees ( delay(B)-delay(C)
[x2,y2,SNR2]                         < d) then A reinforces B,
                            A        else reinforces C
       C
                                   – B is an upstream neighbor
                                     that has a high-rate
                     Sink            gradient toward A for data
                                     that is aggregatable with
           Gradient                  new data [x2, y2, SNR2]
           Low rate data
                                   - d is an adjustable parameter
           Reinforcement
                                SENSIT PI-MTG October 00        12
                  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
                    SENSIT PI-MTG October 00                  13
 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
                      SENSIT PI-MTG October 00                          14
            Braided Multipath
• Disjoint Paths
   – Stringent restriction
   – Allow end-to-end decisions
     only                                               Braided
   – Unsuitable for broadcast                           multi-path
     model
• Braided paths
   – enable distributed decision
     making
   – Offers greater flexibility to
     route around losses
   – May offer greater
     robustness for same energy
     constraints                     Alternate path
   – May be better suited for        (higher latency)
     changing losses in the
     network.
                                                           15
           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


                        SENSIT PI-MTG October 00   16
               Adaptive Fidelity
• extend system lifetime while
  maintaining accuracy
• approach:
  – estimate node density needed                              zzz
    for desired quality                                 zzz
  – automatically adapt to
                                                  zzz

    variations in current density due
    to uneven deployment or node                              zzz
    failure
  – assumes dense initial
    deployment or additional node
    deployment

                       SENSIT PI-MTG October 00                     17
    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
                    SENSIT PI-MTG October 00               18
           Part II:
     System Developments
• API for Diffusion/Network Routing
• Using Random Identifiers




             SENSIT PI-MTG October 00   19
   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

               SENSIT PI-MTG October 00   20
            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


                   SENSIT PI-MTG October 00     21
           Diffusion Routing API
• Objective: Improve current
  Network Routing API to
  better match distributed                       App 1 App 2
  applications needs
• Solution: Allow more control
  over routing decisions and
  packet forwarding                               Diffusion
  – Support in-network
    processing and aggregation
    with flexible application
    interface

                      SENSIT PI-MTG October 00                22
       Future Directions

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




             SENSIT PI-MTG October 00   23
     Random Transaction Identifiers

• Maximize usefulness of every bit
  – each bit transmitted reduces net lifetime
  – can’t 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 selected…like 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
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




                       SENSIT PI-MTG October 00                      25
Testbed Validation of AFF Collision Model:
      5 Transmitters and 1 Receiver




            SENSIT PI-MTG October 00         26
        Part III:
Experimental Infrastructure




         SENSIT PI-MTG October 00   27
          Platform for
      experimentation with
       SCADDS algorithms
• Complementary platform to
  Sensoria nodes:
   – Not for desert-field testing !
     COTS, rather than custom low-         • Specifications:
     power, real-time, integrated               – COTS PC104 CPU module
     sensor platform                                 • AMD ELANSC400, 16MB
       • Can provide larger scale                      RAM+16MB FlashDisk, 4
         networking studies and                        serial/1 parallel ports
         flexibility via COTS                   – Radio: 418Mhz RPC from
       • Model: explore on this testbed           Radiometrix
         and feedback lessons to                     • Moving to RFM
         integrated, Sensoria platform
       • Will be much easier to move            – OS: Slimmed Redhat 6.1.
         back and forth with any Unix              (2.2.x/Libc6)
         variant (e.g., QNX)


                                SENSIT PI-MTG October 00                         28
   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

                   SENSIT PI-MTG October 00                29
              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.

                          SENSIT PI-MTG October 00                                   30
                                                       *Photo From
                                                             http://www.cs.berkeley.edu/~jhill/
                     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
                       SENSIT PI-MTG October 00              31

								
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