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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
