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
SCADDS: Research UpdateOctober 2000
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
SENSIT PI-MTG October 00 3
PART I: Algorithm/Protocol/Diffusi
on Studies
• Diffusion recap • Aggregation• Multipath
SENSIT PI-MTG October 00 4
Diffusion-Recap
• Directed diffusion– Can provide
significantly longer network lifetimes than existing schemes
– Keys to achieving this:• In-network
aggregation• Empirical adaptation
to path
Ave
rage
Dis
sipa
ted
Ene
rgy
(Jo
ule
s/N
od
e/R
ece
ive
d E
ven
t)
Network Size (nodes)
0
0.005
0.01
0.015
0.02
0.025
0.03
0 50 100 150 200 250 300
Diffusion without suppression
flooding
Diffusion with suppression
Omniscient multicast
5
Latency in Data DiffusionCompare 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
Diffusion’s empirical adaptation and in-network processing (suppression) achieves latency as low as optimum (o. multicast).
Del
ay (
Sec
onds
)
Network Size (nodes)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0 50 100 150 200 250 300
Diffusion withoutsuppression
flooding
Diffusion w/suppression o. multicast
SENSIT PI-MTG October 00 6
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 7
Diffusion Work in Progress
• Aggregation mechanisms for energy savings
• Multipath
8
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
0
0.005
0.01
0.015
0.02
0.025
0.03
0 50 100 150 200 250 300
D
issi
pat
ed E
ner
gy
Diffusion- No suppression
Flooding
Omnicient Multicast
Diffusion
• Application-level data processing can improve energy efficiency
9
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
Sink
New Data Source 2
A
B
C
D
E
F
SENSIT PI-MTG October 00 10
Simplified Problem (Continued)
• Naïve local-rules may not work– If local rule always favors aggre
gated data paths, B may be sel ected as aggregation point—
inefficient and higher latency
Data Source 1
Sink
New Data Source 2
A
B
C
D
E
F
11
Desired Aggregation Behavior
[x1,y1,SNR1]
[x2,y2,SNR2]
Sink
GradientLow rate dataReinforcement
• 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 < d), A reinforces B, else reinforces C
- d is an adjustable parameter
B
C A
SENSIT PI-MTG October 00 12
Desired Aggregation Behavior
[x1,y1,SNR1]
[x2,y2,SNR2]
Sink
GradientLow rate dataReinforcement
• A sample local reinforcement rule for new data [x2, y2, SNR2]– if A sees ( delay(B)-delay(C)
< 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
C A
SENSIT PI-MTG October 00 13
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 14
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
15
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.
Alternate path(higher latency)
Braidedmulti-path
SENSIT PI-MTG October 00 16
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 17
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
SENSIT PI-MTG October 00 18
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 19
Part II:System Developments
• API for Diffusion/Network Routing• Using Random Identifiers
SENSIT PI-MTG October 00 20
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 21
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 22
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
Diffusion
App 1 App 2
SENSIT PI-MTG October 00 23
Future Directions
• TDMA• Release updated network routing
API after gaining experience with in-house experiments
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
SENSIT PI-MTG October 00 25
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
Example: A model of address-free fragmentation (16 bit data)
SENSIT PI-MTG October 00 28
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 RAM+16MB FlashDisk, 4 serial/1 parallel ports
– Radio: 418Mhz RPC from Radiometrix
• Moving to RFM
– OS: Slimmed Redhat 6.1. (2.2.x/Libc6)
SENSIT PI-MTG October 00 29
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 30
Leveraging Tiered architecture
*Photo From http://www.cs.berkeley.edu/~jhill/
• 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 31
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
Top Related