Deborah Estrin, Ramesh Govindan, John Heidemann USC/ISI and UCLA SCADDS Staff and Students: Jeremy...

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


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


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.)


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


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


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


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


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


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


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


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


Part II:System Developments

• API for Diffusion/Network Routing• Using Random Identifiers


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


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


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


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


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)


Testbed Validation of AFF Collision Model:5 Transmitters and 1 Receiver


Part III: Experimental Infrastructure


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)


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


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.


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

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