networks of tiny devices embedded in the physical world david culler computer science division u.c....

Networks of Tiny Devices embedded in the Physical World

David Culler Computer Science Division

U.C. Berkeleywww.cs.berkeley.edu/~culler

Intel Research Berkeley

http://www.cs.berkeley.edu/~culler

1/17/2002 TinyOS CSTB 2

Technology Push

• Complete network embedded systems going microscopic

ProcessingStorage

Sensing

Actuation

Communication

LNAmixerPLL basebandfilters

I Q

Power


Application Pull

• Complete NW embedded systems going microscopic

• Huge space of new applications

Circulatory Net

Habitat Monitoring

Condition-based maintenance

Disaster Management

Ubiquitous

computing

Monitoring & Managing Spaces


Bridging the Technology-Application Gap

• Power-aware, communication-centric node architecture

• Tiny Operating System for Range of Highly-Constrained Application-specific environments

• Network Architecture for vast, self-organized collections

• Programming Environments for aggregate applications in a noisy world

• Distributed Middleware Services (time, trigger, routing, allocation)

• Techniques for Fine-grain distributed control• Demonstration Applications


A de facto platform for EmNets

• Developed a series of wireless sensor devices

• TinyOS concurrency framework• Messaging Model• Networking stacks (RF and Serial)• Multihop routing• Several Key components

– sensing, logging, data filters, broadcast

• Simulation tools• DARPA NEST OEP


Many Research Groups on board

• UCB– NEST

– SensorWeb

– Blackout

– Glaser structures

– CBE

– BFD

– BRWC

• UCLA

• USC

• Rutgers winlab

• Intel

• Bosch

• Crossbow

• U Wash

• Rutgers

• UIUC

• NCSA

• U Virginia

• Ohio State

• UCSD

• Dartmouth

• MIT

• Accenture

• and soon many more


A Operating System for Tiny Devices?

• Traditional approaches– command processing loop (wait request, act, respond)

– monolithic event processing

– bring full thread/socket posix regime to platform

• Alternative– provide framework for concurrency and modularity

– never poll, never block

– interleaving flows, events, energy management

=> allow appropriate abstractions to emerge


Tiny OS Concepts

• Scheduler + Graph of Components– constrained two-level scheduling model:

threads + events

• Component:– Commands, – Event Handlers– Frame (storage)– Tasks (concurrency)

• Constrained Storage Model– frame per component, shared stack, no

heap

• Very lean multithreading• Efficient Layering

Messaging Component

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Commands Events


Application = Graph of Components

RFM

Radio byte

Radio Packet

UART

Serial Packet

ADC

Temp photo

Active Messages

clocks

bit

by

tep

ac

ke

t

Route map router sensor appln

ap

pli

ca

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n

HW

SWExample: ad hoc, multi-hop routing of photo sensor readings

3450 B code 226 B data

Graph of cooperatingstate machines on shared stack


Demonstration applications

• 29 Palms• Cory Hall network

– ½ million packets over 3 weeks

• Surge network and environment display• 800 node ad hoc network• CBE • Glaser Shakes• Granlibakken retreat watcher• Robomote

=> continued application focus• more real and long lived• more dynamics• extract architecture and create framework


Example TinyOS study

• UAV drops 10 nodes along road,– hot-water pipe insulation for package

• Nodes self-configure into linear network

• Synchronize (to 1/32 s)

• Calibrate magnetometers

• Each detects passing vehicle

• Share filtered sensor data with 5 neighbors

• Each calculates estimated direction & velocity

• Share results

• As plane passes by, – joins network

– upload as much of missing dataset as possible from each node when in range

• 7.5 KB of code!

• While servicing the radio in SW every 50 us!


Structural performance due to multi-directional ground motions (Glaser & CalTech)

.

Wiring for traditional structural instrumentation+ truckload of equipment

Mote infrastructure15

13

14

6

5`

15

118

Mote Layou

t 129

Comparison of Results


Cory Energy Monitoring/Mgmt System

• 50 nodes on 4th floor• 5 level ad hoc net• 30 sec sampling• 250K samples to database over 6 weeks


Energy Monitoring Network Arch

sensor net

GW GW

802-11

control net

GW

20-ton chiller

PC

scada term

modbus

UCB power monitor net

PC telegraphMYSQL

Browser


Wealth of Research Challenges

• Large numbers of highly constrained (energy & capability), connected devices

– able to be casually deployed in infrastructure (existing or in design)

– imperfect operation and reliability

– operating in aggregate

• New family of issues across all the layers

application

service

network

system

architecture

technology

mg

mt

/ dia

g /

deb

ug

alg

ori

thm

/ th

eory

pro

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a m

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el


Example: Networking

• Hands-on Experience with Large Networks of Tiny Network sensors

intense constraints, freedom of abstraction

• Re-explore entire range of networking issues

– encoding, framing, error handling

– media access control, transmission rate control

– discovery, multihop routing

– broadcast, multicast, aggregation

– active network capsule (reprogramming)

– localization, time synchronization

– security, network-wide protection

– density independent wake-up and proximity est.

• Fundamentally new aspects in each


Simple Epidemic Algorithm Schema

• One (multicast) message handlerif (new mcast) then

take local actionretransmit modified request

• Examples: Network wakeup, command propagation

– Build spanning tree» record parent

• Naturally adapts to available connectivity• Minimal state and protocol overhead

=> surprising complexity in this simple mechanism


Network Discovery: Radio Cells


Network Discovery


Controlled Empirical Study

• Experimental Setup– 13x13 grid of nodes– separation 2ft– flat open surface– Identical length antennas, pointing vertically

upwards.– Fresh batteries on all nodes– Identical orientation of all nodes– The region was clean of external noise sources.

• Range of signal strength settings• Log many runs


Example “epidemic” tree formation


Final Tree


Power Laws ?

• Most nodes have very small degree (ave = .92)• Some have degree = 15% of the population• Few large clusters account for most of the edges

1

10

100

1000

1 10 100

Cluster Size (1 + # children)

Co

un

t

1

10

100

1000

1 10 100

Cluster SizeL

ink

s


Open Territory => Many Children

• Example: Level 1



• Example: Level 2 – variation in distance



• Example: Level 3 – long links


Understanding Connectivity

• 16 transmit power settings• For each transmit power setting,

each node transmits 20 packets.• Receivers log successfully received

packets.• Nodes transmit one after the other in

a token-ring fashion • No collisions.

• Contour plot show probability of reception from center node

• Define “range” a radius where 75% of enclosed nodes receive 75% of packets

• Often good nodes at a distance


Link symmetry in open environment

• Asymmetric Link: Greater than 65% successful reception in one direction and less than 25% successful reception in the other direction

• Symmetric Link: Greater than 65% successful reception in both directions

• others are “bad” links

A -> BB -> A

Frequency


Importance of Asymmetric Links

• 10%-25% asymmetric links.• Many asymmetric links are long links

– || asymmetric long links|| ~ || symmetric long links ||

• Why are long links useful?– Beacon-based Routing: Long links can be used to build low-depth

routing trees– Diffusion: short routing paths

• Protocol design– When to confirm bidirectionality?


Collisions are primary factor

• Nodes out of range may have overlapping cells

– hidden terminal effect

• Collisions => these nodes hear neither parent and become stragglers

• As the tree propagates – folds back on itself– rebounds from the edge– picking up these stragglers.

• This effect was seen in many experiments


Stragglers

• significant fraction of links point ‘backwards’


Key Experience

• Really good at building tinyOS subsystems– non-blocking, split-phase event structures

• Internalized the “state of constant change” paradigm

– ex: maintain routing tree by constantly rebuilding it– soft state that is always suspect– simple one-way protocols

• Operating in the aggregate• Simple mechanisms to accomplish large goals

– MAC, ATC

• Out of the box on networking abstractions– Low-power listen, wake-up, statistical sampling, weighted

aggregation

• Understanding of large scale dynamics


Rich set of additional challenges

• Efficient and robust security primitives

• Application specific virtual machines

• Time & space information in every packet

• Density independent wake-up, aggregation– sensor => can use radio in ‘analog’ mode

• Resilient aggregators

• Programming support for systems of generalized state machines

• Programming the unstructured aggregate– SPMD, Data Parallel, Query Processing, Tuples

• Understanding how an extreme system is behaving and what is its envelope

– adversarial simulation

• Self-configuring, self-correcting systems


The “Law of Miniaturization”

• Each major generation is increasingly smaller, more deeply interactive, arrives when previous is at its strength

• Vast majority of computing will be small, embedded, devices connected to the physical world

– actually the case today, but

– not connected to us, the web, or each other

Time

Integration

Log R

Mainframe

99

Innovation

Minicomputer

Personal ComputerWorkstationServer

networks of tiny devices embedded in the physical world david culler computer science division u.c....

Documents

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tinyos cstb9 application

tinyos cstb6

tinyos cstb4

tinyos cstb5

tinyos cstb7

shared stack slide

q power slide