Toward the Sensor Network Macroscope

1
Toward the Sensor Network Macroscope

• David Culler
• UCB
• With Gil Tolle, Robert Szewczyk,
• Todd Dawson, Kevin Tu and team, (UCB IB)
• Wei Hong, David Gay, and Phil Buonadonna (intel)
• Mobihoc 2005

2
Enabling Technology for Science
the complex

• macroscope
• P. Anthony 1969
• J. de Rosnay, 1979

Perceive
the imperceptible
the atomic
the small
the far
3
The (A) Promise of Sensor Networks

• Dense monitoring and analysis of complex
  phenomena over large regions of space for long
  periods
• Many, small, inexpensive sensing devices
• Frequent sampling over long durations
• Non-perturbing
• Close to the physical phenomena of interest
• Compute, communicate, and coordinate
• Many sensory modes and vantage points
• Observe complex interactions

4
Diverse Applications

• Monitoring Spaces
• Env. Monitoring, Conservation biology, ...
• Precision agriculture,
• built environment comfort efficiency ...
• alarms, security, surveillance, treaty
  verification ...
• Monitoring Things
• condition-based maintenance
• disaster management
• Civil infrastructure
• Interactions of Space and Things

5
Case Study Redwood Ecophysiology

• 70 of H2O cycle is through trees, not ground
• Complex interactions of tree growth and
  environment
• Effected by and effect the microclimate
• Need to understand dynamic processes within the
  trees

6
State of the Art

• Solid understanding of leaf physiology
• Good models, good empirical data, good fit
• Extension to the entire tree canopy is open
  problem
• Various models focused on particular aspects
• Nutrient transport, transpiration,
• Extremely limited empirical basis
• Data Dirth
• Satellite observations wide coverage, low
  resolution, canopy surface
• Spot weather stations single point in space
• Instrument elevator haul data logger along
  vertical transect
• Wide range of sensors climate, sap-flow, dew,
• Goal dense monitoring throughout canopy of
  sampling of trees throughout forest

7
The alternative
8
What was Todd looking for?
10m
20m
34m
30m
36m
2003, unpublished
9
The Systems Challenge

• Monitoring Managing Spaces and Things

applications
Store
Comm.
uRobots actuate
MEMS sensing
Proc
Power
technology
Miniature, low-power connections to the physical
world
10
Study Format

• Site Sonoma Coast Grove of the big Trees
• Near Occidental, CA
• Adjacent to winery
• Interior and Exterior Trees
• 40-50 nodes per tree
• Range of elevations
• Multiple nodes per level, center and periphery
• 25 day duration
• 5 min sample interval
• - all sensors, battery, parent
• Ground level weather station, fog sensors
• Sap Flow sensors
• Internal indicator of level of photosynthetic
  activity

11
Instrument Positions
12
May 15
13
Groundbreaking Science
Unpublished in preparation
14
The Systems Challenge

• Monitoring Managing Spaces and Things

applications
Store
Comm.
uRobots actuate
MEMS sensing
Proc
Power
technology
Miniature, low-power connections to the physical
world
15
How does a bunch of wireless devices become a
(programmable) network?

• Localized algorithms Distributed computation
  where each node performs local operations and
  communicates within some neighborhood to
  accomplish a desired global behavior
• D. Estrin, 21st Century Challenges

16
Macroscopic Programming

• Ideally, scientist would specify the desired
  global behavior
• Compilers would translate this into local
  operations

17
Sensor Databases a start

• Relational databases rich queries described by
  declarative queries over tables of data
• select, join, count, sum, ...
• user dictates what should be computed
• query optimizer determines how
• assumes data presented in complete, tabular form
• First step database operations over streams of
  data
• incremental query processing
• Big step process the query in the sensor net
• query processing content-based routing?
• energy savings, bandwidth, reliability

SELECT AVG(light) GROUP BY roomNo
18
TinyDB
Sam Madden, Joe Hellerstein, Wei Hong, Michael
Franklin
19
Canonical Architecture
20
TinyOS Appln Sensor Kit - TASK
TASK Client Tools
External Tools
JDBC/ODBC
Internet
JDBC
Basestation
TASK Server
TASK Field Tools
Sensor Network
21
TASK Mote Components
Tiny DB
Diagnostics
Tiny Schema Attributes and Commands
Multi-hop routing
Service Scheduler
Watchdog
EEPROM FS
Time Sync.
Abs. Timer
Power Mgmt
TinyOS Core
22
Node Design

• Monitoring Managing Spaces and Things

applications
service
data mgmt
network
system
architecture
Store
Comm.
uRobots actuate
MEMS sensing
Proc
Power
technology
Miniature, low-power connections to the physical
world
23
Mote Platform Evolution
24
Node design lessons

• Components of a sensor net node
• Processor / Radio / Storage / Interface
• Sensor suite
• Power subsystem
• Mechanical design
• Which are specific to the application?
• Let the expert pick the sensors
• Previous experience
• Reference design
• Lab tools for calibration
• Trust

25
Wireless Micro-weather Mote
26
Wireless Micro-weather Mote

• Incident Light Sensors
• TAOS total solar
• Hamamatsu PAR
• Mica2 dot mote
• Power board
• Power supply
• SAFT LiS02 battery, 1 Ah _at_ 2.8V
• Weatherproof Packaging
• HDPE tube with coated sensor boards on both ends
  of the tube
• O-ring seal for two water flows
• Additional PVC skirt to provide extra shade and
  protection against the rain
• Radiant Light Sensors
• PAR and Total Solar
• Environmental Sensors
• Sensirion humidity temp
• Intersema Pressure temp

mote
Deep Collaboration with Intel
27
System

• Monitoring Managing Spaces and Things

applications
service
data mgmt
network
system
architecture
Store
Comm.
uRobots actuate
MEMS sensing
Proc
Power
technology
Miniature, low-power connections to the physical
world
28
Embedded Network System

• TinyOS 1.1.x
• Rich, stable event-driven OS for embedded
  networking
• Component-based design methodology for robustness
• Wireless stack, uart, sensor stack, storage,
  timer
• BMAC expressive, power-aware sub-MAC with
  low-power listening
• MINT route adaptive N-1 multihop min-cost
  routing with link-by-link retransmission
• TinyDB
• Stream SQL-like in-network query processing
• Periodic sampling with local filters stream data
  to root
• Epoch-based power mamangement
• Tiny Application Sensor Kit
• Stream to archival table, metadata table, query
  GUI
• Postgress on stargate linux gateway
• Push the technology to meet the science vision
• Backfill in case reality falls short (logged all
  the physical samples)

29
Traditional Systems

• Well established layers of abstractions
• Strict boundaries
• Ample resources
• Independent Applications at endpoints communicate
  pt-pt through routers
• Well attended

Application
Application
User
System
Network Stack
Transport
Threads
Network
Address Space
Data Link
Files
Physical Layer
Drivers
Routers
30
by comparison ...

• Highly Constrained resources
• processing, storage, bandwidth, power
• Applications spread over many small nodes
• self-organizing Collectives
• highly integrated with changing environment and
  network
• communication is fundamental
• Concurrency intensive in bursts
• streams of sensor data and network traffic
• Robust
• inaccessible, critical operation
• Unclear where the boundaries belong
• even HW/SW will move

• gt Provide a framework for
• Resource-constrained concurrency
• Defining boundaries
• Appln-specific processing and power management
• allow abstractions to emerge

31
TinyOS

• Small, robust, communication centric design
• Resource-constrained concurrency
• Component abstractions
• World-wide adoption
• 65,000 downloads in past year
• Corporate and academic
• Dozen of platforms
• de facto sensor net standard

32
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
• structured event-driven execution
• Never wait or spin

Events
Commands
send_msg(addr, type, data)
power(mode)
init
Messaging Component
Internal State
internal thread
TX_packet(buf)
Power(mode)
TX_packet_done (success)
init
RX_packet_done (buffer)
33
Networking

• Monitoring Managing Spaces and Things

applications
Store
Comm.
uRobots actuate
MEMS sensing
Proc
Power
technology
Miniature, low-power connections to the physical
world
34
Vast Networks of Tiny Devices

• Past 25 years of internet technology built up
  around powerful dedicated devices that are
  carefully configured and very stable
• local high-power wireless subnets at the edges
• 1-1 communication between named computers
• Here, ...
• every little node is potentially a router
• work together in application specific ways
• collections of data defined by attributes
• connectivity is highly variable
• must self-organize to manage topology, routing,
  etc
• and for power savings, radios may be off 99 of
  the time

35
Common Communication Patterns

• Internet
• Many independent pt-pt stream
• Parallel Computing
• Shared objects
• Message patterns (any, grid, n-cube, tree)
• Collective communications
• Broadcast, Grid, Permute, Reduces
• Sensor Networks
• Dissemination (broadcast epidemic)
• Collection
• Aggregation
• Tree-routing
• Neighborhood
• Point-point

Disseminate the Query - eventual
consistency Collect (aggregate) results
The Emergence of Networking Abstractions and
Techniques in TinyOSPhilip Levis, Sam Madden,
David Gay,  Joseph Polastre, Robert Szewczyk,
Alec Woo, Eric Brewer, and David Culler, NSDI'04
36
The Basic Primitive

• Transmit a packet
• Received by a set of nodes
• Dynamically determined
• Depends on physical environment at the time
• What other communication is on-going
• Each selects whether to retransmit
• Potentially after modification
• And if so, when

37
Routing Mechanism

• Upon each transmission, one of the recipients
  retransmit
• determined by source, by receiver, by
• on the edge of the cell

38
The Most Basic Neighborhood

• Direct Reception
• Non-isotropic
• Large variation in affinity
• Asymmetric links
• Long, stable high quality links
• Short bad ones
• Varies with traffic load
• Collisions
• Distant nodes raise noise floor
• Reduce SNR for nearer ones
• Many poor neighbors
• Good ones mostly near, some far

39
BCAST Fundamental building block

• Commands
• Wake-up
• Form routing tree
• Discover route
• Source-destination discovery (DSR, AODV)
• Exploration in directed diffusion
• Time-synchronization
• Constructed from underlying local broadcast

40
Flooding

• Simple Address-Free Algorithm Schema
• if (new bcast msg) then
• take local action
• retransmit modified request
• Naturally adapts to available connectivity
• Minimal state and protocol overhead
• gt surprising complexity in this simple mechanism

41
Radio Cells
42
Flood Spanning Tree
0
43
Empirical Dynamics of Flood

• 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

Ganesan, Krishnamachari, Woo, Culler, Estrin and
Wicker, Complex Behavior at Scale An
Experimental Study of Low-Power Wireless Sensor
Networks , UCLA Computer Science Technical Report
UCLA/CSD-TR 02-0013
44
Final Tree
45
Factors

• Long asymmetric links are common
• Many children
• Nodes out of range may have overlapping cells
• hidden terminal effect
• Collisions gt these nodes hear neither parent
• become stragglers
• As the tree propagates
• folds back on itself
• rebounds from the edge
• picking up these stragglers.
• Mathematically complex because behavior is not
  independent beyond singe cell
• Redundancy
• Geometric overlap gt lt41 additional area

Ni, S.Y., Tseng, Y.C., Chen, Y.S., Sheu, J.P.
The broadcast storm problem in a mobile ad hoc
network. MobiCom'99
46
Selective Retransmission Schemes

• Probabilistic Retransmission
• Fixed prob.
• What would be the right choice?
• Counter
• When hear msg, start random delay
• If hear C msgs during wait, dont retransmit
• Distance
• If nearest node from which msg is heard is less
  than some threshold, dont retransmit
• Location
• If portion of cell not covered by transmitting
  neighbors is less than some threshold, dont
  retransmit
• Cluster-based
• Partition graph into cluster heads, gateways, and
  members
• Members dont transmit

47
Adaptive BCAST rate

• Upon first msg
• Start random delay
• If new msg arrives during delay
• Filter message (eg., discard if signal strength
  below threshold)
• If passes filter, Utilize message
• Start new delay
• Upon expiration of delay
• Complete local processing
• E.g., pick lowest depth node with strongest
  signal as parent
• Retransmit
• Delay is proportion to cell density
• Wait till ngbrs go quiet before transmit
• gt Approx uniform transmissions per unit area,
  regardless of node density
• Exploit long links when appropriate

48
Example Tree
49
Flooding vs Gossip

• In gossip protocols, at each step pick a random
  neighbor
• Assumes an underlying connectivity graph
• Typically used when graph is full connected
• E.g., ip
• Much slower propagation

50
Reliable, Epidemic Dissemination

• TinyDB query dissemination
• Periodically transmit current query
• If hear new query, accept it and start
  retransmitting it
• Does not scale well over density
• Is not responsive at low rate of management
• gt Trickle (Levis, NSDI 04)

51
Epidemic Dissemination - Trickle

• Key Idea maintain constant flux of communication
  per unit area, regardless of node density
• More nbrs gt listen more, talk less
• Announcement rate a 1/cell_density
• Trickle Algorithm
• Listen for random interval
• If too few announcements, announce current
  version
• Low maintenance costs
• Interval increases in absence of new
  announcements
• Quick response
• Contract window upon new announcement
• Low contention
• If hear old version announcement, open
  suppression wider

Trickle A Self-Regulating Algorithm for Code
Propagation and Maintenance in Wireless Sensor
Networks, Levis et al, NSDI'04
52
Collection

• Common use monitoring
• Collection of nodes take periodic samples
• Stream data towards a root node
• Root announces interest
• depth 0
• Nodes listen
• When hear neighbor with smaller depth
• start transmitting data to best lower neighbor
• set own depth to one greater (and include with
  data)
• Data transmission continuously reinforces
  adjusts routes
• Aggregation within nodes or within the tree

53
How Should We Think About Routing?

• Classical View
• Discover the connectivity graph
• Determine the routing subgraph
• relative to traffic pattern
• Compute a path and Route data hop-by-hop
• Destination selection
• Queuing, multiplexing, scheduling,
  retransmission, coding,
• Here?
• What does it mean to be connected?
• What does it mean to route?

54
Building Neighborhoods Routes

• Node transmits to some unknown set
• Candidate nbrs are sources of incoming packets
• Estimate of inbound link reliability
• Occasionally announce inbound link states
• Provides reverse link estimate to outbound
  neighbors
• Basis for cost-based routing
• Cost-based Parent Selection
• depth(me) MIN nbr(me) depth(i)
• loss(me) MIN nbr(me) loss(i)est(me,i)
• trans(me) MIN nbr(me) trans(i)etrans(me,i)
• What about the nbrs that dont fit in the table?
• FIFO, LRU, Frequency

Taming the Challenges of Reliable Multihop
Routing in Sensor Networks, Alec Woo and David
Culler,  SenSys. 2003.
55
Local Operations gt Global Behavior

• Nodes continually sense network environment
• uncertain, partial information
• Packets directed to a parent neighbor
• all other neighbors hear too
• carry additional organizational information
• Each nodes builds estimate of neighborhood
• adjusted with every packet and with time
• Interactively selects parent
• trans 1/ParentRate trans(Parent-gtroot)
• Routes traffic upward
• Collectively they build and maintain a stable
  spanning tree
• takes energy to maintain structure

Predictable global behavior built from local
operations on uncertain data
56
Behavior over Time
Est. Link Quality
Tree Depth
1
2
3
57
The Amoeboed cell
Distance
58
Which node do you route through?
59
What does this mean?

• Always routing through nodes at the hairy edge
• Wherever you set the threshold, the most useful
  node will be close to it
• The underlying connectivity graph changes when
  you use it
• More connectivity when less communication
• Discovery must be performed under load

60
Communication and Power
listen
off
off
RX
TX

• Costs power whenever radio is on
• Transmitting, receiving, or just listening
• Transmit is easy, Rcv is whats tricky
• Want to turn it on just when there is something
  to hear
• Two approaches
• Schedule transmission intervals
• Statically, dynamically, globally, locally
• Make listening cheap

61
TDMA variants

• Time Division Media Access
• Each node has a schedule of awake times
• Typically used in star around coordinator
• Bluetooth, ZIGBEE
• Coordinator hands out slots
• Far more difficult with multihop (mesh) networks
• Further complicated by network dynamics
• Noise, overhearing, interference

62
S-MACYe, Heidemann, and Estrin, INFOCOM 2002

• Carrier Sense Media Access
• Synchronized protocol with periodic listen
  periods
• Integrates higher layer functionality into link
  protocol
• Hard to maintain set of schedules
• T-MAC van Dam and Langendoen, Sensys 2003
• Reduces power consumption by returning to sleep
  if no traffic is detected at the beginning of a
  listen period

Node 1
sleep
sleep
listen
listen
Node 2
63
TinyDB approach

• Divide time into epochs
• Each epoch
• Wake-up
• Propagate query downward
• Propagate / aggregate data upward
• Till epoch expired
• In-transit packets deferred till next epoch
• High contention despite low average duty cycle
• Developed uniform multihop collection/aggregation
  interface
• Tested many routing layers against same
  application
• Shifted to stock mintroute with low-power
  listening
• Today several partially-scheduled alternatives
• FPS (Holte), xmesh (Hill)

64
Low Power Listening (LPL)

• Energy Cost RX TX Listen
• Scheduling tries to reduce listening
• Alternative, reduce listen cost
• Example of a typical low level protocol
  mechanism
• Periodically
• wake up, sample channel, sleep
• Properties
• Wakeup time fixed
• Check Time between wakeups variable
• Preamble length matches wakeup interval
• Robust to variation
• Complementary to scheduling
• Overhear all data packets in cell
• Duty cycle depends on number of neighbors and
  cell traffic

TX
sleep
sleep
sleep
Node 1
time
RX
sleep
sleep
sleep
Node 2
time
65
Communication Scheduling

• TDMA-like scheduling of listening slots
• Node allocates
• listen slots for each child
• Transmission slots to parent
• Hailing slot to hear joins
• To join listen for full cycle
• Pick parent and announce self
• Get transmission slot
• CSMA to manage media
• Allows slot sharing
• Little contention
• Reduces loss overhearing
• Connectivity changes cause mgmt traffic

66
Communication Trade-offs

• Connectivity graph is not static
• Complicates explicit scheduling
• Time Synchronization
• Time of reference required for rendezvous
• Low-power listening (preamble sampling)
• Reduce the cost to listen
• Allows coarser time synch and more flexible
  schedules

67
Power-aware Routing

• Cost-based Routing
• Minimize number of hops
• Minimize loss rate along the path
• Perform local retransmissions, minimize number
  along path
• Energy balance
• Utilize nodes with larger energy resources
• Utilize redundancy
• Nodes near the sink route more traffic, hence use
  more energy
• Give them bigger batteries or provide more of
  them and spread the load
• Randomize routes
• Utilize heterogeneity
• Route through nodes with abundant power sources

68
The Program

• SELECT result_time, epoch, nodeid, parent,
  voltage, depth, humidity, humid_temp, hamatop,
  hamabot
• FROM sensors
• SAMPLE PERIOD 5 min

69
Trust?

• Now that you have unprecedented data streaming
  in, why do you believe it?
• gt calibration
• Rooftop calibration against reference sensors
• Revealed that k weather station contained
  sophisticated lenses over light sensors to
  broaden field of view
• Weather chamber calibration
• Revealed that microsensors are more uniform than
  regions of the chamber

70
Predeployment calibration
71
Deployment methodology

• Program and start all the nodes
• Place them on a rack
• Cycle them through a couple of synthetic days
• Stick them in the truck, drive, climb the tree
• Watch the network form
• Let it run for a few months
• Dumping the data into postgress
• Post calibrate

72
Systems analysis

• GDI revealed much about the network from the data
  stream (output)
• But, didnt know the input
• gt fall back to a data logger
• Log each sample to flash just before sending the
  packet
• Circular buffer of about a month

73
So how did we do?
Too much data
74
Analysis methodology

• Compress time and space gt look at overall
  distribution of values
• Compress space gt time series from distributed
  instrument
• Compress time gt distributions
• Time series over each point in space

75
Overall data yield / lifetime

• The real world IS tougher than the lab
• Management first!
• Need interactive mgmt queries during installation
  and throughout deployment
• Greater visibility
• More resources gt more reliability
• Yes, hop-by-hop retransmission is a good idea

76
Local log yield

• Management!
• Logged till they died (but logs filled up on May
  26)
• Stopped collecting data on June 2 (some still
  ticking)
• Custody transfer
• Beware complexity
• More significant limiter than memory, processing,
  or energy

77
So what about the real data
78
Distributions
79
Time-series of Distributed Instrument
80
Spatial Trends (difference from mean)
81
Dynamics
Movies for every day at http//www.cs.berkeley.edu
/get/sonoma
82
in situ validation
83
Just the beginning

• Real time and post hoc correlations of dense in
  situ instrumentation with satellite data (modis)
• Sensor networks as a part of the larger
  information enterprise
• Weather, remote sensing,
• Correlation with internal physiology
• Sap flow as estimate of photosynthetic activity
• Model-based analysis
• Feed empirical data into models to estimate
  transpiration
• Compare with assumed microclimate
• Sampling across forest regions
• (10 year) Understanding role of trees in
  (coastal) hydrological cycle
• (25 year) Understanding the elements of the
  carbon fixation cycle

84
Sensor Net Application Taxonomy
Power Availability
scarce

• Also
• Scale
• Processing
• Time correlation

trickle
plentiful
burst
benign
Sampling
harsh
Environment
85
Monitoring Environments and Living Things
From ltplantmon_at_intel-research.netgtTo ltwhong_at_int
el-research.netgtDate Tue, 17 May 2005 060011
-0700Subject Help! Water! Water!Your plant
(Bayview, LWPID 10) is dying - PLEASE WATER IT
(humidity 19)
86
Machine Health Monitoring (Intel)
87
Structural Monitoring
88
Footbridge Deployment
mid-span
quarter-span
260ft
5
9
2
7
1
16ft
11
12
Berkeley
SF Bay
14
13
10
3
8
4
L3
L5
L1
L4
L2
Base Station
89
Time-plots of calibrated data
90
Frequency-plots of calibrated data
91
First Vertical Mode of Vibration
1.00
0.74
0.19
-0.73
-0.99
Frequency 1.41 Hz Damping Ratio 2

• Analysis of multiple Time correlated, high sample
  rate readings
• Reliable bulk transfer

92
Second Vertical Mode of Vibration
0.94
1.00
0.68
0.33
0.41
Frequency 1.78 Hz Damping Ratio 1
93
Installation on GGB Span
94
Technology Snapshot

• 100,000 motes in use
• 100-1,000 node deployments
• Many different platforms and companies
• UCB, Crossbow, Intel, EYES, KETI, MIT, Digital
  Sun, TIP, BTNODE3,
• Dust, Sensicast, ember, millennial,
• IEEE standard radio that is usable
• 802.15.4 not perfect, but a whole lot better
  than bluetooth
• Distinct from ZIGBEE the industry effort to
  standardize higher levels of the network
• Inter-platform interoperability
• TELOS, MicaZ, ChipCon dev, iMOTE2
• Emerging open network architecture
• Active and effective international open source
  community
• Miniaturization will happen when it makes
  business sense

95
Nucleus Wireless Sensor Network Management

• lightweight management system to provide
  introspection and debugging in cooperation with
  applications

Design
Application and System Components
Resource Discovery Retrieval of node names and
types Performance Monitoring Remote access to
system attributes Problem Awareness Persistent
logging of system events Separate Network
Layers Uses own collection dissemination
Attributes
Events
RAM
Sensor Network Management System
Names and Descriptors
AttributeRetrieval
EventLogger
Lightweight Collection and Dissemination Protocols
Implementation
Minimal Resource Usage Core requires lt 300 bytes
of RAM Generates little network traffic when
network is not being managed Responds quickly
during interactive management operations
Monitored Systems Task Scheduler Network
Interfaces Persistent Storage Bootloader Protocols
Multiple Naming Schemes Debugging - local names
require less coordination, but management of
heterogeneous systems is harder Management -
global names create a framework that can contain
different applications, but are often incomplete
Tolle, EWSN 05
96
Key Node Design Elements
Flash Storage
timers
proc
data logs
Wireless Net Interface
antenna
RF transceiver
pgm images
WD
Wired Net Interface
serial link USB,EN,
Low-power Standby Wakeup

• Efficient wireless protocol primitives
• Flexible sensor interface
• Ultra-low power standby
• Very Fast wakeup
• Watchdog and Monitoring
• Data SRAM is critical limiting resource

System Architecture Directions for Networked
Sensors, Hill,. Szewcyk, Woo, Culler, Hollar,
Pister,  ASPLOS 2000
97
Newer 802.15.4 Platforms

• Focused on low power
• Sleep - Majority of the time
• Telos 2.4mA
• MicaZ 30mA
• Wakeup
• As quickly as possible to process and return to
  sleep
• Telos 290ns typical, 6ms max
• MicaZ 60ms max internal oscillator, 4ms external
• Process
• Get your work done and get back to sleep
• Telos 4MHz 16-bit
• MicaZ 8MHz 8-bit
• TI MSP430
• Ultra low power
• 1.6mA sleep
• 460mA active
• 1.8V operation

• Standards Based
• IEEE 802.15.4, USB
• IEEE 802.15.4
• CC2420 radio
• 250kbps
• 2.4GHz ISM band
• TinyOS support
• New suite of radio stacks
• Pushing hardware abstraction
• Must conform to std link
• Ease of development and Test
• Program over USB
• Std connector header
• Interoperability
• Telos / MicaZ / ChipCon dev

UCB Telos
Xbow MicaZ
98
Power States at Node Level
Active
Active
Telos Enabling Ultra-Low Power Wireless
Research, Polastre, Szewczyk, Culler, IPSN/SPOTS
2005
99
Prometheus Perpetually Powered Telos

• Solar energy scavenging system for Telos
• Super capacitors buffer energy
• Lithium rechargeable battery as a backup
• Uses MCU to manage charge cycles to extend system
  lifetime
• Manage limited recharges

Perpetual Environmentally Powered Sensor
Networks, Jiang, Polastre, Culler, IPSN/SPOTS,
2005
100
TinyOS 2.x Hardware abstraction model

• Combine the component model with a flexible,
  three-tier hardware abstraction

Hardware independence
101
Robust Dissemination Deluge

• Every byte must (eventually) be correctly
  received by all nodes!
• Reliable Pipelined Epidemic Distribution of
  series of pages
• Constrained storage hierarchy
• Packet (32 bytes) ltlt RAM (4K) ltlt program (128K) lt
  external flash (512K)
• Lossy links, Critical Contention
• Density-aware
• Robust to asymmetric links
• Dynamic adjustment of advertisements
• Minimize set of concurrent data broadcasts
• Spatial multiplexing
• Page Advertise, Request/Fix, Xfer
• Density-aware suppression and snoop on each
• Packet CRC Page CRC
• 159 Byte memory footprint

flash

102
Dissemination Dynamics
103
Mate ASVM

• Three program representations
• Source
• Network transport
• Execution
• TinySQL compiles to bytecode
• For database VM
• Simpler, smaller, faster

104
Role of a sensor net architecture

• Wide range of long-lived applications
• Diverse, constrained, evolving resources
• Low duty cycle
• Small tables
• Loss, noise change
• Embedded in adapting to phy. env.
• In-network processing, not E2E
• Highly application specific
• Few applications over many nodes
• WSN needs a narrow waist too!

105
view of SNA stack
Applications Compose what they need
Tracking Application
Sensing Application
Multiple Network Layer Protocols
106
Bells Law new class per decade
log (people per computer)
streaming information to/from physical world

• Enabled by technological opportunities
• Smaller, more numerous and more intimately
  connected
• Brings in a new kind of application
• Used in many ways not previously imagined

year
107
Small Technology, Broad Agenda

• Social factors
• security, privacy, information sharing
• Applications
• long lived, self-maintaining, dense
  instrumentation of previously unobservable
  phenomena
• interacting with a computational environment
• Programming the Ensemble
• describe global behavior, synthesis local rules
  that have correct, predictable global behavior
• Distributed services
• localization, time synchronization, resilient
  aggregation
• Networking
• self-organizing multihop, resilient, energy
  efficient routing
• despite limited storage and tremendous noise
• Operating system
• extensive resource-constrained concurrency,
  modularity
• framework for defining boundaries
• Architecture
• rich interfaces and simple primitives allowing
  cross-layer optimization
• Components
• low-power processor, ADC, radio, communication,
  encryption, sensors, batteries

108
The Time is Right

• Dont be afraid to go out and tackle REAL
  problems.
• They often reveal interesting challenges.
• The technology is (just barely) ready for it.
• There is much innovation ahead.

109
Acknowledgments

• Supported by
• DARPA Network Embedded Systems Technology (NEST)
  program,
• National Science Foundation,
• Intel, Sun, CrossBow, Microsoft, HP, NTT DoCoMo,
  Samsung
• CITRIS and California MICRO.
• Where to go for more
• www.tinyos.net
• webs.cs.berkeley.edu

Thanks