NEST Midterm Wireless OEP Demo
David Culler
Eric Brewer, David Wagner
Shankar Sastry
NEST PI Meeting
July 14, 2003
Role of MidTerm demo in the program
Challenge Application
composition services
coordination
synthesis
services
services
SW platform
HW platform
sensors actuators processing storage communication
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Platform Timeline
• 6/01 Start of Program Platform Proposal
• 1/02 Delivered 1000 Micas + TinyOS kits
to program projects
• 7/02 Projects demo working components
OEP demos NEST-Tracker
• 2/03 OEP demos NEST-Tracker
composable framework
– Collect services from projects
– SOCOMM and UW demos
• 7/03 Mid-Term Demo on OEP2
– Show feasibility of platform and middleware
• …
• Challenge Application
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Outline
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Introduction
Overview of the Demo
OEP2 HW/SW Platform
Component Services
Mini-demos
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Time synchronization
Ranging
Localization
Sensing
Routing
• Demo
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Overview of the midterm demo
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Establish active sensor field
• Establish spatial and temporal co-ordinates
• Minimize time-to-deploy
 Self-localization (ultra-sound ranging) from few Anchor nodes
 Functional heterogeneity
 Time synchronization to establish rough clock
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Regionalized Entity Detection
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Local signal processing of magnetometer readings
Generate local sighting of strength s at time t
Node with max s over time window elected leader
Aggregates sightings into entity position estimate
Communicate entity detect to entity authority (EA)
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Self-Organized Routing Structure
• Sensor nets: 1-all bcast + all-1 data collection
– Tree construction (bcast, gradient reinforcement, QoS)
• NEST: any-to-few mobile
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Entry Detection and Tracking
• Power management, Distributed sensing, sentry
service (UVA), multihop N-1 routing
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Pursuer Challenges
• Multiple entities in the field
– Disambiguation?
• Mobile destination, multicast “upward”
• Navigation, control, …
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Disambiguation and Hierarchy
• Node Heterogeneity
– Stationary sensor field nodes
» uniform, limited resources, short-range connectivity
» Functional specialization
• Anchored vs localized
• Landmark nodes
– Mobile Pursuer Nodes
» Greater computing resources
» Alternative sensing modes & localization mechanisms
» Alternative communication channels
• Hierarchy of knowledge
– Sensor field nodes detect entities, maintain routing state
– Pursuer nodes maintain entity relationships
» Map of pursuers and evader
» Inter-pursuer coordination
– Entities disambiguated at high levels in hierarchy
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Routing Service Challenge
• Mobile ad hoc routing (eg, MANET) have focused on any-toany independent paths ala internet
– Protocols involve extensive discovery and state maintenance
» DSDR, AODV, …
– either state proportional to size of net or partial flood on any new
connection
• Sensor nets dominated by 1-to-N (broadcast) and N-to-1(data
collection or aggregation)
– simple tree routing structures
– cost-based topology formation with data-driven reinforcement
» Simple cases of directed diffusion
• Here, entity leader (any) routes to few pursuers (anywhere)
– Want simple, bounded-state routing structures
– Small mobility cost
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Landmark-based Routing
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Build routing tree (forest) to landmark node(s)
Entity leader routes up to landmark
Each Pursuer EA handshakes w/ close sensor node (crumb)
Path to landmark forms “crumb trail”
Routes up to landmark, beam-form down to multiple EAs
Incremental crumbs
Alternative routing services: PARC
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Pursuer Control
• Localizes within field coordinates
– Multiple sources: GPS, Ultra-sound, RSSI
• Obtains entity detections from sensor field
• Obtains other pursuer positions
– Pursuer channel or sensor field
• Isolates evader position
– Maintains map
– Tracking and estimation
• Navigates toward evader
– Control loop: head-and-run till next update
– Target based on estimate from evader track
– Coordination and collision avoidance with other pursuer
• When close, STOP
Alternative Control Services: OSU
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Overall Demo Operation
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Sensor field established coordinate grid and time
Sensor field waits in quiescent “sentry state”
Command/Monitor is stationary EA
Plus snooping display for visibility of internal
operation
• Evader enters field is detected and tracked
• Pursuers enter field
– Entity detection, routed to landmark, beamed to EAs
– Pursuers distinguish evader from self entities
– Navigate in co-ordination to pursuer
• Stop when pursuer gets within 1 cell of evader or
evader leaves field
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OEP2 Hardware Platform
• Main board: Mica2 dot
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Atmega microcontroller, Flash, clock
CC1000 frequency agile FSK radio
Small form-factor
Xbow based on Mica
acoustic
• Sensor Board: magnetometer
– Honeywell mag with 2-stage amplification
– Set/reset circuit (5v)
– 4-port digital Pot for biasing and filtering
• Power Subsystem
– Dual-voltage booster/regulator board w/
rechargeable battery
» Controllable 5 v
– Adapter board for conventional recharger
mag
ultrasound
dot
• Ranging boards
– UltraSound
– Acoustic
• Enclosure
• Corresponding Micro-climate and
occupancy detector “stack
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Power Board
• Dual 3.3V and 5V supply
– independently controlled
• 3550mAh capacity at 1.2V on
rechargables
– drawing 25mA constant current (tested in
lab)
– operation down to 0.8V
• ~50uA current at 1.2V in sleep state
with boost converter on
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Enclosure Design
reflector
Exposed components
Watertight compartment
ultrasound
main
mag sense
power
battery
Collision absorption
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OEP2 Software Platform
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TinyOS 1.1 + NesC 1.1
Rich Component Composition Language
ChipCon radio stack
Network Programming
Race detection
TOSSIM + TinyViz
Abstract Timer
Robust Multihop Data Collection
TinyDB
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Higher-Level Node Service Architecture
Localization
Span Tree
Route test
Mag position
appln
services
Service Coordination
Power ctrl
routing
Reset
nbr-hood
messaging
sense
Mgmt
Network
services
Net pgming
Config
Timer
Intra-mote
services
Hardware abstraction
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TinyOS Core Components
Localization
Span Tree
Route test
Mag position
Service Coordination
Power ctrl
routing
Reset
nbr-hood
messaging
sense
• Network Stack
Mgmt
Net pgming
Config
Timer
Hardware abstraction
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– ChipCon
– UART
– I2C
• Magnetometer
• Abstract Timer
– One-shot
– periodic
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Configuration Service
Localization
Span Tree
Route test
Mag position
Service Coordination
Power ctrl
routing
Reset
nbr-hood
messaging
sense
Mgmt
Net pgming
Config
Timer
Hardware abstraction
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• Query & Set
variables
• Any service can
register ‘config’
variable
• Late binding
• Limited
virtualization
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Routing
Localization
Span Tree
Route test
Mag position
Service Coordination
Power ctrl
routing
Reset
nbr-hood
messaging
sense
Mgmt
Net pgming
Config
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Protocol stack
Outbound queue
Multihop
Broadcast
Timer
Hardware abstraction
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Neighborhood Tuple Space
id var1 var2
Localization
Span Tree
Route test
var3
Mag position
Service Coordination
Power ctrl
routing
Reset
nbr-hood
messaging
sense
Mgmt
Net pgming
Config
Timer
Hardware abstraction
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• Data sharing
abstraction
• Per-node logical tuple
space
– Get/Set field
• Abstract neighbor
relation
• Flexible publish/sync
policy
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Power Control Service
Localization
Span Tree
Route test
Mag position
Service Coordination
Power ctrl
routing
Reset
nbr-hood
messaging
sense
Mgmt
Net pgming
Config
Timer
• Provides
start/stop/awake
control for each
component
• Protocol and
sequencing
Hardware abstraction
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Reset and Mgmt Services
Localization
Span Tree
Route test
Mag position
Service Coordination
Power ctrl
routing
Reset
nbr-hood
messaging
sense
Mgmt
Net pgming
Config
Timer
Hardware abstraction
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• Service protocol to
bring components
to a clean, initialized
state
• Health and status
commands
– Blink
– ping
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Service Coordination
Localization
Span Tree
Route test
Mag position
Service Coordination
Power ctrl
routing
Reset
nbr-hood
messaging
sense
Mgmt
Net pgming
Config
Timer
Hardware abstraction
Schedule application
services and
underlying
resources
• Provides start/stop
• One AS active at
any time
• Additional
constraints
– Mag/Msg interaction
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Localization Service
Localization
Span Tree
Route test
Mag position
Service Coordination
Power ctrl
routing
Reset
nbr-hood
messaging
sense
Mgmt
Net pgming
Config
Timer
Hardware abstraction
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• Build field-relative
coordinate grid
• Range
measurements
• Propagate to
determine multihop
ranges from
anchors
• Compute estimated
positions
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Spanning Tree Service
Localization
Span Tree
Route test
Mag position
Service Coordination
Power ctrl
routing
Reset
nbr-hood
messaging
sense
Mgmt
Net pgming
Config
Timer
• Build routing tree
(forest) from
Landmark nodes
• Low-contention,
signal-strength
determined tree
• New bcast
Hardware abstraction
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– Collect readings till quiet
– Filter based on RSSI
– Min hop (filtered) with
max strength
– Retransmit after delay
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Routing Test Service
Localization
Span Tree
Route test
Mag position
Service Coordination
Power ctrl
routing
Reset
nbr-hood
messaging
sense
Mgmt
Net pgming
Config
• Route from node A
to B via landmark
• Execute mgmt cmd
Timer
Hardware abstraction
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Mag Service
Localization
Span Tree
Route test
Mag position
Service Coordination
Power ctrl
routing
Reset
nbr-hood
messaging
sense
Mgmt
Net pgming
Config
Timer
Hardware abstraction
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• Collect mag stream
• EWA into threshold
detector
• Bcast in trigger
• Leader election
• Estimate position as
center of mass
• Route via landmark
to EA
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Entity Authorities
• Maintain “crumb trail” to landmarks
• Collect entity detections via LM routng
• Collection position estimates
– GPS, node RSSI, mag entities, ultrasound
• Collection other pursuer positions
– Alternative channel or LM routing
• Maintain entity map
• Execute navigation control
• Coordinate strategies
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Mini Demos
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TimeSynch Mini-Demo
• Vanderbilt
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Time Sync
• Cross-layer optimization provided by platform
• Eliminates typical large variance on send-delay
– Timestamp outgoing msg after mac delay
– Timestamp incoming
• UCLA demo’d RBS timesynch
• Vanderbilt provided simple multihop direct
method
• Example:
– Ring of nodes maintaining exclusive slots
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Sensing Mini-Demo
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Sensing: Magnetometer Detection
• Based on a 2 stage amplification
with a potentiometer-controlled bias
• Set/Reset circuit
– improved accuracy & no drift
HoneyDot
• 50 uJ of energy per operation;
• 5 mW at 128 Hz
• Improved resolution
– 130 uGauss on a 10-bit Atmel ADC
– Range - 4 gauss
(Earth's magnetic field = 0.5 gauss)
• Software-tunable RC filter
– adaptive sampling and control
– allows for sampling between 5MHz
(sensor BW) and 10 Hz without aliasing
• Further advances with OSU
techniques
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Ranging and Localization Mini-Demo
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Ranging Board
• Lessons from sounder, UCLA
ultrasound, VU ranging
• Dedicated Atmega 8, like motorcontrol board
• Dual proc TinyOS with UART link
• TX
– Command Processor
– Generate 25 KHz 10v signal (range)
• RX
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Triggered by radio chirp
Analog switch
Starts Atmega 8 timer
Analog compare amplified recv with digital
pot threshold.
– Completes timer
– Signals time-stamped event
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25 KHz US
Transceiver
tone
Atmel 8
digital
pot
UART
AM channel
Main TinyOS Mote
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Ultra-Sound Ranger
• UCLA Ultra-sound provides good directional ranging
– High frequency, high amplitude pulse
– PEG requires ranging in the plane
– Rob Szewczyk designed variant of UCLA cone
• Vanderbilt showed good acoustic ranging with high
sample rate and sophisticated processing
– Dual TinyOS approach off-loads sampling
• Relatively simple processing
– 5 cm accuracy in the plane
– 5 m range
• Demo: display range of roving node
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reflector
ultrasound
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Localization for Ease of Deployment
• Few anchor nodes at known positions
• Range between pairs of nodes
• Propagate range estimates to get distance
estimates from anchors
• Calculate estimated location from set of anchors
– Least squares fit
• All three of these steps on-mote and entirely
distributed
=> behavior of algorithm
=> live demonstration on 9-node array outside
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Localization
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9
5
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3
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1
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Localization Accuracy
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Localization Accuracy
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Localization Accuracy
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Live Localization Demo
• 3x3 grid with 4 anchors
Red = Anchor
Blue = Actual
Green = Estimated
2-hop
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Routing Mini Demo
• Goal: route information to the pursuers
• Challenge: pursuers are mobile
– Need to route from one mobile entity (motes near evader) to
another (motes near the pursuer)
• Two solutions:
– “Landmark routing” -- landmark keeps track of the pursuer
and forwards messages from the evader motes
– “Constrained Broadcast” – multicast to possible locations of
the pursuer (Xerox PARC)
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Routing Mini-Demo
• Any-to-one
 global re-broadcast
• Any-to-mobile  repeated re-broadcast
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Berkeley Landmark Routing
• Crumb-trail allows landmark to forward to mobile
– Avoids repeated re-broadcast
• Question: is this too fragile?
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Statistics
• Bandwidth:
#received/sec
• Delivery Rate: # received/# sent
• Energy Cost: # transmissions
• Latency:
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PARC Constrained Broadcast
• Allow sender to initiate global re-broadcast
– Only requires limited repeated re-broadcast
• Question: does this still waste too many messages?
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Routing Results
Berkeley
PARC
• Bandwidth:
5/sec
5/sec
• Delivery Rate:
80%
90%
• Energy Cost:
60/sec
100/sec
• Latency:
.5 sec
.5 sec
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Closing The Loop:
Pursuer Control Overview
Mote Data
Acquisition
GPS
Filtering & Object
Identification
Mote Coordinate
Transformation
Evader State
Estimator
Pursuer State
Estimator
Mote
Motor
Controllers
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Navigation
Constraints
Path
Planner
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Pursuer Control Goals
• Robust to spurious magnetometer readings
• Adaptive to different levels of sensor noise ( mote network
and GPS )
• Accurately correlate magnetometer readings to self, evader,
or disturbances
• Plan route to intercept evader using internal state
estimation of self and evader
• Apply hard navigation constraints
– Do not leave the field
– Do not crash into the evader
• Information-quality adaptive control
– High-noise => slower speed
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Snooping Display
• Display / Control Panel represented as stationary
pursuer
• For additional detail, snoop on overall network
via high-gain antenna
• Simultaneous transmissions may collide only for
display
– Spatial reuse within the network
• Visualize
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Node positions
Routing tree and crumb trail
Mag fields
Estimated evader and pursuer positions
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Scale of Today’s Demo
• Roughly 1/10th scale
• 100 nodes in sensor field of 400 m2
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10x10 grid at 2 m spacing
Radio’s tx power to give about 4m radius
Ranging limit about 3m in grass (10m indoor)
Mag limit about 2 m
» Potentially much greater with OSU signal processing
• Human-driven evader
– Rover
• Single Autonomous pursuer
• Pre-localized
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Questions
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Thanks
• Cory Sharp
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Shawn Schaffert
Phoebus Chen
Phil Levis
Alec Woo
Chris Karlof
Fred Jiang
Naveen Sastry
Rob Szewczyk
Kamin Whitehouse
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Rob von Behren
Joe Polastre
Jaein Jeong
Sukun Kim
Terrence
Neal
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