NEST Midterm Wireless OEP Demo

NEST Midterm Wireless OEP Demo

David CullerEric Brewer, David WagnerShankar Sastry

NEST PI MeetingJuly 14, 2003

7/14/2003 NEST midterm Demo 2

Role of MidTerm demo in the program

HW platform

SW platform

coordination

servicessynthesis

services

composition services

Challenge Application

sensors actuators processing storage communication


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


Outline• Introduction• Overview of the Demo• OEP2 HW/SW Platform• Component Services • Mini-demos

– Time synchronization– Ranging– Localization– Sensing– Routing

• Demo


Overview of the midterm demo


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


Regionalized Entity Detection

• 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)


Self-Organized Routing Structure

• Sensor nets: 1-all bcast + all-1 data collection– Tree construction (bcast, gradient reinforcement, QoS)

• NEST: any-to-few mobile


Entry Detection and Tracking

• Power management, Distributed sensing, sentry service (UVA), multihop N-1 routing


Pursuer Challenges

• Multiple entities in the field– Disambiguation?

• Mobile destination, multicast “upward”• Navigation, control, …


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


Routing Service Challenge• Mobile ad hoc routing (eg, MANET) have focused on any-to-

any 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


Landmark-based Routing

• 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


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, STOPAlternative Control Services: OSU


Overall Demo Operation• 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


OEP2 Hardware Platform• Main board: Mica2 dot

– Atmega microcontroller, Flash, clock– CC1000 frequency agile FSK radio– Small form-factor– Xbow based on Mica

• 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• Ranging boards

– UltraSound– Acoustic

• Enclosure• Corresponding Micro-climate and

occupancy detector “stack

dot

mag ultrasound

acoustic


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


Enclosure Design

Exposed components

Watertight compartment

ultrasoundmain

mag sensepower

battery

reflector

Collision absorption


OEP2 Software Platform• 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


Higher-Level Node Service Architecture

Hardware abstraction

Intra-moteservicesmessaging Config Timer

Network services

Service Coordination

appln services

routing nbr-hood Net pgming

Power ctrl Reset

sense

Localization Span Tree Route test Mag position

Mgmt


Timermessaging sense

TinyOS Core Components

• Network Stack– ChipCon– UART– I2C

• Magnetometer• Abstract Timer

– One-shot– periodic


Config



Power ctrl Reset


Mgmt


Config Timermessaging sense

Configuration Service

• Query & Set variables

• Any service can register ‘config’ variable

• Late binding• Limited

virtualizationHardware abstraction



Power ctrl Reset


Mgmt



Routing

• Protocol stack• Outbound queue• Multihop• Broadcast


Config



Power ctrl Reset


Mgmt


nbr-hood


Neighborhood Tuple Space

• Data sharing abstraction

• Per-node logical tuple space– Get/Set field

• Abstract neighbor relation

• Flexible publish/sync policy


Config


routing Net pgming

Power ctrl Reset


Mgmt

id var1 var2 var3



Power Control Service

• Provides start/stop/awake control for each component

• Protocol and sequencing


Config



Power ctrl Reset


Mgmt



Reset and Mgmt Services

• Service protocol to bring components to a clean, initialized state

• Health and status commands– Blink– ping


Config



Power ctrl Reset


Mgmt




Schedule application services and underlying resources

• Provides start/stop• One AS active at

any time• Additional

constraints– Mag/Msg interaction


Config



Power ctrl Reset


Mgmt



Localization Service

• Build field-relative coordinate grid

• Range measurements

• Propagate to determine multihop ranges from anchors

• Compute estimated positions


Config



Power ctrl Reset


Mgmt



Spanning Tree Service

• Build routing tree (forest) from Landmark nodes

• Low-contention, signal-strength determined tree

• New bcast– Collect readings till quiet– Filter based on RSSI– Min hop (filtered) with

max strength– Retransmit after delay


Config



Power ctrl Reset


Mgmt



Routing Test Service

• Route from node A to B via landmark

• Execute mgmt cmd


Config



Power ctrl Reset


Mgmt



Mag Service

• Collect mag stream• EWA into threshold

detector• Bcast in trigger• Leader election• Estimate position as

center of mass• Route via landmark

to EA


Config



Power ctrl Reset


Mgmt


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


Mini Demos


TimeSynch Mini-Demo

• Vanderbilt


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


Sensing Mini-Demo


Sensing: Magnetometer Detection• Based on a 2 stage amplification with

a potentiometer-controlled bias • Set/Reset circuit

– improved accuracy & no drift • 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

HoneyDot


Ranging and Localization Mini-Demo


Ranging Board• Lessons from sounder, UCLA

ultrasound, VU ranging• Dedicated Atmega 8, like motor-

control board• Dual proc TinyOS with UART link• TX

– Command Processor– Generate 25 KHz 10v signal (range)

• RX– Triggered by radio chirp– Analog switch– Starts Atmega 8 timer– Analog compare amplified recv with digital

pot threshold.– Completes timer– Signals time-stamped event

Atmel 8

25 KHz USTransceiver

tone

digital pot

UARTAM channel

Main TinyOS Mote


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 ultrasound

reflector


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


Localization

1

7

3

4

8

9

1415

1310

5

2

11

6

12


Localization Accuracy






Live Localization Demo

• 3x3 grid with 4 anchors

Red = Anchor

Blue = Actual

Green = Estimated

2-hop


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)


Routing Mini-Demo

• Any-to-one global re-broadcast• Any-to-mobile repeated re-broadcast


Berkeley Landmark Routing

• Crumb-trail allows landmark to forward to mobile– Avoids repeated re-broadcast

• Question: is this too fragile?


Statistics

• Bandwidth: #received/sec

• Delivery Rate: # received/# sent

• Energy Cost: # transmissions

• Latency: time received – time sent


PARC Constrained Broadcast

• Allow sender to initiate global re-broadcast– Only requires limited repeated re-broadcast

• Question: does this still waste too many messages?


Routing Results

Berkeley PARC• Bandwidth: 5/sec 5/sec

• Delivery Rate: 80% 90%

• Energy Cost: 60/sec 100/sec

• Latency: .5 sec .5 sec


Closing The Loop:Pursuer Control Overview

GPS

Mote Data Acquisition

Pursuer State Estimator

Motor Controllers

Path Planner

Filtering & Object Identification

Evader State Estimator

Navigation Constraints

Mote Coordinate Transformation

Mote


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


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– Node positions– Routing tree and crumb trail– Mag fields– Estimated evader and pursuer positions


Scale of Today’s Demo• Roughly 1/10th scale• 100 nodes in sensor field of 400 m2

– 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


Questions


Thanks• Cory Sharp

• Shawn Schaffert• Phoebus Chen• Phil Levis• Alec Woo• Chris Karlof• Fred Jiang• Naveen Sastry• Rob Szewczyk• Kamin Whitehouse

• Rob von Behren• Joe Polastre• Jaein Jeong• Sukun Kim• Terrence• Neal

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NEST Midterm Wireless OEP Demo

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