self organizing wireless sensor network middleware cleanpoint
DESCRIPTION
Self Organizing Wireless Sensor Network Middleware CleanPoint. University of Virginia PI: John A. Stankovic December 2004. Outline. Operational Scenario Goals Overview and Status of Middleware Middleware Services Key Services Power Management/Sentry/Tripwire Service - PowerPoint PPT PresentationTRANSCRIPT
NESTANSCD
University of Virginia
Self Organizing Wireless Sensor Network Middleware
CleanPoint
Self Organizing Wireless Sensor Network Middleware
CleanPoint
University of Virginia PI: John A. Stankovic
December 2004
NESTANSCD
University of Virginia
OutlineOutline
•Operational Scenario•Goals•Overview and Status of Middleware•Middleware Services
– Key Services•Power Management/Sentry/Tripwire Service•Group Management Service•3-Tier Classification•Self-healing
– Other Services
•Lessons Learned•Remaining Work FY ‘05
NESTANSCD
University of Virginia
1. An unmanned plane (UAV) deploys motes
2. Motes establish an sensor network with power management
3.Sensor network detects
vehicles and wakes up the sensor nodes
Zzz...
Energy Efficient Surveillance SystemEnergy Efficient Surveillance System
Diffusion Routing
Neighbor Discovery
Time Synchronization
Parameterization
Sentry Selection
Coordinate Grid
Data Aggregation
Data Streaming
Group Management
Leader Election
Localization
Network Monitor
Tripwire Service
Reconfiguration
Reliable MAC
Leader Migration
Scheduling
State Synchronization
……
Sentry
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University of Virginia
GoalsGoals
•Develop an operational self-organizing sensor network of size 1000
•Cover an area of 1000m x 100m•Stealthy•Lifetime 3-6 months•Timely detection, track and classification
– Large or small vehicle– Person, person with weapon
•Wakeup other devices when necessary– Extend the lifetime of those devices as well
•Exhibit self-healing capabilities
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University of Virginia
ID Task Name
1 Version 1.0 Development
2 Version 1.1 Development
3 More Component Design
4 Component Test
5 Integration
6 Small-Scale Test
7 Large-Scale Test
8 Version 1.1 Commit
9 Version 1.1 Test
10 Lab Testing
11 Filed Test
12 Field Assessment one
13 Version 1.2 Release
14 Version 1.2 Development
15 Version 1.2 Test
16 Field Assessment Two
17 Version 1.3 Release
18 Version 1.3 Development
19 Vesion 1.3 Test
20 Final Demo
5/28
8/6
10/29
12/6
January February March April May June July August September October November December January
Project Milestones FY04Project Milestones FY04
March 3rd May 28th Aug. 6 Oct.29 Dec 6/13 V1.0 V1.1 V1.2 V1.3 Final
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University of Virginia
Summary: DeliverablesSummary: Deliverables
• ANSCD V1.3 middleware code delivered– About 40,000 lines of code and 600 files– About 30 Middleware services provided– Tested with a network with hundred(s) of nodes
• ANSCD Data Packages V1.3 Delivered –System Architecture designed/documented–Mote-Relay Interface designed/documented–Relay requirements defined/documented–Requirements analysis/documented–Demo Test scenario design/documented –ANSCD & Mission GUI Manual documented –Wireless Download Manual documented
• About 20 related papers published
NESTANSCD
University of Virginia
Summary: Objectives Achieved
Summary: Objectives Achieved
Metrics Objective Achieved Metrics Objective Achieved
Coverage (T) 30m by 1000 m (O) 100m by 1000m
Yet to be tested
Sensor Modality Magnet and PIR (T), Acoustic and other (O)
YES
Scale 1000 motes Yet to be tested
Self-Localization Real Coordinates (O) YES
Deployment Manual (T); Airdrop (O)
YES Reconfiguration True(T/O) YES
Ad hoc Routing True(T/O) YES Robustness Backbone True(T/O) YES
False Alarm <5% (T); <1% (O) YES Time Synchronization True(T/O) YES
Detection True (T/O) YES Interface Control Doc True(T/O) YES
Tracking True (T/O) YES Tracking Trace True (T/O) YES
Classification True (T/O) YES Network Topology Report
True(T/O) YES
Accuracy 90%(T); 95%(O) YES Sentry Control True(T/O) YES
Tracking Speed 30 mph (T); 50mph (O)
YES Sentry Health Report True(T/O) YES
Sentry Service True (T/O) YES Source Code True(T/O) YES
Endurance 3 mo (T); 6 mo (O) YES Documents True(T/O) YES
Energy Balance True(T/O) YES Technical Support True(T/O) YES
Stealthness True (T/O) YES Multi-hop Reprogramming
N/A YES
DataDissemination
Relay/RSC (T) SISA(O)
YES Golden Image N/A YES
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University of Virginia
ANSCD Architecture V1.3ANSCD Architecture V1.3
TimeSync
GroupMgmt
SentryService
DynamicConfig
RobustDiffusion Tree
MAC
MICA2 /XSM /XSM2 / MICA2DOT Motes
Application Layer
Middleware Layer
Network Layer
Data Link Layer
EnviroTrack False AlarmFiltering Engine
Display at C&C
AsymmetricDetection
PowerMgmt.
Radio-BaseWakeup
ReportEngine
RelayVelocityRegression
Localization
Classification
TripwireMngt
Frequency-Filter
Sensor Drivers
ContinuousCalibrator
Interference avoidance
Sensing Layer
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Time-Driven System OperationTime-Driven System Operation
RESET
Phase I
System Initialization
Phase III
Localization
Phase VNetwork Partition & Diffusion
Tree Constrcution
Phase VI
Sentry Selection
Phase VII
Health Report
StartPhase VIII
Power Mgmt
Event Tracking
Phase II
Time SyncPhase IV
Asymmetri Detection
Phase VIII
Event Tracking
Power Mgmt
Dormant Section
Tripwire Section
Wakeup Service
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Key Software Components (1)
Key Software Components (1)
•2-way software interface to RSCC and Avalanche (see ICD)
•Flexible Tripwire based power management with sentry and wakeup services
•Group-Based Entity Tracking (EnviroTrack)•Hierarchical Multi-tier Detection and
Classification via heterogeneous sensors (4 PIRs (motion), acoustic, magnetometers)
•Frequency-Filter and continuous threshold adaptations for robust sensing
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Key Software Components (2)
Key Software Components (2)
•Flow control with Aggregate display/health/Tracking message
•Localization (walking GPS)•Radio-based network wakeup •Asymmetry detection for robust routing
establishment •Robust velocity calculation with least
squares estimation•Wakeup service for relay to conserve
energy
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University of Virginia
Key Software Components (3)
Key Software Components (3)
•Stripped-down version of Vanderbilt clock sync
•Multi-hop Dynamic reconfiguration•Multi-hop wireless download (Berkeley’s
Deluge)•Golden image support•Modified B-MAC to avoid communication-
sensing interference
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System Scenario Supported (1)
System Scenario Supported (1)
300 Meters ( 3 tripwire section with 100 motes (5 x 20 ) in each section)
50
me
ters
Base0 Base2Base1
802.11g 802.11gLaptop1 Laptop0 Laptop2
Road
Router (optional)
• Flexibility to define various system architectures• Independent deployment with Tripwires
– ANSCD Middleware V1.3– ANSCD GUI
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System Scenario Supported (2)
System Scenario Supported (2)
•ANSCD Middleware 1.3•Single RSCC •Mission GUI
300 Meters ( 3 tripwire section with 100 motes (5 x 20 ) in each section)
50 meters
Base0
Road
Laptop
RSCC
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University of Virginia
System Scenario Supported (3)
System Scenario Supported (3)
•ANSCD Middleware V1.3 with Tripwires•RSCC•Relay•C2PC•SISA•…
SENSOR RSCC(SENSORNETWORK) MOC/P
Mission GUI
IR/EOCAMERA(s)
SOPHISTICATEDSENSORS (SS)
RFSENSOR
FIELD
RSCC
AdditionalRSCC and Sensor
Networks Long Haul (LH)CommsLink
MOTE-FIELD
MOTEFIELD
SEIWGAntenna
CommsAntenna
TCP/IPPortal
CStat
Mission GUI
C2PCGateway(& Client)
MOC/P
FCD
C2PC Client
LH SocketConverter
RS232Interface MOC
ServerInterface
LH Server
Interface
Socket
Socket
Socket
Ground Station Element
TACTICAL DISPLAY
Long HaulRadio
RELAY
SENSOR RSCC(SENSORNETWORK) MOC/P
Mission GUI
Mission GUI
IR/EOCAMERA(s)
IR/EOCAMERA(s)
SOPHISTICATEDSENSORS (SSU)
RFSENSOR
FIELD
RFSENSOR
FIELD
RSCC
AdditionalRSCC and Sensor
Networks Long Haul (LH)CommsLink
MOTE-FIELD
MOTEFIELD
SEIWGAntenna
CommsAntenna
TCP/IPPortal
CStat
Mission GUI
C2PCGateway(& Client)
MOC/P
FCD
C2PC ClientC2PC Client
LH SocketConverter
RS232Interface
LH SocketConverter
RS232Interface MOC
ServerInterface
LH Server
Interface
Socket
Socket
Socket
Ground Station Element
TACTICAL DISPLAY
Long HaulRadio
RELAY
Hardwired Sensors
Courtesy of Northrop Grumman
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University of Virginia
ANSCD GUI – Vehicle & Person
ANSCD GUI – Vehicle & Person
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University of Virginia
ANSCD GUI – Person w/ WeaponANSCD GUI – Person w/ Weapon
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Mission GUIMission GUI
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University of Virginia
Mote - Relay Interface V1.5Mote - Relay Interface V1.5
Address AMID
GroupID
ByteCount
Flags RecordType
SourceID
MessageID
Data CRC
Format of notification and command messages
Notification Data Records Command Data Records• Tracking Request• Node status Reset• Network configuration
NodeID
X-Coord
Y-Coord
ParentID
#Sentries
#Nodes
Voltage
CmdID
EventID
EventType
LeaderID
Velocity X-Coord
Y-Coord
Conf.Level
MagnetNumber
MotionNumber
AcousticNumber
Event Type Attribute Type
Confidence Value
Accuracy Periodicity
Tracking recordTracking record
Aggregate status recordAggregate status record
Request recordRequest record
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Power ManagementPower Management
•Sentry Service•Tripwire•Rotation
1
4
3
2
Sentry
Non-Sentry
Base node
10mA@3v
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University of Virginia
Tripwire-based SurveillanceTripwire-based Surveillance
•Partition sensor network into multiple sections.
•Turn off all the nodes in dormant sections.•Apply sentry-based power management in
tripwire sections•Periodically, sections rotate to balance energy.
Road
Dormant DormantDormant Active ActiveDormant ActiveActive Dormant Dormant
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Estimation of Network LifetimeEstimation of Network Lifetime
0
10
20
30
40
50
60
70
Time (seconds)
En
erg
y(m
w)
Sentry
NonSentry
Initialization Duration = 5 minutes
Surveillance Duration = 1day
Without system rotation:NonSentry Life Time: 250 daysSentry LifeTime: 7 days
• Lifetime is determined by– Individual Mica 2 mote
consumption • Energy plot for a sentry node • Energy plot for a sleep node
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University of Virginia
Tripwire + SentryTripwire + Sentry
One tripwire section out of every 4 sections with 10% sentry expected 142 days (20x) lifetime.
Power Draw (Tripwire+SBPM vs SBPM)(Based on 10 events per day, 24/7 full Coverage )
0 0.5 1 1.5
Initialization
Sleep
Event Process
Communication
Surveillance
Wakeup
Power Draw(mA@3v)
Tripwire+SBPM
SBPM
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University of Virginia
Lifetime AnalysisLifetime Analysis
Network Life Time
Number of Tripwires (10 regions, 30% sentry, 7 day life)
4 3 2 1
2 AA Batteries
50 days 70 days
105 days
210 days
4 AA Batteries
100 days 140 days
210 days
420 days
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University of Virginia
Group Management
Group Management
IR Camera
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Group Management
Group Management
IR Camera
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University of Virginia
Detection Delay Detection Delay
DETECTION DELAY (S)
CLASSIFICATION DELAY (S)
VELOCITY DELAY (S)
REPORTED VELOCITY (MPH)
ACTUAL VELOCITY (MPH)
2.7 3.2 3.2 25.0/10.9 N/A
1.8 3.2 3.2 24.6 N/A
1.7 2.7 3.2 17.6 N/A
3.8 4.8 5.3 9.3 N/A
1.7 2.7 2.8 11.1 10
2.6 3.1 3.6 18.5 20
1.9 2.4 2.4 23.0 20
2.6 2.9 3.2 12.7 12
0.9 2.5 2.5 22.1 20
4.5 8.1 8.1 6.2 N/A
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University of Virginia
3-Tier Classification3-Tier Classification
Group
Group
Group
Base mote
Report
Report
Performing base level classification
Group leader, performing group level classification
Normal mote, performing sensor (mote) level classification
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First Tier: Robust SensingFirst Tier: Robust Sensing
•PIR Sensing•Magnetic Sensing•Acoustic Sensing
•Commonality:– Initial Threshold Calibration– Continuous Threshold Calibration with changing
environment – Power & Frequency Filtering
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University of Virginia
PIR Sensing Module (1)PIR Sensing Module (1)
• The current PIR detection algorithm using XSM sensors can distinguish walking persons in a range of 12-20 ft in hot environments – About 19 ft/person running – About 12 ft/person walking
• 30-40 ft in cool environments. • Almost all false alarms are reliably
removed.• Radio interference has been also removed.
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PIR Sensing Module (2) PIR Sensing Module (2)
•Environmental factors– Grass and Trees.– Temperature. – Wind and Sunshine.
•Frequency Analysis – Uses high/low-pass filters to filter out noise, so
that no false alarms are generated due to environmental effects.
•Self-adaptive– Continuous filtering and calibration to adapt to
environment.
•Data sampling is turned off for 60 ms when there is radio transmission.
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University of Virginia
PIR Sensing Module (3): DataPIR Sensing Module (3): Data
This figure displays the raw data, the dynamic threshold, and the confidence of the detection. The detection report is based on frequency analysis of the signals and compared with an adaptively adjusted threshold.
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University of Virginia
Magnetometer Sensing (1)Magnetometer Sensing (1)
• Requirement– Detect vehicles and persons with a weapon
• Challenges– ADC reading may saturate– Response latency– Magnetic and electric noise from environment
and mote circuitry– Thermal reading drift– Radio/Mag interference– Short range– XSM-2 has greater noise than XSM-1
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University of Virginia
Magnetometer Sensing (2)Magnetometer Sensing (2)
• Raw ADC reading can saturate
• Translate the pair of POT/ADC values to a single scaled mag point
• Moving average of recent scaled ADC readings.
• Compare to difference between slow and fast moving average
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Magnetometer Sensing (3)Magnetometer Sensing (3)
Response time•Mag sensor chain needs about 40ms to settle. •ADC readings need about 50ms to settle after a potentiometer change.
•The averaging algorithm needs at least 3 initial readings to perform computation.
•A fast-detect logic speeds up detection of obvious signals
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University of Virginia
Magnetometer Sensing (4)Magnetometer Sensing (4)
– Signal/noise ratio• Signals (Scaled ADC readings)
are hard to distinguish for small targets or targets at far distances
– Signals for iron bar moving at 5 ft.
• Use a moving average of recent readings (Mag Points) to filter out noise.
• Mag Points show signals whose amplitude is often lower than that of noise
– Mag Points for iron bar moving at 5 ft.
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University of Virginia
Acoustic Sensing (1)Acoustic Sensing (1)
•Properties:– Power based approach.– Automatic and continuous calibration due to
temperature fluctuations, noisy environments and individual sensor characteristics.
– Differentiates between vehicles, humans, background noise and wind (collaboration with PIR sensors necessary).
•Limitations:– No differentiation between small-big vehicles
currently available.
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University of Virginia
Acoustic Sensing (2)Acoustic Sensing (2)
Three Cars
Initial Calibration
No Detection
Detection whenEnergy Crosses
Standard Deviation
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Acoustic Sensing (3)Acoustic Sensing (3)
•Moving average curve plus 3 times the standard deviation curve = THR curve (called standard deviation on previous slide)
•Count number of crossings of THR out of the last N readings and if percentage is greater than x% then this is a target– X is about 60%
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University of Virginia
DOA controls minimal aggregation degreeto reduce false alarms
Second Tier: Group Aggregation
Second Tier: Group Aggregation
Awareness Range
Detection Range
Node
Member
Follower
Leader
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University of Virginia
System Issues: False alarms System Issues: False alarms
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1 2 3 4 5 6
Degree of aggregation (DOA)
Pro
bab
ilit
y o
f fa
lse
alar
ms
false positives
false negatives
• Probability of false positivesreduces as DOA increases
• Probability of false negativesincreases as DOA increases
•With DOA = 3 we had zero false alarms
•The DOA parameter can be tuned based on sensing range and thedensity with which motes are deployed
Impact of DOA on False Alarms
Spatial-temporal correlated data aggregation can effectively reduce false alarms
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Third Tier: Base Mote (1)Third Tier: Base Mote (1)
• The base mote keeps received tracking messages in FLASH.
• It then makes use of the spatio-temporal correlation to decide which target a tracking message belongs to. (e.g., 30 m and 5 sec)
• When a specific target gets enough (according to a adjustable parameter) messages for one target, a “detection” report is sent from the base mote to the RSCC.
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Third Tier: Base Mote (2)Third Tier: Base Mote (2)
• After the “detection” report is sent and enough information is gathered for classification, a “classification” report is sent from the base mote to RSCC. (2 additional reports beyond detection)
• The base mote also uses a least square calculation to calculate the velocity of the target. A “velocity” report is sent to RSCC. (5 additional reports beyond classification)
• Afterwards, send reports according to an adjustable
flow rate parameter. X D
ista
nce
Time
Slope = X Velocity ( Least Square Estimation)
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University of Virginia
Classification SchemeClassification Scheme
PIR
SensorAcoustic Sensor
Magnetic Sensor
Target Type Status
Detection XFalse Alarm by Wind Done
Detection X Any Target Done
Detection X[n] Person Done
Detection X XPerson with Weapon Done
Detection X X X Vehicle Done
Freq. Analysis X Big/Small Vehicle
Potential
Num Hits X X X Big/Small VehiclePotential
Group Size X X Big/Small VehiclePotential
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University of Virginia
Detection/Classification/Velocity Delay Detection/Classification/Velocity Delay
DETECTION DELAY (S) CLASSIFICATION DELAY (S)
VELOCITY DELAY (S)
REPORTED VELOCITY (MPH)
ACTUAL VELOCITY (MPH)
2.7 3.2 3.2 25.0/10.9 N/A
1.8 3.2 3.2 24.6 N/A
1.7 2.7 3.2 17.6 N/A
3.8 4.8 5.3 9.3 N/A
1.7 2.7 2.8 11.1 10
2.6 3.1 3.6 18.5 20
1.9 2.4 2.4 23.0 20
2.6 2.9 3.2 12.7 12
0.9 2.5 2.5 22.1 20
4.5 8.1 8.1 6.2 N/A
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Self-Healing (1)Self-Healing (1)
•Wide spectrum of capabilities•Not binary
•In Routing– Multiple parents in backbone tree
•No cost for periodic probing•Stealthiness is maintained•Local decision on choosing alternative parent is fast•Re-create n-parent tree on system rotation
•In MAC– For unicast – retransmission of lost packet
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Self-Healing (2)Self-Healing (2)
•At Application Level– Critical messages are transmitted multiple
times to better ensure delivery
•In Sensing– Fail-stop – use of many sensors as targets
move avoids problems here– Byzantine failure – detect node continuously
reporting and shut it down
•In Localization– If node fails to obtain location during walking
GPS, it gets info from neighbors and uses tri-lateration
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Self-Healing (3)Self-Healing (3)
•In System Initialization– Each phase is coordinated and sequential– If a node is not in-step it becomes silent until
next system rotation
•In Tracking– If group leader fails, info is still with the
members and is passed to next leader
•In Wakeup– Decentralized and if some nodes fail to wake-up
it is not a problem because many others will be awake
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University of Virginia
Self-Healing (4)Self-Healing (4)
•Limited Effect– Clock sync, neighbor discovery, etc. are highly
decentralized and local. Single node failures only affect that node and does not propagate to the rest of the network.
•System Rotation– Can correct many issues – Currently, only executed based on time– Could be extended to re-run when many
failures are detected BUT this means extra messages which affects lifetime and stealthiness!
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Other Middleware Services Other Middleware Services
•System Initialization– List of system parameters
•MAC•Routing•Asymmetric Detection•Localization – Walking GPS•Clock Sync•Velocity Calculation
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System InitializationSystem Initialization
•Place motes in field – turn on mote; get location via walking GPS
•Turn on relay and base mote•Turn on RSCC•RSCC requests system parameters to relay•Relay asks base mote for parameters
(from flash)•Base mote sends to relay and relay sends
to RSCC •RSCC then asks each other base mote the
same thing in turn
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System InitializationSystem Initialization
•RSCC then sends out Origin of Reference – broadcast to all relays
•Each relay adds its location to location of RSCC and sends to base mote
•RSCC broadcasts master clock – essentially a start message
•Relay sends start signal to base mote •Base mote sends out parameters and then
begin mote field initialization, e.g., clock sync, localization, etc.
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University of Virginia
System ParametersSystem Parameters
• Multi-hop reconfiguration with tunable parametersParameter Name Units Description of the Parameter Value
GRID_X meter Controls the topology of the network under static localization scheme
Sentry Range meter Controls disperse/density of the sentries.
Power Mode N/A Controls the power consumption of the non-sentries
SD Threshold 1% Threshold to decide whether a link is symmetry or not
Pm TimeOut second The duration a non-sentry should remain awake after it is waken by sentry nodes
FlowRate second Specifies the minimum periodicity with which the tracking updates
PIR Threshold N/A Used to tune the sensitivity of the PIR sensors
DetectionThreshold N/A The minimum number of reports accumulated before a basemote declares the detection
Magnetic Threshold N/A Used to tune the sensitivity of the magnetic sensors
Acoustic Threshold N/A Used to tune the sensitivity of acoustic sensor sensors
shutDownThreshold 1% Used to shutdown chaos motes
Phase Delay second Controls the duration of each phase to accommodate
TrackingPhaseCount Delay The duration of the tracking phase = TrackingPhaseCount * Phase Delay
Settings N/A Defines various kind of binary control
Schedule N/A Defines tripwire sleep/awake schedule
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MAC: B-MACMAC: B-MAC
• A derivative version of CSMA – Listen before send. Linear back off if channel is busy.
• Support dynamic noise floor during carrier sense
• Support MAC Layer reliability through 1 byte ACK
• Support flexible back off scheme to meet requirement of application
• Support lower power listening to trade off fast response
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University of Virginia
Routing (1)Routing (1)
Reliability in routing infrastructure – Asymmetric link detection – MAC level delivery failure detection– Routing layer retransmission– Multi-Parent diffusion tree– Local parent switch in case of failure– Robust to base failure
A B
1
5 3
2
4
6 7Symmetric Link Detection
Local Switch
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University of Virginia
Routing (2)Routing (2)
•Robust diffusion tree with asymmetry detection – It requires no location information.– It requires small portion of nodes awake.– Small cost to maintain (1 byte ACK detection).– It matches to multiple relay scenario.
•Robust diffusion tree with local switch– Robust to failure of parent nodes – Stealthiness (no need to maintain route
periodically)– It requires small portion of nodes to be awake.
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University of Virginia
Asymmetric DetectionAsymmetric Detection
•Neighbors perform discovery via beacons
•Neighbors then also exchange neighbor tables
•Node must hear from a neighbor node and be in that node’s table => symmetric link
•If link is asymmetric – drop neighbor from neighbor table
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• GPS Mote assembly:– Garmin eTrex Legend
GPS device (WAAS enabled)
– MICA2 mote– helmet, RS232 cable,
board, wristband– Memory size: 17 Kbytes
(code), 600 Bytes (data)• Sensor Node: – Mica2, XSM– Memory: 1 Kbytes
(code), data: 120 bytes
Walking GPS
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University of Virginia
• The sensor node deployer (soldier or vehicle) has a GPS Mote assembly attached to it.
• The GPS Mote periodically beacons its location. • Sensor Motes that receive this beacon infer
their location based on the information present in this beacon.
• From the localization perspective, two distinct software components exist.
Sensor Mote
Localization
GPS Mote
GPS
Walking GPS
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University of Virginia
• Two deployment types: – mote powered on at deployment
• first INIT_LOCALIZATION packet gives the location
– mote powered on all the time • INIT_LOCALIZATION stored in circular
buffer, if RSSI > Threshold• Choose best value
• Two stages for Localization:– at deployment time: Walking GPS– during system initialization:
HELP_REQUEST/REPLY, if no location information present (for robustness)
Walking GPS: Sensor Mote
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University of Virginia
Walking GPS Evaluation
• First deployment type: sensor motes turned on at the place of deployment, right before being deployed
• Localization error: 0.8 meters • Standard deviation: 0.5 meters
• Second deployment type: sensor motes turned on all the time.
• Localization error: 1.5 meters • Standard deviation: 0.8 meters
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University of Virginia
• Second deployment type using two GPS devices
• Each line along the length of the grid deployed with a different GPS device
• Localization error: 1.6 meters
• Standard deviation: 0.9 meters
Walking GPS Evaluation
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University of Virginia
Clock Sync (1)Clock Sync (1)
•A strip-down version of Vanderbilt TimeSyn to meet the requirement of ANSCD system
– Normal crystal accuracy 10~50 PPM. Worst case drift 0.03~0.142 second/per day. Average drift is even less.
– Enough for ANSCD requirement
•Used in ANSCD for:– Velocity calculation– Phase transition – Timestamp events
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University of Virginia
Clock Sync (2)Clock Sync (2)
1. Root node accepts time from RSCC through MASTER CLOCK command.
2. Disseminate time through flooding.
3. Time stamping performed right before Timestamp is sent out to avoid un-predictability in MAC access delay
4. Abandon continuous clock drift calibration to achieve stealthiness in operation
5. Rotation to compensate for clock drift
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University of Virginia
Velocity CalculationVelocity Calculation
•Performed at base mote attached to relay•Messages are ordered via the timestamps•Wait for “n” messages before calculating
velocity•Calculate x-comp and y-comp of velocity
separately using least squares curve fitting
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University of Virginia
Lessons Learned (1)Lessons Learned (1)
•System-wide energy solution is needed– Include system init; communication; sensing;
use one flooding for multiple purposes
•Many links are asymmetric – use conservative communication range and an explicit asymmetric detection module
•Timely Delivery of hardware is crucial– Unstable hardware version costs us significant
effort on continuous tuning the sensing & classification algorithms
NESTANSCD
University of Virginia
Lessons Learned (2)Lessons Learned (2)
•Higher bandwidths and more data memory
•Re-send lost messages based on semantics of messages (at application level) – too expensive to re-send every lost packet at MAC layer
•System would be better with higher densities
•Sensing ranges need to be increased
NESTANSCD
University of Virginia
Remaining Work FY ‘05Remaining Work FY ‘05
•Robustness testing and performance evaluation– Large scale testing (1000 motes)
•Aggressive Power Management•Further reduce false alarms•Classify accurately
– Classify small-large vehicles
•Air Drop Localization•Increased self-healing properties•Supporting field tests/demo
– Full integration and testing with sophisticated sensors
NESTANSCD
University of Virginia
EndEnd