NEST
ANSCD
Self Organizing Wireless Sensor
Network Middleware
CleanPoint
University of Virginia
PI: John A. Stankovic
December 2004
University of Virginia
Outline
NEST
ANSCD
• 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
University of Virginia
Energy Efficient Surveillance System
NEST
ANSCD
1. An unmanned plane (UAV) deploys motes
Diffusion Routing
Neighbor
Discovery
Time
Synchronization
Parameterization
Sentry Selection
Coordinate Grid
Zzz...
Data Aggregation
Data Streaming
Group Management
Leader Election
Sentry
Localization
Network Monitor
Tripwire Service
Reconfiguration
Reliable MAC
3.Sensor network detects
vehicles and wakes up
the sensor nodes
Leader Migration
2.
Motes establish an sensor network
with power management
Scheduling
State
Synchronization
University
of Virginia
Goals
NEST
ANSCD
• 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
University of Virginia
Project Milestones FY04
March 3rd
V1.0
May 28th
V1.1
Aug. 6
V1.2
NEST
ANSCD
Oct.29
V1.3
Dec 6/13
Final
University of Virginia
Summary: Deliverables
NEST
ANSCD
• 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
University of Virginia
Summary: Objectives Achieved
NEST
ANSCD
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
Data
Dissemination
Relay/RSC (T)
SISA(O)
YES
Golden Image
N/A
YES
University of Virginia
ANSCD Architecture V1.3
NEST
ANSCD
Application Layer
EnviroTrack
Velocity
Regression
Classification
False Alarm
Filtering Engine
Relay
Middleware Layer
Time
Sync
Group
Mgmt
Sentry
Service
Dynamic
Config
Power
Mgmt.
Locali
zation
Tripwire
Mngt
Sensing Layer
Network Layer
Robust
Diffusion Tree
Report
Engine
Asymmetric
Detection
Radio-Base
Wakeup
Frequency-Filter
Continuous
Calibrator
Data Link Layer
MAC
Interference avoidance
MICA2 /XSM /XSM2 / MICA2DOT Motes
Sensor Drivers
Display at C&C
University of Virginia
Time-Driven System Operation
Phase II
Phase III
Phase IV
Time Sync
Localization
Asymmetri Detection
NEST
ANSCD
Phase V
Network Partition & Diffusion
Tree Constrcution
Dormant Section
Phase I
Start
System Initialization
Event Tracking
Phase VI
Sentry Selection
Phase VIII
Power Mgmt
RESET
Wakeup
Service
Phase VII
Health Report
Power Mgmt
Phase VIII
Event Tracking
Tripwire Section
University of Virginia
Key Software Components (1)
NEST
ANSCD
• 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
University of Virginia
Key Software Components (2)
NEST
ANSCD
• 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
University of Virginia
Key Software Components (3)
NEST
ANSCD
• 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 communicationsensing interference
University of Virginia
System Scenario Supported (1)
NEST
ANSCD
• Flexibility to define various system architectures
• Independent deployment with Tripwires
– ANSCD Middleware V1.3
– ANSCD GUI
Laptop1
Base1
802.11g
Router (optional)
Laptop0
802.11g
Base0
Laptop2
Base2
50 meters
Road
300 Meters ( 3 tripwire section with 100 motes (5 x 20 ) in each section)
University of Virginia
System Scenario Supported (2)
NEST
ANSCD
• ANSCD Middleware 1.3
• Single RSCC
• Mission GUI
RSCC
Laptop
Base0
50 meters
Road
300 Meters ( 3 tripwire section with 100 motes (5 x 20 ) in each section)
University of Virginia
System Scenario Supported (3)
• ANSCD Middleware V1.3 with Tripwires
• RSCC
• Relay
• C2PC
• SISA
•…
Additional
RSCC and Sensor
Networks
Long Haul (LH)
Comms
Link
NEST
ANSCD
C2PC Client
RELAY
Comms
Antenna
RF
SENSOR
FIELD
SEIWG
Antenna
Ground Station
Element
C2PC
Gateway
(& Client)
Long Haul
Radio
TACTICAL
DISPLAY
Mission GUI
Socket
Socket
RS232
Interface
IR/EO
CAMERA(s)
MOC
Server
Interface
FCD
MOTE
FIELD
MOTEFIELD
(SENSOR
NETWORK)
Hardwired
Sensors
SOPHISTICATED
SENSORS
SENSOR
(SS)
(SSU)
Mission
GUI
RSCC
RSCC
LH Socket
Converter
TCP/IP
Portal
CStat
Socket
LH
Server
Interface
MOC/P
MOC/P
Courtesy of Northrop Grumman
University of Virginia
ANSCD GUI – Vehicle & Person
NEST
ANSCD
University of Virginia
ANSCD GUI – Person w/ Weapon
NEST
ANSCD
University of Virginia
Mission GUI
NEST
ANSCD
University of Virginia
Mote - Relay Interface V1.5
Address
AM
ID
Group
ID
Byte
Count
Flags
Record
Type
Source
ID
Message
ID
Data
NEST
ANSCD
CRC
Format of notification and command messages
Notification Data Records
•
Tracking
•
Node status
•
Network configuration
Command Data Records
Request
Reset
Tracking record
Cmd
ID
Node
ID
Event
ID
XCoord
Event Type
Event
Type
Leader
ID
YCoord
Attribute
Type
Velocity
Parent
ID
XCoord
#
Sentries
Confidence
Value
YCoord
#
Nodes
Accuracy
Conf.
Level
Voltage
Magnet
Number
Motion
Number
Acoustic
Number
Aggregate status record
Periodicity
Request record
University of Virginia
Power Management
NEST
ANSCD
• Sentry Service
• Tripwire
• Rotation
3
Sentry
10mA@3v
Base node
1
2
4
Non-Sentry
University of Virginia
Tripwire-based Surveillance
NEST
ANSCD
• 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
Active
Dormant
Active
Dormant
Dormant
Active
Dormant
Active
University of Virginia
Estimation of Network Lifetime
NEST
ANSCD
• Lifetime is determined by
– Individual Mica 2 mote
consumption
• Energy plot for a sentry node
• Energy plot for a sleep node
University of Virginia
Tripwire + Sentry
NEST
ANSCD
Power Draw (Tripwire+SBPM vs SBPM)
(Based on 10 events per day, 24/7 full Coverage )
Wakeup
Surveillance
Communication
Tripwire+SBPM
Event Process
Sleep
SBPM
Initialization
0
0.5
1
1.5
Power Draw(mA@3v)
One tripwire section out of every 4 sections with 10% sentry 
expected 142 days (20x) lifetime.
University of Virginia
Lifetime Analysis
Network
Life Time
NEST
ANSCD
Number of Tripwires
(10 regions, 30% sentry, 7 day life)
4
2 AA
Batteries
50 days
4 AA
Batteries
100 days
3
2
70 days 105 days
140
days
210 days
1
210 days
420 days
University of Virginia
Group Management
NEST
ANSCD
IR Camera
University of Virginia
Group Management
NEST
ANSCD
IR Camera
University of Virginia
Detection Delay
NEST
ANSCD
DETECTION DELAY (S)
CLASSIFICATI
ON 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
University of Virginia
3-Tier Classification
NEST
ANSCD
Base mote
Group
Report
Performing base level
classification
Group
Group leader, performing
group level classification
Group
Normal mote, performing sensor
(mote) level classification
University of Virginia
First Tier: Robust Sensing
NEST
ANSCD
• PIR Sensing
• Magnetic Sensing
• Acoustic Sensing
• Commonality:
– Initial Threshold Calibration
– Continuous Threshold Calibration with changing
environment
– Power & Frequency Filtering
University of Virginia
PIR Sensing Module (1)
NEST
ANSCD
• 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.
University of Virginia
PIR Sensing Module (2)
NEST
ANSCD
• 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.
University of Virginia
PIR 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.
University
NEST
ANSCD
of Virginia
Magnetometer Sensing (1)
NEST
ANSCD
• 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
University of Virginia
Magnetometer Sensing (2)
NEST
ANSCD
• 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
University of Virginia
Magnetometer Sensing (3)
NEST
ANSCD
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
University of Virginia
Magnetometer Sensing (4)
NEST
ANSCD
– 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.
University of Virginia
Acoustic Sensing (1)
NEST
ANSCD
• 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.
University of Virginia
Acoustic Sensing (2)
NEST
ANSCD
Three Cars
Initial
Calibration
No Detection
Detection when
Energy Crosses
Standard Deviation
University of Virginia
Acoustic Sensing (3)
NEST
ANSCD
• 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%
University of Virginia
Second Tier: Group Aggregation
NEST
ANSCD
DOA controls minimal aggregation degree
to reduce false alarms
Awareness Range
Node
Member
Follower
Leader
Detection Range
University of Virginia
System Issues: False alarms
NEST
ANSCD
Impact of DOA on False Alarms
0.7
• Probability of false positives
reduces as DOA increases
Probability of false alarms
0.6
0.5
• Probability of false negatives
false positives
increases as DOA increases
false negatives
0.4
•With DOA = 3 we had zero false
alarms
0.3
0.2
0.1
0
1
2
3
4
Degree of aggregation (DOA)
5
6
•The DOA parameter can be tuned
based on sensing range and the
density with which motes are
deployed
Spatial-temporal correlated data aggregation
can effectively reduce false alarms
University of Virginia
Third Tier: Base Mote (1)
NEST
ANSCD
• 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.
University of Virginia
Third Tier: Base Mote (2)
NEST
ANSCD
• 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)
• Afterwards, send reports
according to an adjustable
flow rate parameter.
X Distance
• 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)
Slope = X Velocity ( Least Square Estimation)
University of Virginia
Time
Classification Scheme
PIR
Sensor
Detection
X
Detection
X[n]
Detection
X
Detection
X
Freq.
Analysis
Group Size
Magnetic
Sensor
X
X
X
X
Status
Done
Any Target
Done
Person
Done
X
Person with
Weapon
Done
X
Vehicle
Done
Big/Small Vehicle
Potenti
al
Big/Small Vehicle
Potenti
al
Big/Small Vehicle
Potenti
al
X
X
Target
Type
False Alarm by
Wind
X
Detection
Num Hits
Acoustic
Sensor
NEST
ANSCD
X
University of Virginia
Detection/Classification/Velocity Delay NEST
ANSCD
DETECTION DELAY (S)
CLASSIFICATIO
N 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
University of Virginia
Self-Healing (1)
NEST
ANSCD
• 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
University of Virginia
Self-Healing (2)
NEST
ANSCD
• 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
University of Virginia
Self-Healing (3)
NEST
ANSCD
• 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
University of Virginia
Self-Healing (4)
NEST
ANSCD
• 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!
University of Virginia
Other Middleware Services
NEST
ANSCD
• System Initialization
– List of system parameters
• MAC
• Routing
• Asymmetric Detection
• Localization – Walking GPS
• Clock Sync
• Velocity Calculation
University of Virginia
System Initialization
NEST
ANSCD
• 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
University of Virginia
System Initialization
NEST
ANSCD
• 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.
University of Virginia
System Parameters
NEST
ANSCD
• Multi-hop reconfiguration with tunable parameters
Parameter 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
second
Controls the duration of each phase to accommodate
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
Phase Delay
TrackingPhaseCount
University of Virginia
MAC: B-MAC
NEST
ANSCD
• 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
University of Virginia
Routing (1)
NEST
ANSCD
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 2
– Robust to base failure
1
5
A
3
4
B
Local Switch
Symmetric Link Detection
6
7
University of Virginia
Routing (2)
NEST
ANSCD
• 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.
University of Virginia
Asymmetric Detection
NEST
ANSCD
• 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
University of Virginia
Walking GPS
NEST
ANSCD
• 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
University of Virginia
Walking GPS
NEST
ANSCD
• 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.
GPS Mote
Sensor Mote
GPS
Localization
University of Virginia
Walking GPS: Sensor Mote
NEST
ANSCD
• 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)
University of Virginia
Walking GPS Evaluation
NEST
ANSCD
• 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
University of Virginia
Walking GPS Evaluation
NEST
ANSCD
• 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
University of Virginia
Clock Sync (1)
NEST
ANSCD
• 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
University of Virginia
Clock Sync (2)
1.
NEST
ANSCD
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
University of Virginia
Velocity Calculation
NEST
ANSCD
• 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
University of Virginia
Lessons Learned (1)
NEST
ANSCD
• 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
University of Virginia
Lessons Learned (2)
NEST
ANSCD
• 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
University of Virginia
Remaining Work FY ‘05
NEST
ANSCD
• 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
University of Virginia
NEST
ANSCD
End
University of Virginia