Transcript VigilNet: An Integrated Sensor Network System for Robust, Energy Efficient Surveillance Tian He Department of Computer Science University of Virginia [email protected] http://www.cs.virginia.edu/~th7c.

VigilNet: An Integrated Sensor Network System for
Robust, Energy Efficient Surveillance
Tian He
Department of Computer Science
University of Virginia
[email protected]
http://www.cs.virginia.edu/~th7c
1
Background
Wireless Sensor Networks (WSN)
A new information processing paradigm based on collaborative
efforts of a large set of self-organizing sensor nodes.
Computation
Sensing
Actuation
Networking
Numerous Applications & Research Challenges
2
Some Related System Work, to name a few
Great Duck Island Project by U.C. Berkeley
Long-term habitat monitoring
Extreme Scaling Project by OSU
Address scaling related issue in large-scale sensor systems.
ZebraNet Project by Princeton University
Wildlife (Zebra) tracking in Africa
NIMS System by UCLA
Spatiotemporal sampling of the environment
Shooter Localization by Vanderbilt University
Localizing the position of the snipers through acoustic sensing
VigilNet System by University of Virginia
Integrated system for robust & energy efficient surveillance
3
Background of VigilNet
VigilNet is one of central projects within the DARPA NEST Program,
which is a five-year tens-million-dollar project
VigilNet
Figure is obtained through the courtesy of DARPA
4
Why VigilNet is Unique?
A mature system for realistic military deployment.
A robust, multi-tier, real-time, energy efficient surveillance
system
One of the largest wireless sensor network systems
deployed and successfully tested in terms of code size
(40,000 lines), number of protocols integrated and network
scale
A key project in NEST program that has been successfully
accepted and classified by Department of Defense (DOD)
5
Outline
Background
Overview of VigilNet
Two Key Services within VigilNet
Power Management with a Fast Wakeup Service
Robust Hierarchical Tracking/Classification
System Evaluation
Conclusion and Future Research Plan
6
VigilNet System Requirements
“Develop an operational, environmentally rugged,
robust, clandestine, self-organizing sensor
network for long term persistent surveillance
of low to moderate duty-cycle events, involving
detection, track, classification of vehicle and
personnel targets over various types of terrain.
….”
Major J.D. Hunsicker
Defense Intelligence Agency, DOD
7
Concept of Operation in VigilNet
Zzz...
Multi-hop Networking
Data Aggregation
Time Sync.
Localization
Group Management
Power management
Reliable MAC
Fast Wakeup
Scheduling
Parameterization
Leader Election
Leader Migration
Sentry Selection
Neighbor Discovery
Reprogramming
Adaptive Calibration
Network Monitor
Event replay
8
System Constraints
Constraints
Energy
Rechargeable
Bandwidth
3.3Wh, only survives a week if on all the time
No
19.2kbps wireless / 20~30 packet/second
Capability
Failure rate
Sensor Quality
8 MHz CPU & 4K data memory
High, in hostile and harsh environment
Low, very easy to generate false alarms
Localization
No special hardware support
9
VigilNet System Architecture
Application Layer
Tracking
Classification
False Alarm
Suppression
Velocity Est.
Relay
Middleware Layer
Time
Sync
Local
izati
on
Group
Mgmt
Dynamic
Config
Sentry
Service
Report
Engine
…
Sensing Layer
Network Layer
Robust
Diffusion
Tree
Tripwire
Mngt
Asymmetry
Detection
Radio-Base
Wakeup
FrequencyFilter
Continuous
Calibrator
Data Link Layer
MAC
Sensor Drivers
MICA2 /XSM /XSM2 / MICA2DOT Motes
Display at C&C
10
Time-Driven Phase Transition in VigilNet
Phase II
Phase III
Phase IV
Time Sync
Localization
Asymmetry Detection
Phase V
Network Partition &
Diffusion Tree
Construction
Surveillance
Phase I
Start System Initialization
Event Tracking
Phase VI
Sentry Selection
Phase VIII
Power Mgmt
Rotation
Wakeup
Service
Phase VII
Health Report
Power Mgmt
Phase VIII
Event Tracking
ActiveTracking
11
Outline
Background
Overview of VigilNet
Two Key Services within VigilNet
Power Management with a Fast Wakeup Service
Robust Hierarchical Tracking/Classification
System Evaluation
Conclusion and Future Research Plan
12
Power Management with Fast Wakeup Service
Research Problem: Reconcile the need for network
longevity with the need for fast and accurate target
detection and classification
Network longevity requires as many sensors off as possible.
Accurate tracking requires as many sensors on as possible to
obtain a high degree of confidence & smooth tracks.
Energy distribution
of different operations
0%
Without power management
99% of energy is consumed in
waiting for potential targets!!!
1%
99%
1%
Initialization
Sleep
Event Process
Communication
Surveillance
13
Solution: Tripwire PM with Wakeup Service
A tripwire-based power management with the support of
sentry & wakeup services
Partition sensor network into multiple sections
Turn off all the nodes in dormant sections
Activate sentries in tripwire sections
Periodically, both sections and sentries rotate to balance energy
Invoke fast wakeup service when a tripwire is triggered
Active
Dormant
Dormant
Dormant
Active
14
Multi-Layer Power Management Strategies
Tripwire Service
W
a
ke
u
p
se
rv
cie
Network-Level
Scheduling
Pt
Sentry Service
Rotation
Ps
Radio Alarm
Pa
Active
Section-Level
Node-Level
Node-Level
Duty Cycle
Scheduling
15
Network-Level: Tripwire Partitioning
Policy: A node belongs to the tripwire section where the
nearest base is located (Voronoi Diagram).
Partition Example 1
Partition Example 2
Optimization of Tripwire Placement: minimize average
distance to the bases (multi-median problem)
n
min   ( x  xi ) ( y  yi ) dxdy
2
i 1 A
2
(x,y): nodes’ positions (xi,yi): base i’s position 16
Section-Level: Sentry Detection Probability
Given a poisson point set P ={P1, P2 , …,Pn, } in plane A
with coverage radius of R, what is the expected detection
probability for a target cutting through A.
Detection probability increases about logarithmically
with both node density (faster) and sensing range
(slower)*
* With same coverage area
17
Node-Level: Duty Cycle Scheduling
Given an average density of K in plane A, each node has a
duty cycle percentage of P and toggle period of T. What is the
average detection probability for a target cutting through the
plane at speed S?
Detection Probability vs. Duty Cycle%
100%
90%
80%
Density = 0.01 V=10m/s
70%
Density = 0.01 V=30m/s
Density = 0.01 V=50m/s
60%
50%
40%
0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1
0.11 0.12 0.13 0.14 0.15 0.16 0.17 0.18 0.19 0.2
The impact of target speed S diminishes when size of plane A
increase
The impact of duty cycle P is significant, however reduces
when P become larger
18
Wakeup Service after Initial Detection
Non-sentry nodes sleep: Periodically toggle their states with 1% duty cycle
Toggle Period
Sleep
1% Wakeup
Sleep
e.g. ( wake up 2ms out 200ms)
Notification by sentries : send messages with a long preamble
Preamble length = TogglePeriod * BitRate SYNC Bytes
DATA
CRC
Non-sentry nodes Wakeup: Once the long preamble is detected, nonsentry nodes lock onto the message and remain active a period of time
BASE
Wakeup process follows the moving
targets and the diffusion tree to the
base
Target
19
Performance: Estimate Network Lifetime
Individual mote consumption
Plot energy profiles for a sentry node
and a sleep/non-sentry node
Sentry percentage over N runs
Active tripwire percentage
20
Performance: Estimate Network Lifetime
Energy Distribution With Tripwire-based Management
(Based on 10 events per day, 24/7 Partial Coverage )
12%
22%
Initialization
12%
2%
1%
Sleep
Event Process
Communication
Survillence
Wakeup
51%
One tripwire section out of every 3 sections with 10%
sentries in tripwire section 106 days lifetime
21
Tradeoff Results
Energy Efficiency Gain
No power management  7 days lifetime
With 10% sentries and energy-efficient wakeup service  56.4
days (8x) lifetime
One tripwire section out of every 3 sections with 10% sentries in
tripwire section 106 days lifetime (15x)
Tracking Performance Penalty
Initial detection delay (3~5s) until an active sentry is triggered
Maximum 200ms wakeup delay for non-sentries to join the
tracking process
22
Outline
Background
Overview of VigilNet
Two Key Services within VigilNet
Power Management with a Fast Wakeup Service
Robust Hierarchical Tracking/Classification
System Evaluation
Conclusion and Future Research Plan
23
Robust Hierarchical Tracking & Classification
Research Problem: Reconcile the need for high
detection sensitivity with the need for a low false
alarm rate.
Limited Detection Range
Cheap sensors
Power constraints
Line of sight issues
High Risk of False Alarms
Moving grass, clouds, changing environments
Low quality of sensor devices
Faulty nodes
24
Solution: Robust Hierarchical Tracking & Classification
Base mote
Group
Report
L4: Performing base
level classification
L3: Group leader, performing
group level classification
Group
Group
L2: Single node, performing node
level classification
Montion
Acoustic
Magnetic
L1: Single sensor, getting raw data and
calculating detection confidence
25
First Tier L1 : Robust Sensing
Challenges
Environmental factors: Moving grass and trees, changing
temperature, wind and sunshine/cloud cover
Un-calibrated sensor hardware: Different sensors render
different readings with same environmental input
Limited sensing & computational power
Solution
Initial & continuous threshold calibration
Data fusion with multiple sensing modality: PIR motion
sensor, Magnetic sensor, Acoustic sensor
Event signature/feature extraction through ARMA filtering
26
Example: PIR Sensing Module (2)
1. Uses high/low-pass ARMA
filters to filter out noise
Raw Data
2. Uses continuous calibration to
set adaptive threshold
Adaptive Threshold
3. Compare filtered data with
adaptive threshold to obtain
detection confidence
Detection Confidence
27
Second Tier L2 : Classification at Node Level
Sensor
Output
Motion
Sensor
Detection
Acoustic
Sensor
Magnetic
Sensor
X
Detection
X
Detection
X[n]
Detection
X[n]
Detection
X[n]
X[n]
# Detection
X[n]
X[n]
Group Size
X[n]
X[n]
Classification Result
False Alarm by Wind
Any Target/FA
Person
X[n]
Person with Weapon
Vehicle
X[n]
Big/Small Vehicle
Big/Small Vehicle
X: Single detection
X[n]: Multiple spatiotemporal related detections
28
Third Tier L3 : Group Tracking/Aggregation
Degree of Aggregation Threshold (DOA) =
minimal required member reports before a
leader confirms detection
Node
Awareness Area
Member
Follower
Leader
Detection Area
29
Fourth Tier L4: Base Mote (2)
Base makes use of the spatiotemporal correlation to
decide which target a tracking message belongs to.
An incremental process by accumulating messages
Detection  Classification  Velocity Calculation
N 1
Vel x 
 ( x  x)(t
i 0
i
i
 t)
N 1
 (t  t )
N 1
where x 
2
x
i 0
i
N
i 0
N 1
Vel y 
(y
i 0
i
N 1
 y )(ti  t )
 (t  t ) 2
N 1
where y 
y
i 0
i
N
i 0
30
Outline
Background
Overview of VigilNet
Two Key Services within VigilNet
Power Management with Fast Wakeup Service
Robust Hierarchical Tracking/Classification
System Evaluation
Programming Abstraction for VigilNet
Conclusion and Future Research Plan
31
System Evaluation Scenario by 3rd Party
300 meters, 30 motes each line, 4 non-uniform lines
2
A
C
B
0
N
Tent
C&C
•200 XSM Motes
•3 Bases
•one/two/three sections
Mote Field
2
0
0
M
1
300M by 200 M T shape
D
32
200 XSM Motes in 3 Sections (Field Result)
33
Detection Classification Tracking
1.Initial Detection
2.Classification
3.Periodic updates
34
Performance: DOA Impact on False Alarms
DOA: minimal degree of aggregation for target confirmation
Probability of false alarms
0.035
When DOA threshold increases
• Probability of false positives
0.03
False Positive
0.025
decreases
• Probability of false negatives
increases
False Negative
0.02
0.015
•With DOA = 3 we had zero false
alarms
0.01
0.005
0
1
2
3
4
5
Degree of aggregation (DOA)
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
35
Performance: Detection & Classification
False Alarms (over about 2 hours)
No false negatives ( able to detect/track all the targets)
1 false positive
90% target classifications are correct
A few times classified a person as a vehicle because of strong
wind, which shakes antenna and generates acoustic noise.
36
Delay in Detection/Classification/Tracking
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
Average detection delay 2.42 seconds
Average classification delay 3.56 seconds
Average delay to get velocity estimation 3.75 seconds
37
Accuracy of Tracking
Target Type
Average
Tracking
Error (m)
Std. Dev. Of
Tracking
Errors (m)
Actual
Velocity
(mph)
Calculated
Velocity (mph)
Walking Person
6.19
3.28
3±1
2.9
Running Person
6.67
3.89
7±1
6.9
Vehicle
7.06
3.98
10±1
10.5
Vehicle
5.91
3.02
20±1
23.5
1
5.58
4.76
10±1
9.2
2
6.33
3.52
10±1
9.9
Two
Vehicles
Average tracking localization error is 6.29m with Std 3.7m
Average velocity estimation error is 6.0%
38
Other Evaluation Scenarios
Phase Test Item
System Initialization
1
Track and classify persons
2
3
4
5
Track and classify persons with weapon
6
7
8
Partition network into multiple tripwires
9
10
Tracking and classify vehicles at various speeds
Tracking multiple targets (people/ vehicles/ people w/w)
Activate and Deactivate tripwire sections
Tracking with multiple tripwires (dormant section  active section)
Fault Tolerance with base mote failure
Fault Tolerance with random failure of 20% motes
39
Contribution of VigilNet
VigilNet is a key project within the NEST program that has
been successfully transferred to the Department of Defense.
A mature large-scale system for realistic deployment
Around 40,000 lines of source code
Over 30 protocols/middleware services integrated
Support multiple platforms (MICA2/MICA2DOT/XSM)
Many research ideas are published in premier conferences
40
VigilNet 1.3 Release
Software, manual, publications, demo video etc.
can be downloaded at VigilNet website:
www.cs.virginia.edu/nest
Also accessible in Tinyos contribution Release
Software after V1.3 release is classified and will
not be available in public domain
41
Other Related Research Topics in VigilNet
Overall System
Mobisys 2004, Sensys 2004
Transaction on Sensor Network
Programming Paradigm
ICDCS 2004
Localization
MobiCom 2003
Transaction on
Embedded System
Routing
ICDCS 2003
Best Paper
Nomination
Application Layer
EnviroTrack
Sensing Coverage
Sensys 2003
Velocity
Regression
False Alarm
Filtering Engine
Middleware Layer
Locali
zation
Group
Mgmt
Sentry
Service
Dynamic
Config
Power
Mgmt.
Data
Aggreg
ation
Time
Sync
Tripwire
Mngt
z
Network Layer
Routing
Radio Modeling
Mobisys 2004
Infocom 2005
Classification
Asymmetric
Detection
Wakeup
Frequency-Filter
Continuous
Calibrator
Data Link Layer
MAC
Interference avoidance
MICA2 /XSM /XSM2 / MICA2DOT Motes
Sensor Drivers
42
Full Integrated System (as of Dec 2004)
Additional
RSCC and Sensor
Networks
Long Haul (LH)
Comms
Link
VigilNet System
C2PC Client
RELAY
Long Range
Relay
Comms
Antenna
RF
SENSOR
FIELD
Remote
Command &
Control
SEIWG
Antenna
Ground Station
Element
C2PC
Gateway
(& Client)
Long Haul
Radio
TACTICAL
DISPLAY
Mission GUI
Socket
Socket
Satellite Long
Haul Link
MOC
Server
Interface
FCD
MOTE
FIELD
Back End
(Global Control)
RS232
Interface
IR/EO
CAMERA(s)
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
Back End
MOC/P
MOC/P
Courtesy of Northrop Grumman & DARPA
43
Research Plans
Improve scalability of VigilNet to up 10,000 nodes
Generate aggregate behavior through primitive local
interactions among sensor nodes
Tackle scalability issues with stateless design
Improve efficiency of VigilNet further by exploiting
cross-layer design
Breaking the boundary of layers for efficiency
Performance composability among multiple protocols
44
Research Plans
Infrastructure support for sensor system performance
tuning & achieve experimental repeatability
Realistic large scale simulation support for VigilNet (e.g
realistic radio pattern [Mobisys’04])
Programming support for event replay (EnviroLog Project)
Facilitate transferring VigilNet capabilities into other
domains by supporting high-level programming
abstractions.
Medical/Health Care Applications
Environmental application (underwater acoustic detection..)
…
45
Question?
Thanks
Related publications, software release, manual etc:
http://www.cs.virginia.edu/~th7c
46
Performance: Localization Evaluation (Walking GPS)
Evaluated by best fit of localization results into of a grid
Localization error: 0.8 meters
Standard deviation: 0.5 meters
47
Power Management
Sentry Service
Tripwire
Rotation
3
Sentry
10mA@3v
Base node
1
2
4
Non-Sentry
48
Other Power Efficient Strategies Implemented
Diffusion Tree with minimum dominating set
Application independent data aggregation
37% overhead in sending a message ( preamble, sync, crc)
Application specific data aggregation
Group based data aggregation
Implicit acknowledgement for per-hop reliability
Incremental Activation
49
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
1
2
5
A
3
4
B
Local Switch
Symmetric Link Detection
6
7 50
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.
51
Asymmetric 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
52
Walking GPS
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
53
Walking GPS
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
54
Walking GPS: Sensor Mote
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)
55
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
56