energy-aware scheduling with quality of surveillance guarantee in wireless sensor networks
DESCRIPTION
Energy-Aware Scheduling with Quality of Surveillance Guarantee in Wireless Sensor Networks. Jaehoon Jeong , Sarah Sharafkandi and David Du {jjeong,ssharaf,du}@cs.umn.edu. Contents. Introduction Related Work Problem Formulation Energy-Aware Sensor Scheduling - PowerPoint PPT PresentationTRANSCRIPT
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U of Minnesota
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Energy-Aware Scheduling Energy-Aware Scheduling withwith Quality of Surveillance Guarantee Quality of Surveillance Guarantee in in
Wireless Sensor NetworksWireless Sensor Networks
Jaehoon Jeong, Sarah Sharafkandi and David Du{jjeong,ssharaf,du}@cs.umn.edu
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ContentsContents
1. Introduction
2. Related Work
3. Problem Formulation
4. Energy-Aware Sensor Scheduling
5. Optimality of Sensor Scheduling
6. QoSv-Guaranteed Sensor Scheduling
7. Sensor Scheduling for Complex Roads
8. Performance Evaluation
9. Conclusion
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IntroductionIntroduction1. Motivation
We investigate the properties of the Linear Sensor Network (e.g., Road Network in transportation system).
These properties can be used for a variety of applications: Localization, Vehicle Detection, and Vehicle Tracking.
2. Applications of Our Sensing Scheduling Algorithm① Surveillance for Security around City’s Border② Crossroad Signal Control in Transportation System
3. Objectives① Maximization of Lifetime of Wireless Sensor Network② Control of Detection Quality
Quality of Surveillance Guarantee (QoSv)
4. Contributions① Energy-aware Sensor Scheduling feasible for Mobile Target
Detection and Tracking② QoSv-Guaranteed Sensor Scheduling for Complex Roads
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Surveillance of City Border Roads (1/2)Surveillance of City Border Roads (1/2)
Inner BoundaryCITY
Outer Boundary
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Surveillance of City Border Roads (2/2)Surveillance of City Border Roads (2/2)
Inner BoundaryCITY
Outer Boundary
S1
Road Segment
S2 S3 Sn. . . . .
Sensing Coverage
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Vehicle Detection for Road Traffic Vehicle Detection for Road Traffic MeasurementMeasurement
54 St.
53 St.
52 St.
51 St.
EwingAve.
DrewAve.
ChowenAve.
BeardAve.
vehicle
vehicle
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Related WorkRelated Work
1. Temporally and Spatially Partial Coverage① The region under surveillance is covered partially in
terms of time and space.
② Our scheduling algorithm utilizes this partial coverage to save sensing energy.
2. Quality of Surveillance (QoSv)① Our QoSv is defined as the reciprocal of the average
detection time.
② Other QoSv was originally defined as the reciprocal value of the expected travel distance until the first detection.
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Problem FormulationProblem Formulation
1. Assumptions① The sensors knows their location and are time-synchronized.
② The sensing range is uniform-disk.
③ The cost of turn-off operation is ignorable.
④ The vehicle’s maximum speed is bounded.
2. Objective To maximize the sensor network lifetime to satisfy the
following conditions Provide the reliable detection of every vehicle, Guarantee the desired average detection time, and Facilitate the mobile target tracking after the target detection.
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Sensor Network Model for Sensor Network Model for Road SegmentRoad Segment
S1
Road Segment
Vehicle
S2 S3 Sn. . . . .
Sensing Coverage
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Key Idea to Our SchedulingKey Idea to Our Scheduling
How to have some sleeping time to save energy? We observe that the vehicle needs time l/v to pass the road segment. Time l/v is the sleeping time for all the sensors on the road segment.
S1
Road Segment Length = l
Vehicle
S2 S3 Sn. . . . .
Speed = v
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Energy-Aware Sensor SchedulingEnergy-Aware Sensor Scheduling
1. Our sensor scheduling consists of two phases:① Initialization Phase
② Surveillance Phase Working Period + Sleeping Period
sn ... s2 s1
0
sn ... s2 s1
En
erg
y C
on
sum
pti
on
[J]
Time [sec]
Sleeping (I)Initialization
Working (W) Working (W)
. . . . . sn ... s2 s1
Working (W)
Sleeping (I)
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Sensing Sequence for Sensing Sequence for Vehicle DetectionVehicle Detection
S1 . . . . .(b)
Sensor Scheduling Sequnce
S2 S3 S4 SnSn-1Sn-2
S1 . . . . .(c) S2 S3 S4 SnSn-1Sn-2
S1 . . . . .(d) S2 S3 S4 SnSn-1Sn-2
S1 . . . . .(e) S2 S3 S4 SnSn-1Sn-2
S1 . . . . .(f) S2 S3 S4 SnSn-1Sn-2
Detected
S1Vehicle . . . . .(a)
All sensors are sleeping
S2 S3 S4 SnSn-1Sn-2
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Optimality of Sensor SchedulingOptimality of Sensor Scheduling
1. Sensor Network Lifetime
The following energy can be saved through sleeping:
Number of Surveillance Periods
Working Period
Sleeping Period
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Considerations on Turn-On and Considerations on Turn-On and Warming-UP OverheadsWarming-UP Overheads
1. Each Sensor’s Lifetime without Sleeping
2. Sensor Network Lifetime through Sleeping
Case 1: Turn-On Overhead is greater than Sleeping benefit
Case 2: Turn-On Overhead is less than
Sleeping benefit
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QoSv-Guaranteed Sensor SchedulingQoSv-Guaranteed Sensor Scheduling
1. Average Detection Time for Constant Vehicle Speed
Approximate Average Detection Time (ADT)
2. Average Detection Time for Bounded Vehicle Speed
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Determination of Scheduling Determination of Scheduling ParametersParameters
1. Scheduling Parameters are① The sensor network length (l)
② The working time (w)
③ The sleeping time (s)
2. Sensor Network Length (l)
3. Working Time (w)
4. Sleeping Time (s)where
S1
Sensor Network Length
Vehicle
S2 S3 Sn. . . . .
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Sensor Scheduling for Complex Roads Sensor Scheduling for Complex Roads (1/4)(1/4)
Road Network between the Inner and Outer Boundaries
O1
O2
I2
I3
I4
Outer Boundary Inner Boundary
I5
I1
Vehicle
CITY
Road
Network
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Sensor Scheduling for Complex Roads Sensor Scheduling for Complex Roads (2/4)(2/4)
A Connected Graph for an Exemplary Road Network The Road Network is represented as a Connected Graph
between the Inner and Outer Boundaries.
O1
O2
I2
I3
I4
Outer Boundary Inner Boundary
I5
P1
P2
P3 P6
P5
P4
I1
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Sensor Scheduling for Complex Roads Sensor Scheduling for Complex Roads (3/4)(3/4) Construction of Scheduling Plan in Road Network
Determine the starting points Si to satisfy the required QoSv through Search Algorithm.
O1
O2
I2
I3
I4
Outer Boundary Inner Boundary
I5
P1
P2
P3 P6
P5
P4
I1S1
S2S3
S4
S6
S5
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Sensor Scheduling for Complex Roads Sensor Scheduling for Complex Roads (4/4)(4/4) Scanning in Road Network
One scanning can be split into multiple scanning. Multiple scanning can be merged into one scanning for
sensing energy.
O1
O2
I2
I3
I4
Outer Boundary Inner Boundary
I5
P1
P2
P3 P6
P5
P4
I1S1
S2S3
S4
S6
S5
split merge
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Performance EvaluationPerformance Evaluation
1. Metrics① Sensor Network Lifetime according to Working Time and
Turn-on Energy
② Average Detection Time according to Working Time and Road Segment Length (i.e., Sensor Network Length)
③ Required Average Scanning Number for Sensing Error Probability
2. Validation of Numerical Analysis① We validated our numerical analysis of our scheduling
algorithm through simulation.
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Sensor Network LifetimeSensor Network Lifetime according to according to Working Time and Turn-on EnergyWorking Time and Turn-on Energy
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Average Detection TimeAverage Detection Time according to according to Working Time and Road Segment LengthWorking Time and Road Segment Length
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Required Average Scanning NumberRequired Average Scanning Number for for Sensing Error ProbabilitySensing Error Probability
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ConclusionConclusion
1. We proposed an Energy-Aware Scheduling Algorithm to satisfy the required QoSv in Linear Sensor Network. QoSv is defined as the reciprocal value of
Average Detection Time (ADT).
2. Our Algorithm can be used for ① Surveillance for City’s Border Roads, and
② Traffic Signal Control in Crossroads.
3. Future Work We develop the specific algorithm for traffic signal
control in the transportation system.
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Q & AQ & A