UNIVERSITY OF SOUTHERN CALIFORNIA
ELECTION: Energy-efficient and LowlatEncy sCheduling Technique for wIreless
sensOr Networks
S. Begum, S. Wang, B. Krishnamachari, A. Helmy
Electrical Engineering-Systems
University of Southern California
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UNIVERSITY OF SOUTHERN CALIFORNIA
Motivation
R
r
BS
• Sensor network of homogenous active sensors
• Monitor some phenomenon to detect abnormalities
• Application: chemical monitoring, machine fault
detection
• Exhibits spatio-temporal correlation
• Phases of operation:
• Phase1 (normal operation): Energy efficiency
• Phase2 (event detection+): Latency and responsiveness
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UNIVERSITY OF SOUTHERN CALIFORNIA
Motivation
•
•
•
LEACH: Heinzleman et. al., HICSS 2000
R
• Data driven, passive sensor
• Achieves energy efficiency
• Periodic clustering
r
• Rotation of cluster head
• High latency
TEEN: Manjeshwar et. al., IPDPS 2001
• Event driven, passive sensor
• Periodic cluster and rotation of cluster head
• Sleeps with fixed sleep cycle
• Achieves low latency
• Sense continuously
• Stay awake when the event is detected (threshold reached)
ELECTION:
• Event driven, active sensor
• Takes advantage of the spatio-temporal correlation to adaptively adjust
sleep cycle
• Achieve energy efficiency in phase 1: turn radios off
• Ensures low latency and high responsiveness in phase2
BS
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UNIVERSITY OF SOUTHERN CALIFORNIA
Assumptions
• Active/smart sensors
• Able to sense the environment in a responsive and
timely manner
• Schedules sensors and communication radios
independently
• The underlying phenomenon exhibits spatiotemporal correlation
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UNIVERSITY OF SOUTHERN CALIFORNIA
Outline
•
•
•
•
Motivation
Description of Algorithms
Performance Analysis
Conclusion
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System Parameters
• Initial sleep cycles: Sin
• Data threshold: Dth
• Gradient threshold: Gth
• Gradient: rate of change of the phenomenon
• Sleep reduction function: Fsr
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UNIVERSITY OF SOUTHERN CALIFORNIA
Basic Algorithms
Timing Diagram
Phase0:Synchronization
CH formation
TDMA aggregation
Phase2: Report
(sense + communication)
Phase1:Monitor
(sense only: with phenomenon
dependant scheduling)
State Transition Diagram
g(t) < Gth  s(t+1) = s(t)
Init
Synch
Sleep
D(t) < Dth, g(t) > Gth 
CH Selection
d(t) > Dth
Active
d(t) < Dth
CH Advertisement
s(t+1) = Fsr(s(t), g(t))
Phase 1: Radio off
CH
CM
CM
Phase 2
Point at which threshold crosses
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UNIVERSITY OF SOUTHERN CALIFORNIA
Adapting Sleep Cycles
s(t+1) = Fsr(s(t), g(t))
Geared Sleep Reduction Function (Fsr)
•
•
Adjust sleep cycle based
previous sleep cycle and
gradient
Temporal correlation  a
node wakeup at the event
of threshold crossing
Spatial correlation  All
sensors measuring same
phenomenon wake up at
the same time
s(t+1) =
½ s(t)
0.0 < g(t) < 0.005
¼ s(t)
0.005 < g(t) < 0.01
.
.
.
Temperature (deg.)
•
g(t) < 0.0
120
300
100
250
80
200
60
150
40
100
Temp.
20
50
Sleep cycle
0
0
40
45
50
55
60
Time (in thousand seconds)
System Parameters: Sin= 250 sec, Dth= 95 degrees
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Sleep cycle (sec.)
s(t)
UNIVERSITY OF SOUTHERN CALIFORNIA
Performance Metrices
• Energy
• Total energy dissipation
• Sensing energy
• Communication: Cluster formation + Reporting
• Latency
• Delay between report generation and actual
time of threshold being reached
• Responsiveness
• Difference between reported data value and
threshold (e.g. degree of temperature)
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UNIVERSITY OF SOUTHERN CALIFORNIA
Energy Analysis
ELECTION = AEsT1/s + AEsT2/Tr + A/Ec + A/ Er T2/Tr
LEACH = AEsT/Tr + A/EcT/Tc + A/ErT/Tr
TEEN = AEsT + A/EcT/Tc + A/ErT2/Tr
Ec >> Es  Savings in cluster formation
Es > Ec  Savings in sensing (w.r.t. TEEN)
Es: Energy dissipation of a single sensing operation
Ec: Energy dissipation in a single cluster formation
Er: energy dissipation in a single report
T: Network life
T1, T2: duration of phase 1, phase 2
Tr: Reporting interval
Tc: Cluster formation interval (Le, Te)
: Node density
: Average node degree
A: Total area of the network
: Percentage of node CH (Le, Te)
s: Expected sleep duration (El)
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UNIVERSITY OF SOUTHERN CALIFORNIA
Latency and Responsiveness
Protocol
Latency
(Avg): L
Latency
(Worst): 
Respons. (Worst): 
ELECTION
½ Last sleep
duration
Last sleep
duration
Gmax Sin
LEACH
½ Tr
Tr
Gmax * 
TEEN
½S
S
Gmax * S
Gmax: Max gradient threshold it responds to (El)
Sin: Initial sleep duration (El)
S: Fixed sleep cycle (Le, Te)
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UNIVERSITY OF SOUTHERN CALIFORNIA
Simulation Setup
• High level simulation
• ELECTION
• TEEN
• Hybrid
• Fixed sleep cycle (like TEEN)
• On demand cluster formation (like ELECTION)
• Network simulated
• 36 uniformly distributed sensors
• Network divided into 4 quadrant
• Each quadrant is assigned a sensing pattern
• Phenomenon simulated
• Phenomenon 1: changes 100 times during entire
simulation
• Phenomenon 2: changes 20 times
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Simulation Parameters
• Simulation time: 600K seconds
• ELECTION
• Geared sleep reduction function
• Initial sleep cycle (Sin): 256 secs
• TEEN
• Cluster formation interval (Tc): 6K secs
• Fixed sleep cycle: 50 secs
• Hybrid
• Cluster formation: on demand
• Fixed sleep cycle: 50 secs
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Remaining Energy Analysis
Average Remaining Energy (in unit):
Phenomenon 1 (changes 100 times): Es/Etx = 1%
Phenomenon 1 (changes 100 times): Es/Etx = 10%
5000
5000
ELECTION
4000
4000
Hybrid
3000
3000
TEEN
2000
2000
1000
1000
0
0
0
100
200
300
400
500
600
Ele ction
Hybrid
Te e n
0
100
200
300
400
500
600
Simula tion time (in thousa nd se conds)
Simula tion time (in thous a nd s e conds )
Phenomenon 2 (changes 20 times): Es/Etx = 10%
5000
ELECTION
4000
Hybrid
3000
TEEN
2000
1000
0
0
100
200
300
400
500
600
Simula tion time (in thous a nd s e conds )
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Delay and Responsiveness
Delay (in seconds)
35
30
25
20
15
10
5
E LE CTION
0
Hybrid/TE E N
1
5
9
13
17
21
25
29
21
25
29
S im u la tio n ru n
Responsiveness (in degrees)
0.5
ELECTION
0.4
Hybrid/TEEN
0.3
0.2
0.1
0
1
5
9
13
17
Simulation run
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Limitations
• Dependency on the underlying phenomenon
• A priori information of the environment may not be
available
• Not suitable for phenomenon that does not
exhibit spatio-temporal correlation (e.g.
seismic monitoring)
• Synchronization problem in phase 1
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UNIVERSITY OF SOUTHERN CALIFORNIA
Conclusion
• New sleep scheduling scheme for wireless
active sensor networks
• Exploit spatio-temporal correlation of
physical phenomenon
• Adaptively adjust sleep cycle
• Outperforms LEACH and TEEN with respect
to energy, latency and responsiveness
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