fei yang, isabelle augé-blum
DESCRIPTION
Delivery ratio-maximized wakeup scheduling for ultra-low duty-cycled WSNs under real-time constraints. Fei Yang, Isabelle Augé-Blum National Institute of Applied Sciences of Lyon in the Telecommunications department ( 法國里昂國立應用科學學院 ). Computer Networks 2011. 898410120 陳正昌 2011/03/28. - PowerPoint PPT PresentationTRANSCRIPT
Delivery ratio-maximized wakeup scheduling for ultra-low duty-cycled WSNs
under real-time constraints
Fei Yang, Isabelle Augé-BlumNational Institute of Applied Sciences of Lyon in the Telecommunications department
( 法國里昂國立應用科學學院 )
Computer Networks 2011
898410120 陳正昌 2011/03/28
Page: 2WMNL
Performance Evaluation
Outline
Introduction and Goals
Wakeup Scheduling Algorithm
Conclusions
Page: 3WMNL
Performance Evaluation
Outline
Introduction and Goals
Wakeup Scheduling Algorithm
Conclusions
Page: 4WMNL
Introduction
• WSNs have been widely used in many applications.
• The data flows of WSN applications can be mainly classified into four types
– Event-driven
– Query-driven
– Continuous
– Hybrid
Page: 5WMNL
Introduction
• The characteristics of event-driven WSN applications are
– Not have data most of the time
– Have to report to the sink with real-time constraints
• Nodes spend most of the time on idle listening.
Page: 6WMNL
Introduction
• Typical power consumptions for an IEEE 802.15.4 radio (CC2420).
– Transmit : 52.2 mW
– Receive : 56.4 mW
– Listen : 56.4 mW
– Sleep : 3 W
• Sensor nodes are battery-powered
– Energy saving is an important issue in WSNs.
Page: 7WMNL
Introduction
• Duty-cycled approach can prolongs the sensor lifetime.
…Time
Scheduling period
Page: 8WMNL
Introduction
• Duty-cycle will negatively affect other performances
– End-to-end delay
– Connectivity
• Although some existing scheduling algorithms can reduce the end-to-end delay
– Didn’t takes routing into account
– Didn’t have a bounded delay
– Didn’t takes unreliable links into account
Page: 9WMNL
Goals
• Proposes a novel forwarding scheme for ultra-low duty-cycle WSNs.
– Improve the energy efficiency
– Decrease end-to-end delay
– Increase delivery ratio
– Guarantee bounded delay on the messages
– Distributed scheduling
Page: 10WMNL
Performance Evaluation
Outline
Introduction and Goals
Wakeup Scheduling Algorithm
Conclusions
Page: 11WMNL
…Time
Network Assumptions
• All nodes are locally synchronized with their neighbors.
• Only one node sends the alarm when the event happens.
• One duty-cycle period is divided into many slots and have same duration.
• Each node wakes up for only one slot during one period.
• The node can wake up for more than one slot when it has packets to send.
B B BA A A
Page: 12WMNL
Basic Idea
Sink
…
Time
…
31 slots
Slots1~5
Slots6~10
Slots11~15
Slots16~20
Slots21~25
Slots26~30
Slot 31
0.9
0.8
0.7
0.6
0.5
Slot 1
Slot 2
Slot 3
Slot 4
Slot 5
Expected Delivery Ratio
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Wakeup Scheduling Algorithm
Expected Delivery Ratio (EDR)
1
01
1k
jnn
r
kni jkk
PPEDRFEDR
Hop Count (HC)
otherwise
&&1 if
11 jjj nn
ni
ni
nni
LQHCHC
HC
HCHC
Page: 14WMNL
∞∞
Wakeup Scheduling Algorithm
Hop Count (HC)
otherwise
&&1 if
11 jjj nn
ni
ni
nni
LQHCHC
HC
HCHC
D
A
B
Sink
Cα=0.5
0.9
0.8
0.3
0.8
0.8 0.6
1
0
1
3
2 ∞∞
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Wakeup Scheduling Algorithm
Expected Delivery Ratio (EDR)
1
01
1k
jnn
r
kni jkk
PPEDRFEDR
D
A
B
SinkC
0.7
0.4
0.6
0.6
0.7
0.8
100
95
94
93
π(FD)={B, C, A}
EDR(π(FD))=
0.6*0.4
+(1-0.6)*0.7*0.6
+(1-0.6)*(1-0.7)*0.8*0.7
=0.4752
Page: 16WMNL
Wakeup Scheduling Algorithm
Expected Delivery Ratio (EDR)
1
01
1k
jnn
r
kni jkk
PPEDRFEDR
D
A
B
SinkC
0.7
0.4
0.6
0.6
0.7
0.8
100
93
94
95
π(FD)={A, C, B}
EDR(π(FD))=
0.8*0.7
+(1-0.8)*0.7*0.6
+(1-0.8)*(1-0.7)*0.6*0.4
=0.6584 > 0.4752
Page: 17WMNL
Wakeup Scheduling Algorithm
Expected Delivery Ratio (EDR)
1
01
1k
jnn
r
kni jkk
PPEDRFEDR
Hop Count (HC)
otherwise
&&1 if
11 jjj nn
ni
ni
nni
LQHCHC
HC
HCHC
Wakeup Slot (WS) Selection
iii EDRSRHCSRTWS 1
Selectable Range (SR)
upboundHC
TSR …
Time
…
T
Page: 18WMNL
Wakeup Scheduling Algorithm
Expected Delivery Ratio (EDR)
1
01
1k
jnn
r
kni jkk
PPEDRFEDR
Hop Count (HC)
otherwise
&&1 if
11 jjj nn
ni
ni
nni
LQHCHC
HC
HCHC
Wakeup Slot (WS) Selection
i
upboundi
upboundi EDR
HC
THC
HC
TTWS 1
0~1
1 , i
upboundi
upboundi HC
HC
TTHC
HC
TTWS
Page: 19WMNL
Wakeup Scheduling AlgorithmWakeup Slot (WS) Selection
i
upboundi
upboundi EDR
HC
THC
HC
TTWS 1
Sink
HC=1HC=2HC=3HC=4HC=5HC=6
HCupbound=6
54 slots
…Time0 8 9 17 18 26 27 35 36 44 45 53
Page: 20WMNL
Wakeup Scheduling AlgorithmWakeup Slot (WS) Selection
i
upboundi
upboundi EDR
HC
THC
HC
TTWS 1
SinkB
C
A
HC=5HC=6
HCupbound=6
54 slots
…Time0 8 9 17 18 26 27 35 36 44 45 53
EDRC=0.6
EDRB=0.4
EDRA=0.8 102.099 AWS
146.099 BWS
124.099 CWS
Slot T
(HC=0, EDR=1)
Page: 21WMNL
Wakeup Scheduling Algorithm
Distributed Wakeup Scheduling
Sink broadcasts a packet that includes its
EDR(1), WS(T) and HC(0)
Every node except the sink runs the following algorithm
if receives a packet from one of the neighbors
Calculates the new HC
Calculates the new EDR
if the change of EDR is higher than a threshold
or the HC is changed
Calculates the new WS
Broadcasts the new values
endif
endif
Expected Delivery Ratio (EDR)
1
01
1k
jnn
r
kni jkk
PPEDRFEDR
Hop Count (HC)
otherwise
&&1 if
11 jjj nn
ni
ni
nni
LQHCHC
HC
HCHC
Wakeup Slot (WS) Selection
i
upboundi
upboundi EDR
HC
THC
HC
TTWS 1
Page: 22WMNL
Wakeup Scheduling Algorithm
Sink
…
Time
…
54 slots
Slots45~53
(HCi, EDRi, WSi)
(0, 1, 54)
(1, 0.95, ∞)
(1, 0.9, ∞)
Page: 23WMNL
(1, 0.95, 47)
(1, 0.9, 50)
Wakeup Scheduling Algorithm
Sink
…
Time
…
54 slots
Slots36~44
Slots45~53
(HCi, EDRi, WSi)
(0, 1, 54)
(2, 0.92, ∞)
(2, 0.9, ∞)
(2, 0.88, ∞)
(2, 0.86, ∞)
(2, 0.92, 38)
(2, 0.9, 39)
(2, 0.88, 40)
(2, 0.86, 41)
Page: 24WMNL
(1, 0.95, 47)
(1, 0.9, 50)
Wakeup Scheduling Algorithm
Sink
…
Time
…
54 slots
Slots27~35
Slots36~44
Slots45~53
(HCi, EDRi, WSi)
(0, 1, 54)
(2, 0.92, 38)
(2, 0.9, 39)
(2, 0.88, 40)
(2, 0.86, 41)
(3, 0.89, ∞)
(3, 0.88, ∞)
(3, 0.85, ∞)
(3, 0.83, ∞)
(3, 0.8, ∞)
(3, 0.89, 28)
(3, 0.88, 29)
(3, 0.85, 31)
(3, 0.83, 32)
(3, 0.8, 33)
Page: 25WMNL
(3, 0.89, 28)
(3, 0.88, 29)
(3, 0.85, 31)
(3, 0.83, 32)
(3, 0.8, 33)
(1, 0.95, 47)
(1, 0.9, 50)
Wakeup Scheduling Algorithm
Sink
…
Time
…
54 slots
Slots0~8
Slots9~17
Slots18~26
Slots27~35
Slots36~44
Slots45~53
(HCi, EDRi, WSi)
(0, 1, 54)
(2, 0.92, 38)
(2, 0.9, 39)
(2, 0.88, 40)
(2, 0.86, 41)
(4, 0.85, 19)
(4, 0.83, 20)
(4, 0.82, 21)
(4, 0.78, 23)
(4, 0.76, 24)
(5, 0.80, 11)
(5, 0.78, 12)
(5, 0.76, 13)
(5, 0.75, 14)
(5, 0.73, 16)
(6, 0.75, 2)
(6, 0.73, 3)
(6, 0.70, 5)
(6, 0.68, 6)
(6, 0.6, 7)
Page: 26WMNL
(3, 0.89, 28)
(3, 0.88, 29)
(3, 0.85, 31)
(3, 0.83, 32)
(3, 0.8, 33)
(1, 0.95, 47)
(1, 0.9, 50)
Wakeup Scheduling Algorithm
Sink
…
Time
…
54 slots
Slots0~8
Slots9~17
Slots18~26
Slots27~35
Slots36~44
Slots45~53
(HCi, EDRi, WSi)
(0, 1, 54)
(2, 0.92, 38)
(2, 0.9, 39)
(2, 0.88, 40)
(2, 0.86, 41)
(4, 0.85, 19)
(4, 0.83, 20)
(4, 0.82, 21)
(4, 0.78, 23)
(4, 0.76, 24)
(5, 0.80, 11)
(5, 0.78, 12)
(5, 0.76, 13)
(5, 0.75, 14)
(5, 0.73, 16)
(6, 0.75, 2)
(6, 0.73, 3)
(6, 0.70, 5)
(6, 0.68, 6)
(6, 0.6, 7)
12 20 29 39 47
54
Page: 27WMNL
Performance Evaluation
Outline
Introduction and Goals
Wakeup Scheduling Algorithm
Conclusions
Page: 28WMNL
Performance Evaluation
Simulation Parameters
Simulator WSNet
Deploy Nodes 250 nodes
Network Size 150m * 150m
Slots 750, 1000, 2000, 3000 slots
Hop Count Bound 10 hops
Run Time 100
Modulation FSK
Data Rate 19.2 kbps
Page: 29WMNL
Performance Evaluation
Performance metrics
Delivery Ratio
End-to-End Delay
Energy Consumption
Impact factor
Density and Link Quality
Duty Cycle
Sink Position
Page: 30WMNL
Performance Evaluation
Experiments
Experiment 1 Each node only considers the neighboring nodes with the lower HC
Experiment 2Every node considers the neighboring nodes with not only the lower HC but also the
same HC
Sink
HC=1HC=2HC=3HC=4HC=5HC=6
Page: 31WMNL
Performance Evaluation
Comparison
WSEDR
Random
i
upboundi
upboundi EDR
HC
THC
HC
TTWS 1
random
HC
THC
HC
TTWS
upboundi
upboundi
Page: 32WMNL
Performance Evaluation
Delivery Ratio (Duty-Cycle : 0.1%, Sink Location : (75,75))
Experiment 1 Experiment 2
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Delivery Ratio (α : 0.3, Sink Location : (75,75))
Experiment 1 Experiment 2
Performance Evaluation
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Delivery Ratio (Density : 29 neighbors, α : 0.3, Duty-Cycle : 0.1%)
Experiment 1 Experiment 2
Performance Evaluation
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End-to-End Delay (Duty-Cycle : 0.1%, Sink Location : (75,75))
Experiment 1 Experiment 2
Performance Evaluation
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End-to-End Delay (α : 0.3, Sink Location : (75,75))
Experiment 1 Experiment 2
Performance Evaluation
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End-to-End Delay (Density : 29 neighbors, α : 0.3, Duty-Cycle : 0.1%)
Experiment 1 Experiment 2
Performance Evaluation
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Energy Consumption (Duty-Cycle : 0.1%, Sink Location : (75,75))
Experiment 1 Experiment 2
Performance Evaluation
Page: 39WMNL
Energy Consumption (α : 0.3, Sink Location : (75,75))
Experiment 1 Experiment 2
Performance Evaluation
Page: 40WMNL
Energy Consumption (Density : 29 neighbors, α : 0.3, Duty-Cycle : 0.1%)
Experiment 1 Experiment 2
Performance Evaluation
Page: 41WMNL
Performance Evaluation
Outline
Introduction and Goals
Wakeup Scheduling Algorithm
Conclusions
Page: 42WMNL
Conclusion
• Proposes a novel forwarding scheme for ultra-low duty-cycle WSNs.
– Improve the energy efficiency
– Decrease end-to-end delay
– Maximizes the delivery ratio
– Distributed scheduling
– Highly suitable for ultra-low duty-cycle real-time event-driven WSN
Page: 43WMNL
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