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1Challenge the future
STAFFETTA: exploit duty-cycling
for opportunistic data collection
M.Cattani, A.Loukas, M.Zimmerling, M.Zuniga, K.Langendoen
2Challenge the future
Data collection in WSN:
problem statement
sender
receiver
rendezvous time
Sink is a special node that collect all information and has no
resource constraints
Nodes deliver their data to the sink over a multi-hop path and
are battery powered à duty cycle their radio
Duty cycle makes communication expensive due to the time
and energy required by nodes to rendezvous
Collect data from a wireless network as fast as
possible under severe resource constraints
3Challenge the future
Data collection in WSN:
existing solutions
Choose as forwarder the
closest node to the sink
(shortest path)
Not always the fastest route
Best paths are over-used
Tree-based routing
(unicast primitive) next-hop
4Challenge the future
Data collection in WSN:
existing solutions
From a set of best forwarders,
choose the first one to wake
up (fastest path)
Not always the shortest route
Robustness due to path
diversity
Opportunistic routing
(anycast primitive)forwarder set
5Challenge the future
Data collection in WSN:
existing solutions
Choose as forwarder the
closest node to the sink
(shortest path)
Not always the fastest route
Best paths are over-used
Tree-based routing
(unicast primitive)
From a set of best forwarders,
choose the first one to wake
up (fastest path)
Not always the shortest route
Robustness due to path
diversity
Opportunistic routing
(anycast primitive)
6Challenge the future
Data collection in WSN:
Staffetta’s idea
Bias opportunistic choices towards nodes closer
to the sink (fast choices are also the shortest)
7Challenge the future
Data collection in WSN:
Staffetta’s idea
S
1
2
3
4
tree-based opportunistic opportunistic
with staffetta
rendezvous timerendezvous time rendezvous time
Bias opportunistic choices towards nodes closer
to the sink (fast choices are also the shortest)
can take time to reach best forwarder
8Challenge the future
Data collection in WSN:
Staffetta’s idea
S
1
2
3
4
tree-based opportunistic opportunistic
with staffetta
rendezvous timerendezvous time rendezvous time
Bias opportunistic choices towards nodes closer
to the sink (fast choices are also the shortest)
the more forwarders, the faster (but longer paths)
9Challenge the future
Data collection in WSN:
Staffetta’s idea
S
1
2
3
4
tree-based opportunistic opportunistic
with staffetta
rendezvous timerendezvous time rendezvous time
Bias opportunistic choices towards nodes closer
to the sink (fast choices are also the shortest)
the more active, the faster the forwarding
10Challenge the future
MECHANISM
Staffetta’s
11Challenge the future
Staffetta mechanism
1
2
4
8
15
33 32
31
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27
16
24
23
26
10
18
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17
25
14
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3
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33 32
31
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27
16
24
23
26
10
18
20
19
17
25
14
11
13
3
6
7
1) Exploit relation between the activity of nodes
and rendezvous time to create a gradient
sink
12Challenge the future
Staffetta mechanism
1
2
4
8
15
33 32
31
28
22
27
16
24
23
26
10
18
20
19
17
25
14
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3
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1
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31
28
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27
16
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26
10
18
20
19
17
25
14
11
13
3
6
7
1) Exploit relation between the activity of nodes
and rendezvous time to create a gradient
2) Use Activity Gradient to improve the efficiency
of opportunistic data collection protocols
13Challenge the future
How to create an activity gradient
a) Give to nodes a fixed energy budget (DCmax)
b) Let nodes wake up as frequently as possible
c) In the presence of a sink, obtain an activity gradient
1) Exploit relation between the activity of nodes
and rendezvous time to create a gradient
14Challenge the future
0
5
10
15
20
25
30
WakeupFreq.[Hz]
Without Staffetta With Staffetta
0
30
60
90
120
150
Fwd.delay[ms]
Rendezvous w/o Staffetta Rendezvous Staffetta Data Transmission
10
15
20
25
30
yCycle[%]
DCmax
How to create an activity gradient
1) Exploit relation between the activity of nodes
and rendezvous time to create a gradient
0
5
10
15
20
WakeupFre
0
30
60
90
120
150
Fwd.delay[ms]
Rendezvous w/o Staffetta Rendezvous Staffetta Data Transmission
0
5
10
15
20
25
30
DutyCycle[%]
DCmax
1 2 3 4 5
Hops from Sink
Topology
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
S
15Challenge the future
How to exploit an activity gradient
2) Use Activity Gradient to improve the efficiency
of opportunistic data collection protocols
Shorter forwarding delays
Biased opportunistic choices (more likely to reach good nodes)
Exploit temporal links
Handle high traffic loads around the sink
Increased adaptability to network dynamics
16Challenge the future
EVALUATION
Staffetta’s
17Challenge the future
Protocols comparison
DUTY CYCLE MAC Routing metric
ORW
FIXED
GENERIC
LPL +
ANYCAST
Expected Duty Cycle (ORW)
Queue Backlog (BCP)
STAFFETTA None - Random Walk
We evaluated Staffetta against 1 opportunistic
protocol and 3 routing metrics on 2 testbeds
18Challenge the future
Comparison with ORW
ORW ST.EDC ORW ST.EDC ORW ST.EDC
0
50
100
150
200
250
300
Rate 1/30 Hz Rate 1/60 Hz Rate 1/240 Hz
Performance improvements
Latency 80-450x
Duty Cycle 2-9x
Delivery Ratio up to 1.5x
ORW ST.EDC ORW ST.EDC ORW ST.EDC
0.8
0.85
0.9
0.95
1.0
Rate 1/30 Hz Rate 1/60 Hz Rate 1/240 Hz
ORW ST.EDC ORW ST.EDC ORW ST.E
0
1
2
3
4
5
6
Rate 1/30 Hz Rate 1/60 Hz Rate 1/240 H
latency[s]deliveryratio
dutycycle[%]
19Challenge the future
Latency improvements
EDC ST.EDC QB ST.QB RW ST.RW
0
10
20
30
EDC ST.EDC QB ST.QB RW ST.RW
0
50
100
150
200
Staffetta achieves low latencies by reducing
the forwarding time and the path length
pathlength[hop]
latency[s]
20Challenge the future
Delivery Ratio improvements
EDC ST.EDC QB ST.QB RW ST.RW
0
50
100
150
200
EDC ST.EDC QB ST.QB RW ST.RW
0
0.2
0.4
0.6
0.8
1
Staffetta improves the delivery ratio by
reducing queue backlog (but ...)
latency[s]
deliveryratio
21Challenge the future
Duty Cycle improvements
EDC ST.EDC QB ST.QB RW ST.RW
0
5
10
15
20
Staffetta reduces the energy consumption
even if it increases the nodes’ activity
wakeupfreq.[Hz]
dutycycle[%]
EDC ST.EDC QB ST.QB RW ST.RW
0
2
4
6
8
10
12
22Challenge the future
Adaptability
1
2
3
Nodes close to 1
Nodes close to 2
Nodes close to 3
23Challenge the future
Adaptability
0 100 200 300 400 500 600
0
4
8
12
WakeupFreq.[Hz] Nodes close to position 1
0 100 200 300 400 500 600
0
4
8
12
WakeupFreq.[Hz]
Nodes close to position 2
0 100 200 300 400 500 600
Time [s]
0
4
8
12
WakeupFreq.[Hz]
Nodes close to position 3
sink moves
24Challenge the future
0 100 200 300 400 500 600
0
4
8
12
WakeupFreq.[Hz] Nodes close to position 1
0 100 200 300 400 500 600
0
4
8
12
WakeupFreq.[Hz]
Nodes close to position 2
0 100 200 300 400 500 600
Time [s]
0
4
8
12
WakeupFreq.[Hz]
Nodes close to position 3
Adaptability sink moves
25Challenge the future
Adaptability
140 160 180 200 220 240 260
0
100
200
300
Latency[s]
Nodes close to pos. 1 Nodes close to pos. 2 Nodes close to pos. 3
140 160 180 200 220 240 260
Time [s]
0
0.5
1
1.5
2
Throughput[msg/s]
140 160 180 200 220 240 260
0
1
2
3
4
Latency[s]
Nodes close to pos. 1 Nodes close to pos. 2 Nodes close to pos. 3
140 160 180 200 220 240 260
Time [s]
0
0.5
1
1.5
2
Throughput[msg/s]
FIXED
STAFFETTA
26Challenge the future
CONCLUSIONS
27Challenge the future
Conclusion
Staffetta’s simple rule is able to
• Achieve desired network lifetime
• Adapt to network changes
and generate an activity gradient that
• Biases opportunistic choices towards the sink
• Reduces latency, path length and energy consumption
github.com/cattanimarco/Staffetta-Sensys-2016
28Challenge the future
THANKS A LOT

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Staffetta: Smart Duty-Cycling for Opportunistic Data Collection

  • 1. 1Challenge the future STAFFETTA: exploit duty-cycling for opportunistic data collection M.Cattani, A.Loukas, M.Zimmerling, M.Zuniga, K.Langendoen
  • 2. 2Challenge the future Data collection in WSN: problem statement sender receiver rendezvous time Sink is a special node that collect all information and has no resource constraints Nodes deliver their data to the sink over a multi-hop path and are battery powered à duty cycle their radio Duty cycle makes communication expensive due to the time and energy required by nodes to rendezvous Collect data from a wireless network as fast as possible under severe resource constraints
  • 3. 3Challenge the future Data collection in WSN: existing solutions Choose as forwarder the closest node to the sink (shortest path) Not always the fastest route Best paths are over-used Tree-based routing (unicast primitive) next-hop
  • 4. 4Challenge the future Data collection in WSN: existing solutions From a set of best forwarders, choose the first one to wake up (fastest path) Not always the shortest route Robustness due to path diversity Opportunistic routing (anycast primitive)forwarder set
  • 5. 5Challenge the future Data collection in WSN: existing solutions Choose as forwarder the closest node to the sink (shortest path) Not always the fastest route Best paths are over-used Tree-based routing (unicast primitive) From a set of best forwarders, choose the first one to wake up (fastest path) Not always the shortest route Robustness due to path diversity Opportunistic routing (anycast primitive)
  • 6. 6Challenge the future Data collection in WSN: Staffetta’s idea Bias opportunistic choices towards nodes closer to the sink (fast choices are also the shortest)
  • 7. 7Challenge the future Data collection in WSN: Staffetta’s idea S 1 2 3 4 tree-based opportunistic opportunistic with staffetta rendezvous timerendezvous time rendezvous time Bias opportunistic choices towards nodes closer to the sink (fast choices are also the shortest) can take time to reach best forwarder
  • 8. 8Challenge the future Data collection in WSN: Staffetta’s idea S 1 2 3 4 tree-based opportunistic opportunistic with staffetta rendezvous timerendezvous time rendezvous time Bias opportunistic choices towards nodes closer to the sink (fast choices are also the shortest) the more forwarders, the faster (but longer paths)
  • 9. 9Challenge the future Data collection in WSN: Staffetta’s idea S 1 2 3 4 tree-based opportunistic opportunistic with staffetta rendezvous timerendezvous time rendezvous time Bias opportunistic choices towards nodes closer to the sink (fast choices are also the shortest) the more active, the faster the forwarding
  • 11. 11Challenge the future Staffetta mechanism 1 2 4 8 15 33 32 31 28 22 27 16 24 23 26 10 18 20 19 17 25 14 11 13 3 6 7 1 2 4 8 15 33 32 31 28 22 27 16 24 23 26 10 18 20 19 17 25 14 11 13 3 6 7 1) Exploit relation between the activity of nodes and rendezvous time to create a gradient sink
  • 12. 12Challenge the future Staffetta mechanism 1 2 4 8 15 33 32 31 28 22 27 16 24 23 26 10 18 20 19 17 25 14 11 13 3 6 7 1 2 4 8 15 33 32 31 28 22 27 16 24 23 26 10 18 20 19 17 25 14 11 13 3 6 7 1) Exploit relation between the activity of nodes and rendezvous time to create a gradient 2) Use Activity Gradient to improve the efficiency of opportunistic data collection protocols
  • 13. 13Challenge the future How to create an activity gradient a) Give to nodes a fixed energy budget (DCmax) b) Let nodes wake up as frequently as possible c) In the presence of a sink, obtain an activity gradient 1) Exploit relation between the activity of nodes and rendezvous time to create a gradient
  • 14. 14Challenge the future 0 5 10 15 20 25 30 WakeupFreq.[Hz] Without Staffetta With Staffetta 0 30 60 90 120 150 Fwd.delay[ms] Rendezvous w/o Staffetta Rendezvous Staffetta Data Transmission 10 15 20 25 30 yCycle[%] DCmax How to create an activity gradient 1) Exploit relation between the activity of nodes and rendezvous time to create a gradient 0 5 10 15 20 WakeupFre 0 30 60 90 120 150 Fwd.delay[ms] Rendezvous w/o Staffetta Rendezvous Staffetta Data Transmission 0 5 10 15 20 25 30 DutyCycle[%] DCmax 1 2 3 4 5 Hops from Sink Topology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 S
  • 15. 15Challenge the future How to exploit an activity gradient 2) Use Activity Gradient to improve the efficiency of opportunistic data collection protocols Shorter forwarding delays Biased opportunistic choices (more likely to reach good nodes) Exploit temporal links Handle high traffic loads around the sink Increased adaptability to network dynamics
  • 17. 17Challenge the future Protocols comparison DUTY CYCLE MAC Routing metric ORW FIXED GENERIC LPL + ANYCAST Expected Duty Cycle (ORW) Queue Backlog (BCP) STAFFETTA None - Random Walk We evaluated Staffetta against 1 opportunistic protocol and 3 routing metrics on 2 testbeds
  • 18. 18Challenge the future Comparison with ORW ORW ST.EDC ORW ST.EDC ORW ST.EDC 0 50 100 150 200 250 300 Rate 1/30 Hz Rate 1/60 Hz Rate 1/240 Hz Performance improvements Latency 80-450x Duty Cycle 2-9x Delivery Ratio up to 1.5x ORW ST.EDC ORW ST.EDC ORW ST.EDC 0.8 0.85 0.9 0.95 1.0 Rate 1/30 Hz Rate 1/60 Hz Rate 1/240 Hz ORW ST.EDC ORW ST.EDC ORW ST.E 0 1 2 3 4 5 6 Rate 1/30 Hz Rate 1/60 Hz Rate 1/240 H latency[s]deliveryratio dutycycle[%]
  • 19. 19Challenge the future Latency improvements EDC ST.EDC QB ST.QB RW ST.RW 0 10 20 30 EDC ST.EDC QB ST.QB RW ST.RW 0 50 100 150 200 Staffetta achieves low latencies by reducing the forwarding time and the path length pathlength[hop] latency[s]
  • 20. 20Challenge the future Delivery Ratio improvements EDC ST.EDC QB ST.QB RW ST.RW 0 50 100 150 200 EDC ST.EDC QB ST.QB RW ST.RW 0 0.2 0.4 0.6 0.8 1 Staffetta improves the delivery ratio by reducing queue backlog (but ...) latency[s] deliveryratio
  • 21. 21Challenge the future Duty Cycle improvements EDC ST.EDC QB ST.QB RW ST.RW 0 5 10 15 20 Staffetta reduces the energy consumption even if it increases the nodes’ activity wakeupfreq.[Hz] dutycycle[%] EDC ST.EDC QB ST.QB RW ST.RW 0 2 4 6 8 10 12
  • 22. 22Challenge the future Adaptability 1 2 3 Nodes close to 1 Nodes close to 2 Nodes close to 3
  • 23. 23Challenge the future Adaptability 0 100 200 300 400 500 600 0 4 8 12 WakeupFreq.[Hz] Nodes close to position 1 0 100 200 300 400 500 600 0 4 8 12 WakeupFreq.[Hz] Nodes close to position 2 0 100 200 300 400 500 600 Time [s] 0 4 8 12 WakeupFreq.[Hz] Nodes close to position 3 sink moves
  • 24. 24Challenge the future 0 100 200 300 400 500 600 0 4 8 12 WakeupFreq.[Hz] Nodes close to position 1 0 100 200 300 400 500 600 0 4 8 12 WakeupFreq.[Hz] Nodes close to position 2 0 100 200 300 400 500 600 Time [s] 0 4 8 12 WakeupFreq.[Hz] Nodes close to position 3 Adaptability sink moves
  • 25. 25Challenge the future Adaptability 140 160 180 200 220 240 260 0 100 200 300 Latency[s] Nodes close to pos. 1 Nodes close to pos. 2 Nodes close to pos. 3 140 160 180 200 220 240 260 Time [s] 0 0.5 1 1.5 2 Throughput[msg/s] 140 160 180 200 220 240 260 0 1 2 3 4 Latency[s] Nodes close to pos. 1 Nodes close to pos. 2 Nodes close to pos. 3 140 160 180 200 220 240 260 Time [s] 0 0.5 1 1.5 2 Throughput[msg/s] FIXED STAFFETTA
  • 27. 27Challenge the future Conclusion Staffetta’s simple rule is able to • Achieve desired network lifetime • Adapt to network changes and generate an activity gradient that • Biases opportunistic choices towards the sink • Reduces latency, path length and energy consumption github.com/cattanimarco/Staffetta-Sensys-2016