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1Challenge the future
STAFFETTA: exploit duty-cycling
for opportunistic data collection
M.Cattani, A.Loukas, M.Zimmerling,...
2Challenge the future
Data collection in WSN:
problem statement
sender
receiver
rendezvous time
Sink is a special node tha...
3Challenge the future
Data collection in WSN:
existing solutions
Choose as forwarder the
closest node to the sink
(shortes...
4Challenge the future
Data collection in WSN:
existing solutions
From a set of best forwarders,
choose the first one to wa...
5Challenge the future
Data collection in WSN:
existing solutions
Choose as forwarder the
closest node to the sink
(shortes...
6Challenge the future
Data collection in WSN:
Staffetta’s idea
Bias opportunistic choices towards nodes closer
to the sink...
7Challenge the future
Data collection in WSN:
Staffetta’s idea
S
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tree-based opportunistic opportunistic
with staffe...
8Challenge the future
Data collection in WSN:
Staffetta’s idea
S
1
2
3
4
tree-based opportunistic opportunistic
with staffe...
9Challenge the future
Data collection in WSN:
Staffetta’s idea
S
1
2
3
4
tree-based opportunistic opportunistic
with staffe...
10Challenge the future
MECHANISM
Staffetta’s
11Challenge the future
Staffetta mechanism
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12Challenge the future
Staffetta mechanism
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13Challenge the future
How to create an activity gradient
a) Give to nodes a fixed energy budget (DCmax)
b) Let nodes wake...
14Challenge the future
0
5
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WakeupFreq.[Hz]
Without Staffetta With Staffetta
0
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Fwd.delay[ms...
15Challenge the future
How to exploit an activity gradient
2) Use Activity Gradient to improve the efficiency
of opportuni...
16Challenge the future
EVALUATION
Staffetta’s
17Challenge the future
Protocols comparison
DUTY CYCLE MAC Routing metric
ORW
FIXED
GENERIC
LPL +
ANYCAST
Expected Duty Cy...
18Challenge the future
Comparison with ORW
ORW ST.EDC ORW ST.EDC ORW ST.EDC
0
50
100
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Rate 1/30 Hz Rate 1/6...
19Challenge the future
Latency improvements
EDC ST.EDC QB ST.QB RW ST.RW
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EDC ST.EDC QB ST.QB RW ST.RW
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100
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20Challenge the future
Delivery Ratio improvements
EDC ST.EDC QB ST.QB RW ST.RW
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100
150
200
EDC ST.EDC QB ST.QB RW ST...
21Challenge the future
Duty Cycle improvements
EDC ST.EDC QB ST.QB RW ST.RW
0
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Staffetta reduces the energy cons...
22Challenge the future
Adaptability
1
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Nodes close to 1
Nodes close to 2
Nodes close to 3
23Challenge the future
Adaptability
0 100 200 300 400 500 600
0
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WakeupFreq.[Hz] Nodes close to position 1
0 100 200...
24Challenge the future
0 100 200 300 400 500 600
0
4
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WakeupFreq.[Hz] Nodes close to position 1
0 100 200 300 400 500 ...
25Challenge the future
Adaptability
140 160 180 200 220 240 260
0
100
200
300
Latency[s]
Nodes close to pos. 1 Nodes close...
26Challenge the future
CONCLUSIONS
27Challenge the future
Conclusion
Staffetta’s simple rule is able to
• Achieve desired network lifetime
• Adapt to network...
28Challenge the future
THANKS A LOT
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Staffetta: Smart Duty-Cycling for Opportunistic Data Collection

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Staffetta is a duty-cycling mechanism that improves opportunistic data collection in wireless sensor networks. Staffetta dynamically adjusts each node’s wake-up frequency, such that nodes closer to the sink are more active than nodes at the edge of the network.

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Staffetta: Smart Duty-Cycling for Opportunistic Data Collection

  1. 1. 1Challenge the future STAFFETTA: exploit duty-cycling for opportunistic data collection M.Cattani, A.Loukas, M.Zimmerling, M.Zuniga, K.Langendoen
  2. 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. 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. 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. 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. 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. 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. 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. 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
  10. 10. 10Challenge the future MECHANISM Staffetta’s
  11. 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. 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. 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. 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. 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
  16. 16. 16Challenge the future EVALUATION Staffetta’s
  17. 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. 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. 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. 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. 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. 22. 22Challenge the future Adaptability 1 2 3 Nodes close to 1 Nodes close to 2 Nodes close to 3
  23. 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. 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. 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
  26. 26. 26Challenge the future CONCLUSIONS
  27. 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
  28. 28. 28Challenge the future THANKS A LOT

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