staffetta: smart duty-cycling for opportunistic data collection
TRANSCRIPT
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
senderreceiver
rendezvous time
Sink is a special node that collect all information and has no resource constraintsNodes deliver their data to the sink over a multi-hop path and are battery powered à duty cycle their radioDuty 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
MECHANISMStaffetta’s
11Challenge the future
Staffetta mechanism
1
2
4
815
33 32
31
28
22
271624
23
2610
18
20
1917
25
14
11
13
3
6
7
1
2
4
815
33 32
31
28
22
271624
23
2610
18
20
1917
25
14
11
13
3
6
7
1) Exploit relation between the activity of nodes and rendezvous time to create a gradientsink
12Challenge the future
Staffetta mechanism
1
2
4
815
33 32
31
28
22
271624
23
2610
18
20
1917
25
14
11
13
3
6
7
1
2
4
815
33 32
31
28
22
271624
23
2610
18
20
1917
25
14
11
13
3
6
7
1) Exploit relation between the activity of nodes and rendezvous time to create a gradient2) 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
Wake
up F
req. [H
z]
Without Staffetta With Staffetta
0
30
60
90
120
150
Fw
d. dela
y [m
s]
Rendezvous w/o Staffetta Rendezvous Staffetta Data Transmission
0 5
10 15 20 25 30
Duty
Cyc
le [%
]
DCmax
1 2 3 4 5Hops from Sink
Topolo
gy
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
S
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 25 30
Wa
keu
p F
req
. [H
z]
Without Staffetta With Staffetta
0
30
60
90
120
150
Fw
d.
de
lay
[ms]
Rendezvous w/o Staffetta Rendezvous Staffetta Data Transmission
0 5
10 15 20 25 30
Du
ty C
ycle
[%
]
DCmax
1 2 3 4 5Hops from Sink
To
po
log
y
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 protocolsShorter 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
EVALUATIONStaffetta’s
17Challenge the future
Protocols comparison
DUTY CYCLE MAC Routing metricORW
FIXEDGENERIC
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
300Rate 1/30 Hz Rate 1/60 Hz Rate 1/240 HzPerformance improvements
Latency 80-450xDuty Cycle 2-9xDelivery Ratio up to 1.5x
ORW ST.EDC ORW ST.EDC ORW ST.EDC0.8
0.85
0.9
0.95
1.0Rate 1/30 Hz Rate 1/60 Hz Rate 1/240 Hz
ORW ST.EDC ORW ST.EDC ORW ST.EDC0
1
2
3
4
5
6Rate 1/30 Hz Rate 1/60 Hz Rate 1/240 Hz
late
ncy
[s]
deliv
ery
ratio
duty
cyc
le [
%]
19Challenge the future
Latency improvements
EDC ST.EDC QB ST.QB RW ST.RW0
10
20
30
EDC ST.EDC QB ST.QB RW ST.RW0
50
100
150
200
Staffetta achieves low latencies by reducing the forwarding time and the path length
path
leng
th [h
op]
late
ncy
[s]
20Challenge the future
Delivery Ratio improvements
EDC ST.EDC QB ST.QB RW ST.RW0
50
100
150
200
EDC ST.EDC QB ST.QB RW ST.RW0
0.2
0.4
0.6
0.8
1
Staffetta improves the delivery ratio by reducing queue backlog (but ...)
late
ncy
[s]
deliv
ery
ratio
21Challenge the future
Duty Cycle improvements
EDC ST.EDC QB ST.QB RW ST.RW0
5
10
15
20
Staffetta reduces the energy consumption even if it increases the nodes’ activity
wak
eup
freq.
[Hz]
duty
cyc
le [
%]
EDC ST.EDC QB ST.QB RW ST.RW0
2
4
6
8
10
12
22Challenge the future
Adaptability
1
2
3
Nodes close to 1Nodes close to 2Nodes close to 3
23Challenge the future
Adaptability
0 100 200 300 400 500 6000
4
8
12W
ake
up F
req. [H
z] Nodes close to position 1
0 100 200 300 400 500 6000
4
8
12
Wake
up F
req. [H
z] Nodes close to position 2
0 100 200 300 400 500 600
Time [s]
0
4
8
12
Wake
up F
req. [H
z] Nodes close to position 3
sink moves
24Challenge the future
0 100 200 300 400 500 6000
4
8
12W
ake
up F
req. [H
z] Nodes close to position 1
0 100 200 300 400 500 6000
4
8
12
Wake
up F
req. [H
z] Nodes close to position 2
0 100 200 300 400 500 600
Time [s]
0
4
8
12
Wake
up F
req. [H
z] Nodes close to position 3
Adaptability sink moves
25Challenge the future
Adaptability140 160 180 200 220 240 2600
100
200
300
La
ten
cy [
s] Nodes close to pos. 1 Nodes close to pos. 2 Nodes close to pos. 3
140 160 180 200 220 240 260Time [s]
0
0.5
1
1.5
2
Th
rou
gh
pu
t [m
sg/s
]
140 160 180 200 220 240 260 0
1
2
3
4
Late
ncy
[s] Nodes close to pos. 1 Nodes close to pos. 2 Nodes close to pos. 3
140 160 180 200 220 240 260Time [s]
0
0.5
1
1.5
2
Th
rou
gh
pu
t [m
sg/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