esc: energy synchronized communication in sustainable sensor networks
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October 14, 2009 Yu (Jason) Gu @ ICNP ‘09
ESC: Energy Synchronized Communication in Sustainable
Sensor Networks
Yu (Jason) Gu, Ting Zhu and Tian He
Department of Computer Science and Engineering
October 14, 2009 Yu (Jason) Gu @ ICNP ‘09
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Background
• Sustainable Sensor Networks– Aimed to operate unattended for
a very long period of time (tens of years)
– Scavenge energy from ambient environment (e.g., solar energy)
– Energy is stored in ultra capacitors or batteries TwinStar Platform
(MobiSys’09)
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Why Different ?• Scavenged energy varies
significantly both in Time and Space.
• Only can afford low-duty-cycle operation
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• Energy Storages (batteries, capacitors) are limited in capacity.
• Energy conservation with reduced performance during energy-rich periods is wasteful.
• In sustainable sensor networks, energy management shall focus on balancing (synchronizing) energy supply with demand, instead of saving as much energy as possible.
Conserving Energy is not Always Beneficial!
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ESC Design Objective
• Transparent middleware• Only adjust RF activities at
individual nodes• Support existing routing
protocols• Distributed implementation
at individual nodes
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ESC Optimization Objective• Minimizing the average delay of arbitrary traffic
patterns in the presence of energy dynamics by allocating (increase/reduce) duty cycles in an optimal way.– Energy-rich time:
• Increased duty-cycle reduce a maximal amount of network delay
– Energy-poor time:• Decreased duty-cycle increase a minimal amount of
delay
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Agenda
• Motivations and Design ObjectiveMotivations and Design Objective• Network Model and Delay Modeling• Energy Synchronization Control• Evaluation• Conclusion
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How to Represent Working Schedule?
1 2 83
Node Working Schedule : { 1, 83 }
active activedormant dormant
84
Period = 100
Node Duty Cycle : 2 / 100 = 2%
An Active Instance
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Delivery Latency in Low-Duty-Cycle Networks
1 2 3 4{1} {41} {71} {91}
Sleep latency is 40 Sleep latency is 30 Sleep latency is 20
End-to-end communication delay is 90
Sleep latency dominates communication delay!
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Cross-Traffic Delay
A
B
C
D
E
F
G
H
I
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Cross-Traffic Delay
D
A
B
E
F
A
B
C
D
E
F
G
H
I
Predecessor Successor
• Expected delay for packets from all predecessors to corresponding successors via node D.
• Capture the most generic many-to-many communication pattern
• We aim at minimizing cross-traffic delay so as to minimize network wide delay
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Cycle Representation of Working Schedule
21 22 63
active active
64
Period = 100
active active
121
Period = 100
122 163 164
21
63
0t=11
Sleep Latency is 10.t=91
Sleep Latency is 30.
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Delay Modeling : Single Link Delay
A Dp
t1
t2
tn
t3
…
Working schedule of node D
For a packet sent by predecessor A at time t:
tDAD(t) = p×(t2-t)
+(1-p) ×p× (t3-t)
+(1-p) ×(1-p) ×p× (t4-t) + …
t4
October 14, 2009 Yu (Jason) Gu @ ICNP ‘09
1414Delay Modeling: From a Predecessor to a Successor
A D E
Cross traffic delay from A through D to E is:
Sending time: t
DAD(t)
DAE(t) = DAD(t) + p1×DDE(t1) + p2×DDE(t2)
t1
t2
tn
…
p1
p2
pn
…
DDE(t1)DDE(t2)
DDE(tn)…
+ … + pn×DDE(tn)
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Delay Modeling: From all Predecessors to all Successors
D
A
B
E
F
Predecessor Successor
Weighted average for packets from all packet ready times at predecessors to all successors
DD = W1×DAE
W1
+ W2 ×DAF + W3 × DBE + W4 × DBF
W2
W3
W4
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Agenda
• Motivations and Design ObjectiveMotivations and Design Objective• Network Model and Delay ModelingNetwork Model and Delay Modeling• Energy Synchronization Control• Evaluation• Conclusion
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Energy Synchronization Control: Decrease Duty-Cycle
Dt1
t2
…tn
{t2, t3,…, tn} D1
{t1, t3,…, tn} D2
…{t1,t2,…, tn-1} Dn
• Method (exhaustive search): – Remove an active instance from the
working schedule one by one, calculating corresponding new cross-traffic delay
– Remove the active instance yields the minimal new delay
• Time complexity is O(n), but n is bounded and small in low-duty-cycle network.
Min{D1,D2,…,Dn} = D2
Remove t2 from working schedule
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Energy Synchronization Control: Increase Duty-Cycle (1)
Cross-traffic delay at node D is a constant between a time interval ( e.g., (1,81) ) formed by a predecessor A and a successor E
A D E
{1} {81}{21}{53}
20 60
52 28
Cross-traffic delay: 80
Cross-traffic delay: 80
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Energy Synchronization Control: Increase Duty-Cycle (2)
1
35
99
76
A D E{1, 76} {35, 99}
(1,35), (35,76), (76,99), (99,1)
Only need to attempt to augment active instance for these 4 intervals (instead of all possible 100 time instances). The complexity is also a constant
D1
D2
D3
D4
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Significance of the Stair Effect of Cross-Traffic Delay
Predecessor schedule: {36, 53, 80}Successor schedule: {90, 151, 189}
36 53 80 90 151 189
Period = 200
200 vs. 6 times !
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Bursty Duty-Cycle Increase/Decrease
• Exhaustive search yields exponential complexity
• Greedy solution is optimal !– For increase/decrease n active instances– Apply active instance increase/decrease n
times– Complexity is linear
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Agenda
• Motivations and Design ObjectiveMotivations and Design Objective• Network ModelNetwork Model• Modeling of Cross-Traffic DelayModeling of Cross-Traffic Delay• Energy Synchronization ControlEnergy Synchronization Control• Evaluation• Conclusion
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Evaluation• Test-bed Implementation
– 30 MicaZ nodes, random placement, 4-hop network
• Large-Scale Simulation– Up to 1200 nodes, 100 repeated experiments for
each data point• Routing Protocols
– Link-Quality-based: ETX in MobiCom’03– Sleep-Latency-based: DESS in INFOCOM’05
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Test-bed Performance
ESC effectively synchronize cross-traffic delay with energy-harvesting rate
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Test-bed Delay Distribution
65% delay gap at 80% percentile 200% delay gap at 100% percentile
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Impact of Duty CycleRandom Avg. Delay is 1010
ESC Avg. Delay is 684
ESC has over 30% less avg. delay than the Random scheme
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Conclusion• We are the first to propose the concept of
Energy Synchronized Computing. – The first installment is an Energy Synchronized
Communication middleware for existing network protocols.
• Discover the stair-effect of cross-traffic delay. • Design a constant time complexity energy
synchronization middleware that can be generically applied to many existing routing algorithms.
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