esc: energy synchronized communication in sustainable sensor networks

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


2

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


44

• 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


66

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


77

Agenda

• Motivations and Design ObjectiveMotivations and Design Objective• Network Model and Delay Modeling• Energy Synchronization Control• Evaluation• Conclusion


88

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


99

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


1111

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


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


1717

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


2020

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 !


2121

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


2222

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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http://mess.cs.umn.edu

http://www.nsf.gov/index.jsp

esc: energy synchronized communication in sustainable sensor networks

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