networking low-power energy harvesting devices: measurements and algorithm

“Long-duration, Solar-powered Wireless Sensor Networks.” EmNets’07

Model only

Network4 nodes

MCU

Power management in energy harvesting sensor networks,” ACM Trans. Embed. Comput. Syst., 2007

Model driven

Network MCU

“Design, modeling, and capacity planning for micro-solar power sensor networks.” IPSN’08

Model only

Network 19 Tmotes

MCU

“Steady and Fair Rate Allocation for Rechargeable Sensors in Perpetual Sensor Networks.” SenSys’08

Model driven

Network150 Tmote

MCU

“SolarStore: Enhancing Data Reliability In Solar-powered Storage-centric Sensor Networks.“ MobiSys’09

Model driven

Network Embedded system

”Minimum Variance Energy Allocation for Solar-Powered System” DCOSS’09

Model driven

Network9 nodes

Embedded system

”On the Limits of Effective Hybrid Micro-Energy Harvesting on Mobile CRFID Sensors.” MobiSys’10

Model only

Individual system

Embedded system

“A Weather-Condition Prediction Algorithm for Solar-Powered Wireless Sensor Nodes” WICOM’10

Model only

Individual system

Embedded system

Accurate Modeling and Prediction of Energy Availability in Energy Harvesting Real-Time Embedded Systems” Green Computing’10

Model only

Individual system

Embedded system

Cloudy Computing: Leveraging Weather Forecast in Energy Harvesting Sensor System.” SECON10’

Model driven

Network5 TelosB

MCU

Networking Low-Power Energy Harvesting Devices: Measurements and Algorithms.” INFOCOM’11

Model driven

Individual system/Networks

MCU

Networking Low-Power Energy Harvesting Devices: Measurements and Algorithm

Gorlatova M., Wallwater A., Zussman G.INFOCOM 2011

Outline

• Introduction• Model• Measurement• Energy profile• Algorithm for predictable profile and

stochastic profile• Result

Introduction

• Achieve time-fair resource allocation since energy varies among different time

• Other research focus on fairness – Data generation rate (SenSys08)– among nodes (JSAC06)

• Proposed energy allocation algorithms across the different time slot to optimize– energy spending rate for single node– energy communication rate for a link

• Indoor irradiance measurements study.

Dimensions of algorithm design

• Environmental energy model• Energy storage type• Ratio of energy storage capacity to energy harvested• Time granularity– nodes characterize received energy – Make decision from sec ~days

• Problem /Network size– energy harvesting affects nodes’ decisions– Link decisions, routing, rate adaptation

Environmental energy model

• Predictable profiles– Ideal– Accurate for the future

• Partially predictable• Stochastic • Model-free

Energy storage type• Rechargeable battery– Ideal linear model– Changes of stored energy vs harvested power

• Capacitor– Non-linear model– Power harvested depend both on the energy

provided and on the amount of energy stored

Contributions

• Indoor : partially-predictable energy model• Time granularity : day, improving prediction• Fair allocation of resources along the time– Energy spending rate for a node– Data rate for a link

• Predictable energy profiles– Lexicographic maximization– Utility maximization

• Stochastic model– Markov Decision Process

Model

• K slots timei = {0,1,…K-1}

• D=AηH• Q(i)=q(D(i),B(i)) for capacitor•

Objective

• Optimization energy spending rate s(i) for single node• Optimization energy communication rate ru(i), rv(i) for a

link• Utility maximization frame work to find

– spending rate s(i)– communication rate ru(i), rv(i)– α-fair function U(s(i)) = s(i)1-α/(1-α), for α>0, α≠1

log(s(i)), for α=1• Consider predictable profile energy model

•

•

• Dynamic programming-based algorithm

• h(i,B(i))=max[ U(s(i)) + h(i+1,min(B(i)+Q(i)-s(i),C))]• Determine vector {s(0),…s(K-1)} maximizes h(0,B0)• Running time O(K[C/△]2)

• For linear storage q(D(i),B(i)) = D(i)• Progressive Filling algorithm

• Running time O(K[K+QT/△]),QT= ΣQ(i)+(B0-BK)• If linear storage is large, s(i) = QT/K

Measurement

• Long-term measurement of indoor irradiance• Office buildings at Columbia Uni. since 2009/6• TAOS TSL230rd photometric sensors• LabJack U3 DAQ

• Hd : mean of the daily irradiation• σ : standard deviation• r : bit rate, throughout a day when exposed Hd

– r = A(10cm2) x η(1%) x Hd /(3600x24)/(10e-9)– EnHANTs costs 1nJ/bit

How to predict Hd?

• Exponentially weighted moving-average (EWMA)– Error is relatively high

• For L-1, avg. prediction error > 0.4Hd

• L-2, avg. prediction error > 0.5Hd

• Outdoor, avg. prediction error = 0.3Hd

• Weather forecast [secon10] may be improved– For L-1, correlation coefficient of Hd and weather =

0.35– For L-6, correlation coefficient =0.8

Work week pattern

• For L-2, student office, on shelf far from window• Hd = 1.63 on weekdays, Hd=0.37 on weekend• 9.7hr/day lighting on weekday, <1hr on weekend• Avg. error prediction error 0.5Hd -> 0.26Hd if

separate weekdays and weekends.• L-1 and L-5, correlation coefficient =0.58• L-1 and L-5 facing same direction, correlation

coefficient =0.71

Short term energy profiles

• HT, T= 0.5 hr• L-3, daylight-

dependent variations• L-2, either 0 or

45uW/cm2

• partially predictable energy model

Mobile measurements

• mobile device carried around indoor and outdoor locations

• Indoor : 70uW/cm2

• Outdoor : 32mW/cm2

• Poorly predictable• Stochastic energy model

Link: optimizing Data rate

•

•

• ru(i) = rv(i) = r(i)

• Extension of TFR algorithm

• {ru(0),..ru(K-1)}, {rv(0,..rv(K-1)} maximize h(0,B0u,B0v)

• Complexity : • For linear storage, LPF algo.,

Decoupled Rate Control algorithm

• DRC algorithm• Determine su(i), sv(i) independently using PF

algorithm• r(i) = min(su(i), sv(i))/(ctx + crx)

Stochastic Energy model

• Energy harvested in a slot is and i.i.d (identical independent distribution)random variable D

• [d1,..dM] with probability [p1,…pM]• Spending Policy Determination (SPD) problem– Given distribution D, determine s(i)

• Markov Decision Process(MDP)

• Apply dynamic programming, from i=K-1 for each {i,B(i)}

• For each storage B(i), s(i) approached optimal• Running time

• Link Spending Policy Determination

Problem(LSPD)

• Apply dynamic programming• For each {i, Bu(i), Bv(i)}

• Maximization is over all {ru(i), rv(i)}, such that• ctxru(i) + crxrv(i) = su(i) ≤ Bu(i)• ctxrv(i) + crxru(i) =sv(i) ≤ Bv(i)• Complexity: O([Cu/△]2 [Cv/]2MuMvK)

Numerical results

• Energy profile L-3 input• s(i) are obtained by PF algorithm for linear

storage(left)• s(i) for TFU problem for nonlinear storage

(right)

• L-1, L-2 energy profile• Optimal communication rate {ru(i), rv(i)}

• Optimal energy spending policies (SPD)• L-1 profile as random variable D• Optimal s(i)

• Optimal communication rate ru(i), rv(i)

Conclusion

• First long-term indoor radiant energy measurements campaign that provides useful traces

• Developed algorithms for predictable environment that uniquely determine the spending policies for linear and non-linear energy storage models

• Developed algorithms for stochastic environments that can provide nodes with simple pre-computed decisions policies

“Long-duration, Solar-powered Wireless Sensor Networks.” EmNets’07

Model only

Network4 nodes

MCU

Power management in energy harvesting sensor networks,” ACM Trans. Embed. Comput. Syst., 2007

Model driven

Network MCU

“Design, modeling, and capacity planning for micro-solar power sensor networks.” IPSN’08

Model only

Network 19 Tmotes

MCU

“Steady and Fair Rate Allocation for Rechargeable Sensors in Perpetual Sensor Networks.” SenSys’08

Model driven

Network150 Tmote

MCU

“SolarStore: Enhancing Data Reliability In Solar-powered Storage-centric Sensor Networks.“ MobiSys’09

Model driven

Network Embedded system

”Minimum Variance Energy Allocation for Solar-Powered System” DCOSS’09

Model driven

Network9 nodes

Embedded system

”On the Limits of Effective Hybrid Micro-Energy Harvesting on Mobile CRFID Sensors.” MobiSys’10

Model only

Individual system

Embedded system

“A Weather-Condition Prediction Algorithm for Solar-Powered Wireless Sensor Nodes” WICOM’10

Model only

Individual system

Embedded system

Accurate Modeling and Prediction of Energy Availability in Energy Harvesting Real-Time Embedded Systems” Green Computing’10

Model only

Individual system

Embedded system

Cloudy Computing: Leveraging Weather Forecast in Energy Harvesting Sensor System.” SECON10’

Model driven

Network5 TelosB

MCU

Networking Low-Power Energy Harvesting Devices: Measurements and Algorithms.” INFOCOM’11

Model driven

Individual system/Networks

MCU

Model only/model driven

objective Network/individual system

MCU / embedded

P. Corke, et al. “Long-duration, Solar-powered Wireless Sensor Networks.” EmNets’07

Model only Deployed 2 years, estimation component efficiency

Network4 sensor nodes

MCU

J. Taneja, et al. “Design, modeling, and capacity planning for micro-solar power sensor networks.” IPSN’08

Model only Battery comparison and experience of deployment

Network 19 Tmotes

MCU

K.Fan, et al., “Steady and Fair Rate Allocation for Rechargeable Sensors in Perpetual Sensor Networks.” SenSys’08

Model driven Deployed outside for 2 month maximum data collection rate

Network150 Tmote

MCU

Model only/model driven

objective Network/individual system

MCU / embedded

Y. Yang,et al. “SolarStore: Enhancing Data Reliability In Solar-powered Storage-centric Sensor Networks.“ MobiSys’09

Model driven Data replication , outdoor/ indoor testbed

Network Embedded system

D.Noh, et al, ”Minimum Variance Energy Allocation for Solar-Powered System” DCOSS’09

Model driven Energy allocation for time slots(1hr)

Network9 nodes

Embedded system

C.Moser, et al., “Power Management in Energy Harvesting Embedded Systems with Discrete Service Levels” ISLPED’09

Model driven Optimization for scheduling by dynamic programming

Individual system

Embedded system

Jeremy G., et al. ”On the Limits of Effective Hybrid Micro-Energy Harvesting on Mobile CRFID Sensors.” MobisSys’10

Model only Power consumption benchmark

Individual system

Embedded system

Model only/model driven

objective Network/individual system

MCU / embedded

Zhaoting J., et al. “A Weather-Condition Prediction Algorithm for Solar-Powered Wireless Sensor Nodes” WICOM’10

Model only EWMA vs Weather Conditioned EWMA

Individual systemsimulation

Embedded system

Jun. L., et al.”Accurate Modeling and Prediction of Energy Availability in Energy Harvesting Real-Time Embedded Systems” Green Computing’10

Model only Prediction of harvested energy

Individual systemsimulation

Embedded system

N.Sharma, et al. ”Cloudy Computing: Leveraging Weather Forecast in Energy Harvesting Sensor System.” SECON’10

Model driven Forecast weather energy model, lexicographically fair rate

Network5 TelosB

MCU

M.Gorlatove, et al.”Networking Low-Power Energy Harvesting Devices: Measurements and Algorithms.” INFOCOM’11

Model driven Derive algorithm for energy spend rate/ link rate

Individual system/Network simulation

MCU

• Alpha ->0, globally optimize• Alpha ->1, proportional fairness• ->2, harmonic mean fairness• -> infinite , generalized max min

allocation

Introduction

• Perpetual energy harvesting wireless device– Solar, piezoelectric and thermal energy harvesting– Ultra-low-power wireless communication– Rechargeable sensor networks

• Focus on devices that harvest environmental light energy

• Energy-harvesting-aware algorithm and system– Lack of data and analysis of energy availability for

indoor/outdoor environment– 16 months indoor light energy measurement

• light measurement and resource allocation