networking low-power energy harvesting devices: measurements and algorithm
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
“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