Emerging Techniques for Energy
Management in Practical WSNs
Giuseppe Anastasi
Dept. Information Engineering, University of Pisa
E-mail: g.anastasi@iet.unipi.it
Web: http://www.ing.unipi.it/~anastasi/
PerLabBased on work carried out in cooperation with
Cesare Alippi, Manuel Roveri, Cristian Galperti (Polytechnic of Milan)
Mario Di Francesco (University of Pisa)

Outline
 Energy-efficient data acquisition
 Motivations
 Main approaches
 Our contribution
 Conclusions

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Current snapshot
 Increasing number of sensor network deploymentsfor real-life applications
 Progressive diffusion of commercial devices
 sensors sensor nodes

WSNs cannot be regarded any moreas an interesting research topic only

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Future perspectives
ON World Inc., “Wireless Sensor Networks – Growing
Markets, Accelerating Demands”, July 2005
http://www.onworld.com/html/wirelesssensorsrprt2.htm

 Prediction
 127 millions of sensor nodes operational in 2010
 particularly in the field of industrial applications

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Future perspectivesEmbedded WiSeNTs project (funded by the EuropeanCommunity, FP6) roadmap, November 2006.
http://www.embedded-wisents.org/dissemination/roadmap.html

 Prediction
 The WSN market share will grow considerably upto 2015
 especially in the fields of logistics, automationand control

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Limitations
Energy limitation remains the main barrier tothe diffusion of this technology
 Main approaches
 Low-power design
 Energy harvesting Energy conservation
 Energy efficient networking protocols
 Energy-efficient application design
 Cross-layering
…
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Energy Conservation Schemes

G. Anastasi, M. Conti, M. Di Francesco, A. Passarella, Energy Conservation in
Wireless Sensor Networks, Ad Hoc Networks Journal, submitted for publication
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A common assumption
 Traditional assumption about energy consumption

data transmission is much more expensive
than data sensing and processing Recent deployments have highlighted that this assumption
doesn’t hold in many practical application scenariosEmerging Techniques for Energy Management in Practical WSNs

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Power Consumption of Common
RadiosPower Consumption
RadioProducer
Transmission( at 0 dBm)

Reception

CC2420(Telos)

Texas
Instruments

35 mW

38 mW

CC1000(Mica2/Mica2dot)

Texas
Instruments

42 mW

29 mW

TR1000(Mica)

RF Monolithics

36 mW

9 mW

J. Polastre, A Unifying Link Abstraction for Wireless Sensor Networks, Ph.D. Thesis,
University of California at Berkeley, 2005.
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Power Consumption of SomeSensorsSensor

Producer

Sensing

PowerConsumption

STCN75STMTemperature

0.4 mW

QST108KT6

STM

Touch

7 mW

iMEMS

ADI

Accelerometer(3 axis)30 mW

2200 Series, 2600 Series

GEMS

Pressure

50 mW

T150

GEFRAN

Humidity

90 mW

LUC-M10PEPPERL+FUCHS

Level Sensor

300 mW

CP18, VL18, GM60, GLV30

VISOLUX

Proximity

350 mW

TDA0161

STM

Proximity

420 mW

FCS-GL1/2A4-AP8X-H1141

TURCK

Flow Control

1250 mW

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Sensor Energy Consumption
 Energy for sensing cannot be neglected due to use of active transducers
 e.g., sonar and radar need of highly energy consuming A/D converters
 e.g., acoustic or seismic sensors

 presence of sensing arrays e.g., CCD or CMOS image sensor

 acquisition time much longer than transmission time

Schemes for effective management of sensor
energy consumption must be devised
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Management of sensor energy
consumption
Energy Efficient
Data Acquisition

Duty Cycling

HierarchicalSensing

Adaptive
Sensing

Model-based
Active Sensing

AdaptiveSampling

V. Raghunathan, S. Ganeriwal, M. Srivastava, Emerging Techniques for Long
Lived Wireless Sensor Networks, IEEE Communication Magazine, April 2006.
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Hierarchical Sensing Basic idea Using different sensors with different power
consumption and resolution properties
 Accuracy/energy consumption trade-off Triggered sensing Low-power low resolution sensors trigger high-powerhigh-accuracy sensors

 Multi-scale sensing Low-resolution wide area sensors are used to identify
areas of interests
 High resolution sensors are, then, switched on for
more accurate measurements
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Triggered Sensing: An example
 Low-power Low-cost Video sensor [DSD 2008] Video surveillance, traffic control, people detection, …
 CMOS video camera (550 mW)
 Pyroelectric InfraRed (PIR) sensor (2 mW)
 Bluetooth/ZigBee module (100 mW)
 Energy harvesting system (solar cells)

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Multi-scale sensing: an example
 I-Mouse [Tseng 2007]
 Fire detection system
 Static sensor monitors the temperature Anomaly detected in a given region  Mobile sensors are sent for deeper investigation
 They collect data (take snapshots)
 Then, come back to the control center Appropriate actions are taken by the control center

Y.-C. Tseng, Y.C. Wang, K.-Y. Cheng, Y.-Y. Hsieh, iMouse: An Integrated Mobile
Surveillance and Wireless Sensor System, IEEE Computer, Vol. 40, N. 6,June 2007.
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Management of sensor energy
consumption
Energy Efficient
Data Acquisition

Duty Cycling

HierarchicalSensing

AdaptiveSensing

Model-based
Active SensingAdaptiveSampling

V. Raghunathan, S. Ganeriwal, M. Srivastava, Emerging Techniques for Long
Lived Wireless Sensor Networks, IEEE Communication Magazine, April 2006.
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Adaptive Sampling Adapts the sampling rate to the dynamics of thephenomenon under monitoring Exploits Temporal Correlation
 Spatial Correlation
 The available energy may also be considered Reduces at the same time the energy consumption
for data acquisition and communication
 Lower amount of data to transmit
 Lower number of sensor nodes to activate

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Adaptive Sampling (cont’d)
Key Questions
 When to change?
 How to change?

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Adaptive sampling (cont’d) Correlation-based reliable event transport [Akan 2003]
 Distortion vs. reporting frequency model: D(f)
 Goal: achieve the desired distortion level D* with the minimum
reporting frequency
 Event to Sink Reliable Transport (ESRT) protocol
 Achieves reliable event detection with minimum energy expenditureand congestion (centralized approach) Adaptive Sampling – [Jain-Chang, 2004]
 Nodes adapt their sampling rate within a certain range
 Kalman Filter used to predict future activity

 If the desired modification exceeds the allowed range, nodes ask for
additional bandwidth Decentralized adaptation scheme + (centralized) bandwidth allocation
mechanism Goal: bandwidth/energy usage optimization
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Adaptive sampling (cont’d) FloodNet Adaptive Routing (FAR) [Zhou 2007]
 Adaptive sampling + energy-aware routing Adaptive sampling is based on a flood prediction model Centralized approach Decentralized Adaptive Sampling [Kho 2007] Sampling rate adapted on the basis of the available energy
 Nodes are powered by solar cells

 Goal: minimize the total uncertainty error, given that the sensor cantake a maximum number of samples on that day

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Adaptive sampling (cont’d) Backcasting [Willet 2004]
 More nodes should be active in regions where the variation of thesensed quantity is high Preview phase: only a subset of nodes are activated for an initial
estimate Refinement phase: the control center can activate more nodes inregions where the spatial correlation is low

 Correlation-based Collaborative MAC (CC-MAC) [Vuran 2006]
 Minimizes the number of sampling nodes while achieving the desired
level of distortion D*
 The base station derives the correlation radius (based on distortionlevel D* and spatial correlation model) and broadcasts it to sensor
nodes Only a single node within the radius samples and reports data
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Management of sensor energy
consumption
Energy Efficient
Data Acquisition

Duty Cycling

HierarchicalSensing

AdaptiveSensing

Model-based
Active Sensing

AdaptiveSampling

V. Raghunathan, S. Ganeriwal, M. Srivastava, Emerging Techniques for Long
Lived Wireless Sensor Networks, IEEE Communication Magazine, April 2006.
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Model-based Active Sensing Basic idea
 Learn the spatio-temporal relationships among measurements
 and use this knowledge to make the sensing process energy efficient A model of the phenomenon to be monitored is built And updated dynamically, based on measurements from sensor nodes

 The sensor node decides whether
 To acquire a new sample through a measurement
 To estimate this new sample, with the desired accuracy, through themodel Different kind of models
 Probabilistic models, Regressive models, …
 The most appropriate model is application specific

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Model-based Active Sensing(cont’d) Barbie-Q (BBQ) Query System [Deshpande 2004]
 Probabilistic model (based on time-varying multivariate Gaussians) andquery planner (base station) The model is built and updated dynamically based on sensor reading Using this model, the system decides the most efficient way to answer
the query with the required confidence Some values are acquired from sensors, some others are derived from themodel Utility-based Sensing and Comm. (USAC) [Padhy 2006]
 Glacial environment monitoring Linear regression model (sensor node) data are expected to be piecewise linear functions of time If the next observed data is within the CI the sampling rate is reduced forenergy efficiency
 Otherwise, the sampling rate is set to the maximum to incorporate thechange in the model
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Our Contribution

Snow Sensor

Power Consumption: 59 mW
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Snow Sensor Node
 Multi-frequency capacitive measuring unit composed by a probe multi-frequency injection board capable of measuring capacity of thedielectric at different frequencies

 Temperature sensor
 Mote Sensor node Processing Wireless communication

23

Data transmissionTemperature
measurement

1Emerging Techniques for Energy Management in Practical WSNs

Snow capacitance measurement
at 100Hz and at 100 kHz

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Snow Monitoring Applications
45000

Snow
Snow

40000

Equivalent capacity

Snow
35000

Snow

30000

Snow
Snow
Ice

25000

Ice

20000

Ice
Ice

15000

Ice

10000

Ice
Ice
Ice

5000

Air
Water

050 Hz100 Hz200 Hz500 Hz800 Hz

1 KHz

5 KHz

10 KHz 50 KHz 100 KHz

Measuring frequency

Measure the snow
dielectric constant

Quantify the presenceof water, ice and air
in the snow

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Monitor thesnow coverage statusATSN 2008

Snow Sensor
 The sensing activity is very power consuming Three readings for each measure are done to achieve a stable andreliable value Power Consumption: 59 mW

 The system is powered by a rechargeable battery pack
 An energy harvesting may also be present
 Energy must be managed very efficiently
2

3

Data transmission

Temperature
measurement

1
Emerging Techniques for Energy Management in Practical WSNs

Snow capacitance measurement
at 100Hz and at 100 kHz
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Energy Conservation
 Twofold Approach switch off the sensor between consecutive samples
 Trivial solution
 Reduce the energy consumption by 83%

 adapt the sampling frequency to the process undermonitoring

Adaptive Sampling Algorithm
 The idea: find dynamically the minimum sample ratecompatible with the monitored signal ↓ sample rate  ↓ sampling energy consumption↓ transmission energy consumption
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Our proposal
 Nyquist Theorem:
Fmax  Fs > 2 Fmax

 Fmax is not known in advance and changes over time Track the dynamics of the process under monitoring andadapt the sampling frequency accordingly Modified CUSUM change detection test We modified the traditional CUSUM test to assess the nonnon-stationarity of themaximum frequency in the signal’s power spectrum.

 General approach Not only for snow monitoring Suitable for slowly varying processes

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Frequency Change Detection

Modified CUSUM test
1.

Estimate the maximum signal
frequency Fmax

W-sample training set

Fs=c*Fmax, c>2

2.

Define two alternative thresholds
Fup and Fdown

3.

If the current estimated maximum
frequency Fcurr is closer to Fup /Fdown
than Fmax for h consecutive
samples, a change is detected in
the maximum frequency of thesignal

4.

A new sampling frequency Fs isdefined (Fs=c*Fcurr, c>2)

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Algorithm Sampling Algorithm
Estimate Fmax by considering the initial W samples and set Fs = c * Fmax.;
Define Fup = (1 + (c-2)/4) * Fmax and Fdownp = (1 – (c-2)/4) * Fmax;
h1=0 and h2=0;
for (i=W+1; i < DataLength; i++) {Estimate the current maximum frequency Fcur on the subsequence (i-W, i)if ( | Fcurr - Fup | < | Fcurr - Fmax | )h1= h1+1;
else if ( | Fcurr - Fdown | < | Fcurr - Fmax | )h2= h2+1;
else {
h1=0;
h2= 0;}
if (h1 > h )|| (h2 > h) {Fs = c * Fcurr.;
Fup = (1 + (c-2)/4) * Fcurr;
Fdown = (1 – (c-2)/4) * Fcurr;}}C. Alippi, G. Anastasi, C. Galperti, F. Mancini, M. Roveri, Adaptive Sampling for Energy Conservation in WirelessSensor Networks for Snow Monitoring Applications, Proc. IEEE MASS 2007, MASS-GHS Workshop, Pisa (Italy),October 8, 2007.
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Cluster-based Architecture
Base Station

Cluster Head
Cluster Node

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Data Collection Protocol

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Simulation Setup Network scenario
 Cluster-based architecture Adaptive Sampling Algorithm executed at BS Dataset: real snow measurements
 4 datasets derived in different days
 6000 samples acquired with a fixed period of 15s
 about 24 hours

 Message loss: Bernoulli process
 Loss compensation
 Missed samples are replaced by the previous ones

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Figures of Merit Sampling Fraction, number ofsamples acquired by the Adaptivealgorithm w.r.t. the number ofsamples acquired using fixed-rate

 MRE:

1 N xi − xi∑
N i =1 xi

Emerging Techniques for Energy Management in Practical WSNs

provides an indicationof the energy savedwih the Adaptive
Sampling Algorithm

gives a measure of therelative error introducedin the data sequence
reconstructed at the BS

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Parameter selection: c, h and W
 Parameter c: confidence parameter for the maximum
frequency detection (c > 2, Nyquist) Parameter h: critical to the robustness of the algorithm
 low values (e.g., 1 or 2): quick detection but possible false positives
 high values (e.g., 1000): few false positives but less prompt in
detecting the changes

 Parameter W: critical to the accuracy of the algorithm low values: not accurate estimation but low energy consumption
 high values: accurate estimation of Fs but high energy consumption

 A-priori knowledge about the process, if available, can beused for a proper parameter setting
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Parameters Algorithm parameters
W = 512, h = 40, c = 2.1
Radio Parameters
Parameter

Communication Protocol
Parameters
Parameter

ValueValue

hello message size

6 bytes

RadioCC1000

ch-ready/bs-ready message size

10 bytes

Frame size

36 bytes

data message size

21 bytesBit rate

19.2 Kbps

ack message size

2 bytes

notify message size

13 bytes

Transmit Power (0 dBm)

42 mW

Receive Power

Frame size

15 s

29 mW

Slot size

1sIdle Power

29 mW

Retransmission timeout (t_out)

Sleeping Power0.6 µW

Max number of retransmissions (max_rtx)

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Simulation Results Sampling Fraction (Energy Saving)
The Adaptive Sampling Algorithm
reduces significantly the number ofsamples the snow sensor has toacquire (67-79%)

The algorithm can save a lot ofenergy consumed by both thesensor and radio subsystems

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Simulation Results (2) MRE for Low/High Frequency Capacity
Low
Frequency

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HighFrequency

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Simulation Results (3) MRE for the temperature Original andreconstructed sequences

The MRE for ambient temperature is high in all
the scenarios. This is because temperature
values ranges from -3 to 23 C. Small absolutevalues can cause an high error
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Impact of delivery ratio
Delivery Ratio

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Sampling Fraction

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Impact of delivery ratio
Energy consumed
by the sensor

Emerging Techniques for Energy Management in Practical WSNs

Energy consumed
by the radio

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Energy Consumption

Power management schemePower cons.

Activity ratio

No Power Management(Always On)

880 mJ/sample

100%

Duty-cycle

150 mJ/sample

Duty-cycle + Adaptive
Sampling

Emerging Techniques for Energy Management in Practical WSNs

(1 sample every 15 sec)

(1 sample every 15 sec)

17%
3.5-5.5%ATSN 2008

Conclusions
 The Adaptive Sampling Algorithm reduces the % ofsamples
 by 67-79% with respect to fixed over-sampling (1 sample every 15 sec) and, correspondingly, the energy consumption
 for sensing and communication
 The MRE remains at acceptable values

 General methodology
 Can be used for slowly changing phenomena

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Conclusions
 Hierarchical Sensing Very energy efficient Application specific Adaptive Sampling Quite general and efficient Often centralized due to the high computational requirements
 Usually a single direction (time or space) is explored Model-based Active Sensing Very promising approach Should be improved in the direction of decentralization
 Key question: which is the optimal class of models for a specificapplication scenario?

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Questions?

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