& MOKRENKO Olesia [email protected] Lesecq S., Lombardi W., Puschini D., Debicki O. (CEA LETI)...

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MOKRENKO [email protected]

Lesecq S., Lombardi W., Puschini D., Debicki O. (CEA LETI) Albea C. (Université Toulouse III, LAAS)

Work partially funded by the Artemis ARROWHEAD project under grant agreement nb. 332987http://www.arrowhead.eu/

Predictive Control strategy to extend the lifespan of Wireless Sensor Networks

28 may 2014

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Context & Motivations

Smart world“Smart” devices (computation & decision capability )

Ensure control of a system over Wireless Sensor Network (WSN):Energy limited capacityHeterogeneity of technologies “Dynamicity” of system to control

Objective of the work:Ensure control objectives under energy constraints and system dynamicity

Smart Buildings

Smart Grids

Industrial Automation

Water Distribution

Swarm Robotics



Related workSmart devices existence

Different functioning modes

Routing and communication protocols

Networked Control SystemsEnsure Quality of Service (QoS)

… related to Control theory of the WSNEnergy conservation methodologies

Dynamic Power Management (DPM) Application Layer

Transport Layer

Network Layer

Data Link Layer

Physical Layer

Power Management Plane

Connection Management Plane

Task Management Plane



System modeling (1/2)Single-hop heterogeneous sensor network architecture

Wireless sensor node model

Pro

cess

ing

Subs

yste

m

Sensing Subsystem

Power Supply

Subsystem

Communication Subsystem

Example of sensor functioning modes

Sleep OffOn

Typical cycle for a node working in mode «On»

Sink

Sensor node

Current cons.

Rx

Tx

MCU / Sensor



System modeling (2/2)WSN energetic model

Average energy consumption for each sensor node

Constraints imposed by the system modelBounded capacity of the battery

𝑥𝑘+1=𝐴 𝑥𝑘+𝐵𝑢𝑘

¿ −

Sensor / Node …

…

…

…

𝑥

𝑡𝑖𝑚𝑒𝑘 𝑘+1

𝑥𝑘+1

𝑥𝑘

?

0≤ 𝑥 𝑖≤𝑋𝑖𝑚𝑎𝑥

𝑋 𝑖𝑚𝑎𝑥

0

Node has a unique working mode



Control objectives

Control objectivesDynamically re-configure a WSN in order to provide the requested services and performance levels with a minimum number of active sensor nodes

Dynamic Power Management in the WSNEnsure given “global objectives” → dynamic “mission”

For mode :Sink

Off

OnSleep

On

Sleep

Sleep



Control design

Minimization problem

Model Predictive Control solved by Mixed-Integer Quadratic Programming (MIQP)

Inequality and equality constraintsReal and binary variablesMIQP problem is solved on-line at each decision time

𝑢∗=argmin𝑢

(‖𝑥‖𝑄2+‖𝑢‖𝑅2 )

𝑋 𝑖𝑚𝑎𝑥

Subject to:

• Bounded capacity of the battery

• Node has a unique working mode

• Binary control:

• Extra functional constraints – mission:

0



Application example (1/3)Benchmark

6 heterogeneous sensor nodes but with similar functionality + sink 3 different functioning modes

Dynamic mission

Model Predictive Control

Modes Processing SS Communication SS Sensing SS

Active Tx / Rx On

Sleep Off Off

Off Off Off

Sensor node

6.56 0.92 0

8.72 1.11 0

7.75 1.08 0

9.43 1.26 0

7.54 1.29 0

7.20 1.03 0

Average current consumption (modified from [1])

[1] Fourty, Nicolas, Adrien Van Den Bossche, and Thierry Val. "An advanced study of energy consumption in an IEEE 802.15. 4 based network: Everything but the truth on 802.15. 4 node lifetime." Computer Communications 35.14 (2012): 1759-1767.

𝑀𝑖𝑠𝑠𝑖𝑜𝑛

𝑡𝑖𝑚𝑒8 am0 6 pm 8 am 6 pm



Application example (2/3): simulationFunctioning modes of sensor nodes

Simulation started in midnight

Sensor node remaining energy evolution without and with DPMwithout DPM with DPM

Sensor node

Mode []

Mode []

Mode []

7,872 1,104 0

10,464 1.332 0

9,300 1.296 0

11,316 1.512 0

27,898 4,773 0

26,640 3,811 0

Rem

ain

ing

en

erg

y [

mW

h]

Rem

ain

ing

en

erg

y [

mW

h]



Application example (3/3): simulation

Total remaining energy comparison (with and without DPM)

Rem

ain

ing

en

erg

y [

mW

h]

time [h]

Lifespan > 2× initial lifespan



Conclusion & Future works

Dynamic Power Management via Model Predictive Control is proposed

Centralized control strategyFor the considered example: Initial Lifespan × 2Dynamicity of system partially taken into account

Future worksDecentralized controlHarvesting system in the sensor nodeImplementation on a test-bench (for control validation)

SinkOff

OnSleep

On

Sleep

Sleep

On

Sleep??

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Control design

Minimization problem

Transformed by Model Predictive Control (by Mixed-Integer Quadratic Programming (MIQP))

Real and binary variablesInequality and equality constraintsMIQP problem is solved on-line at each decision time

Subject to:



Sensor node

[1] Chen, Fred, et al. "Energy-Aware Design of Compressed Sensing Systems for Wireless Sensors Under Performance and Reliability Constraints." Circuits and Systems I: Regular Papers, IEEE Transactions on 60.3 (2013): 650-661.

Modified from [1]

ComputingSubsystem

Flash memor

y

Processor

Sensing SubsystemSensor(s)

Read / write

interface

AFE

Power Supply Subsystem

Energy supply management / DC-DC

converter

Node power

management

Electricity storage

Energy harvest

er

Digitalbaseband

Communication Subsystem

Radio transceiver



Sensor node battery characteristics

Sensor node battery characteristics Each sensor node embeds two AA batteries



Algorithm of fulfill a mission

Mission:



Wireless network constraints

Imperfections and constraints of wireless network (related QoS):

Quantization errors in the signals transmitted over the network due to the finite word length of the packetsPacket dropouts result from transmission errors in physical network links or from buffer overflows due to congestionTime-varying transmission intervals and delays intervals depend on highly variable network conditions such as congestion and channel qualityCommunication constraints caused by the sharing of the network by multiple nodes and the fact that only one node is allowed to transmit its packet per transmission

Energy limited capacity

Heemels, WP, Maurice H., et al. "Networked control systems with communication constraints: Tradeoffs between transmission intervals, delays and performance." Automatic Control, IEEE Transactions on 55.8 (2010): 1781-1796.Naghshtabrizi, Payam, and Joao P. Hespanha. "Implementation considerations for wireless networked control systems." Wireless Networking Based Control. Springer New York, 2011. 1-27.

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