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

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Presentation on theme: "& MOKRENKO Olesia Lesecq S., Lombardi W., Puschini D., Debicki O. (CEA LETI) Albea C. (Université Toulouse III, LAAS)"— Presentation transcript:

1 & www.cea.fr MOKRENKO Olesia olesia.mokrenko@cea.fr 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. 332987 http://www.arrowhead.eu/ Predictive Control strategy to extend the lifespan of Wireless Sensor Networks 28 may 2014

2 Cliquez pour modifier le style du titre MOKRENKO Olesia| 28 may 2014 © CEA. All rights reserved | 2| 2 & Context & Motivations Smart world “Smart” devices (computation & decision capability ) Ensure control of a system over Wireless Sensor Network (WSN): Energy limited capacity Heterogeneity of technologies “Dynamicity” of system to control Objective of the work: Ensure control objectives under energy constraints and system dynamicity Objective of the work: Ensure control objectives under energy constraints and system dynamicity Smart Buildings Smart Grids Industrial Automation Automation Water Distribution Swarm Robotics

3 Cliquez pour modifier le style du titre MOKRENKO Olesia| 28 may 2014 © CEA. All rights reserved | 3| 3 & Related work Smart devices existence Different functioning modes Routing and communication protocols Networked Control Systems Ensure Quality of Service (QoS) … related to Control theory of the WSN Energy 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

4 Cliquez pour modifier le style du titre MOKRENKO Olesia| 28 may 2014 © CEA. All rights reserved | 4| 4 & System modeling (1/2) Single-hop heterogeneous sensor network architecture Wireless sensor node model Processing Subsystem Sensing Subsystem Power Supply Subsystem Communication Subsystem Example of sensor functioning modes SleepOffOn Typical cycle for a node working in mode «On» Sink Sensor node Current cons. Rx Tx MCU / Sensor

5 Cliquez pour modifier le style du titre MOKRENKO Olesia| 28 may 2014 © CEA. All rights reserved | 5| 5 & System modeling (2/2) WSN energetic model Average energy consumption for each sensor node Constraints imposed by the system model Bounded capacity of the battery Sensor / Node… … … … Node has a unique working mode

6 Cliquez pour modifier le style du titre MOKRENKO Olesia| 28 may 2014 © CEA. All rights reserved | 6| 6 & Control objectives Dynamically 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 WSN Ensure given “global objectives” → dynamic “mission” Sink Off On Sleep On Sleep

7 Cliquez pour modifier le style du titre MOKRENKO Olesia| 28 may 2014 © CEA. All rights reserved | 7| 7 & Control design Minimization problem Model Predictive Control solved by Mixed-Integer Quadratic Programming (MIQP) Inequality and equality constraints Real and binary variables MIQP problem is solved on-line at each decision time Subject to:

8 Cliquez pour modifier le style du titre MOKRENKO Olesia| 28 may 2014 © CEA. All rights reserved | 8| 8 & Application example (1/3) Benchmark 6 heterogeneous sensor nodes but with similar functionality + sink 3 different functioning modes Dynamic mission Model Predictive Control ModesProcessing SSCommunication SSSensing SS ActiveTx / RxOn SleepOff Sensor node 6.560.920 8.721.110 7.751.080 9.431.260 7.541.290 7.201.030 [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.

9 Cliquez pour modifier le style du titre MOKRENKO Olesia| 28 may 2014 © CEA. All rights reserved | 9| 9 & Application example (2/3): simulation Functioning modes of sensor nodes Simulation started in midnight Sensor node remaining energy evolution without and with DPM without DPM with DPM Sensor node 7,8721,1040 10,4641.3320 9,3001.2960 11,3161.5120 27,8984,7730 26,6403,8110 Remaining energy [mWh]

10 Cliquez pour modifier le style du titre MOKRENKO Olesia| 28 may 2014 © CEA. All rights reserved | 10 & Application example (3/3): simulation Total remaining energy comparison (with and without DPM) Remaining energy [mWh] time [h] Lifespan > 2× initial lifespan

11 Cliquez pour modifier le style du titre MOKRENKO Olesia| 28 may 2014 © CEA. All rights reserved | 11 & Conclusion & Future works Dynamic Power Management via Model Predictive Control is proposed Centralized control strategy For the considered example: Initial Lifespan × 2 Dynamicity of system partially taken into account Future works Decentralized control Harvesting system in the sensor node Implementation on a test-bench (for control validation) Sink Off On Sleep On Sleep On Sleep ??

12 Centre de Grenoble 17 rue des Martyrs 38054 Grenoble Cedex Centre de Saclay Nano-Innov PC 172 91191 Gif sur Yvette Cedex Thank you

13 Cliquez pour modifier le style du titre MOKRENKO Olesia| 28 may 2014 © CEA. All rights reserved | 13 & Control design Minimization problem Transformed by Model Predictive Control (by Mixed-Integer Quadratic Programming (MIQP)) Real and binary variables Inequality and equality constraints MIQP problem is solved on-line at each decision time

14 Cliquez pour modifier le style du titre MOKRENKO Olesia| 28 may 2014 © CEA. All rights reserved | 14 & 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] Computing Subsystem Flash memory Processor Sensing Subsystem Sensor(s) Read / write interface AFE Power Supply Subsystem Energy supply management / DC-DC converter Node power management Electricity storage Energy harvester Digital baseband Communication Subsystem Radio transceiver

15 Cliquez pour modifier le style du titre MOKRENKO Olesia| 28 may 2014 © CEA. All rights reserved | 15 & Sensor node battery characteristics Each sensor node embeds two AA batteries

16 Cliquez pour modifier le style du titre MOKRENKO Olesia| 28 may 2014 © CEA. All rights reserved | 16 & Algorithm of fulfill a mission Mission:

17 Cliquez pour modifier le style du titre MOKRENKO Olesia| 28 may 2014 © CEA. All rights reserved | 17 & 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 packets Packet dropouts result from transmission errors in physical network links or from buffer overflows due to congestion Time-varying transmission intervals and delays intervals depend on highly variable network conditions such as congestion and channel quality Communication 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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