Advisor : Prof. Tajana Simunic Rosing. CSE Dept., University of California, San Diego

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1 Aruna Ravinagarajan Advisor : Prof. Tajana Simunic Rosing CSE Dept., University of California, San Diego System Energy Efficiency Lab

2 Structural Health Monitoring (SHM) SHM: Process of monitoring a structure over time and identifying damage A wireless sensor network (WSN) Monitors a physical space or object Environment Humans and animals Structures Remote Location: Needs long lasting energy source System Energy Efficiency Lab

3 System Energy Efficiency Lab SHM How is it done?

4 SHM How is it done? Stuart G Taylor, Kevin M Farinholt, Eric B Flynn, Eloi Figueiredo, David L Mascarenas, Erik A Moro, Gyuhae Park, Michael D Todd and Charles R Farrar, A mobile-agent-based wireless sensing network for structural monitoring applications. Meas. Sci. Technol (14pp) System Energy Efficiency Lab

5 SHiMmer for Structural Health Monitoring PZT s Shimmer System Energy Efficiency Lab

6 SHiMmer for Structural Health Monitoring Localized embedded node for detection of damage in structures Local analysis on SHiMmer is mandatory due to data size, 4 MB per test High DSP computational workload Max power 370mW at 400 MIPS Peak power consumption 1.1W when sensing/actuating Powered only with energy harvesting System Energy Efficiency Lab

7 Energy Harvesting Challenges: Solar energy is not uniformly distributed over time Daily weather and seasons change the total input energy The task scheduler needs to manage energy consumption and accuracy of computation System Energy Efficiency Lab 7

8 Motivation Problem Definition: WSNs powered only via energy harvesting Operating with severe energy constraints Too much data to continually transmit Localized processing a must Key challenge: Minimize energy costs while maximizing accuracy of computation System Energy Efficiency Lab

9 Related work Related work: Energy Harvesting in WSNs Guarantee energy neutrality and adapt duty cycle [Kansal et al., DAC 06] Task Scheduling in WSNs Power Management with discrete service levels [Moser et al., ISLPED 09] Adapting Task Utility in externally triggered WSN [J.Steck et al., INSS 09] System Energy Efficiency Lab

10 Steady State Mode Periodic Lifetime Monitoring Identifies damage that accumulates over long period of time Effective bridge maintenance costs millions of $$$ every year By performing steady state monitoring, reduces burden on bridge s annual maintenance System Energy Efficiency Lab 10

11 External Request Mode Event 1967, Silver Bridge 2007, Minneapolis Bridge System Energy Efficiency Lab 11

12 External Request Mode Event Extra measurements are required to verify the structure s integrity Execution Time Constraint Given a time limit, what is the highest level of data accuracy? Data Accuracy Constraint Given a minimum data accuracy, how long will it take to execute tasks? System Energy Efficiency Lab 12

13 Challenge and Contribution To run an intensive workload, Task Scheduler needs to manage: Energy Consumption Accuracy of computation Contribution: Regression based algorithm to optimize resource utilization Applying DVFS techniques scaled by available energy System Energy Efficiency Lab 13

14 Active Ultrasonic SHM 16 PZT sensors provide 120 different sensing paths 120 waveform data give about 4 Mbyte Filtering signal using FFTs Convolving filtered set of data with baseline signals Damage detection consists of combining data using a correlation function A higher number of measurements increases detection and localization accuracy System Energy Efficiency Lab 14

15 Setting up Regression Model BlackFin integrated DVFS defines three different DSP working modes: Active High: V core =1.20V, f DSP =300 MHz, P Max =370mW Active Low: V core =0.85V, f DSP =150 MHz, P Max =220mW Idle: V core =0.85V, f DSP =75 MHz, P Max =35mW System Energy Efficiency Lab 15

16 Setting up Regression Model The energy and time consumption, accuracy for every task in the task graph can be determined based on the number of paths, frequency and power System Energy Efficiency Lab 16

17 Setting up Regression Model System Energy Efficiency Lab 17

18 Steady State Algorithm Steady State activity performed every 15 min (900 sec) : T slot E available represents the available energy in buffer Based on regression model, DVFS mode is automatically selected by the scheduler in order to maximize number of path measurements with respect to energy availability System Energy Efficiency Lab 18

19 Steady State Algorithm The available energy is calculated as: Available Energy E available t = E buffer t E th _ min E buffer t = E buffer t 1 + E harvested t E consumed t where E th_min is the minimum amount of energy to execute an external request System Energy Efficiency Lab 19

20 Steady State Algorithm Available Energy Execution time is estimated using relation obtained from regression : T e execution t DSP DSP = t, t DSP E available t { f, f } High DSP low where t DSP and e DSP are model coefficients that depend on the DVFS mode (high/low frequency) e Estimate Execution Time System Energy Efficiency Lab 20

21 Steady State Algorithm Available Energy Estimate Execution Time The DVFS mode is selected to max N paths according to estimated limits: Select DVFS mode e E e DSP DSP T execution execution t DSP ( fi) < { f, f } High t E low = E available execution ( fi) System Energy Efficiency Lab 21

22 Steady State Algorithm Available Energy Estimate Execution Time Select DVFS mode Execute tasks according to task graph for N paths Transmit Result System Energy Efficiency Lab 22

23 External Request Algorithm External Requests (ER) set two constraints: Maximum Execution Time Minimum SHM Accuracy The DVFS mode is selected according to estimated limits: DVFS mode selection Tconstrain max performed E in manner similar required = edsp OR min to steady state tdsp Accuracy min Erequired = edsp min adata E > E > E available th _ min required Available Energy Max Time OR Min Accuracy Select DVFS mode Execute tasks according to task graph for N required paths Transmit Result System Energy Efficiency Lab 23

24 Experimental set-up Inputs to the system: Variable solar energy conditions to the energy harvester Task sequence as shown in Task Graph DVFS active frequency and power modes Periodic activity time slots: T slot = 900s Constant number of External Requests per day: 25 Results compared to an iterative search algorithm for task scheduling in SHM 2 2 J. B. Steck et al. "Adapting Performance in Energy Harvesting Wireless Sensor Network for Structural Health Monitoring Applications," 6th International Conference on Networked Sensing Systems, 2009 System Energy Efficiency Lab 24

25 Number of measurements increase 50% measurement increase with regression algorithm 15-20% further increase with DVFS System Energy Efficiency Lab 25

26 Measurement accuracy improvement 16% accuracy increase with regression algorithm 27% accuracy increase with DVFS System Energy Efficiency Lab 26

27 Increase in number of served ER 95% of external request served with regression algorithm 20% of energy saving with DVFS System Energy Efficiency Lab 27

28 Conclusions With our task scheduler, SHiMmer for SHM analysis achieved: Up to 85% increase in the number of daily measurements Up to 27% increase in result accuracy Up to 95% of external request service requests processed The algorithm improves performance through an efficient combination of: The adoption of the regression algorithm, optimizing the usage of available resources An efficient usage of DVFS System Energy Efficiency Lab 28