EN FA11 Network Embedded Systems/Sensor Networks Week 7: Energy Management. Marcus Chang, Andreas CS JHU

Transcription

1 EN FA11 Network Embedded Systems/Sensor Networks Week 7: Energy Management Marcus Chang, Andreas CS JHU 1

2 Power Management Where does all my power go? Simulation Collect power measurements during deployment Collect run-time traces from deployment What is the most efficient way to use the available power? Fixed lifetime requirement? Local power usage policy Global energy optimization 2

3 Simulation TOSSIM COOJA/MSPSim Good for simulating large deployments Need real traces to be accurate 3 Image: COOJA/MSPSIM

4 Energy Metering for Free: Augmenting Switching Regulators for Real-Time Monitoring Prabal Dutta, Mark Feldmeier, Joseph Paradiso, and David Culler Appeared in IPSN

5 Power Measurement Options Attach external amperemeter Can be expensive for large networks Use onboard ADC Can be disruptive/interfere with measurements 5

6 Mote Power Supply Switching Regulator Takes variable input voltage V in and changes it into stable V out Charge-discharge cycle Analogy: filling and emptying a bucket of water V LX switches between V in, V out, and ground 6

7 Switching Regulator Linear response between switching frequency and load icount: Connect V LX to a counter module in MCU Correlate counts with current 7

8 Evaluation Accuracy (range: 5μA 50 ma): ±15% 1 count 0.5 μj Overhead (range: 10μA 100 ma): 1% % Counter overflow handling Responsiveness: dependent on the load 8

9 Quanto: Tracking Energy in Networked Embedded Systems Rodrigo Fonseca, Prabal Dutta, Philip Levis, and Ion Stoica Appeared in OSDI

10 Energy Profiling Combine application profiling with energy measurements Log power state changes Log power consumption in each stage Log application profile Estimate each components power consumption by solving linear equations: XΠ = Y 10

11 Power State Tracking 11

12 Activity Tracking 12

13 Blink 13

14 LPL Overhearing 14

15 Overhead Blink example 597 log entries over 48 seconds 7 KiB RAM CPU time: 0.12% total time 71.05% CPU active time Periodic log offloading 15

16 Virtual Battery: An Energy Reserve Abstraction for Embedded Sensor Networks Qing Cao, Debessay Fesehaye, Nam Pham, Yusuf Sarwar, and Tarek Abdelzaher 16

17 Problem Statement We want to be as power efficient at possible On the other hand, The application is running for a reason If energy is available, because of fixed lifetime or energy harvesting, we should optimize its usage 17

18 Virtual Battery Energy Reserve Manager = Bank Each task requests an account with conditions W fraction of real battery allocated N number of energy installments L expected lifetime of the task C credit line, fraction of remaining energy B maximum energy burn rate 18

19 All Roads Lead to Rome Tasks are only allowed to run if there is enough energy on the account 19

20 Lance: Priority-Driven Data Storage and Extraction for Sensor Networks G. Werner-Allen, M. Welsh, S. Dawson-Haggerty Appeared in SenSys

21 Problem Statement Sensor networks are used for data intensive applications Structural monitoring, preventive maintenance, acoustic monitoring Sampling volume exceeds limited bandwidth and storage resources Applications must direct limited resources to retrieving the most interesting signals from the network 21

22 Motivating Application: Volcano Monitoring 22 Graph from M. Welsh

23 Volcano Monitoring Sensor Node 23 Graph from M. Welsh

24 Problem Definition Resource limitations prevent the system from retrieving all collected measurements Need to select which data to download Key idea: Use application-defined priority to drive resource allocation Assign utility to each Application Data Unit (ADU) Define cost for downloading ADUs Use utility and cost to prioritize download sequence 24

25 Lance Architecture 25

26 Summarization Functions Nodes use application-specified summarization function to compute summary s i of ADU Periodically transmit summaries to the gateway 26

27 Cost Estimation Cost c i includes three energy costs involved in downloading an ADU E d : energy required to download from node E r : energy cost of intermediate nodes E o : energy cost of overhearing nodes 27

28 Lance Optimizer Responsible for scheduling downloads based on knowledge of ADUs in the network and their associated values and costs Use reliable delivery protocol to retrieve data Algorithm: Greedy heuristic approximation of multidimensional knapsack problem Consider only feasible ADUs Download ADU with highest score Scoring function combines summary and cost 28

29 Policy Modules Download priority for an ADU may depend on global network state Correlated event detection: detect spike in priority across several nodes and download same time window from the whole network Policy modules: User-supplied functions to inspect, filter, or modify raw ADU summaries 29

30 Testbed Experiments Scoring Functions 30

31 Testbed Experiment 31

32 Correlation Policy 32

33 Field Deployment Ran 8 sensor nodes for a total of 71 hours Successfully downloaded 1232 ADUs (77 MB of data) 308 downloads failed due to timeouts: success rate 80% Total storage summaries span ADUs (688 MB of data) Lance downloaded 11% of the data acquired by the network 33

34 Summary Where does all my power go? Simulation Energy Profiling How to use the battery efficiently? Local policies Network wide policies Next week: Time Synchronization 34

35 Schedule Week 1: Introduction and Applications Week 2: Mote Hardware Week 3: Embedded Programming Week 4: Medium Access Control Week 5: Link Estimation and Tree Routing Week 6: IP Networking Week 7: Energy Management Week 8: Time Synchronization Week 9: Review and Midterm Week 10: Operating Systems and Programming Languages Week 11: Advanced Networking Topics Week 12: Localization Week 13: Energy Harvesting Week 14: TBD 35