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@scale: Insights from a Large, Long Lived Appliance Energy WSN

@scale: Insights from a Large, Long Lived Appliance Energy WSN. Authors. Stephen Dawson - Haggertyy Steven Lanziseraz Jay Tanejay Richard Brownz David Cullery Computer Science Division Environmental Energy Technologies Division University of California. Abstract .

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@scale: Insights from a Large, Long Lived Appliance Energy WSN

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  1. @scale: Insights from a Large, Long Lived Appliance Energy WSN

  2. Authors • Stephen Dawson - Haggertyy • Steven Lanziseraz • Jay Tanejay • Richard Brownz • David Cullery Computer Science Division Environmental Energy Technologies Division University of California

  3. Abstract • Presents insights obtained from conducting a year-long, 455 meter deployment of wireless plug-load electric meters in a large commercial building. • Authors developed a stratified sampling methodology for surveying the energy use of Miscellaneous Electric Loads (MELs) in commercial buildings. • Over deployment period, they collected over nine hundred million individual readings. • scalable IPv6 routing protocol which supports point-to-point routing and multiple points of egress is used. • Found that the set of links they use is dynamic; not using such a dynamic set results in paths that are twice as long. • Finally, conducted a detailed survey of the accuracy possible with inexpensive AC metering hardware. • Based on a 21-point automated calibration of a population of 500 devices, they find that it is possible to produce nearly utility-grade metering data.

  4. Topics • Introduction • Miscellaneous Electric Loads • System Design • Network Insights • Energy Science • Conclusion

  5. 1. Introduction • A large, long lived Massive Application. • Motivation. • Developing new Insights. • Stratified Sampling.

  6. 2. Miscellaneous Electric Loads • Miscellaneous Electric Loads(MELs). • MELs Device Inventory • Device Sampling Methodology

  7. 3.System Design

  8. System can be decomposed into 3 tier’s :

  9. Metering Tier : • MSP430 microcontroller integrated with 802.15.4 radio and Analog Devices ADE7753 energy metering chip. • Each device runs the Tiny OS and uses blip (IPv6/6loWPAN stack)

  10. Back Haul Tier : • Load Balancing Routers • Provide connectivity to and from metering elements. • HYDRO(A Hybrid Routing Protocol for low power and lossy Networks) • Along with meters the devices make up a IPV6 subnet where all devices participate in routing protocol.

  11. Data Center Tier : • Receives UDP packets • Data packets from the meters traverse several network segments en route to the data center, • 6loWPAN network • IPv6 tunnel • IPv6 Internet

  12. 3.1 Data Generation • The metering devices sample average power and total energy for every 10 seconds. • Sends packets every 20 seconds with the two readings. • Data packet includes clocks and counters for time synchronization.

  13. Packet Delivery Ratio : PDR is the metric for Network Performance. Ratio of packets delivered to packets originated

  14. 4. Network Insights • HYDRO builds a directed acyclic graph (DAG) towards a set of load-balancing routers (LBRs). • Hydro contains numerous mechanisms to improve scalability and reliability in shifting links and deployment sizes. • With 455 nodes spread across four floors, routing through 7 LBRs, the average node density was at least 16; we estimate this by counting the number of distinct links reported over the life of the deployment.

  15. 4.1 Routing Requirements • In production, Application collects data using MP2P traffic pattern. This pattern is optimized in HYDRO by maintaining a DAG towards egress routers with large amount of redundancy. • Ability to scale using Multiple LBRs. HYDRO supports this by extending the routing topology over a backhaul link, in our case its building Ethernet.

  16. 4.2 Data Loss • Since the network is in real environment, with large number of devices experiences data loss.

  17. One of the most unexpected sources of missing data was that of device unplugging.

  18. 4.3 Network Dynamics • Running a routing protocol in a large network over a year gives us a rich set of data with which to observe link dynamics. • First, we have application-layer data reported by the sensor nodes to the data repository. • Second, HYDRO routers apprise the LBRs of their link-state at regular intervals so that they can build source routes back into the network.

  19. Running a routing protocol over a long period of time in a real environment confirms many hypotheses about link behavior over time

  20. 5. Energy Science • Accuracy Analysis : For energy meters, calibration transforms measurements taken by the device into engineering units usable for scientific comparison. This process presents several challenges : Accuracy Requirements : In the MELs regime, loads seldom consume more than 300W, with a large proportion below 60W. Utility-grade electric meters to be accurate to within 2% of load from 60W to 3:6kW, ignoring the lowest range.

  21. Simple, individualized calibration: There are differences among metering devices caused by variations in the manufacturing process, but the calibration equations need to be simple enough to be computed on the devices themselves. Thus, each device needs to be tested, calibrated, and programmed separately.

  22. Insights revealed while developing calibration function are : First, after analyzing raw data from several hundred meters, the raw values exhibited highly linear behavior but only over limited domains. Therefore, single-point calibration is insufficient.

  23. A piecewise linear function using multi-point calibration may be used, consisting of three portions, where the first only ensures that the meter returns a zero reading at zero load, and the boundary between the second and third segment is chosen to minimize the overall error in the calibration.

  24. The second insight is that despite larger percentage error at lower load levels, absolute error remains quite low when examined across all meters.

  25. 6. Conclusion • Therefore, the results obtained present several new findings and insights which allows deployments at larger scales.

  26. Thank you, srujan Kusumba

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