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Separation of Sensor Control and Data in Closed-Loop Sensor Networks

Separation of Sensor Control and Data in Closed-Loop Sensor Networks. Victoria Manfredi, Jim Kurose, Naceur Malouch, Chun Zhang, Michael Zink SECON 2009. Outline. Why separate sensor control and data? Closed-Loop Sensor Networks Meteorological Application Network, Sensing, Tracking Models

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Separation of Sensor Control and Data in Closed-Loop Sensor Networks

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  1. Separation of Sensor Control and Data in Closed-Loop Sensor Networks Victoria Manfredi, Jim Kurose, Naceur Malouch, Chun Zhang, Michael Zink SECON 2009

  2. Outline • Why separate sensor control and data? • Closed-Loop Sensor Networks • Meteorological Application • Network, Sensing, Tracking Models • Simulation Results • Summary • Future Work • Why separate sensor control and data? • Closed-Loop Sensor Networks • Meteorological Application • Network, Sensing, Tracking Models • Simulation Results • Summary • Future Work

  3. Why separate sensor control and data? • Sensor network • Closed-loop sensor network Bursty, high-bandwidth data Many-to-one routing to sink Congestion Wireless links Data Data spatially, temporally redundant Prefer to delay, drop data Sensor Controls How does prioritizing sensor control traffic over data traffic impact application-level performance?

  4. Why separate sensor control and data? Related Work • Service differentiation for different classes of traffic • e.g., [Fredj et al, Sigcomm 2001]  Do not consider effects of prioritizing only sensor control in a sensor network • Prioritizing network control • e.g., SS7, ATM, [Kyasanur et al, Broadnets 2005]  Our focus: prioritizing sensor control • Networked control systems • e.g., [Lemmon et al, SenSys 2003] • data/sensor control are measurements/feedback of classical control system  We assume amount of data  sensor control

  5. Outline • Why separate sensor control and data? • Closed-Loop Sensor Networks • Meteorological Application • Network, Sensing, Tracking Models • Simulation Results • Summary • Future Work

  6. Closed-loop Sensor Networks • Prioritizing sensor control • impact on packet delays? • impact on data collected? • Control loop delay Priority control delay Data delay FIFO control delay Data Data from control k Data from control k-1 Control k k-1 k+1  = Update interval Small  data delay, large  control delay  more data collected in time to compute next sensor control

  7. Better Quality Data • More data samples • Cramer-Rao bound: SD(W) ≥ 1 / n I • accuracy  sub-linearly with n • Effect of data packet drops? • accuracy  sub-linearly with n Radars, Sonars, Cameras, … Fisher information Std Dev of W from  # of iid samples Compute unbiased estimator W (sample mean) of parameter  (population mean) Sensing accuracy  and slowly with # of samples

  8. Outline • Why separate sensor control and data? • Closed-Loop Sensor Networks • Meteorological Application • Network, Sensing, Tracking Models • Simulation Results • Summary • Future Work

  9. CASA • Collaborative Adaptive Sensing of the Atmosphere • dense (sensor) network of low-power meteorological radars • observe severe weather in lower 3km of atmosphere • Collaborative • multiple radars coordinated • Adaptive • can focus beam on phenomena CASA radar network is a closed-loop sensor network

  10. Storm Tracking Application: 3 Coupled Models ld   Network model: control, data delays, depend on scheduling (FIFO, priority) Sensing model: given scan, quantity and quality of data, estimated storm location Tracking model: predict storm location based on current, past estimates and observations using Kalman filters ld  Timeliness of control, data affects amount of sensed data gathered ld lc Qualityof estimated storm location affects tracking (xk,yk) (xk-1,yk-1) Qualityof tracking affects scan angle, quality of estimates

  11. Network Model ld   ld Obtain sensor control and data packet delays • Wireless network • radar data sent to control center, sensor control back to radars • much more data traffic than sensor control traffic • Delays at bottleneck link dominate control-loop delay  ld lc control Deterministic arrivals  data other Bursty arrivals Obtain delays for FIFO, priority queuing using simulation

  12. = Sensing Model Convert packet delays into sensing error • Radar • transmits pulses to estimate reflectivity at point in space • Reflectivity • # of particles in volume of atmosphere • standard deviation, radar SNR where N = c (D - (a+b))/q scan angle width sensing time Smaller angle, longer time sensing  lower sensing error

  13. r d z = 30 dBz Tracking Model Convert sensing error into location error, perform tracking (xk,yk) • Location of storm centroid • equals location of peak reflectivity • standard deviation, • Kalman filters • generate trajectory of storm centroid • track storm centroid (xk-1,yk-1) distance from radar mid-range reflectivity value z used in measurement covariance matrix Goal: track storm centroid with highest possible accuracy

  14. Outline • Why separate sensor control and data? • Closed-Loop Sensor Networks • Meteorological Application • Network, Sensing, Tracking Models • Simulation Results • Summary • Future Work

  15. Data Quantity vs Quality 360 scans,  = 5sec, very bursty traffic CDF FIFO achieves at least 80% as many samples as priority ~80% of time Priority has at least 90% as much uncertainty as FIFO ~90% of the time * NFIFO / Npriority * r,Priority / r,FIFO During times of congestion, prioritizing sensor control  quantity, quality of data

  16. Tracking Quality + + RMSE = # intervals  (truet-obst)2 √ t=1 # intervals + + idx = 1 idx = 25 idx = 55 Per-interval performance gains/losses may accumulate across multiple update intervals

  17. Outline • Why separate sensor control and data? • Closed-Loop Sensor Networks • Meteorological Application • Network, Sensing, Tracking Models • Simulation Results • Summary • Future Work

  18. Summary and Future Work • Results parallel [Fredj et al, Sigcomm 2001] for diffserv: • Future work • how do errors accumulate across control update intervals? • other applications where gains can accumulate? • challenge, importance of quantifying impact of system design decisions on application-level performance When network congestion, prioritizing sensor control in closed-loop sensor network  quantity, quality of data, and gives better application-level performance “that performance is generally satisfactory in a classical best effort network as long as link load is not too close to 100%,” and that “there appears little scope for service differentiation beyond the two broad categories of `good enough’ and ’too bad.’ ”

  19. Thank You! Questions? Contact: Victoria Manfredi vmanfred@cs.umass.edu More info: www-net.cs.umass.edu/~vmanfred

  20. Data Quantity vs Quality  sensing accuracy 1/sqrt(N) prioritize sensor control 1/2 control loop delay  data samples (N) 360 scans,  = 5sec, very bursty traffic CDF FIFO achieves at least 80% as many samples as priority ~80% of time Priority has at least 90% as much uncertainty as FIFO ~90% of the time * NFIFO / Npriority * r,Priority / r,FIFO During times of congestion, prioritizing sensor control  quantity, quality of data

  21. More Data • Control loop delay • Prioritizing sensor control  to zero,  virtually unchanged FIFO :  -  -  Priority :  -  Priority control delay   Data delay FIFO control delay Data from control k Data from control k-1 k k+1  = Update interval % gain in time collecting data is at most  / ( -  - ) More data, but % gain depends on size of update interval

  22. (xk,yk) (xk-1,yk-1) Kalman filter xk := estimated (location, velocity) yk := measured (location, velocity) noisy, with std deviation sr(q,a+b) Measure: radar data received, measured position yk, with sr(q,a+b) Filter: estimate xk based on yk, predicted x-k Estimated state error covariance matrix, depends on velocity noise model, sr(q,a+b) Predict: next x-(k+1) 99% confidence region, gives qk+1 to scan next time step

  23. idx = Simulation Set-up • Network parameters • Kalman filter parameters • initialize based on storm data • 10 NS-2 simulation runs, 100,000 sec each Vary burstiness of ``other” traffic, r1 = 1s control= 1/ pkts/s on off 1= po 2= (1-p)o data= 2000/30 pkts/s  r2 = 1s other= 2000/30 pkts/s Index of dispersion control+data+other  133.37 pkts/s  = 148.5 pkts/s avg load  0.90

  24. Data Quantity Number of times more voxels scanned under priority than under FIFO idx = 55 idx = 25 idx = 1  (seconds) As   and burstiness , gains from prioritizing increase

  25. Data Quality Assuming  = 360 Reflectivity Standard Deviation Number of Pulses F(x)  = 30sec  = 30sec idx1 F(x) idx1 idx55  = 5sec idx55  = 5sec idx55 idx55 idx1 idx1 x = r,Priority / r,FIFO x = NFIFO / NPriority Small decision epoch, bursty traffic: FIFO achieves ~80% as many pulses as priority ~80% of time Small decision epoch, bursty traffic: priority has at least 90% as much uncertainty as FIFO ~90% of the time

  26. Number of Pulses FIFO and Priority each achieve about 6x as many pulses per voxel for  = 30 sec vs  = 5 sec, and total # of pulses is independent of 

  27. Effect of Packet Loss FIFO: sensor control packets dropped Capacity: when >1000, data dropped r (with loss) / r (no loss) Priority: no sensor control packets dropped  = pkts / second As system goes into overload sensing accuracy degrades (more) gracefully when sensor control is prioritized

  28. Related Work Prioritize Network Control • SS7 telephone signaling system • ATM networks, IP networks • 1998: Kalampoukas, Varma, Ramakrishan, 2002: Balakrishnan et al, • priority handling of TCP acks • 2005: Kyasanur, Padhye, Bahl • separate control channel for controlling access to shared medium in wireless Service Differentiation for Different Classes of Traffic • 2001: Bhatnager, Deb, Nath • assign priorities to packets, forwarding higher-priority packets more frequently over more paths to achieve higher delivery prob • 2005: Karenos, Kalogeraki, Krishnamurthy • allocate rates to flows based on class of traffic and estimated network load • 2006: Tan, Yue, Lau • bandwidth reservation for high-priority flows in wireless sensor networks • 2008: Kumar, Crepadir, Rowaihy, Cao, Harris, Zorzi, La Porta • differential service for high priority data traffic versus low-priority data traffic in congested areas of sensor network Our focus: prioritize sensor control Networked Control Systems • data, sensor control sent over network • constrained to be feedback and measurements of classical control system • ratio of data to control much smaller than that of closed-loop sensor network • 2001: Walsh, Ye • put error from network delays in control eqns • 2003: Lemmon, Ling, Sun • drop selected data during overload by analyzing effect on control equations Do not consider effects of prioritizing only sensor control in a sensor network • Sub-class of closed-loop sensor networks considered here

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