280 likes | 293 Views
Explore the importance of separating sensor control from data in closed-loop sensor networks, focusing on a meteorological application. Understand the impact of prioritizing control traffic over data traffic on application-level performance and data collection. Utilize models to study the effects on packet delays, data quantity, sensing accuracy, and storm tracking. Learn about Collaborative Adaptive Sensing of the Atmosphere (CASA) and the intricate network, sensing, and tracking models involved. Simulation results and implications for future work are discussed.
E N D
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 • 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
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?
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
Outline • Why separate sensor control and data? • Closed-Loop Sensor Networks • Meteorological Application • Network, Sensing, Tracking Models • Simulation Results • Summary • Future Work
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
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
Outline • Why separate sensor control and data? • Closed-Loop Sensor Networks • Meteorological Application • Network, Sensing, Tracking Models • Simulation Results • Summary • Future Work
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
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
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
= 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
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
Outline • Why separate sensor control and data? • Closed-Loop Sensor Networks • Meteorological Application • Network, Sensing, Tracking Models • Simulation Results • Summary • Future Work
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
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
Outline • Why separate sensor control and data? • Closed-Loop Sensor Networks • Meteorological Application • Network, Sensing, Tracking Models • Simulation Results • Summary • Future Work
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.’ ”
Thank You! Questions? Contact: Victoria Manfredi vmanfred@cs.umass.edu More info: www-net.cs.umass.edu/~vmanfred
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
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
(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
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= po 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
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
Data Quality Assuming = 360 Reflectivity Standard Deviation Number of Pulses F(x) = 30sec = 30sec idx1 F(x) idx1 idx55 = 5sec idx55 = 5sec idx55 idx55 idx1 idx1 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
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
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
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