Victoria Manfredi, Jim Kurose, Naceur Malouch, Chun Zhang, Michael Zink
SECON 2009
Separation of Sensor Control and Data in Closed-Loop Sensor Networks
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Why separate sensor control and data?Closed-Loop Sensor NetworksMeteorological Application
– Network, Sensing, Tracking Models
Simulation ResultsSummary Future Work
Why separate sensor control and data?Closed-Loop Sensor NetworksMeteorological Application
– Network, Sensing, Tracking Models
Simulation ResultsSummary Future Work
Outline
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Many-to-one routing to sink
Congestion
Bursty, high-bandwidth data
Wireless links
How does prioritizing sensor control traffic over data traffic impact application-level performance?
Data
Sensor Controls
Data spatially, temporally redundant
Prefer to delay, drop data
Why separate sensor control and data?
Sensor network
Closed-loop sensor network
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Why separate sensor control and data?
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
Related Work
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Why separate sensor control and data?Closed-Loop Sensor NetworksMeteorological Application
– Network, Sensing, Tracking Models
Simulation ResultsSummary Future Work
Outline
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Data
Controlk-1 k k+1
Data from control k-1
Data from control k
Data delay FIFO control delay
Priority control delay
Small data delay, large control delay more data collected in time to compute next sensor control
= Update interval
Closed-loop Sensor Networks
Prioritizing sensor control – impact on packet delays?– impact on data collected?
Control loop delay
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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
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Sensing accuracy and slowly with # of samples
Std Dev of W from
Fisher information
# of iid samples
Compute unbiased estimator W (sample mean) of parameter (population mean)
Radars, Sonars, Cameras, …
Better Quality Data
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Why separate sensor control and data?Closed-Loop Sensor NetworksMeteorological Application
– Network, Sensing, Tracking Models
Simulation ResultsSummary Future Work
Outline
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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
CASA radar network is a closed-loop sensor network
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(xk-1,yk-1)
(xk,yk)
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
Quality of estimated storm location affects tracking
Quality of tracking affects scan angle, quality of estimates
Timeliness of control, data affects amount of sensed data gathered
Storm Tracking Application: 3 Coupled Models
d
d
c
d
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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
Network ModelObtain sensor control and
data packet delays
d
d
c
d
Bursty arrivals
Deterministic arrivals
control
data
other
Obtain delays for FIFO, priority queuing using simulation
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Radar– transmits pulses to estimate reflectivity at point in space
Reflectivity– # of particles in volume of atmosphere– standard deviation,
=
Sensing ModelConvert packet delays into
sensing error
sensing timescan angle width
radar SNR where Nc
Smaller angle, longer time sensing lower sensing error
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Location of storm centroid– equals location of peak reflectivity– standard deviation,
Kalman filters– generate trajectory of storm centroid– track storm centroid
r d
30 dBzz =
z used in measurement covariance matrix
Convert sensing error into location error, perform tracking
(xk-1,yk-1)
(xk,yk)
Tracking Model
mid-range reflectivity value
distance from radar
Goal: track storm centroid with highest possible accuracy
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Why separate sensor control and data?Closed-Loop Sensor NetworksMeteorological Application
– Network, Sensing, Tracking Models
Simulation ResultsSummary Future Work
Outline
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NFIFO / Npriority
Data Quantity vs QualityC
DF
r,Priority / r,FIFO
360 scans, = 5sec, very bursty traffic
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
**During times of congestion, prioritizing sensor
control quantity, quality of data
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idx = 1 idx = 25
Per-interval performance gains/losses may accumulate across multiple update intervals
t=1
# intervals
# intervals
RMSE =
(truet-obst)2
√
+
+
+
Tracking Quality
idx = 55
+
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Why separate sensor control and data?Closed-Loop Sensor NetworksMeteorological Application
– Network, Sensing, Tracking Models
Simulation ResultsSummary Future Work
Outline
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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
“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.’ ”
Summary and Future Work
When network congestion, prioritizing sensor control in closed-loop sensor network quantity, quality of
data, and gives better application-level performance
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Thank You!
Questions?
Contact: Victoria Manfredi [email protected]
More info: www-net.cs.umass.edu/~vmanfred
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NFIFO / Npriority
Data Quantity vs QualityC
DF
r,Priority / r,FIFO
360 scans, = 5sec, very bursty traffic
prioritize sensor control
1/2 control loop delay
data samples (N)
sensing accuracy 1/sqrt(N)
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
**During times of congestion, prioritizing sensor
control quantity, quality of data
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Control loop delay
Prioritizing sensor control to zero, virtually unchanged
FIFO : - - Priority : -
k k+1
Data from control k-1
Data from control k
= Update interval
Data delay FIFO control delay
Priority control delay
More Data
% gain in time collecting data is at most
/ ( - - )
More data, but % gain depends on size of update interval
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Kalman filter
(xk-1,yk-1)(xk,yk)
Measure: radar data received, measured position yk, with r(,+)
Filter: estimate xk based on yk, predicted x-
k
Predict: next x-(k+1) 99%
confidence region, gives k+1 to scan next time step
Estimated state error covariance matrix, dependson velocity noise model, r(,+)
xk := estimated (location, velocity)
yk := measured (location, velocity)
noisy, with std deviation r(,+)
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Network parameters
Kalman filter parameters– initialize based on storm data
10 NS-2 simulation runs, 100,000 sec each
Simulation Set-up
data= 2000/30
pkts/s
other= 2000/30
pkts/s
off on
r1 = 1s
r2 = 1s
1= po 2= (1-p)o
control+ data+other 133.37 pkts/s = 148.5 pkts/s
avg load 0.90
idx =
control= 1/ pkts/s
Vary burstiness of ``other” traffic,
Index of dispersion
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Data Quantity
(seconds)
As and burstiness , gains from prioritizing increase
Number of times more voxels scanned under
priority than under FIFOidx = 55
idx = 25
idx = 1
25
Data Quality
Small decision epoch, bursty traffic: FIFO achieves ~80% as many pulses
as priority ~80% of time
idx1
idx55
= 5sec
idx55
Number of Pulses
= 30sec
idx55 = 5sec
idx55idx1
Reflectivity Standard Deviation
= 30secidx1
idx1
Small decision epoch, bursty traffic: priority has at least 90% as much
uncertainty as FIFO ~90% of the time
x = NFIFO / NPriorityx = r,Priority / r,FIFO
F(x
)
F(x
)
Assuming = 360
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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
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Effect of Packet Loss
As system goes into overload sensing accuracy degrades (more) gracefully when sensor control is prioritized
Capacity: when >1000, data dropped
Priority: no sensor control packets dropped
= pkts / second
r (w
ith lo
ss)
/
r (n
o lo
ss)
FIFO: sensor control packets dropped
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Related Work
Networked Control Systems
Prioritize Network Control
Do not consider effects of prioritizing only sensor control
in a sensor network
Our focus: prioritize sensor control
Sub-class of closed-loop sensor networks considered here
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
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
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