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

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